In this paper, we propose a policy-guided Monte Carlo Tree Search (MCTS) decoder that achieves near maximum-likelihood decoding (MLD) performance for short block codes. The MCTS decoder searches for test error patterns (TEPs) in the received information bits and obtains codeword candidates through re-encoding. The TEP search is executed on a tree structure, guided by a neural network policy trained via MCTS-based learning. The trained policy guides the decoder to find the correct TEPs with minimal steps from the root node (all-zero TEP). The decoder outputs the codeword with maximum likelihood when the early stopping criterion is satisfied. The proposed method requires no Gaussian elimination (GE) compared to ordered statistics decoding (OSD) and can reduce search complexity by 95\% compared to non-GE OSD. It achieves lower decoding latency than both OSD and non-GE OSD at high SNRs.

Traditional wireless communications rely solely on bit-level channel coding for error correction, without exploiting the inherent linguistic structure of the data source. This paper proposes a large language model (LLM) Viterbi decoder that integrates LLM priors into the Viterbi decoding for text transmission over AWGN channels. The proposed decoder maintains multiple candidate paths during the Viterbi decoding and periodically evaluates path reliabilities using a fine-tuned Byte-level T5 (ByT5) language model. By combining channel reliability metrics with semantic probability from the LLM, it outputs the path that maximizes the joint likelihood of channel observations and linguistic coherence. Simulations show that our decoder achieves significant performance gains over conventional Viterbi decoding in terms of both block error rate (BLER) and semantic similarity. For convolutional codes with constraint length 3, it achieves approximately 1.5 dB more coding gain in BLER, with over 50% improvements in semantic similarity. The framework can extend to other structured data sources beyond text.

This paper proposes a novel robust covert integrated sensing and communication (RC-ISAC) system, where mobile Warden tracking is leveraged to assist covert communication design. We focus on a typically overlooked yet highly threatening Warden-blocked scenario, in which temporary tracking loss prevents timely updates of covert communication strategy. To overcome this challenge, the reconfigurable intelligent surface (RIS) is introduced to establish a controllable sensing link that bypasses the obstacle. Furthermore, a robust extended Kalman filtering (R-EKF) strategy with a sensing-failure fallback mechanism is developed to achieve reliable Warden tracking, where sensing failures are detected and promptly addressed through re-scanning of the Warden. In addition, a high-capacity covert optimization (HCO) scheme is proposed to improve the covert transmission performance and maintain reliable Warden tracking, which is achieved by the joint design of ISAC sensing-communication beamforming and RIS passive beamforming. Simulation results demonstrate that the proposed RC-ISAC system achieves superior robustness and covert transmission performance compared with the no-RIS baseline and the element-wise optimization baseline.

This paper investigates covert prompt transmission for secure and efficient large language model (LLM) services over wireless networks. We formulate a latency minimization problem under fidelity and detectability constraints to ensure confidential and covert communication by jointly optimizing the transmit power and prompt compression ratio. To solve this problem, we first propose a prompt compression and encryption (PCAE) framework, performing surprisal-guided compression followed by lightweight permutation-based encryption. Specifically, PCAE employs a locally deployed small language model (SLM) to estimate token-level surprisal scores, selectively retaining semantically critical tokens while discarding redundant ones. This significantly reduces computational overhead and transmission duration. To further enhance covert wireless transmission, we then develop a group-based proximal policy optimization (GPPO) method that samples multiple candidate actions for each state, selecting the optimal one within each group and incorporating a Kullback-Leibler (KL) divergence penalty to improve policy stability and exploration. Simulation results show that PCAE achieves comparable LLM response fidelity to baseline methods while reducing preprocessing latency by over five orders of magnitude, enabling real-time edge deployment. We further validate PCAE effectiveness across diverse LLM backbones, including DeepSeek-32B, Qwen-32B, and their smaller variants. Moreover, GPPO reduces covert transmission latency by up to 38.6% compared to existing reinforcement learning strategies, with further analysis showing that increased transmit power provides additional latency benefits.

This paper investigates a general integrated sensing and covert communication (ISCC) system, where a base station (BS) transmits signals to covert users (CUs) while simultaneously sensing a dynamic target that acts as a warden (WA), maliciously attempting to eavesdrop on the covert communication. An essential task in realizing ISCC is the beamforming design, which however, is complicated by the dynamic nature of both the WA and the CUs in practice, i.e., (i) the rapid movement of the WA and (ii) the time-varying number of CUs. To address these challenges, in this paper, we develop a versatile recurrent graph neural network (RGNN)-based beamforming design framework, where the penalty method is first employed to transform the constrained optimization problem into an unconstrained one, and then an RGNN is customized to effectively output the beamforming vectors. Through implicitly learning features from the historical warden detection channels and the instantaneous channel state information among CUs and BS, the proposed approach could predict the next-time slot beamforming matrix while accommodating a scalable number of CUs, thus eliminating repeated WA channel estimation and re-optimization when handling dynamic scenarios. Moreover, a convolutional long short-term memory (CLSTM)-augmented message passing GNN (CL-MPGNN) is developed to realize the RGNN framework. In particular, a CLSTM module is first adopted to exploit the spatial-temporal features from the input to facilitate an effective predictive beamforming. Then, a set of message-passing layers is employed to guarantee the scalability of the beamforming design. Simulations verify the effectiveness of the proposed algorithm in terms of the covert communication performance, the covert communication-sensing tradeoff, and the generalizability, respectively.

Corrosion semantic segmentation (CSS) is essential for early and accurate detection and positioning of corrosion in complex real-life scenarios. However, the unique characteristics of corrosion patterns, including the diverse forms, blurred boundaries, and intra-class heterogeneity, pose significant challenges in CSS. To address these challenges, we propose a Prototype-based Multi-dimension Sample-Adaptive Intensity Mapping with Density Sampling network (PMSAD) for CSS. PMSAD leverages nonparametric nearest prototype retrieving to enhance intra-class cohesion and inter-class separation, thereby handling the challenge of diverse forms. In PMSAD, prototypes are equally assigned to each class during training to mitigate class imbalance and capture intra-class variations. In addition, we elaborately design and implement three core components in PMSAD, including Multi-Scale Dual Attention (MSDA), Multi-dimension Sample-adaptive Intensity Mapping (MSAIM), and Density Sampling (DS). The MSDA enhances feature discrimination, facilitating robust representation learning. The end-to-end MSAIM adaptively adjusts RGB channel intensity contrasts of the input corrosion image to enhance feature robustness, counteracting the effects of uneven natural illumination. The DS is proposed for training refinement to tackle fuzzy boundaries and internal interference between corrosion classes. It focuses on high-density, high-error regions, offering refined guidance to correct intra-cluster centers and reduce inter-cluster similarity. Extensive evaluations on real-world datasets, including coarse and relabeled fine-grained dataset, validate the superior performance and generalization ability of PMSAD, achieving the new state-of-the-art performance in precise boundary delineation and accurate corrosion classification. The code is available at: https://github.com/c1oTTpD/PMSAD.

As vehicular edge computing (VEC) grows increasingly demanding, distributed task offloading is brought up as a potential solution. However, the high mobility and limited resources of vehicles, coupled with uncertain service reliability, make it difficult to guarantee timely and correct execution of computation-intensive tasks. Existing task offloading models overlook critical aspects such as punctuality and correctness, limiting their ability to select reliable service vehicles in dynamic and potentially adversarial environments. To address this gap, we propose a performance-reputation integrated task offloading scheme for reliable VEC. We introduce a dual-dimensional reputation model (D2Rep) that jointly evaluates vehicles based on the degree of punctuality, result correctness probability, and historical reputation value. Building on this, we develop the performance value-maximized vehicular computation offloading (PFVMax-VCO) scheme, which combines reputation value, link reliability, and projected success rate into a unified performance value to guide multi-objective optimization of delay, energy consumption, and reliability. To support the realistic evaluation, we implement a SUMO-Veins-OMNeT++ integrated simulation framework. Experimental results demonstrate that the proposed PFVMax-VCO scheme outperforms existing solutions in terms of execution cost, result correctness probability, and task success rate, while enabling robust and dynamic reputation management for VEC systems.

Recent advancements in computing and autonomous driving technologies have led to the integration of new functionalities into intelligent automotive, such as environmental perception, path planning, assisted driving, and entertainment services. This integration requires the rapid processing of critical tasks, including collision detection, emergency braking, lane keeping, and Vehicle-to-Everything (V2X) communication, exceeding past functional demands. Consequently, high-performance multi-core processors have emerged as the preferred hardware solution due to their superior processing speeds, energy efficiency, and parallel task execution capabilities. This paper explores various applications of multi core processors in intelligent automotive systems, systematically reviewing recent advancements in multi-core scheduling methods. It categorizes and analyzes approaches to task loading, task migration, efficiency improvement, safety assurance, communication, and resource contention in both general and automotive contexts. To objectively assess these methods, the paper establishes reference standards for evaluating scheduling methods and system architectures and provides analytical methods aligned with these standards. Finally, the paper discusses future challenges, considers trends in intelligent automotive systems and multi-core processors, and offers recommendations for future research in this field.

Reconfigurable intelligent surface (RIS) has emerged as a promising technology for future 6G wireless communications. However, the passive nature of RIS and the high-dimensional cascaded channels pose significant challenges for channel estimation (CE), particularly in practical scenarios where decomposition dictionaries cannot be predefined. This paper proposes a novel three-stage joint CE and positioning (JCEP) framework for RIS-assisted communication systems. It first performs the initial CE based on a predefined row dictionary that exploits the structural properties of cascaded channels, and then conducts positioning based on the initial CE results. Finally, it refines the CE results by incorporating the positioning output to construct customized column dictionaries. The framework employs a unitary approximate message passing sparse Bayesian learning (UAMP-SBL) based channel estimator that adapts to both initial and CE refinement stages. For positioning, we design a graph attention network (GAT) to achieve robust positioning performance in dynamic environments. Furthermore, in the CE refinement, we introduce a location-aware dictionary design that leverages position priors to reduce computational overhead. Additionally, we employ meta-learning to enable rapid adaptation to new environments. Extensive simulations show that our framework achieves superior performance in CE and positioning accuracy with low complexity.

Wireless Human-Machine Collaboration (WHMC) represents a critical advancement for Industry 5.0, enabling seamless interaction between humans and machines across geographically distributed systems. As the WHMC systems become increasingly important for achieving complex collaborative control tasks, ensuring their stability is essential for practical deployment and long-term operation. Stability analysis certifies how the closed-loop system will behave under model randomness, which is essential for systems operating with wireless communications. However, the fundamental stability analysis of the WHMC systems remains an unexplored challenge due to the intricate interplay between the stochastic nature of wireless communications, dynamic human operations, and the inherent complexities of control system dynamics. This paper establishes a fundamental WHMC model incorporating dual wireless loops for machine and human control. Our framework accounts for practical factors such as short-packet transmissions, fading channels, and advanced HARQ schemes. We model human control lag as a Markov process, which is crucial for capturing the stochastic nature of human interactions. Building on this model, we propose a stochastic cycle-cost-based approach to derive a stability condition for the WHMC system, expressed in terms of wireless channel statistics, human dynamics, and control parameters. Our findings are validated through extensive numerical simulations and a proof-of-concept experiment, where we developed and tested a novel wireless collaborative cart-pole control system. The results confirm the effectiveness of our approach and provide a robust framework for future research on WHMC systems in more complex environments.

Physical layer security (PLS) has emerged as a promising technology to protect critical and sensitive information against unauthorized devices. To address the key challenge of acquiring channel state information (CSI) of passive eavesdroppers in PLS implementation, we propose a novel sensing-assisted PLS scheme with the aid of reflecting reconfigurable intelligent surface (STAR-RIS). It employs a self-refine sensing scheme utilizing the artificial noise (AN) signals to iteratively estimate the eavesdroppers’ positions for CSI calculation. We aim to maximize the secrecy capacity based on the sensing-estimated CSI while tracking the eavesdroppers in full-duplex (FD) mode with integrated sensing and communication (ISAC) signals comprising artificial noise (AN). This is achieved by jointly designing the beamforming vector of information signals, the beamforming vector of AN signals, and the coefficients of the STAR-RIS. To optimize these coupled variables, we introduce an alternating optimization (AO) scheme to solve the problem recursively. In particular, we tackle the non-convexity of the beamforming optimizations for information and AN signals with the successive convex approximation (SCA) scheme and adopt a semi-definite relaxation (SDR) scheme to design the reflection and refraction coefficients of the STAR-RIS. The numerical results validate that the proposed scheme ensures secure communications against multiple eavesdroppers without any prior eavesdropper channel information. In addition, the proposed scheme can significantly improve SC performance by up to 66. 7% compared to the benchmarks without the sensing-assisted function.

This paper investigates the reliability enhancement of integrated access and backhaul (IAB) networks in urban environments by jointly leveraging reconfigurable intelligent surfaces (RIS) and unmanned aerial vehicles (UAVs). We propose a RIS and UAV collaboration-enhanced IAB (RUC-IAB) network, where UAVs serve as mobile IAB nodes and the RIS is employed to establish robust line-of-sight (LoS) backhaul links. Our collaborative approach effectively mitigates both blockage-induced and signal-to-noise ratio (SNR)-limited outages, which are the two primary factors compromising transmission reliability in urban IAB networks. To further reduce the outages caused by data accumulation at the IAB node, we develop a joint UAV deployment and RIS beamforming optimization (URO) scheme to balance the access and backhaul transmission rates. In this scheme, a closed-form lower bound on the non-outage probability is derived to facilitate low-complexity UAV placement, and a semidefinite relaxation (SDR)-based method is proposed to optimize the RIS phase shifts. Simulation results show that the proposed URO scheme achieves a 44.72% reduction in average outage probability compared to the phase-alignment-based scheme across various backhaul distances.

The integration of aerial reconfigurable intelligent surfaces (ARISs) into IoT networks offers transformative potential for enhancing secure and energy-efficient communication in the presence of blockages and eavesdropping threats. This paper proposes to integrate ARIS into Internet of Things (IoT) networks to simultaneously improve communication reliability, enforce information freshness, and defend against eavesdropping. We formulate a joint optimization problem to minimize the average total transmit energy of IoT devices through the coordinated design of unmanned aerial vehicle (UAV) trajectory, transmit power allocation, ARIS phase shifts, and device scheduling, subject to rigorous constraints on age of information (AoI), UAV energy budget, and secrecy rate guarantees. The optimization problem is formulated as a dynamic programming problem. To address the complexity of long-term dynamic optimization, we employ Lyapunov optimization to decompose it into a per-slot deterministic optimization problem, which can be solved without requiring future state information. However, the per-slot problem is a mixed-integer non-convex optimization problem, making it inherently challenging to solve optimally. To address this, we propose an efficient algorithm that effectively balances the tradeoff between minimizing average total energy consumption and stabilizing average total queue backlogs. Simulation results demonstrate that our algorithm reduces average transmit energy by 46% compared to the round-robin comparison scheme while strictly adhering to information freshness and UAV energy constraints.

As a new communications architecture, the Space-Air-Ground integrated network (SAGIN) integrates satellites, airborne platforms, and terrestrial networks to enhance global connectivity and support robust and flexible communication capabilities. Efficient task offloading and resource allocation are crucial for SAGIN to meet the quality of service (QoS) requirements at low cost. In this paper, we formulate task offloading and resource allocation as a time-sequential decision-making problem, aiming to maximize task completion within available communication and computational resources. We propose an online approach referred to as graph fusion deep reinforcement learning (GF-DRL). GF-DRL incorporates a graph feature extraction network that utilizes a graph convolutional network (GCN) to extract features from both the task graph and user equipment (UE) graph, along with two attention mechanisms (hard and soft) to merge the two graphs. We also propose an action encoding and mapping network to generate both discrete (offloading) and continuous (allocation) decisions in an end-to-end manner. Simulation results validate the effectiveness of our proposed GF-DRL compared to state-of-the-art task offloading resource allocation approaches.

Reconfigurable intelligent surface (RIS) has emerged as a promising wireless communication technology in the 6G era. Its ability to adaptively reflect signals offers improved coverage and low energy consumption. Existing channel estimation methods for RIS primarily rely on sparse signal recovery techniques with large overcomplete dictionaries, which results in prohibitive computational complexity. To address this issue, we employ the vision transformer (ViT) model for adaptive user positioning and propose a novel user position based dictionary design approach, to effectively reduce dictionary size and solve the off-grid problem. This design approach is incorporated into a unified framework, where user positioning and channel estimation are performed jointly for integrated sensing and communications. A modified unitary approximate message passing sparse Bayesian learning algorithm with an early stopping scheme is proposed to address potential overfitting issues in channel estimation. Extensive simulation results demonstrate the effectiveness and robustness of our proposed framework.

Non-binary convolutional codes (NBCCs) offer significant performance advantages in modern communication systems, but their optimal decoding using classical maximum a posteriori probability (MAP) algorithms is computationally intensive. This paper proposes a low-complexity linear MAP (LMAP) decoding method for rate-1/2 NBCCs. By representing the MAP forward and backward decoding processes as shift register structures operating on probability mass functions (PMFs) of signal estimates, the method supports single direction (forward and backward) SISO decoding, and can achieve the optimal bidirectional MAP decoding performance. The decoder structure is determined offline, and the decoding process only involves simple shift register operations, finite field convolutions, and permutations. Simulation results demonstrate that the proposed LMAP decoder achieves identical error performance to the conventional BCJR MAP decoder, while significantly reducing decoding latency and hardware complexity.

In this paper, we propose a novel cross-domain channel estimation (CDCE) algorithm for orthogonal frequency division multiplexing (OFDM) systems, leveraging the unique characteristics of the delay-Doppler (DD) domain channel. Specifically, the proposed algorithm transforms the time-frequency (TF) domain pilot sequence of OFDM into the DD domain and applies a two-dimensional (2D) twisted-convolution for acquiring a coarse estimation of the underlying channel delay and Doppler. Then, the OFDM channel estimation is formulated as a sparse signal recovery problem in the TF domain according to the dictionary derived based on the obtained delay and Doppler estimates. Furthermore, a low-complexity ℓ1-regularized least-square estimator is proposed to effectively solve this problem. Moreover, we further develop a performance analysis framework of the proposed scheme based on the ambiguity function (AF) of the adopted pilot sequence. Our numerical results demonstrate noticeable estimation performance improvement compared to conventional OFDM channel estimation methods, particularly in the presence of high channel mobility.

In this paper, we propose a novel goal-oriented method for identifying stationary Internet of Things (IoT) devices, with the performance robust to the number of inactive devices. We start by formulating a two-dimensional atomic norm minimization problem that captures the angular group-sparsity of the wireless channel. Building on this, we propose a goal-oriented optimization problem that retains only the angular information required to identify active stationary IoT devices. This problem is then reformulated as an equivalent semi-definite programming (SDP) problem, enabling efficient detection of active users. Unlike traditional methods that rely on orthogonal preambles or pilot assignments for joint active user detection and channel estimation, our approach operates without pilots, enabling blind identification of the line-of-sight angles of active stationary devices. Simulation results demonstrate that the proposed method achieves high detection accuracy and low false alarm rates, offering a scalable and robust solution for enabling massive connectivity in future wireless networks.

Accurate indoor tracking is a critical component of modern location-based services, fundamentally transforming the way we interact with indoor environments. Traditional state space model (SSM) based tracking often struggles in complex environments due to its reliance on fixed and oversimplified transition and observation functions. In this paper, we propose a novel deep state space model (DSSM) approach for indoor tracking that overcomes these limitations by leveraging trainable neural networks (NNs) in place of fixed transition and observation functions. The proposed DSSM retains the structured representation of SSMs while improving the ability to effectively capture the complex dynamics of both target movements and measurement errors. The proposed model incorporates physics constraints to enable self-supervised learning, eliminating the need for labeled data during training. We evaluate our framework using real-world time of flight (ToF) measurements, demonstrating its superior tracking accuracy compared to conventional methods.

Acquiring prior knowledge of trajectory distributions in specific environments can significantly expedite the optimisation process in robot motion planning. Leveraging successful past plans and utilising trajectory generative models as priors offers a clear advantage. Previous studies have proposed various methods to harness these priors, such as using prior samples for initialisation or incorporating the prior distribution into trajectory optimisation through inference. Recently, diffusion models have demonstrated effectiveness in encoding multi-modal data in high-dimensional settings. In this study, we introduce a methodology that integrates Stein Variational Gradient Descent (SVGD) with Gaussian Process Motion Planning (GPMP), leveraging diffusion models as multi-modal priors. This approach combines the advantages of deep generative model and Bayesian inference to reduce the computation time required to approximate the posterior distribution of trajectories, particularly when adapting to new, unseen environments. In addition, we incorporate path signatures into our method to enhance the diversity of the posterior distribution, thereby improving the optimality of trajectories in multi-modal settings. To validate our approach, we conduct comparative assessments against multiple baseline methods across various scenarios, including 2D planar robots and robotic manipulators.

In this paper, we propose a linear representation of BCJR maximum a posteriori probability (MAP) decoding of a rate$1 / 2$convolutional code (CC), referred to as the linear MAP decoding (LMAP). We discover that the MAP forward and backward decoding can be implemented by the corresponding dual soft input and soft output (SISO) encoders using shift registers. The bidrectional MAP decoding output can be obtained by combining the contents of respective forward and backward dual encoders. Represented using simple shift-registers, LMAP decoder maps naturally to hardware registers and thus can be easily implemented. Simulation results demonstrate that the LMAP decoding achieves the same performance as the BCJR MAP decoding, but has a significantly reduced decoding delay. For the block length 64, the CC of the memory length 14 with LMAP decoding surpasses the random coding union (RCU) bound by approximately 0.5 dB at a BLER of$10^{-3}$, and closely approaches both the normal approximation (NA) and meta-converse (MC) bounds.

This paper provides the first analytical characterization of the achievable guesswork complexity of ordered statistics decoding (OSD) in binary AWGN channels. This complexity is defined as the number of test error patterns (TEPs) processed by OSD immediately upon finding the correct codeword estimate. We show that for an order-$k$OSD of a$(n, k)$code, the average achievable guesswork complexity is tightly approximated by$e^{-k p_{e}} I_{0}\left(2 k \sqrt{p_{e}}\right)$, where$I_{0}$is the modified Bessel function and$p_{e}$is determined by the code rate and SNR. Furthermore, for an order-$m$OSD$(m

Vital sign monitoring plays a key role in modern healthcare, supporting applications ranging from chronic disease management to more advanced elderly care. While traditional systems rely on contact-based devices, recent advances in WiFi sensing allow contactless monitoring using channel state information (CSI), offering a more convenient and unobtrusive approach. However, most existing WiFi-based methods mainly concentrate on monitoring in static conditions, rendering them unsuitable for real-world scenarios such as measuring respiration rate during walking. To address this gap, in this paper we propose MovBeat, a respiration prediction system designed to deliver high-accuracy respiratory monitoring when the subject is in motion. By integrating a contrastive learning framework with attention-based feature extraction, MovBeat effectively mitigates interference from the environment and body movements. Experimental results demonstrate that MovBeat achieves over 90 % accuracy in monitoring respiration on the move - an improvment of approximately 20 % compared to traditional methods. Comprehensive evaluations in diverse movement states, including both line-of-sight (LoS) and non-line-of-sight (NLoS) environments, demonstrate the robustness and generalizability of MovBeat in real-world scenarios.

Reconfigurable antennas have excellent dynamic adaptability to alter their operational state in response to environmental changes, and is considered as one potential technology towards future communication systems. However, acquiring accurate channel state information (CSI) for all radiation patterns with low pilot overheads imposes a significant challenge in pattern reconfigurable MIMO (PR-MIMO) communication systems. To address this issue, in this paper we propose a novel two-stage channel estimation approach based on antenna grouping (AG) to obtain the CSIs for all radiation patterns efficiently. In the first stage, all antennas at the transmitter employ the same radiation pattern, while in the second stage, the antennas at the transmitter are divided into groups according to the number of radiation patterns, where antennas in different groups employ different radiation patterns, while antennas within the same group employ the same radiation pattern. When the exact number of channel paths is known, a closed-form channel extrapolation algorithm and a singular value decomposition (SVD)-based channel extrapolation algorithm are proposed, depending on the value of the channel paths and whether the angle information is known. Extensive simulation results illustrate that the proposed algorithms can accurately extrapolate the CSI of all radiation patterns, with dramatically reduced pilot overheads compared to the conventional channel estimation methods.

Space-air-ground integrated networks (SAGIN), a cornerstone of 6G, face challenges in task offloading and resource allocation (TORA) due to their heterogeneity, dynamic topology, and high mobility. To address these, we formulate a multi-objective TORA problem under dynamic topologies to minimize latency and inter-node power consumption. We propose a dynamic heterogeneous graph neural network (DHGNN) that, combined with reinforcement learning, adaptively updates node features to capture cross-domain dependencies. Simulations demonstrate that our method outperforms existing GDRL approaches in reward, latency, and power efficiency.

Abstract Driven by the vision of ubiquitous connectivity and wireless intelligence, the evolution of ultra-dense constellation-based satellite-integrated Internet is underway, now taking preliminary shape. Nevertheless, the entrenched institutional silos and limited, nonrenewable heterogeneous network resources leave current satellite systems struggling to accommodate the escalating demands of next-generation intelligent applications. In this context, the distributed satellite information networks (DSIN), exemplified by the cohesive clustered satellites (CCS) system, have emerged as an innovative architecture, bridging information gaps across diverse satellite systems, such as communication, navigation, and remote sensing, and establishing a unified, open information network paradigm to support resilient space information services. This survey first provides a profound discussion about innovative network architectures of DSIN, encompassing distributed regenerative satellite network architecture, distributed satellite computing network architecture, and reconfigurable satellite formation flying, to enable flexible and scalable communication, computing and control, fundamentally enhancing network resilience. The DSIN faces challenges from network heterogeneity, unpredictable channel dynamics, sparse resources, and decentralized collaboration frameworks. To address these issues, a series of enabling technologies is identified, including channel modeling and estimation, cloud-native distributed MIMO cooperation, new waveform design, grant-free massive access, nonorthogonal multicast, distributed phased array antennas, high-speed inter-satellite communication, network routing, and the proper combination of all these diversity techniques. Furthermore, to heighten the overall resource efficiency, the cross-layer optimization techniques are further developed to meet upper-layer deterministic, adaptive and secure information services requirements. In addition, emerging research directions and new opportunities are highlighted on the way to achieving the DSIN vision.

This article established a novel multi-input multioutput (MIMO) communication network, in the presence of full-duplex (FD) transmitters and receivers with the assistance of dual-side intelligent omni surface (IOS). Compared with the traditional IOS, the dual-side IOS allows signals from both sides to reflect and refract simultaneously, which further exploits the potential of metasurfaces to avoid frequency dependence, and size, weight, and power (SWaP) limitations. By considering both the downlink and uplink transmissions, we aim to maximize the weighted sum rate, subject to the transmit power constraints of the transmitter, the users and the dual-side reflecting and refracting phase shifts constraints. However, the formulated sum rate maximization problem is not convex, hence we exploit the weighted minimum mean square error (WMMSE) approach, and tackle the original problem iteratively by solving two subproblems. For the beamforming matrices optimization of the downlink and uplink, we resort to the Lagrangian dual method combined with a bisection search to obtain the results. Furthermore, we resort to the quadratically constrained quadratic programming (QCQP) method to optimize the reflecting and refracting phase shifts of both sides of the IOS. Simulation results validate the efficacy of the proposed algorithm and demonstrate the superiority of the dual-side IOS.

Space-Air-Ground integrated network (SAGIN) is a crucial component of the 6G, enabling global and seamless communication coverage. This multi-layered communication system integrates space, air, and terrestrial segments, each with computational capability, and also serves as a ubiquitous computing platform. An efficient task offloading and resource allocation scheme is key in SAGIN to maximize resource utilization efficiency, meeting the stringent quality of service (QoS) requirements for different service types. In this paper, we introduce a dynamic SAGIN model featuring diverse antenna configurations, two timescale types, different channel models for each segment, and dual service types. We formulate a problem of sequential decision-making task offloading and resource allocation. Our proposed solution is an innovative online approach referred to as graphic deep reinforcement learning (GDRL). This approach utilizes a graph neural network (GNN)-based feature extraction network to identify the inherent dependencies within the graphical structure of the states. We design an action mapping network with an encoding scheme for end-to-end generation of task offloading and resource allocation decisions. Additionally, we incorporate meta-learning into GDRL to swiftly adapt to rapid changes in key parameters of the SAGIN environment, significantly reducing online deployment complexity. Simulation results validate that our proposed GDRL significantly outperforms state-of-the-art DRL approaches by achieving the highest reward and lowest overall latency.

The existing task-agnostic and resource-constrained satellite communication fails to meet diverse task demands in the upcoming sixth-generation (6G) network. In this paper, to enable the ubiquitous intelligent services with massive traffic for global users through satellite-Integrated Internet, we first propose a novel semantic metric named utility loss of information (UoI), which can capture the task-oriented aspects by quantifying both value loss of semantic mismatch, and energy loss of unnecessary transmissions. Then, we design a UoI minimization data generation and transmission (UMGT) scheme for task-adaptive communications in satellite-Integrated Internet with energy constraint and reliability requirement. For the time-varying satellite-terrestrial link with high bit error rate (BER) and delayed feedback, we derive the closed-form expressions of BER, and apply the long erasure coding (LEC) to combat the deep fading. Subsequently, we transform the optimization problem to minimize the upper bound of an unconstrained Lyapunov drift-plus-penalty (DPP). Further, we propose two deep reinforcement learning (DRL) algorithms to intelligently choose when to generate data, how to adjust the number of LEC packets and whether to retransmit, thereby minimizing the average UoI. Simulation results validate that our UMGT scheme can achieve the lowest UoI than several state-of-the-art schemes, and demonstrate its adaptability to various task demands.

Wireless networked control systems (WNCSs) are critical to Industry 4.0, enabling applications like drone swarms and autonomous robots. The tight interdependence between communication and control demands integrated design, yet traditional approaches treat them separately, leading to inefficiencies. Existing codesign methods often rely on simplified models for single-loop or independent multi-loop systems, overlooking the complexities of large-scale WNCSs. These include coupled control loops, time-correlated wireless channels, sensing-control trade-offs, and computational challenges. To address these challenges, we propose a practical WNCS model that captures correlated dynamics among spatially distributed sensors and actuators sharing limited wireless resources over multi-state Markov block-fading channels. To solve the resulting high-dimensional codesign problem, we develop a deep reinforcement learning (DRL) algorithm that scales efficiently by managing hybrid action spaces, capturing communication-control dependencies, and maintaining robust performance under time-correlated dynamics and resource constraints. Simulations demonstrate that our DRL approach outperforms benchmarks, providing a scalable and effective solution for large-scale industrial WNCSs.

Modern wireless systems face interference due to rising spectrum efficiency demands and increasingly aggressive network designs. Despite its optimality, the huge complexity of the maximum likelihood (ML) detection hinders its deployment in the future wireless communication systems, which require low latency and high energy efficiency. In this paper, we develop a novel generalized framework for data detection in interference channels. In particular, we factorize the joint likelihood function of the transmitted symbols to obtain the marginal distribution of a single symbol following the sum-product (SP) algorithm. Motivated by the fact that the complexity of the SP algorithm is dominated by the summation process, we introduce Gaussian and Gaussian mixture models to reduce the state space of symbols, which helps to reduce the detection complexity. The proposed hybrid detection framework consists of three kinds of symbol distributions, i.e., original discrete, Gaussian, and Gaussian mixture distributions. To strike a balance between complexity and error performance, we can simply modify the components of different symbol distributions, offering high flexibility in practical applications. Furthermore, we analyze the performance of our proposed detection scheme and discuss the design guidelines for the mixture Gaussian messages. Simulation results demonstrated the effectiveness of the proposed algorithm.

Geometry plays a significant role in monocular 3D object detection. It can be used to estimate object depth by using the perspective projection between object's physical size and 2D projection in the image plane, which can introduce mathematical priors into deep models. However, this projection process also introduces error amplification, where the error of the estimated height is amplified and reflected into the projected depth. It leads to unreliable depth inferences and also impairs training stability. To tackle this problem, we propose a novel Geometry Uncertainty Propagation Network (GUPNet++) by modeling geometry projection in a probabilistic manner. This ensures depth predictions are well-bounded and associated with a reasonable uncertainty. The significance of introducing such geometric uncertainty is two-fold: (1). It models the uncertainty propagation relationship of the geometry projection during training, improving the stability and efficiency of the end-to-end model learning. (2). It can be derived to a highly reliable confidence to indicate the quality of the 3D detection result, enabling more reliable detection inference. Experiments show that the proposed approach not only obtains (state-of-the-art) SOTA performance in image-based monocular 3D detection but also demonstrates superiority in efficacy with a simplified framework. The code and model will be released at https://github.com/SuperMHP/GUPNet_Plus.

In response to the requirements of precise information conveying and goal-oriented transmitting with minimal cost for the upcoming satellite-integrated Internet, we focus on a semantic communication metric named utility loss of information (UoI), which is generalized to capture the tradeoff of the value and energy loss of information. The former is quantified by the duration and severity of mismatch transceivers, and the latter is evaluated by unnecessary data generation and transmissions. To achieve the optimal tradeoff between value and energy loss of information for status update, we formulate a joint optimization problem to design a UoI-optimal policy to generate and transmit data for a multi-state Markov source. By regarding the limited energy, we transform the above problem to a constrained Markov decision process (CMDP), and rigorously prove the UoI-optimal policy has a dual-threshold structure. Then, we derive the closed-form expressions for average UoI and generation and transmission ratio. Moreover, we propose a simplified relative value iteration (SRVI) algorithm based on the theoretical derivations, combined with the bisection search to find two optimal thresholds for the UoI-optimal policy. Simulation results verify that our UoI-optimal policy achieves the optimal tradeoff among timeliness, reliability, and energy efficiency, and outperforms several state-of-the-art semantic-aware policies.

In this paper, we investigate the far-field and near-field codebook-based beam training for multi-user modular extremely large-scale multiple-input multiple-output (XL-MIMO) communications, utilizing a modular extremely large-scale uniform linear array (XL-ULA) at the base station (BS). Unlike conventional collocated XL-ULA with all adjacent elements separated by signal wavelength scale, the modular XL-ULA has different inter-module and intra-module spacings, rendering the existing near-field polar-domain codebook design ineffective. To cater to the modular array architecture, one straightforward approach to beam codebook design is to remove those elements corresponding to the modular space in the conventional polar-domain codebook. However, such a naive polar-domain codebook for modular XL-ULA results in undesired grating lobes. To overcome this challenge, we propose a novel near-field optimization-based codebook design, by exploiting the priori knowledge about the users’ potential angular/distance range to minimize the levels of side lobes while maintaining the main lobe beamforming gain. Furthermore, based on the designed near-field codebooks, an efficient multi-beam training scheme enabled by grating lobes is devised for multi-user modular XL-MIMO communications. Numerical results verify the effectiveness of the proposed near-field optimization-based codebook for modular XL-MIMO in mitigating inter-user interference (IUI) for ultra-dense users, as well as the efficiency of the multi-beam training scheme.

This paper focuses on the frozen set design for precoded polar codes decoded by the successive cancellation list (SCL) algorithm. We propose a novel frozen set design method, whose computational complexity is low due to the use of analytical bounds and constrained frozen set structure. We derive new bounds based on the recently published complexity analysis of SCL decoding with near maximum-likelihood (ML) performance. To predict the ML performance, we employ the state-of-the-art bounds relying on the code weight distribution. The bounds and constrained frozen set structure are incorporated into the genetic algorithm to generate optimized frozen sets with low complexity. Our simulation results show that the constructed precoded polar codes of length 512 have a superior frame error rate (FER) performance compared to the state-of-the-art codes under SCL decoding with various list sizes.

This paper presents a novel auto-encoder based end-to-end channel encoding and decoding. It integrates deep reinforcement learning (DRL) and graph neural networks (GNN) in code design by modeling the generation of code parity-check matrices as a Markov Decision Process (MDP), to optimize key coding performance metrics such as error-rates and code algebraic properties. An edge-weighted GNN (EW-GNN) decoder is proposed, which operates on the Tanner graph with an iterative message-passing structure. Once trained on a single linear block code, the EW-GNN decoder can be directly used to decode other linear block codes of different code lengths and code rates. An iterative joint training of the DRL-based code designer and the EW-GNN decoder is performed to optimize the end-end encoding and decoding process. Simulation results show the proposed auto-encoder significantly surpasses several traditional coding schemes at short block lengths, including low-density parity-check (LDPC) codes with the belief propagation (BP) decoding and the maximum-likelihood decoding (MLD), and BCH with BP decoding, offering superior error-correction capabilities while maintaining low decoding complexity.

Multi-antenna millimeter-wave (mmWave) communication systems have become a promising approach to improve throughput. However, mmWave narrow beams are susceptible to blockages, making it difficult to guarantee continuous services for ultra-reliable and low-latency communications (URLLC). To address this issue, we propose a novel dual-path beam tracking framework and develop a Recurrent Neural Network-based Constrained Deep Reinforcement Learning (RCDRL) algorithm to optimize the beam search sets of the beam tracking algorithm. The objective is to minimize the total time and frequency resources allocated for beam sweeping, beam tracking, and data transmission, subject to the constraint on the service interruption probability of URLLC. A pre-training method is developed to improve the initial performance and stability of the RCDRL algorithm. Comprehensive evaluation results indicate that the proposed approach outperforms other baseline methods in terms of the tradeoff between service interruption probability and resource utilization efficiency. Specifically, the RCDRL algorithm reduces the service interruption probability by three orders of magnitude compared with a standardized single-beam tracking method at the cost of sacrificing the resource utilization efficiency by 14.6%. In addition, our policy achieves lower service interruption probability and higher resource utilization efficiency compared with an existing dual-beam tracking algorithm.

Due to the utilization of large antenna arrays at base stations (BSs) and the operations of wireless communications in high frequency bands, mobile terminals often find themselves in the near-field of the array aperture. In this work, we address the signal processing challenges of integrated near-field localization and communication in uplink transmission of an integrated sensing and communication (ISAC) system, where the BS performs joint near-field localization and signal detection (JNFLSD). We show that JNFLSD can be formulated as a matrix factorization (MF) problem with proper structures imposed on the factor matrices. Then, leveraging the variational inference (VI) and unitary approximate message passing (UAMP), we develop a low complexity Bayesian approach to MF, called UAMP-MF, to handle a generic MF problem. We then apply the UAMP-MF algorithm to solve the JNFLSD problem, where the factor matrix structures are fully exploited. Extensive simulation results are provided to demonstrate the superior performance of the proposed method.

This work studies information freshness of a V2I status updating link in IoV. The status updating link is modeled as a multi-source Ber/Geo/1/1 non-preemptive or preemptive queue. We focus on statistical characteristics of the age of information (AoI) and peak AoI (PAoI). To fully track the AoI evolutions under non-preemptive and preemptive policies, Markov three-dimensional age process (3DAP) and two-dimensional age process (2DAP) are respectively introduced. Their first element is the AoI process; The second one stands for if an update of the concerned source is in transmission and its current age; The third element of 3DAP denotes if an update of another source is in transmission. An analytical approach for studying the AoIs and PAoIs in discrete-time multi-source systems is presented. By studying the state transitions, balance equations, and stationary distributions of 3DAP and 2DAP, analytical expressions of the distributions and averages of AoIs and PAoIs under both queueing policies are derived. Moreover, the optimal probabilistic update selection mechanism (PUSM) that maximizes overall freshness is derived in closed-form for the two-source case. Numerical results validate effectiveness of the theoretical analyses and reveal usefulness of the retransmission. It is found that in terms of improving the overall freshness, the PUSM should be designed to make effective update generation probabilities of sources as close as possible.

Sparse vector coding (SVC) is a promising coding technique to achieve high transmission reliability and low latency for short packet communications. However, for SVC with conventional combination-based sparse mapping, a small increase of transmitted bits may lead to excessively long sparse vectors, resulting in unsatisfactory transmission performance when coding efficiency is high. In this paper, we propose a generalized index redefinition (IR)-based SVC (GIR-SVC) to significantly enhance the efficiency of SVC. The IR mechanism enables multiple index bit streams to share position resources in SVC, with the help of constellation labels. GIR-SVC constructs the sparse vector using a hybrid IR mechanism that integrates the unlabeled IR and the pairwise-grouping-based labeled IR, which allows efficient mapping and de-mapping of index bits without requiring index tables. Consequently, the proposed GIR-SVC can be efficiently decoded without the index table using sparse recovery algorithms. Theoretical analysis is conducted to validate the block error rate (BLER) performance of GIR-SVC. Simulations show that GIR-SVC can significantly reduce the decoding delay compared to existing approaches, while maintaining the high transmission reliability.

We consider a joint uplink and downlink scheduling problem of a fully distributed wireless networked control system (WNCS) with a limited number of frequency channels. Using elements of stochastic systems theory, we derive a sufficient stability condition of the WNCS, which is stated in terms of both the control and communication system parameters. Once the condition is satisfied, there exists a stationary and deterministic scheduling policy that can stabilize all plants of the WNCS. By analyzing and representing the per-step cost function of the WNCS in terms of a finite-length countable vector state, we formulate the optimal transmission scheduling problem into a Markov decision process and develop a deep reinforcement learning (DRL)-based framework for solving it. To tackle the challenges of a large action space in DRL, we propose novel action space reduction and action embedding methods for the DRL framework that can be applied to various algorithms, including deep Q-network (DQN), deep deterministic policy gradient (DDPG), and twin delayed DDPG (TD3). Numerical results show that the proposed algorithm significantly outperforms benchmark policies.

This paper investigates guesswork over ordered statistics and formulates the achievable guesswork complexity of ordered statistics decoding (OSD) in binary additive white Gaussian noise (AWGN) channels. The achievable guesswork complexity is defined as the number of test error patterns (TEPs) processed by OSD immediately upon finding the correct codeword estimate. The paper first develops a new upper bound for guesswork over independent sequences by partitioning them into Hamming shells and applying Hölder’s inequality. This upper bound is then extended to ordered statistics, by constructing the conditionally independent sequences within the ordered statistics sequences. Next, we apply these bounds to characterize the statistical moments of the OSD guesswork complexity. We show that the achievable guesswork complexity of OSD at maximum decoding order can be accurately approximated by the modified Bessel function, which increases exponentially with code dimension. We also identify a guesswork complexity saturation threshold, where increasing the OSD decoding order beyond this threshold improves error performance without further raising the achievable guesswork complexity. Finally, the paper presents insights on applying these findings to enhance the design of OSD decoders.

In this paper, we study beamforming design for multi-user MIMO cognitive radio systems, where a secondary base station transmits multiple data streams to multiple secondary users while imposing interference on primary users. We focus on the weighted sum rate (WSR) maximization problem with the sum power constraint (SPC) and the interference constraints (ICs) by optimizing the beamforming matrices. Firstly, through an analysis of the generalized utility optimization problem, we prove that the WSR maximization problem with a single quadratic constraint can be simplified to an unconstrained WSR maximization problem with adaptive covariance matrices, which can be further solved by the weighted minimal mean square error (WMMSE) method with much lower complexity. Then, we propose the modified subgradient method (MSM)-reduced (R)-WMMSE algorithm for the general scenario and the null-space projection (NSP)-R-WMMSE algorithm for the special scenario with zero ICs. Finally, theoretical and numerical results show superior performances of the proposed algorithms compared to benchmark schemes in terms of computational complexity.

In this paper, we develop a graph neural network (GNN)-assisted bilinear inference approach to enhance the receiver performance of the MIMO system through message passing-based joint channel estimation and data detection (JCD). Specifically, based on the bilinear generalized approximate message passing (BiG-AMP) framework and conditional correlation of signal, we propose a GNN-assisted BiG-AMP (GNN-BiGAMP) approach, which integrates a GNN module into the data-detection-loop to compensate the inaccurate marginal likelihood approximation. By leveraging the coupling between the channel and received symbols, a bilinear GNN-assisted BiG-AMP (BiGNN-BiGAMP) JCD receiver is further proposed. This method incorporates two GNNs with similar graph representation into the bilinear posterior estimation loops, which not only compensates for approximation errors but also alleviates performance loss due to premature variance convergence, thereby enhancing the receiver performance significantly. To fully exploit the supervised information from channel estimation and data detection, we propose a multitask learning based training scheme, which coordinates GNNs with different tasks in two loops. Simulation results show that our proposed GNN-assisted JCD receivers significantly outperform other JCD counterparts in terms of both channel estimation and data detection.

Blockchain-empowered end-edge collaborative computing is a promising technology for enhancing the timeliness and trustworthiness of Industrial Internet of Things (IIoT). However, integrating task offloading with blockchain consensus inevitably escalates resource consumption across communication, computation, and energy domains. Thus, the joint optimization of task offloading, resource allocation and blockchain consensus is very important for IIoT. This paper studies a general end-edge collaborative computing scenario with multiple end devices and multiple edge servers. We first propose a novel dynamic blockchain (DBC) scheme by developing a dynamic leader election mechanism and designing a dynamic consensus waiting time window. Then, by fully considering the constraints of multi-task size and deadline, communication bandwidth, computing frequency, battery capacity, Byzantine fault tolerant and trustworthiness, we formulate the trustworthy processing efficiency (TPE) maximization problem with respect to end-edge task division, communication and computation resource allocation, leader election and consensus waiting window. To address this problem, we transform it into a Markov decision process and design a compound reward by fully considering the penalty for computing timeout and consensus failure. After that, we propose a rotating multi-agent deep reinforcement learning (R-MADRL) algorithm tailored to the proposed DBC scheme, where an entropy-based dual-critic DRL algorithm is proposed for rotating multi-agent training and decentralized execution. Extensive experiments validate the effectiveness and superiority of the proposed DBC with R-MADRL, where three benchmark DRL algorithms and three blockchain consensus schemes are compared. The results demonstrate that R-MADRL achieves stable convergence with more than 60.32% TPE reward than other algorithms while the task timeout ratio of DBC is reduced by more than 66.49% compared with other schemes.

In this paper, we propose a hierarchical optimization approach that guarantees the maximum age of information (AoI) for uncrewed aerial vehicle (UAV) assisted Internet-of-Things (IoT) data collection. Our model is based on an energy-efficient simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) beamforming model. We formulate the optimization to minimize the UAV flight energy consumption subject to a maximum average AoI threshold by optimizing the UAV trajectory, IoT device scheduling, and STAR-RIS beamforming. To solve this, we develop an optimization-based hierarchical deep reinforcement learning (OH-DRL) algorithm that decomposes the formulated problem into an inter-cluster UAV visiting policy and STAR-RIS-based intra-cluster IoT scheduling policy. In OH-DRL, we jointly optimize the two policies in a high-level loop and a low-level loop, respectively. In the high-level loop, we design an AoI-guided DRL algorithm to determine the AoI-guaranteed UAV hovering position with minimal flight distance. In the low-level loop, a semidefinite relaxation (SDR)-based optimization algorithm further reduces the UAV’s flying time by minimizing the average AoI. Simulation results validate that OH-DRL achieves better convergence performance and energy-saving efficiency across different network scales. Compared to the state-of-the-art DRL algorithm, OH-DRL reduces the UAV flight energy consumption by 14.4% and decreases the number of training episodes required for convergence by 66%

Space-Air-Ground integrated network (SAGIN) is a crucial component of the 6G, enabling global and seamless communication coverage. An efficient task offloading and resource allocation scheme is key in SAGIN to maximize resource utilization efficiency, meeting the stringent quality of service (QoS) requirements for different service types. In this paper, we introduce a dynamic SAGIN model featuring diverse antenna configurations, two timescale types, different channel models for each segment, and dual service types. We formulate a problem of sequential decision-making task offloading and resource allocation. Our proposed solution is an innovative online approach referred to as graphic deep reinforcement learning (GDRL). This approach utilizes a graph neural network (GNN)-based feature extraction network to identify the inherent dependencies within the graphical structure of the states. Simulation results validate that our proposed GDRL significantly outperforms state-of-the-art deep reinforcement learning (DRL) approaches by achieving the highest reward and lowest overall latency.

We consider matrix factorization (MF) with certain constraints, which finds wide applications in various areas. Leveraging variational inference (VI) and unitary approximate message passing (UAMP), we develop a Bayesian approach to MF with an efficient message passing implementation, called UAMP-MF. With proper priors imposed on the factor matrices, UAMP-MF can be used to solve a range of problems formulated as MF, such as dictionary learning, compressive sensing with matrix uncertainty, robust principal component analysis, etc. Numerical examples are provided to show that UAMP-MF significantly outperforms state-of-the-art algorithms in terms of computational complexity, recovery accuracy and robustness.

Network slicing (NS) is a promising technology that supports diverse requirements for next-generation low-latency wireless communication networks. However, the tampering attack is a rising issue of jeopardizing NS service-provisioning. To resist tampering attacks in NS networks, we propose a novel optimization framework for reliable NS resource allocation in a blockchain-secured low-latency wireless network, where trusted base stations (BSs) with high reputations are selected for blockchain management and NS service-provisioning. For such a blockchain-secured network, we consider that the latency is measured by the summation of blockchain management and NS service-provisioning, whilst the NS reliability is evaluated by the BS denial-of-service (DoS) probability. To satisfy the requirements of both the latency and reliability, we formulate a constrained computing resource allocation optimization problem to minimize the total processing latency subject to the BS DoS probability. To efficiently solve the optimization, we design a constrained deep reinforcement learning (DRL) algorithm, which satisfies both latency and DoS probability requirements by introducing an additional critic neural network. The proposed constrained DRL further solves the issue of high input dimension by incorporating feature engineering technology. Simulation results validate the effectiveness of our approach in achieving reliable and low-latency NS service-provisioning in the considered blockchain-secured wireless network.

Mobile edge computing (MEC) enables mobile devices (MDs) to offload their computational tasks to the network edge, significantly reducing transmission delay and energy consumption. In this paper, we develop a novel partial NOMA (PNOMA) based task offloading scheme in a multi-layer mobile computing network (MD-MEC-Cloud). PNOMA combines the high throughput of NOMA with the low interference of OMA for efficient, low-latency transmission. Furthermore, the PNOMA-based multi-layer collaborations enable rapid task processing across various computing requirements. We formulate a non-convex mixed-integer optimization problem aimed at minimizing the average delay across all MDs. To address this challenging problem, we propose an algorithm called reincarnating proximal policy optimization (RPPO), which uses online inference solutions to significantly reduce complexity. In addition, we incorporate accumulated apriori information into RPPO for fast retraining and design both a reward function and an evaluation phase to ensure the communication/computation constraints are met with a high probability. Simulation results demonstrate that the proposed task offloading scheme outperforms existing methods.

In this paper, we propose an efficient decoding algorithm for short low-density parity check (LDPC) codes by carefully combining the belief propagation (BP) decoding and ordered statistics decoding (OSD) algorithms. Specifically, a modified BP (mBP) algorithm is applied for a certain number of iterations prior to OSD to enhance the reliability of the received message, where an offset parameter is utilized in mBP to control the weight of the extrinsic information in message passing. By carefully selecting the offset parameter and the number of mBP iterations, the number of errors in the most reliable positions (MRPs) in OSD can be reduced by mBP, thereby significantly improving the overall decoding performance of error rate and complexity. Simulation results show that the proposed algorithm can approach the maximum-likelihood decoding (MLD) for short LDPC codes with only a slight increase in complexity compared to BP and a significant decrease compared to OSD. Specifically, the order- (m -1) decoding of the proposed algorithm can achieve the performance of the order-m OSD.

Probabilistic Movement Primitives (ProMPs) and their variants are powerful methods for enabling robots to learn complex tasks from human demonstrations, where motion trajectories are represented as stochastic processes with Gaussian assumptions. However, despite their computational efficiency, these methods have limited expressiveness in capturing the diversity found in human demonstrations, which are typically characterized by the multi-modality of motions. For example, when picking up an object partially obscured by an obstacle, some individuals may opt to go to the right, while others may choose the left side of the object. In this paper, we introduce Stein Movement Primitives (SMPs), a novel approach to probabilistic movement primitives. We formulate motion primitive adaptation as a non-parametric probabilistic inference using Stein Variational Gradient Descent (SVGD), thus avoiding any explicit posterior distribution assumptions and enabling the direct representation of the multi-modality in human demonstrations. We illustrate how our method can adapt robot motion to different scenarios while maintaining high similarity to the original demonstrations, even when the demonstrations are multi-modal. Experimentally, we demonstrate our approach to several domain adaptation problems using the LASA dataset and with a real robotic arm.

The acquisition of accurate channel state information (CSI) is of utmost importance since it provides performance improvement for wireless communication systems. However, acquiring accurate CSI, which can be done through channel estimation or channel prediction, is an intricate task due to the complexity of the time-varying and frequency selectivity of the wireless environment. To this end, we propose an efficient machine learning (ML)-based technique for channel prediction in orthogonal frequency-division multiplexing (OFDM) sub-bands. The novelty of the proposed approach lies in the training of channel fading samples used to estimate future channel behavior in selective fading.

WiFi sensing has emerged as a promising technique in the healthcare industry, enabling contact-free monitoring of vital signs by detecting changes in WiFi signals resulting from physiological activities. State-of-the-art WiFi sensing uses channel state information (CSI) to analyze signal characteristics, capturing subtle changes due to heartbeats and breathing. However, existing methods face challenges in concurrently measuring respiration and heart rates, and they exhibit high sensitivity to environmental factors and individual differences, limiting the detection accuracy of a trained model in real-world environments. In this paper, we propose a novel multi-task contrastive learning framework for concurrent detection of respiration and heart rates. We introduce multi-task learning with hard-shared layers to exploit the physiological link between breathing and heartbeat. Additionally, we leverage contrastive learning to improve our model's ability to differentiate and prioritize CSI changes related to respiratory and cardiac activi-ties. The experimental results demonstrate the proposed model's ability to accurately measure respiratory and heart rates in challenging scenarios, including long-distance and non-line-of-sight conditions, even when utilizing omnidirectional antennas.

Mounting a reconfigurable intelligent surface (RIS) on an unmanned aerial vehicle (UAV) holds promise for improving traditional terrestrial network performance. Unlike conventional methods deploying passive RIS on UAVs, this study delves into the efficacy of an aerial active RIS (AARIS). Specifically, the downlink transmission of an AARIS network is investigated, where the base station (BS) leverages rate-splitting multiple access (RSMA) for effective interference management and benefits from the support of an AARIS for jointly amplifying and reflecting the BS’s transmit signals. Considering both the non-trivial energy consumption of the active RIS and the limited energy storage of the UAV, we propose an innovative element selection strategy for optimizing the on/off status of active RIS elements, which adaptively and remarkably manages the system’s power consumption. To this end, a resource management problem is formulated, aiming to maximize the system energy efficiency (EE) by jointly optimizing the transmit beamforming at the BS, the element activation, the phase shift and the amplification factor at the active RIS, the RSMA common data rate at users, as well as the UAV’s trajectory. Due to the dynamicity nature of UAV and user mobility, a deep reinforcement learning (DRL) algorithm is designed for resource allocation, utilizing meta-learning to adaptively handle fast time-varying system dynamics. According to simulations, integrating meta-learning yields a notable 36% increase in system EE. Additionally, substituting AARIS for fixed terrestrial active RIS results in a 26% EE enhancement.

Whilst blockchain technology holds promise for secure Internet of Things (IoT) data management, its deployment in the massive Internet of Unmanned Aerial Vehicles (IoUAV) still faces significant challenges to satisfy strict requirements for low-latency query services and cost-effective resource consumption. To address these challenges, we present a lightweight multi-type data (MTD) blockchain architecture called LMChain with cost-effective MTD block scheduling. Specifically, LMChain incorporates cross-layer MTD blocks, wherein resource-constrained UAVs retain only lightweight block headers. Block bodies with high query probability are stored in fog nodes, while others are offloaded to cloud storage. Based on the MTD block structure, we develop a cost-effective block scheduling scheme to minimize the overall cost associated with LMChain storage and querying. A cooperative deep reinforcement learning (CDRL) algorithm is designed to efficiently schedule MTD blocks between the fog and cloud layers. Simulation results show that our LMChain significantly reduces the IoUAV blockchain system’s storage resource requirements and overall cost while supporting low-latency query services, making it well-suited for massive IoUAV applications.

Federated learning (FL) is a promising solution to overcome data island and privacy issues in intrusion detection systems (IDSs) for the Industrial Internet of Things (IIoT). However, the heterogeneity of various IIoT devices poses formidable challenges to FL-based intrusion detection, especially the training cost relating to delay and energy consumption. In this article, we propose a delay and energy-efficient asynchronous FL (AFL) framework for intrusion detection (DEAFL-ID) in heterogeneous IIoT. Specifically, we address the shortcomings of low efficiency and high energy consumption in existing FL-based solutions involving all idle IIoT devices. To do so, we formulate an AFL-based optimal device selection problem which aims to select high-quality training devices in advance by exploring the device advantages in detection accuracy, delay reduction, and energy saving. Subsequently, a deep Q-network (DQN)-based learning algorithm is developed to quickly solve the above high-dimensional problem. In addition, to further improve the detection performance, we build a hybrid sampling-assisted convolutional neural network (CNN)-based IDS model, which can eliminate the imbalance of IIoT data and enable the selected devices to fully extract data features. Through simulations, we demonstrate that DEAFL-ID achieves a significant improvement in training cost and detection performance compared with existing IDS schemes.

We propose real-time remote estimation of a dual-process status update system for mission-critical applications using non-orthogonal multi-access (NOMA) and orthogonal multi-access (OMA) techniques. We consider the finite block length regime so that, with OMA, the status updates of each process are transmitted using time slot sharing. Meanwhile, with NOMA, we use a power domain packet combining to allow simultaneous status updates of each process in each time slot. To compensate for the reliability loss due to short packet lengths and multi-access, we utilize packet retransmission with hybrid automatic repeat request (HARQ). Specifically, we propose OMA-HARQ and NOMA-HARQ transmission control policies, where multi-access resource sharing is jointly designed with HARQ. We propose dynamic and static scheduling policies by optimizing time-sharing or power-sharing ratios between sensors over time. The dynamic policy utilizes higher flexibility to optimize the reliability under restricted age-of-information (AoI), leading to the best estimation mean-squared-error (MSE) performance. We formulate and solve policy optimization problems, where both long-term average MSE and its variance minimization are the targets. We obtain optimal policies to minimize the composite objective function of costs of each process using the Markov decision process (MDP) framework and relative value iteration algorithm. An intensive simulation study shows significant performance improvement over existing approaches.

Fingerprint-based indoor localization holds great potential for the Internet of Things. Despite numerous studies focusing on its algorithmic and practical aspects, a notable gap exists in theoretical performance analysis in this domain. This paper aims to bridge this gap by deriving several lower bounds and approximations of mean square error (MSE) for fingerprint-based localization. These analyses offer different complexity and accuracy trade-offs. We derive the equivalent Fisher information matrix and its decomposed form based on a wireless propagation model, thus obtaining the Cramér-Rao bound (CRB). By approximating the Fisher information provided by constraint knowledge, we develop a constraint-aware CRB. To more accurately characterize nonlinear transformation and constraint information, we introduce the Ziv-Zakai bound (ZZB) and modify it for adapt deterministic parameters. The Gauss–Legendre quadrature method and the trust-region reflective algorithm are employed to make the calculation of ZZB tractable. We introduce a tighter extrapolated ZZB by fitting the quadrature function outside the well-defined domain based on the Q-function. For the constrained maximum likelihood estimator, an approximate MSE expression, which can characterize map constraints, is also developed. The simulation and experimental results validate the effectiveness of the proposed bounds and approximate MSE.

Grant-free random access is promising in achieving massive connectivity with sporadic transmissions in massive machine-type communications (mMTCs) for Internet of Things (IoT) applications, where the handshaking between the access point (AP) and users is skipped, leading to high multiple access efficiency. In grant-free random access, the AP needs to identify the active users and perform channel estimation and signal detection. Conventionally, pilot signals are required for the AP to achieve user activity detection and channel estimation before active user signal detection, which may still result in substantial overhead and latency. In this article, to further reduce the overhead and latency, we investigate the problem of grant-free random access without the use of pilot signals in a millimeter-wave (mmWave) multiple input and multiple output (MIMO) system, where the AP performs blind joint user activity detection, channel estimation, and signal detection (UACESD). We show that the blind joint UACESD can be formulated as a constrained composite matrix factorization problem, which can be solved by exploiting the structures of the channel matrix and signal matrix. Leveraging a unitary approximate message passing-based matrix factorization (UAMP-MF) algorithm, we design a message passing-based Bayesian algorithm to solve the blind joint UACESD problem. Extensive simulation results demonstrate the effectiveness of the blind grant-free random access scheme.

This article considers a challenging scenario of machine-type communications, where we assume Internet of Things (IoT) devices send short packets sporadically to an access point (AP) and the devices are not synchronized in the packet level. High-transmission efficiency and low latency are concerned. Motivated by the great potential of multiple-input-multiple-output nonorthogonal multiple access (MIMO-NOMA) in massive access, we design a grant-free MIMO-NOMA scheme, and in particular differential modulation is used so that expensive channel estimation at the receiver (AP) can be bypassed. The receiver at AP needs to carry out active device detection and multidevice data detection. The active user detection is formulated as the estimation of the common support of sparse signals, and a message-passing-based sparse Bayesian learning (SBL) algorithm is designed to solve the problem. Due to the use of differential modulation, we investigate the problem of noncoherent multidevice data detection, and develop a message-passing-based Bayesian data detector, where the constraint of differential modulation is exploited to drastically improve the detection performance, compared to the conventional noncoherent detection scheme. Simulation results demonstrate the effectiveness of the proposed active device detector and noncoherent multidevice data detector.

Wireless-networked control system (WNCS) connecting sensors, controllers, and actuators via wireless communications is a key enabling technology for highly scalable and low-cost deployment of control systems in the Industry 4.0 era. Despite the tight interaction of control and communications in WNCSs, most existing works adopt separate design approaches. This is mainly because the co-design of control-communication policies requires large and hybrid state and action spaces, making the optimal problem mathematically intractable and difficult to be solved effectively by classic algorithms. In this article, we systematically investigate deep-learning (DL)-based estimator-control-scheduler co-design for a model-unknown nonlinear WNCS over wireless fading channels. In particular, we propose a co-design framework with the awareness of the sensor’s Age-of-Information (AoI) states and dynamic channel states. We propose a novel deep reinforcement learning (DRL)-based algorithm for controller and scheduler optimization utilizing both model-free and model-based data. An AoI-based importance sampling algorithm that takes into account the data accuracy is proposed for enhancing learning efficiency. We also develop novel schemes for enhancing the stability of joint training. Extensive experiments demonstrate that the proposed joint training algorithm can effectively solve the estimation–control–scheduling co-design problem in various scenarios and provide significant performance gain compared to separate designs and some benchmark policies.

Machine learning has been considered a promising approach for indoor localization. Nevertheless, the sample efficiency, scalability, and generalization ability remain open issues of implementing learning-based algorithms in practical systems. In this paper, we establish a zero-shot learning framework that does not need real-world measurements in a new communication environment. Specifically, a graph neural network that is scalable to the number of access points (APs) and mobile devices (MDs) is used for obtaining coarse locations of MDs. Based on the coarse locations, the floor-plan image between an MD and an AP is exploited to improve localization accuracy in a floor-plan-aided deep neural network. To further improve the generalization ability, we develop a synthetic data generator that provides synthetic data samples in different scenarios, where real-world samples are not available. We implement the framework in a prototype that estimates the locations of MDs. Experimental results show that our zero-shot learning method can reduce localization errors by around 30% to 55% compared with three baselines from the existing literature.

Human-centric Metaverse services requires novel communication and networking technologies to achieve seamless connectivity for Metaverse users. Reconfigurable intelligent surface (RIS) in 5G and beyond networks can provide highly reliable communication connections, superior user quality of service (QoS), seamless user connections, and extensive signal coverage for Metaverse. Deploying RIS in unmanned aerial vehicle (UAV) networks for Metaverse can enormously improve the signal propagation environment and human-centric communication experiences. Considering the channel uncertainty of the air-ground cascade communication link in Metaverse, an RIS-aided multi-UAV cross-layer network system is proposed. Under the cross-tier interference limitation and the rate outage probability constraint, the system EE improved by maximizing the minimal energy efficiency (EE) of UAV units. Different from the existing RIS schemes, which suffer from the significant channel acquisition cost or power consumption, this paper first proposes a topology design scheme of irregular RIS, which Metaverse user only connects a few RIS elements to obtain high EE. Secondly, with the imperfect cascade channel state information (CSI) error model, the rate outage probability constraint is approximated by Bernstein type inequality to enhance the seamless human-centric connectivity service. Hence a low complexity scheme is invoked to co-design the power control parameter at the UAV transmitter and RIS reflecting phase. Finally, affluent simulation curves verify that the irregular RIS controller deployment combined with low power loss topology design and low-complexity phase shift design contributes to improve human-centric QoS for Metaverse service.

This paper proposes a blockchain-secured deep reinforcement learning (BC-DRL) optimization framework for data management and resource allocation in decentralized wireless mobile edge computing (MEC) networks. In our framework, we design a low-latency reputation-based proof-of-stake (RPoS) consensus protocol to select highly reliable blockchain-enabled BSs to securely store MEC user requests and prevent data tampering attacks. We formulate the MEC resource allocation optimization as a constrained Markov decision process that balances minimum processing latency and denial-of-service (DoS) probability. We use the MEC aggregated features as the DRL input to significantly reduce the high-dimensionality input of the remaining service processing time for individual MEC requests. Our designed constrained DRL effectively attains the optimal resource allocations that are adapted to the dynamic DoS requirements. We provide extensive simulation results and analysis to validate that our BC-DRL framework achieves higher security, reliability, and resource utilization efficiency than benchmark blockchain consensus protocols and MEC resource allocation algorithms.

This paper considers the joint optimization of resource allocation and power control for rate-splitting multiple access (RSMA) based low earth orbits (LEO) satellite-terrestrial networks, where resource sharing between terrestrial and LEO satellite communications is optimized and the LEO satellite serves multiple ground stations (GSs) simultaneously through RSMA technique. Particularly, to make full use of RSMA technique, the LEO satellite needs to appropriately schedule transmitting power to common and private streams. Therefore, the key issue is to jointly optimize the resource allocation and power control to fully utilize the benefits of resource sharing and RSMA. However, the combination of continuous power control and discrete resource allocation becomes the bottleneck for providing an effective solution with limited system information. To deal with this problem, we propose a deep reinforcement learning (DRL)-based framework which jointly employs deep Q-network (DQN) algorithm for discrete resource allocation and proximal policy optimization (PPO) algorithm for continuous power control to maximize a joint objective. Simulation experiments evaluate the performance of the proposed scheme compared with several baseline schemes and the results show the advantages of the proposed scheme.

Reconfigurable intelligent surface (RIS) has evolved into a promising approach to significantly improve both spectral and energy efficiencies of wireless communications. Different from the traditional fully-passive and fully-active RISs, a novel hybrid RIS composed of both active and passive reflecting elements has recently emerged, which can leverage their combined advantages to effectively mitigate the RIS-induced multiplicative path loss. In this paper, we investigate a hybrid active-passive RIS assisted wireless system from a rate-maximization perspective. Specifically, we firstly consider the multi-antenna multi-user system and aim to maximize the system weighted sum rate (WSR) by jointly optimizing the transmit precoding matrices and the active-passive RIS reflection matrix. The optimal semi-closed-form solution to each subproblem is obtained by jointly exploring the activeness of constraints and leveraging the majorization-minimization (MM) technique. To gain more useful insights into the rate maximization, we also study the special single-antenna single-user scenario, in which it is revealed that both the optimal transmit beamsteering direction and the optimal phase shifts at the hybrid RIS are independent of actual reflection amplitudes of the hybrid RIS. Numerical results demonstrate the lower complexity and superior rate performance of our proposed algorithms as compared to the existing schemes adopting the fully-passive RIS. Moreover, it is revealed that the hybrid RIS can strike a flexible balance between the square-order beamforming gain of the fully-passive RIS and the power amplification gain of the fully-active RIS by adjusting the active/passive element allocation.

This paper focuses on the design of high-performance polar codes with dynamic frozen bits that can be compactly specified. We split the code design problem into the frozen set design and the frozen bit expression design problems. To solve the first problem, we analyze the connection between the code minimum distance and the frozen set. This analysis leads to a novel frozen set structure that ensures a low frame error rate (FER) under successive cancellation list (SCL) decoding. Given a bit-channel reliability sequence, our frozen set structure reduces the problem of frozen set design to that of selecting three integer numbers. We develop a reinforcement learning technique to find the values of these integer numbers minimizing the FER under SCL decoding. We then propose a simple deterministic method producing efficient expressions for dynamic frozen bits. Simulation results show that the proposed compactly-specified polar codes of lengths 512 to 4096 outperform the state-of-the-art polar code constructions under SCL decoding in the high SNR regime.

In this paper, we propose a novel artificial intelligence (AI) based adaptive polar coding scheme that adapts to various channel conditions and quality of service requirements. To ensure tight adaptation, we develop a new AI-based performance prediction framework for the precoded polar codes under the successive cancellation list (SCL) decoder. This AI-based framework relies on a neural network and recent advancements in the analysis of precoded polar codes, SCL and SC decoders. Then we apply the proposed framework to optimise precoded polar codes for various target frame error rates (FER), signal-to-noise ratios (SNR) and decoding list sizes$L$, where the code length is fixed to a power of two, but the code rate may vary. We predict the throughput and maximise it over the code rates with bit-level granularity. The proposed approach paves the way towards online adaptive polar coding with high error-correction capability. The constructed codes can be compactly specified using the reliability sequence from the 5G New Radio standard and a single parameter whose value is specific to each code. The simulation results show that the proposed codes outperform 5G polar codes with CRC11 under SCL decoding with various$L$.

A three-dimensional constellation-basedM-ary differential chaos shift keying modulation system, referred to as3D-M-DCSK system, is proposed in this paper. In particular, we exploit the excellent cross-correlation properties of chaotic signals to intelligently combine the 3D constellation mapping and the DCSK scheme, thus increasing the minimum Euclidean distance (MED) between the adjacent mapped symbols. In this way, the date rate and transmission reliability of conventional two-dimensional constellation mapping can be effectively improved. The theoretical expressions for the symbol error rate (SER) of the proposed 3D-M-DCSK system are derived under the additive white Gaussian noise (AWGN) channel as well as the multipath Rayleigh fading channel. Additionally, the peak-to-average-power-ratio (PAPR) performance of the proposed 3D-M-DCSK system is carefully analyzed. Simulation results show that the proposed 3D-M-DCSK system achieves outstanding improvement in terms of transmission reliability and PAPR performance compared to the state-of-the-artM-DCSK system in both AWGN and multipath Rayleigh fading channels.

In this paper, we develop a secure multi-layer mobile edge computing (MEC) system where an unmanned aerial vehicle (UAV) equipped with a reconfigurable intelligent surface (RIS) acts as an aerial edge server and assists the offloading from multiple ground users to a base station (BS), in the presence of a full-duplex active eavesdropper (AE). To enhance the computing performance, we consider a partially offloading scheme where the computational task at each user can be executed at itself and offloaded to the UAV edge server and the BS via the UAV-enabled RIS, respectively. To maximize the total number of secure computing tasks among all users, we design a low complexity iterative algorithm by jointly optimizing the RIS phase shift, UAV deployment, power and computing resource allocation subject to certain power constraints. Numerical results show that compared to benchmark offloading schemes, our proposed UAV-RIS aided multi-layer MEC design improves the computing performance by at least 12.91%. Numerical results also demonstrate the impact of the full-duplex AE and validate the robustness of our proposed solution.

This paper considers an unmanned aerial vehicle (UAV)-enabled multi-package delivery system, where a cargo UAV collects the parcels of ground users, and finally delivers them to the destination. One key aspect of this system is to ensure a stable and reliable connection between the UAV and the base station (BS) throughout the mission for the safety of the UAV flight. To this end, we minimize the communication outage time between the UAV and the BSs while maximizing the value of the packages picked up via optimizing the UAV path and pick-up design. Although the formulated problem is difficult to solve due to its non-convexity, we propose a connectivity-aware delivery (CAD) framework that divides the delivery mission into the path design phase and the pick-up design phase to address this challenging problem. Specifically, in the path design phase, we design the optimal flight path between any two package collection points of the UAV based on deep reinforcement learning to reduce the expected communication outage duration. In the pick-up design phase, we propose a genetic algorithm based pick-up algorithm which decides the selection and order of the packages to be picked by the UAV to maximize the value of the picked-up parcels under the constraints of the UAV’s load and energy. Extensive experiments and comparative studies demonstrate the superior performance of our framework in terms of both the outage rate and total value of the picked packages.

This paper investigates an aerial reconfigurable intelligent surface (RIS)-aided communication system under the probabilistic line-of-sight (LoS) channel, where an unmanned aerial vehicle (UAV) equipped with an RIS is deployed to assist two ground nodes in their information exchange. An optimization problem with the objective of maximizing the minimum average achievable rate is formulated to jointly design the communication scheduling, the RIS’s phase shift, and the three-dimensional (3D) UAV trajectory. To solve such a non-convex problem, we propose an efficient iterative algorithm to obtain its suboptimal solution. Simulation results show that our proposed design significantly outperforms the existing schemes and provides new insights into the elevation angle and distance trade-off for the UAV-borne RIS communication system.

Network slicing is a key enabler of 5G and beyond networks to satisfy the diverse quality of service (QoS) requirements of different services simultaneously. In network slicing, radio access network (RAN) slicing is essential to establish a functional network slice by connecting mobile devices and mapping virtualized resource units to different slices. This requires a highly efficient resource allocation scheme to maximize resource utilization efficiency and meet the diverse QoS requirements. In this paper, we propose a dynamic RAN slicing model that incorporates multiple distributions to accommodate different user request types and diverse priorities among traffic types in the same slice, where the total available resources are dynamically changing over time. We formulate resource allocation as a time-sequential dynamic optimization problem that takes into account system stability, resource limitation, different timescales, long-term system performance, and user priority. We propose a deep reinforcement learning-based (DRL-based) approach referred to as prediction-aided weighted DRL (PW-DRL) to online infer the power allocation and user acceptance decisions that can maximize a predefined reward function. Additionally, a prediction network is formulated to capture the correlation between current and future states. Simulation results validate that our proposed PW-DRL significantly outperforms state-of-the-art approaches by achieving the highest long-term reward and fastest convergence.

The integration of mobile edge computing (MEC) and wireless power transfer (WPT) can effectively improve the computing ability and energy sustainability of energy-constrained wireless devices in the Internet of Things (IoT) networks. Intelligent reflecting surface (IRS) has recently emerged as an effective technique to improve the performance of wireless systems by intelligently reconfiguring wireless environments. This paper studies the exploitation of IRS to improve the secure computation performance of WPT-MEC systems with a passive eavesdropper. A wireless access point (AP) first charge multiple users with the emitted energy signals, and then the users perform local computing and partial offloading to complete their computation tasks with the harvested energy in the presence of an eavesdropper, where the local computing can be executed during the whole process of WPT and offloading. Meanwhile, deploying IRS can improve the energy capture and secure offloading performance of the users. We maximize the secure computation task bits of users by jointly optimizing the AP energy transmit beamforming, the IRS phase shifts, the transmit power, users’ offloading time, and the local computation frequency of users, which are tangled with each other. An iterative optimal algorithm is developed to solve this non-convex problem by combining Taylor expansion method, semidefinite relaxation (SDR) algorithm, the Lagrange duality theory and Karush-Kuhn-Tucker (KKT) conditions. The numerical results show that the proposed scheme can effectively increase the secure computation task bits compared with other benchmark schemes, especially for the maximum transmit power of AP, the improvement is above 45$\%$.

The ever-increasing number of ubiquitous Internet of Things (IoT) applications entails a high demand for scarce communication and network resources. To meet this stringent requirement, mobile edge computing (MEC) is envisioned as a transformative technique to significantly streamline the existing network operations. Recently, device-to-device (D2D) communication has been proposed as a promising technology in 5G and beyond networks with a significantly increased transmission efficiency, especially suitable for small-packet task exchanges. In this paper, we incorporate D2D communication into the multi-layer computing network and propose a full-dimensional task offloading scheme by jointly optimizing task offloading decisions and computation/communication resource allocation. We formulate it as mixed-integer nonlinear programming (MINLP) problem, where the optimal branch-and-bound (B&B) algorithm with the full strong branching (FSB) variable selection policy features an extremely high complexity. To address this challenge, we propose inverse reinforcement learning with graph neural networks (GIRL) to generate a new variable selection policy that closely matches the FSB variable selection. Without sacrificing the global optimality, the GIRL can directly infer the variable selection with a much lower complexity, significantly accelerating the original B&B algorithm. Simulation results show that the GIRL achieves a lower complexity without sacrificing the global optimality. Furthermore, our proposed full-dimensional task offloading scheme achieves better performance than the existing schemes in terms of average delay for all mobile devices (MDs).

Remote state estimation of large-scale distributed dynamic processes plays an important role in Industry 4.0 applications. In this paper, we focus on the transmission scheduling problem of a remote estimation system. First, we derive some structural properties of the optimal sensor scheduling policy over fading channels. Then, building on these theoretical guidelines, we develop a structure-enhanced deep reinforcement learning (DRL) framework for optimal scheduling of the system to achieve the minimum overall estimation mean-square error (MSE). In particular, we propose a structure-enhanced action selection method, which tends to select actions that obey the policy structure. This explores the action space more effectively and enhances the learning efficiency of DRL agents. Furthermore, we introduce a structure-enhanced loss function to add penalties to actions that do not follow the policy structure. The new loss function guides the DRL to converge to the optimal policy structure quickly. Our numerical experiments illustrate that the proposed structure-enhanced DRL algorithms can save the training time by 50% and reduce the remote estimation MSE by 10% to 25%, when compared to benchmark DRL algorithms. In addition, we show that the derived structural properties exist in a wide range of dynamic scheduling problems that go beyond remote state estimation.

We investigate signal detection in multiple-input-multiple-output (MIMO) communication systems with hardware impairments, such as power amplifier nonlinearity and in-phase/quadrature imbalance. To deal with the complex combined effects of hardware imperfections, neural network (NN) techniques, in particular deep neural networks (DNNs), have been studied to directly compensate for the impact of hardware impairments. However, it is difficult to train a DNN with limited pilot signals, hindering its practical application. In this work, we investigate how to achieve efficient Bayesian signal detection in MIMO systems with hardware imperfections. Characterizing combined hardware imperfections often leads to complicated signal models, making Bayesian signal detection challenging. To address this issue, we first train an NN to ‘model’ the MIMO system with hardware imperfections and then perform Bayesian inference based on the trained NN. Modelling the MIMO system with NN enables the design of NN architectures based on the signal flow of the MIMO system, minimizing the number of NN layers and parameters, which is crucial to achieving efficient training with limited pilot signals. We then represent the trained NN with a factor graph, and design an efficient message passing based Bayesian signal detector, leveraging the unitary approximate message passing (UAMP) algorithm. The implementation of a turbo receiver with the proposed Bayesian detector is also investigated. Extensive simulation results demonstrate that the proposed technique delivers remarkably better performance than state-of-the-art methods.

Beam management is one of the most challenging issues in low-earth orbit (LEO) satellites, where the antenna direction is dynamic, and the storage, computing, and communication resources are limited. In this work, we develop a zero-shot learning approach to maximize the average data rate of a user by optimizing beam tracking policy in both spatial and temporal domains. In the spatial domain, we develop a graph recurrent neural network (GRNN) with only 60 training parameters to predict the next beam direction. Compared with an existing recurrent neural network with more than 30, 000 parameters, the GRNN can reduce the storage requirement remarkably. In the temporal domain, we design a Twin deep Q-network (DQN) to determine the time to trigger beam tracking. To improve the generalization ability of GRNN and Twin DQN, we apply meta-learning to train them with different antenna directions and test them in unseen scenarios. Simulation results show that our zero-shot learning approach does not need new data samples in unseen scenarios. Thus, it does not introduce extra computing and communication overheads. Additionally, the achievable average data rate is around 10% to 20% higher than different benchmarks.

In this paper, we develop a deep learning-based bandwidth allocation policy that is: 1) scalable with the number of users and 2) transferable to different communication scenarios, such as non-stationary wireless channels, different quality-of-service (QoS) requirements, and dynamically available resources. To support scalability, the bandwidth allocation policy is represented by a graph neural network (GNN), with which the number of training parameters does not change with the number of users. To enable the generalization of the GNN, we develop a hybrid-task meta-learning (HML) algorithm that trains the initial parameters of the GNN with different communication scenarios during meta-training. Next, during meta-testing, a few samples are used to fine-tune the GNN with unseen communication scenarios. Simulation results demonstrate that our HML approach can improve the initial performance by 8.79%, and sample efficiency by 73%, compared with existing benchmarks. After fine-tuning, our near-optimal GNN-based policy can achieve close to the same reward with much lower inference complexity compared to the optimal policy obtained using iterative optimization. Numerical results validate that our HML can reduce the computation time by approximately 200 to 2000 times than the optimal iterative algorithm.

Pattern reconfigurable multiple-input multiple-output (PR-MIMO) can manipulate the wireless channel according to different communication requirements. In this paper, we discuss the potential of constructive interference (CI)-based symbol-level precoding (CI-SLP) in PR-MIMO communication systems. The joint design problem that optimizes the SLP strategy and the radiation pattern of PR-MIMO for phase-shift keying (PSK) modulation is formulated to maximize the worst serviced user’s communication quality. Since the optimization variables are softly-coupled, we employ the alternating optimization framework to decompose the joint design problem into the SLP design sub-problem and the pattern design sub-problem. We simplify the pattern design sub-problem and propose an interior-point algorithm, where a sequential optimization-based scheme is further proposed as a sub-optimal solution with low complexity. Furthermore, the discussion is extended to quadrature amplitude modulation (QAM) modulated systems, where a special stopping criterion is proposed to guarantee the performance gain of the proposed scheme. The practical realization of the designed reconfigurable antenna array is also discussed, where we propose a design scheme using programmable metasurface antennas based on time-division switching to enable quick and adaptive pattern reconfiguration. Numerical results demonstrate that the radiation pattern configurability can further enhance the benefit of SLP over conventional precoding approaches.

Reconfigurable intelligent surfaces (RIS) can dynamically reconstruct wireless environments to enhance spectral efficiency. However, most existing studies have ignored the impact of the phase error and imperfect amplitude gain of the RIS. In this paper, we investigate the practical RIS-aided multiuser communication systems by maximizing the sum of users’ average achievable rate, filling the current research gap. Specifically, a novel RIS phase shift design approach, namely amplitude feedback (AF), is proposed by utilizing the coupling relationship between the amplitude and phase to derive the optimal phase shift under the worst phase error. The feasibility of AF is demonstrated by proving the measurability of amplitude response through the electromagnetic theory. We propose a channel estimation design with low pilot overhead and provide an effective closed-form achievable rate to approximate the average achievable rate. Moreover, an efficient optimization algorithm is proposed to achieve the optimal closed-form precoding at the base station and the optimal trade-off design between the amplitude and phase of RIS, and the algorithm is extended to active RIS systems. Numerical results demonstrate that our proposed AF method and optimization algorithm can efficiently improve the performance in terms of achievable sum rate compared to existing methods.

In this paper, we optimize the resource allocation for channel estimation and data transmission and the packet size to maximize the resource utilization efficiency subject to the constraints of ultra-reliable low-latency communications (URLLC). With practical channel estimation, the packet error probability (PEP) does not have a closed-form expression. To solve the problem, we develop novel model-based and model-free unsupervised deep learning algorithms to train a deep neural network for resource allocation and data transmission. Two types of reliability constraints are considered over a wireless link: 1) average PEP constraint; 2) constraint on the probability that PEP is higher than a threshold. The simulation results show that the learning algorithms can guarantee both types of reliability constraints. Compared with a benchmark that maximizes the number of symbols for data transmission and uses the maximum ratio transmission precoding, the learning method with the codebook-based precoding achieves a lower average signal-to-interference-plus-noise ratio (SINR), but improves the resource utilization efficiency by three times. It is because the resource utilization efficiency of URLLC is dominated by the tail distribution of SINR, not the average SINR, and the SINR of the benchmark has a much longer tail distribution than the learning method.

Network slicing is key to enabling 6G and beyond networks to simultaneously meet the diverse quality of service (QoS) requirements of various services. In network slicing, radio access network (RAN) slicing is essential to establish a functional network slice by connecting mobile devices and mapping virtualized resource units to different slices. This demands an efficient resource allocation scheme that maximizes resource utilization while meeting diverse QoS requirements. In this paper, we propose a new dynamic resource allocation framework that encompasses three types of services. We formulate a dynamic resource allocation problem that features a mixed action space and has both long-term power and instantaneously available resource unit constraints. We propose a deep reinforcement learning (DRL)-based approach referred to as prediction-aided weighted DRL (PW-DRL), which infers the power allocation and user acceptance decisions to maximize a predefined reward function. Additionally, we propose a prediction network that significantly improves the DRL learning process under limited resources by supplying future state information. Simulation results validate that our proposed PW-DRL significantly outperforms state-of-art DRL approaches by achieving the highest long-term reward and fastest convergence.

Cell-free massive multiple-input multiple-output (MIMO) deploys a large number of distributed access points (APs) without cell edges, offering seamless connectivity with significantly increased spectral efficiency and system capacity, but suffering degraded energy efficiency. In this paper, we develop a green energy scheme by simultaneously optimizing power allocation and AP selection. We formulate it as a non-convex mixed-integer nonlinear programming problem (MINLP), which is NP-hard. To address this challenging problem, we propose a learning-based algorithm that embeds non-convex optimization into contemporary deep reinforcement learning (DRL), referred to as optimization-embedded soft actor-critic with graph transformer networks (OSAC-G). OSAC-G enjoys the benefits of directly online inferring solutions for the non-convex problem with a much lower computational complexity compared to conventional non-convex optimization. Simulation results demonstrate that the green energy scheme significantly decreases energy consumption compared to the existing ones.

The rapid development of Internet of Things (IoT) applications requires efficient computing and communication resource allocation strategies to streamline the existing network operations. These strategies could be formulated as mixed-integer nonlinear programming (MINLP) problems, where the optimal branch-and-bound (B&B) with the full strong branching (FSB) variable selection policy features an extremely high complexity. We propose inverse reinforcement learning with graph neural networks (GNNIRL) to generate a new variable selection policy that closely matches the FSB variable selection. Without sacrificing the optimality, the GNNIRL can directly infer the variable selection with a significantly lower complexity, which is also verified by simulation.

Remote state estimation of large-scale distributed dynamic processes plays an important role in Industry 4.0 applications. In this paper, by leveraging the theoretical results of structural properties of optimal scheduling policies, we develop a structure-enhanced deep reinforcement learning (DRL) framework for optimal scheduling of a multi-sensor remote estimation system to achieve the minimum overall estimation mean-square error (MSE). In particular, we propose a structure-enhanced action selection method, which tends to select actions that obey the policy structure. This explores the action space more effectively and enhances the learning efficiency of DRL agents. Furthermore, we introduce a structure-enhanced loss function to add penalty to actions that do not follow the policy structure. The new loss function guides the DRL to converge to the optimal policy structure quickly. Our numerical results show that the proposed structure-enhanced DRL algorithms can save the training time by 50% and reduce the remote estimation MSE by 10% to 25%, when compared to benchmark DRL algorithms.

In this work, we propose a novel decoding algorithm for short block codes based on an edge-weighted graph neural network (EW-GNN). The EW-GNN decoder operates on the Tanner graph with an iterative message-passing structure, which algorithmically aligns with the conventional belief propagation (BP) decoding method. In each iteration, the “weight” on the message passed along each edge is obtained from a fully connected neural network that has the reliability information from nodes/edges as its input. Compared to existing deep-learning-based decoding schemes, the EW-GNN decoder is characterised by its scalability, meaning that 1) the number of trainable parameters is independent of the codeword length, and 2) an EW-GNN decoder trained with shorter/simple codes can be directly used for longer/sophisticated codes of different code rates. Furthermore, simulation results show that the EW-GNN decoder outperforms the BP and deep-learning-based BP methods from the literature in terms of the decoding error rate.

In this paper, we present a novel unmanned aerial vehicle (UAV) aided physical layer authentication (PLA) frame-work to detect the origin of the received signal between a legitimate transmitter and a malicious adversary, based on the physical properties of channel characteristics and geographical locations. First, we model the authentication hypothesis test at the UAV based on the signal-to-noise ratio (SNR) of each transmission and analyze the probability density functions (PDFs) of SNR differences. Then, we derive the explicit expressions of false alarm probability (FAP) and miss detection probability (MDP), both of which depict the occurrence of detection error. Next, with the aim of minimizing the MDP subject to a given FAP constraint, the detection threshold and UAV deployment are jointly optimized. Numerical results verify the accuracy of our derived expressions and demonstrate the impact of distribution rate and adversary’s location on the detection performance. Moreover, numerical results also highlight the superiority of our proposed solution using SNR differences over benchmark strategy in high-rise urban environment.

This paper considers computation offloading for mobile applications with task-dependency requirements in mobile edge computing (MEC) systems. Based on the online arrival patterns and various delay constraints of practical applications, we focus on minimizing the system deadline violation ratio (DVR) to improve the overall reliability performance. Specifically, we propose a DVR minimization computation offloading scheme with task migration and merging, in which the task migration and merging model is designed to construct an overall directed acyclic graph (DAG) for all currently dependent tasks. We consider a multi-slot MEC system where applications arrive slot-by-slot without prior knowledge of future arrivals. Then given the number of application arrivals at each time slot, we equivalently transform the DVR minimization problem into a problem that maximizes the number of completed applications in a finite time horizon. The above problem is challenging to determine the optimal task execution order for different applications with various task dependencies and delay constraints. To address this, we develop a migration-enabled multi-priority task sequencing algorithm, which creatively introduces several task priority metrics and determines the optimal task execution order. Then, a deep deterministic policy gradient (DDPG)-based learning algorithm is developed to find the optimal offloading policy. Experimental results demonstrate that the proposed scheme can reduce the system DVR by 60.34%~70.3% compared with existing benchmark schemes under various network scenarios.

Rate-Splitting Multiple Access (RSMA) has recently found favour in the multi-antenna-aided wireless downlink, as a benefit of relaxing the accuracy of Channel State Information at the Transmitter (CSIT), while in achieving high spectral efficiency and providing security guarantees. These benefits are particularly important in high-velocity vehicular platoons since their high Doppler affects the estimation accuracy of the CSIT. To tackle this challenge, we propose an RSMA-based Internet of Vehicles (IoV) solution that jointly considers platoon control and FEderated Edge Learning (FEEL) in the downlink. Specifically, the proposed framework is designed for transmitting the unicast control messages within the IoV platoon, as well as for privacy-preserving FEEL-aided downlink Non-Orthogonal Unicasting and Multicasting (NOUM). Given this sophisticated framework, a multi-objective optimization problem is formulated to minimize both the latency of the FEEL downlink and the deviation of the vehicles within the platoon. To efficiently solve this problem, a Block Coordinate Descent (BCD) framework is developed for decoupling the main multi-objective problem into two sub-problems. Then, for solving these non-convex sub-problems, a Successive Convex Approximation (SCA) and Model Predictive Control (MPC) method is developed for solving the FEEL-based downlink problem and platoon control problem, respectively. Our simulation results show that the proposed RSMA-based IoV system outperforms both the popular Multi-User Linear Precoding (MU–LP) and the conventional Non-Orthogonal Multiple Access (NOMA) system. Finally, the BCD framework is shown to generate near-optimal solutions at reduced complexity.

Emerging Internet of Things applications pursue both data timeliness and fidelity at the fusion center (FC), raising challenges for network design. This work investigates the optimal node number achieving the best timeliness-fidelity tradeoff. Specifically, we consider a wireless network where homogeneous sensors observe one source simultaneously and deliver their observations to the FC over orthogonal block fading channels. Two scenarios are considered: The FC waits for observations from all nodes and the FC waits for observations from partial nodes. We evaluate the data timeliness and fidelity using the age of information (AoI) and the mean squared error (MSE), respectively. We first present a tight approximation of the average AoI in closed-form and derive a tight lower bound on the MSE of the sensing system. Then, sub-optimal numbers of sensor nodes minimizing a weighted-sum of the average AoI and the MSE are obtained in closed-forms for both scenarios based on high signal-to-noise ratio regime analysis. Iteration algorithms are further provided to approach the optimums. It is shown that the optimal partial number of nodes is proportional to the square root of total number of nodes asymptotically. Numerical results validate the accuracy and effectiveness of the solutions in improving the timeliness-fidelity tradeoff.

In this paper, we propose a reconfigurable intelligent surface (RIS)-assisted frequency-modulated (FM) differential chaos shift keying (DCSK) scheme with simultaneous wireless information and power transfer (SWIPT), called RIS-FM-DCSK-SWIPT scheme, for low-power, low-cost, and high-reliability wireless communication networks. In particular, the proposed scheme is developed under a non-linear energy-harvesting (EH) model which can accurately characterize the practical situation. The proposed RIS-FM-DCSK-SWIPT scheme has an appealing feature that it does not require channel state information, thus avoiding the complex channel estimation. We further derive the closed-form theoretical expressions for the energy shortage probability and bit error rate (BER) of the proposed scheme over the multipath Rayleigh fading channel. In addition, we investigate the influence of key parameters on the performance of the proposed transmission scheme in two different scenarios, i.e., RIS-assisted access point (RIS-AP) and dual-hop communication (RIS-DH). Finally, we carry out various Monte-Carlo experiments to verify the accuracy of the theoretical derivation, illustrate the performance advantage of the proposed scheme, and give some design insights for future study.

To achieve reliable and efficient transmissions in free-space optical (FSO) communication, this paper designs a new protograph low-density parity-check (PLDPC) coded generalized spatial multipulse position modulation (GSMPPM) system over weak turbulence channels. Specifically, we investigate the PLDPC code, generalized space shift keying (GSSK) modulation, and MPPM constellation. First, we propose a type of novel GSMPPM constellations that intelligently integrates the GSSK into MPPM, referred to as asymmetric dual-mode (ADM) constellations, so as to improve the performance of the PLDPC-coded GSMPPM system. Furthermore, exploiting a protograph extrinsic information transfer (PEXIT) algorithm, we construct a type of improved PLDPC code, referred to as I-PLDPC code, which outperforms the existing PLDPC codes over weak turbulence channels. Analytical and simulation results show that the proposed ADM constellations and the proposed I-PLDPC code can obtain noticeable performance gains over their counterparts. Therefore, the proposed PLDPC-coded GSMPPM system with ADM constellations is competent to satisfy the high-reliability requirement for FSO applications.

The booming of the industrial Internet of Things (IIoT) brings an exponential increase in industrial devices, calling for more flexible and low-cost communications. The fifth generation and beyond (B5G) communication technologies provide a dedicated solution by supporting two industry-targeted technologies: Massive machine-type communications (mMTC) and ultra reliable low-latency communications (URLLC). In this article, we design a B5G-aided quality test system in advanced manufacturing, where various sensors are connected to the base station (BS) and send contextual information via mMTC. The BS and quality test machine transmit short length commands and small size feedback to each other, respectively, via URLLC. We formulate a long-term optimization problem to improve the product qualification rate by maximizing the expected average reward with limited testing capacity and changing configurations. To address this problem, we develop a novel context-aware combinatorial quality test (CC-QT) algorithm based on bandit learning (BL), which integrates contextual information to predict the product quality, and a combinatorial method to decrease the complexity of the BL process. Furthermore, we derive a performance upper bound of the proposed CC-QT and analyze its computational complexity. Experimental results illustrate the performance of CC-QT and substantiate its superiority over the existing algorithms.

In this paper, we utilize a concatenation scheme to construct new families of quantum error correction codes achieving the quantum Gilbert-Varshamov (GV) bound asymptotically. Weconcatenate alternant codes with any linear code achievingthe classical GV bound to construct Calderbank-Shor-Steane (CSS) codes. We show that the concatenated code can achieve the quantum GV bound asymptotically and can approach the Hashing bound for asymmetric Pauli channels. By combing Steane’s enlargement construction of CSS codes, we derive a family of enlarged stabilizer codes achieving the quantum GV bound for enlarged CSS codes asymptotically. Asapplications, we derive two families of fast encodable and decodable CSS codes with parameters$\mathscr {Q}_{1}=[[N,\Omega (\sqrt {N}),\Omega (\sqrt {N})]]$, and$\mathscr {Q}_{2}=[[N,\Omega (N/\log N),\Omega (N/\log N)/\Omega (\log N)]]$. We show that$\mathscr {Q}_{1}$can be encoded very efficiently by circuits of size$O(N)$and depth$O(\sqrt {N})$. For an input error syndrome,$\mathscr {Q}_{1}$can correct any adversarial error of weight up to half the minimum distance bound in$O(N)$time.$\mathscr {Q}_{1}$can also be decoded in parallel in$O(\sqrt {N})$time by using$O(\sqrt {N})$classical processors. For an input error syndrome, we proved that$\mathscr {Q}_{2}$can correct a linear number of${X}$-errors with high probability and an almost linear number of${Z}$-errors in$O(N)$time. Moreover,$\mathscr {Q}_{2}$can be decoded in parallel in$O(\log (N))$time by using$O(N)$classical processors.

Deep neural network (DNN)-task enabled mobile edge computing (MEC) is gaining ubiquity due to outstanding performance of artificial intelligence. By virtue of characteristics of DNN, this paper develops a joint design of task partitioning and offloading for a DNN-task enabled MEC network that consists of a single server and multiple mobile devices (MDs), where the server and each MD employ the well-trained DNNs for task computation. The main contributions of this paper are as follows: First, we propose a layer-level computation partitioning strategy for DNN to partition each MD's task into the subtasks that are either locally computed at the MD or offloaded to the server. Second, we develop a delay prediction model for DNN to characterize the computation delay of each subtask at the MD and the server. Third, we design a slot model and a dynamic pricing strategy for the server to efficiently schedule the offloaded subtasks. Fourth, we jointly optimize the design of task partitioning and offloading to minimize each MD's cost that includes the computation delay, the energy consumption, and the price paid to the server. In particular, we propose two distributed algorithms based on the aggregative game theory to solve the optimization problem. Finally, numerical results demonstrate that the proposed scheme is scalable to different types of DNNs and shows the superiority over the baseline schemes in terms of processing delay and energy consumption.

Integrating mobile edge computing (MEC) in the ultra-dense network (UDN) is a key enabler to meet the service demand by allowing smart devices to perform uninterrupted task offloading via densely deployed MEC servers. In most cases, the smart devices randomly move around the whole network. Consequently, the popular ‘`MEC-centralized decision’' offloading approach could be inapplicable, as joint decision-making among multiple MEC servers becomes difficult due to time synchronization and information exchange overhead. In this paper, we take a user-centric approach to minimize a long-term delay for a given task duration under a price budget constraint. To address this problem, we develop a novel contextual sleeping bandit learning (CSBL) algorithm, which integrates contextual information and sleeping characteristic to accelerate the learning convergence and leverage Lyapunov optimization to deal with the price budget constraint. Furthermore, we extend to a multiple offloading scenario where multiple MEC servers can be selected in each offloading round and propose a CSBL-multiple (CSBL-M) algorithm to address the exponential increase of the offloading selections. For both CSBL and CSBL-M, we derive the upper bounds of learning regret and provide rigorous proofs that they asymptotically approach the Oracle algorithm within bounded deviations for finite task duration.

With the development of MEC services, MEC brokers will emerge to facilitate the purchase and management of resources for individual MEC users. Both data communication and computing resources offered by MEC service providers can be purchased by pay-as-you-go (PAYG) or reserved plans. Besides data and computing plans for each type of resource, we also consider combo plans specifically designed for MEC services covering both resources. In this paper, we propose a smart online aggregated reservation (SOAR) framework for MEC brokers to minimize their cost of reserving resources for multiple users without the knowledge of future demands. In our framework, a task aggregation algorithm is designed to aggregate the users’ demands in each PAYG billing cycle to improve the plan utilization, and plan reservation algorithms are proposed to decide when to reserve which plans. The performance gap (competitive ratio) between SOAR and optimal solution which knows all future demands in advance, is analyzed and derived in closed-form. The performance gap is proved to be the minimum among all deterministic online algorithms. Trace-driven simulations verify the cost advantage of our SOAR framework, which can save nearly 40 percent of cost for users through the brokerage service.

A new differential chaos shift keying scheme with multidimensional index modulation, referred to as MIM-DCSK scheme, is proposed in this paper. This design objective of the proposed MIM-DCSK scheme is to enhance the data rate, energy efficiency, and spectral efficiency of traditional DCSK scheme. In the proposed MIM-DCSK scheme, in addition to the information bits allocated for physical transmission, multidimensional transmission entities, namely the time slot, carrier, and Walsh code are simultaneously considered as indices to convey additional information bits, thus achieving high data rate, spectral efficiency, and energy efficiency. The theoretical bit-error-rate (BER) expressions of the MIM-DCSK scheme are derived over additive white Gaussian noise (AWGN) and multipath Rayleigh fading channels. Furthermore, the data rate, complexity, spectral efficiency, and energy efficiency of the MIM-DCSK scheme are analyzed. Simulation results verify the accuracy of the theoretical analysis and illustrate the superiority of the proposed scheme. The proposed MIM-DCSK scheme is a promising solution for low-power and low-cost short-wireless communications.

Remote state estimation where sensors send their measurements of distributed dynamic plants to a remote estimator over shared wireless resources is essential for mission-critical applications of Industry 4.0. Existing algorithms on dynamic radio resource allocation for remote estimation systems assumed oversimplified wireless communications models and can only work for small-scale settings. In this work, we consider remote estimation systems with practical wireless models over the orthogonal multiple-access and non-orthogonal multiple-access schemes. We derive necessary and sufficient conditions under which remote estimation systems can be stabilized. The conditions are described in terms of the transmission power budget, channel statistics, and plants’ parameters. For each multiple-access scheme, we formulate a novel dynamic resource allocation problem as a decision-making problem for achieving the minimum overall long-term average estimation mean-square error. Both the estimation quality and the channel quality states are taken into account for decision making. We systematically investigated the problems under different multiple-access schemes with large discrete, hybrid discrete-and-continuous, and continuous action spaces, respectively. We propose novel action-space compression methods and develop advanced deep reinforcement learning algorithms to solve the problems. Numerical results show that our algorithms solve the resource allocation problems effectively and provide much better scalability than the literature.

Reconfigurable multiple-input multiple-output can provide performance gains over traditional MIMO by reshaping the channels, i.e., introducing more channel realizations. In this paper, we focus on the achievable rate maximization pattern design for reconfigurable MIMO systems. Firstly, we introduce the matrix representation of pattern reconfigurable MIMO (PR-MIMO), based on which a pattern design problem is formulated. To further reveal the effect of the radiation pattern on the wireless channel, we consider pattern design for both the single-pattern case where the optimized radiation pattern is the same for all the antenna elements, and the multi-pattern case where different antenna elements can adopt different radiation patterns. For the single-pattern case, we show that the pattern design is equivalent to a redistribution of gains among all scattering paths, and an eigenvalue optimization based solution is obtained. For the multi-pattern case, we propose a sequential optimization framework with manifold optimization and eigenvalue decomposition to obtain near-optimal solutions. Numerical results validate the superiority of PR-MIMO systems over traditional MIMO in terms of achievable rate, and also show the effectiveness of the proposed solutions.

The rateless multiple access (RMA) scheme is a promising distributed multiple access scheme to achieve simultaneous high reliability, low latency and massive connectivity. In this paper, we investigate the maximum likelihood (ML) decoding performance of the RMA scheme in an Additive white Gaussian noise (AWGN) channel with binary phase-shift keying (BPSK) modulation. For the first time, this paper derives the ensemble weight distribution of the RMA scheme. We derive an upper bound on the decoding error performance of the RMA scheme under ML decoding in an AWGN channel with BPSK modulation. Using the derived bound as the fitness function, we adopt the continuous genetic algorithm to optimize the parameters of the RMA scheme. Simulation results show the tightness of the derived bound and the superiority of the optimized degree distribution over the conventional degree distributions.

Remote state estimation, where many sensors send their measurements of distributed dynamic plants to a remote estimator over shared wireless resources, is essential for mission-critical applications of Industry 4.0. Most of the existing works on remote state estimation assumed orthogonal multiple access and the proposed dynamic radio resource allocation algorithms can only work for very small-scale settings. In this work, we consider a remote estimation system with non-orthogonal multiple access. We formulate a novel dynamic resource allocation problem for achieving the minimum overall long-term average estimation mean-square error. Both the estimation quality state and the channel quality state are taken into account for decision making at each time. The problem has a large hybrid discrete and continuous action space for joint channel assignment and power allocation. We propose a novel action-space compression method and develop an advanced deep reinforcement learning algorithm to solve the problem. Numerical results show that our algorithm solves the resource allocation problem effectively, presents much better scalability than the literature, and provides significant performance gain compared to some benchmarks.

The implementation of integrated sensing and communication (ISAC) highly depends on the effective beamforming design exploiting accurate instantaneous channel state information (ICSI). However, channel tracking in ISAC requires large amount of training overhead and prohibitively large computational complexity. To address this problem, in this paper, we focus on ISAC-assisted vehicular networks and exploit a deep learning approach to implicitly learn the features of historical channels and directly predict the beamforming matrix for the next time slot to maximize the average achievable sum-rate of system, thus bypassing the need of explicit channel tracking for reducing the system signaling overhead. To this end, a general sum-rate maximization problem with Cramer-Rao lower bounds-based sensing constraints is first formulated for the considered ISAC system. Then, a historical channels-based convolutional long short-term memory network is designed for predictive beamforming that can exploit the spatial and temporal dependencies of communication channels to further improve the learning performance. Finally, simulation results show that the proposed method can satisfy the requirement of sensing performance, while its achievable sum-rate can approach the upper bound obtained by a genie-aided scheme with perfect ICSI available.

This paper investigates how to build an active attack detection framework that is driven by fundamental channel modeling and practical wireless datasets. Firstly, we propose the concept of interpretable channel fingerprints (ICFs), which correspond to the spatial-temporal parameters in real physical wireless signal propagation channels. Based on this, we design an adversarial autoencoder (AAE) with a semi-supervised learning network, which takes as inputs the power spectrum of quantized ICFs and enables small sample learning multiclassification tasks for different types of wireless channel active attacks. We have experimentally verified the performance of our AAE network using the Wireless InSite ray tracing software. Our results show that the proposed semi-supervised network outperforms the fully-supervised network especially in small sample conditions. We highlight the need for careful selection of the hyperparameters for learning rate and mini-batch size, and the system parameters for the ICF power spectrum resolution. We show that the detection accuracy of the proposed AAE model can reach more than 98% with only a small number of input samples.

Rate-splitting multiple access (RSMA) is an emerging multiple access strategy, with non-orthogonal multiple access (NOMA) and space division multiple access (SDMA) as its two special cases. RSMA divides the messages required by users into the private and common parts, and the private streams are naturally suitable for the secure transmission. In this paper, we establish a unified rate-splitting framework to ensure the secure transmission for the three multiple access systems mentioned above. The precoders at the multi-antenna transmitter are conjointly optimized to improve the transmission rate of common message. Successive interference cancellation (SIC) is utilized, and the private message for the downlink broadcasting RSMA network can be effectively hidden in the high-power common message. Simulation results demonstrate that the proposed rate-splitting framework can effectively guarantee the secure transmission of the secrecy information.

Pursuing both data freshness and preciseness in emerging Internet of Things applications brings big challenge for network design. This work investigates the optimal node number achieving the best fidelity-timeliness tradeoff. Specifically, we consider a wireless sensor network where multiple sensors observe one source simultaneously and deliver the observations to Fusion Center (FC) over orthogonal channels. We assume that the FC waits for observations from only partial nodes. We evaluate the fidelity and timeliness using mean squared error (MSE) and age of information (AoI) metric respectively. Firstly, explicit expressions of AoI and MSE are derived. Secondly, a closed-form approximate optimal number of sensor nodes is obtained to achieve the minimum weighted-sum of AoI and MSE. Iteration algorithm is further provided to approach the optimum. It is proved that the optimal number of partial nodes is proportional to the square root of number of total nodes. Numerical results verify that the proposed near-optimal node number is accurate and can significantly improve the fidelity-timeliness performance.

In this paper, we design the joint decoding (JD) of non-orthogonal multiple access (NOMA) systems employing short block length codes. We first proposed a low-complexity soft-output ordered-statistics decoding (LC-SOSD) based on a decoding stopping condition, derived from approximations of the a-posterior probabilities of codeword estimates. Simulation results show that LC-SOSD has the similar mutual information transform property to the original SOSD with a significantly reduced complexity. Then, based on the analysis, an efficient JD receiver which combines the parallel interference cancellation (PIC) and the proposed LC-SOSD is developed for NOMA systems. Two novel techniques, namely decoding switch (DS) and decoding combiner (DC), are introduced to accelerate the convergence speed. Simulation results show that the proposed receiver can achieve a lower bit-error rate (BER) compared to the successive interference cancellation (SIC) decoding over the additive-white-Gaussian-noise (AWGN) and fading channel, with a lower complexity in terms of the number of decoding iterations.

The industrial Internet of Things (IIoT) interconnects an exponential number of industrial devices, and more flexible and low-cost communications are widely in demand. The fifth-generation (5G) communication provides two industrial-target technologies, massive machine-type communications (mMTC) and ultra-reliable low-latency communications (URLLC), to meet the demand. We design a 5G-aided quality test system, where various sensors are connected to the base station (BS) and send contextual information via mMTC. The BS and quality test machine transmit short-length commands and small-size feedback to each other via URLLC. The problem is formulated as a long-term optimization one with the purpose of improving the product qualification rate. We develop a novel contextual combinatorial quality test (CC-QT) algorithm to solve the problem. We further derive a performance upper bound of the proposed CC-QT and analyze its computational complexity. Experimental results illustrate the performance of CC-QT and substantiate its superiority over the existing algorithms.

In this paper, we focus on the pattern reconfigurable multiple-input multiple-output (PR-MIMO), a technique that has the potential to bridge the gap between electro-magnetics and communications towards the emerging Electro-magnetic Information Theory (EIT). Specifically, we focus on the pattern design problem aimed at maximizing the channel capacity for reconfigurable MIMO communication systems, where we firstly introduce the matrix representation of PR-MIMO and further formulate a pattern design problem. We decompose the pattern design into two steps, i.e., the correlation modification process to optimize the correlation structure of the channel, followed by the power allocation process to improve the channel quality based on the optimized channel structure. For the correlation modification process, we propose a sequential optimization framework with eigenvalue decomposition to obtain near-optimal solutions. For the power allocation process, we provide a closed-form power allocation scheme to redistribute the transmission power among the modified subchannels. Numerical results show that the proposed pattern design scheme offers significant improvements over legacy MIMO systems, which motivates the application of PR-MIMO in wireless communication systems.

In this article, we present practical stochastic modeling and detailed performance analysis of our double blockchain (DBC) from Haoet al.(2021) for secure information and reputation data management in large-scale wireless Internet of Things (IoT) networks. Specifically, the DBC is a private blockchain deployed on a cloud-fog communication network which is composed of an information blockchain (IBC) storing large amounts of IoT data in the cloud layer and a reputation blockchain (RBC) storing reputation data of the IoT devices in the near-terminal fog layer. The locations of the fog layer nodes are modeled according to a random Poisson point process (PPP) over a given 2-D area to approximate the stochastic property of real-world wireless node deployments. Furthermore, we assume that the number of IoT devices transmitting to the fog nodes also follow a random Poisson distribution. Based on these models, we derive novel closed-form expressions for the storage size, transmission latency, and tampering time of the IoT fog nodes in our DBC architecture. Numerical simulations highlight high storage scalability, low latency, and superior security of the DBC design, and provide insights into the performance gains for different fog node and IoT device densities.

This article proposes a wireless key generation solution for secure low-latency communications with active jamming attack prevention in wireless networked control systems (WNCSs) of Industrial Internet of Things (IIoT) applications. We first identify a new vulnerability in physical-layer key generation schemes using wireless channel and random pilots (RPs) in static environments. We derive a closed-form expression for the probability that the RP-based key is successfully attacked by a long-term eavesdropper at a fixed location. To prevent such attacks, we propose a one-time pad (OTP) encrypted transmission solution assisted by one-way self-interference (SI), which has low-latency, high-security benefits, and active attack detection capability. The performance of the proposed scheme is analytically compared with two benchmark RP-based schemes, and its advantages are verified in a ray-tracing-based simulation environment. We further investigate the impact of critical design parameters, which reveal fundamental insights for the deployment and implementation of our proposed secure communications scheme.

Wireless secret key generation has emerged as a promising technique for Internet-of-Things (IoT) systems to establish shared encryption keys between the server and legitimate mobile user. This article focuses on the use of multidomain joint information to achieve a high key generation rate (KGR) and the implementation of a reliable, low-complexity secret key generation mechanism for distributed antenna systems (DAS) with orthogonal-frequency division multiplexing (OFDM). We present a space-time-frequency channel state information (CSI)-based key generation scheme based on a two-step approach of adaptive link selection and stepwise decorrelation algorithms. The performance is evaluated in terms of KGR, key disagreement rate (KDR), randomness, and computational complexity by using both a standardized channel model and real-world measurements. Numerical results show that our proposed low-complexity algorithms effectively utilize the space-time-frequency CSI to multiply the KGR in both indoor and outdoor environments. Through adaptive link selection in DAS, the KDR is maintained within a correctable range, thereby ensuring the validity of generated keys in dynamic environments. Further applying stepwise decorrelation reduces the computational complexity by more than half while satisfying all eight key generation randomness tests in the NIST test suite.

Mobile-edge computing (MEC) and intelligent reflecting surface (IRS) have attracted much attention as promising technologies for the next-generation mobile networks and Internet of Things (IoT). In this article, we investigate how to improve the security of the MEC system with the assistance of IRS and artificial noise (AN) in the IoT. By adjusting the phase of the IRS, the users’ signals can be enhanced and the eavesdroppers’ signal can be weakened. In addition, the full-duplex base station (FD-BS) transmits AN to destroy the eavesdroppers’ signal and further enhance the users’ security. We minimize the users’ secure energy consumption by jointly optimizing the base station receive beamforming vectors, AN covariance matrix, IRS phase shifts, users’ offloading time, transmit power, and local computation tasks. The formulated problem is a nonconvex problem that is hard to solve directly, so we decompose it into tractable subproblems and develop an alternating optimization approach by combining the semidefinite relaxation (SDR) algorithm and Dinkelbach’s method. The results show that the proposed scheme can greatly reduce the secure energy consumption compared with other benchmark scheme.

Double auctions play a pivotal role in stimulating active participation of a large number of users comprising both task requesters and workers in mobile crowdsourcing. However, most existing studies have concentrated on designing offline two-sided auction mechanisms and supporting single-type tasks and fixed auction service models. Such works ignore the need of dynamic services and are unsuitable for large-scale crowdsourcing markets with extremely diverse demands (i.e., types and urgency degrees of tasks required by different requesters) and supplies (i.e., task skills and online durations of different workers). In this article, we consider a practical crowdsourcing application with an on-demand service strategy. Especially, we innovatively design three online service models, namely, online single-bid single-task (OSS), online single-bid multiple-task (OSM), and online multiple-bid multiple-task (OMM) models to accommodate diversified tasks and bidding demands for different users. Furthermore, to effectively allocate tasks and facilitate bidding, we propose a truthful online double auction mechanism for each service model based on the McAfee double auction. By doing so, each user can flexibly select auction service models and corresponding auction mechanisms according to their current interested tasks and online duration. To illustrate this, we present a three-demand example to explain the effectiveness of our on-demand service strategy in realistic crowdsourcing applications. Moreover, we theoretically prove that our mechanisms satisfy truthfulness, individual rationality, budget balance, and consumer sovereignty. Through extensive simulations, we show that our mechanisms can accommodate the various demands of different users and improve social utility, including platform utility and average user utility.

As an attempt to tackle the low-data-rate issue of the conventional LoRa systems, we propose two novel frequency-bin-index (FBI) LoRa schemes. In scheme I, the indices of starting frequency bins (SFBs) are utilized to carry the information bits. To facilitate the actual implementation, the SFBs of each LoRa signal are divided into several groups prior to the modulation process in the proposed FBI-LoRa system. To further improve the system flexibility, we formulate a generalized modulation scheme and propose scheme II by treating the SFB groups as an additional type of transmission entity. In scheme II, the combination of SFB indices and that of SFB group indices are both exploited to carry the information bits. We derive the theoretical expressions for bit error rate (BER) and throughput of the proposed FBI-LoRa system with two modulation schemes over AWGN and Rayleigh fading channels. Simulation results not only verify the accuracy of theoretical BER and throughput analyses but also show that the proposed FBI-LoRa schemes can significantly increase the transmission throughput compared with the existing LoRa systems at the expense of a slight loss in BER performance. The proposed FBI-LoRa system is a promising alternative for high-data-rate Internet of Things (IoT) applications.

Rateless codes can adapt to the wireless channel conditions without accurate channel state information (CSI) at the transmitter side, avoiding CSI feedback and retransmission, and thus are a promising channel coding approach to meet the stringent requirements of ultrareliable low-latency communications (uRLLCs). This article investigates a self-adaptive ordered statistics decoder (S-OSD) scheme for finite block length nonbinary Raptor code (NBRC). Aiming at minimizing the decoding complexity, some strategies for our S-OSD scheme are designed, including the segmentation rules of most reliable basis, the generating and discarding rules of test error patterns, and the stop criteria, respectively. In addition, an upper bound of block error rates (BLERs) for the NBRC under OSD is derived, which can be used to estimate the number of NBRC symbols required to successfully decode the input information via the S-OSD. Simulation results show that the complexity of our S-OSD scheme is greatly reduced comparing to the existing OSD schemes, while achieving very low BLER in the short block length regime.

This paper investigates the integrated sensing and communication (ISAC) in vehicle-to-infrastructure (V2I) networks. To realize ISAC, an effective beamforming design is essential which however, highly depends on the availability of accurate channel tracking requiring large training overhead and computational complexity. Motivated by this, we adopt a deep learning (DL) approach to implicitly learn the features of historical channels and directly predict the beamforming matrix to be adopted for the next time slot to maximize the average achievable sum-rate of an ISAC system. The proposed method can bypass the need of explicit channel tracking process and reduce the signaling overhead significantly. To this end, a general sum-rate maximization problem with Cramer-Rao lower bounds-based sensing constraints is first formulated for the considered ISAC system taking into account the multiple access interference. Then, by exploiting the penalty method, a versatile unsupervised DL-based predictive beamforming design framework is developed to address the formulated design problem. As a realization of the developed framework, a historical channels-based convolutional long short-term memory (LSTM) network (HCL-Net) is devised for predictive beamforming in the ISAC-based V2I network. Specifically, the convolution and LSTM modules are successively adopted in the proposed HCL-Net to exploit the spatial and temporal dependencies of communication channels to further improve the learning performance. Finally, simulation results show that the proposed predictive method not only guarantees the required sensing performance, but also achieves a satisfactory sum-rate that can approach the upper bound obtained by the genie-aided scheme with the perfect instantaneous channel state information available.

In this paper, an IRS-aided integrated sensing and communications (ISAC) system operating in the terahertz (THz) band is proposed to maximize the system capacity. Transmit beamforming and phase-shift design are transformed into a universal optimization problem with ergodic constraints. Then the joint optimization of transmit beamforming and phase-shift design is achieved by gradient-based, primal-dual proximal policy optimization (PPO) in the multi-user multiple-input single-output (MISO) scenario. Specifically, the actor part generates continuous transmit beamforming and the critic part takes charge of discrete phase shift design. Based on the MISO scenario, we investigate a distributed PPO (DPPO) framework with the concept of multi-threading learning in the multi-user multiple-input multiple-output (MIMO) scenario. Simulation results demonstrate the effectiveness of the primal-dual PPO algorithm and its multi-threading version in terms of transmit beamforming and phase-shift design.

This work investigates a remote status updating system where the transmission process is modeled as a multi-stream M/G/1/1 non-preemptive system. We derive the closed-form expression of the average AoI of each stream in a heterogeneous case, where the distributions of service time are different for streams. To obtain more insights, we apply the results in a homogeneous system, where the service time distributions are identical, and find that preemption of packets would not always lead to the reduction of AoI, especially when the variance coefficient of the service time is small. We further optimize the generation rate to minimize the sum of average AoI. The results in heterogeneous cases show that given the same average service time for all streams, a higher generation rate should be allocated to the stream with a small service time variance. For the homogeneous cases with different AoI urgency weights for each stream, a higher generation rate should be reserved for the stream with more urgent AoI requirements for timeliness improvement. Besides, a lower bound on sum of average AoI is also provided, which only depends on the service rate and the number of data streams in homogeneous systems. Numerical results validate our theoretical analysis.

The problem of computing the Hamming weight distribution of linear codes is considered in this paper. A novel method to enumerate all codewords up to a certain Hamming weight for binary linear block codes in general and in particular for the punctured, shortened, precoded polar codes is introduced. The proposed approach performs a recursive decomposition of the codes using construction X4 that is typically used to combine codes of different lengths. This allows to enumerate the low-weight codewords of the overall code as combinations of the low-weight codewords of the component codes. Numerical results show that the proposed approach can efficiently compute the exact partial weight distribution of the 5G New Radio punctured/shortened polar codes with CRC11 and pure polar codes. In the former and latter cases, the low-weight codeword number is up to 106 and 108, respectively. Besides, randomly punctured and shortened polar codes and randomly precoded polar codes are also considered. To the best of the authors’ knowledge, this is the first method able to solve these problems.

Integrated sensing and communication (ISAC) is an up-and-coming technique for future 6G networks. However, the communication message carried by the detection waveform will face the risk of being eavesdropped, which leads to the challenge of wireless security for ISAC networks. In this paper, we leverage non-orthogonal multiple access (NOMA) to support more users for the ISAC network, with the precoding well designed to guarantee the security. Specifically, we formulate a joint precoding optimization problem to maximize the sum secrecy rate for multiple users via artificial jamming, where the superimposed signal for NOMA users can be concurrently employed for the target detection. Since the optimization problem is non-convex, it is transformed into a convex one based on successive convex approximation (SCA), where the Taylor’s approximation and second-order cone (SOC) constraint are further applied. Then, we propose an iterative algorithm, through which the original optimization problem can be solved effectively. Simulation results show that the proposed secure NOMA-ISAC scheme can guarantee the secure transmission while ensuring the sensing performance.

In this paper, we propose a new linear-equation ordered-statistics decoding (LE-OSD). Unlike the OSD, LE-OSD uses high reliable parity bits rather than information bits to recover codeword estimates, which is equivalent to solving a system of linear equations (SLE). Only test error patterns (TEPs) that create feasible SLEs, referred to as the valid TEPs, are used to obtain codeword estimates. We introduce several constraints on the Hamming weight of TEPs to limit the overall decoding complexity. Furthermore, we analyze the block error rate (BLER) and the computational complexity of the proposed approach. It is shown that LE-OSD has a similar performance to OSD in terms of BLER, which can asymptotically approach Maximum-likelihood (ML) performance with proper parameter selections. Simulation results demonstrate that the LE-OSD has a significantly reduced complexity compared to OSD, especially for low-rate codes, that usually require high decoding order in OSD. Nevertheless, the complexity reduction can also be observed for high-rate codes. In addition, we further improve LE-OSD by applying the decoding stopping condition and the TEP discarding condition. As shown by simulations, the improved LE-OSD has a considerably reduced complexity while maintaining the BLER performance, compared to the latest OSD approaches from literature.

The integration of mobile edge computing (MEC) into an ultra-dense network (UDN) can provide ubiquitous task offloading services to computation-demanding users leveraging densely deployed micro base stations. The conventional multi-user task offloading strategies are performed centrally, where a central node makes global task offloading decisions on server selection and resource allocation. In practice, the deployment becomes prohibitively complex with the increasing number of users as it involves high communication overhead and complex global optimization operations. In this paper, we develop a novel decentralized task offloading strategy in UDN, enabling users to independently make local task offloading decisions. We formulate the associated optimization problem to minimize the long-term average task delay among all users. On this basis, we develop a novel calibrated contextual bandit learning (CCBL) algorithm, where users can learn the computational delay functions of micro base stations and predict the task offloading decisions of other users in a decentralized manner. The convergence of the proposed CCBL algorithm is verified via the approachability theory. Moreover, we transfer the target of calibrated learning from all micro base stations to a single user and propose a user-oriented CCBL algorithm to further decrease the computational complexity and increase the convergence rate. Simulation results illustrate that our proposed algorithm outperforms the existing decentralized algorithms and approaches the centralized one.

The computation power of baseband units (BBUs) is a major source of power consumption in millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems with a large number of users due to complex signal processing. The effective reduction of computation power is critical for improving system energy efficiency. In this paper, the principle of reducing the computation power of BBUs is first investigated in mmWave massive MIMO systems with a hybrid precoding structure. A recursive constraint in decomposing the baseband precoding matrix is derived for reducing the computation power of hybrid precoding systems. Furthermore, the optimal number of sub-matrices minimizing the maximum error in decomposing the baseband precoding matrix is obtained. Based on the proposed principle, consisting of the recursive constraint and the optimal number of sub-matrices, a fast Monte Carlo baseband precoding (FMCBP) algorithm is developed to reduce the computation power of BBUs and improve system energy efficiency. Simulation results show that the total transmission rate and energy efficiency of mmWave systems are coupled with the computation power of BBUs, based on the FMCBP algorithm. Moreover, the FMCBP algorithm maximally improves the energy efficiency of multi-user mmWave massive MIMO communication systems by 124 percent, compared with the conventional equivalent zero-forcing algorithm.

Mobile edge computing (MEC) is a promising approach that can reduce the latency of task processing by offloading tasks from user equipments (UEs) to MEC servers. Existing works always assume that the MEC server is capable of executing the offloaded tasks, without considering the impact of improper load on task processing efficiency. In this article, we present a two-stage computing offloading scheme to minimize the task processing delay while managing the server load properly. To minimize the task processing delay, each UE optimizes how much workload to be offloaded to the MEC server. To improve the task processing efficiency of the server, we arrange the processing order of offloading tasks by introducing an aggregative game with an instantaneous load billing mechanism. The proposed game can obtain the optimal task offloading and processing strategy with limited information and a small number of iterations. Simulation results show that our scheme approaches the optimal offloading strategy in terms of minimizing task processing delay for each UE and improving processing efficiency for the server.

Belief propagation (BP) is a popular message passing algorithm for distributed cooperative localization. However, due to the nonlinearity of measurement functions, BP implementation has no closed-form expression and requires message approximations. While nonparametric BP can be used, it suffers from a high computational complexity, thus being impractical in energy-constrained networks. In this paper, a parametric Bayesian method with Gaussian BP implementation is proposed for distributed cooperative localization. With linearization of the Euclidean norm in ranging measurements, the joint posterior distribution of agents’ locations is successively approximated with a sequence of high-dimensional Gaussian distributions. At each iteration of the successive Gaussian approximation, vector-valued Gaussian BP is further adopted to compute the marginal distributions of agents’ locations in a distributed way. It is proved by the principle of majorization-minimization that the proposed successive Gaussian approximation is guaranteed to converge, and the sequence of the estimated agents’ locations converges to a stationary point of the objective function of the maximum a posteriori estimation. Furthermore, although cooperative localization involves loopy network topologies, in which convergence property of Gaussian BP is generally unknown, it is proved in this paper that vector-valued Gaussian BP converges, making the proposed parametric BP-based method being the first one achieving convergence guarantee. Compared to the nonparametric BP counterpart, the proposed method has a much lower computational complexity and communication overhead. Simulation results demonstrate that the proposed method achieves a superior performance in localization accuracy compared to existing cooperative localization methods.

In this paper, we consider a mobile edge computing (MEC)-based secure computation offloading system, and design a practical multi-eavesdropper model including two specific scenarios of non-colluding and colluding eavesdropping. Furthermore, we design a requirement satisfaction model by exploring practical variations in user request patterns for security provisioning, delay reduction and energy saving. Based on these, we propose a satisfaction-maximized secure computation offloading (SMax-SCO) scheme, and then formulate an optimization problem aiming at maximizing users’ requirement satisfactions subject to secrecy offloading rate, tolerable delay, task workload and maximum power constraints. Since the optimization problem is nonconvex, we present an efficient successive convex approximation (SCA)-based algorithm to obtain suboptimal solutions. We demonstrate that the proposed SMax-SCO scheme achieves a significant improvement in security performance and requirement satisfaction compared with existing schemes. Moreover, we conclude that SMax-SCO can resist eavesdropping attacks of multiple eavesdroppers and even colluding eavesdroppers.

This paper considers a wireless network with a base station (BS) conducting timely status updates to multiple clients via adaptive non-orthogonal multiple access (NOMA)/orthogonal multiple access (OMA). Specifically, the BS is able to adaptively switch between NOMA and OMA for the downlink transmission to optimize the information freshness of the network, characterized by the Age of Information (AoI) metric. For the simple two-client case, we formulate a Markov Decision Process (MDP) problem and develop the optimal policy for the BS to decide whether to use NOMA or OMA for each downlink transmission based on the instantaneous AoI of both clients. The optimal policy is shown to have a switching-type property with obvious decision switching boundaries. A suboptimal policy with lower computation complexity is also devised, which is shown to achieve near-optimal performance via numerical simulations. For the more general multi-client scenario, the optimal solution is the computationally intractable due to the large state and action spaces. As such, we devote to provide a feasible suboptimal policy with low computation complexity. Specifically, inspired by the proposed suboptimal policy of the two-client scenario, we formulate a nonlinear optimization problem to determine the optimal power allocated to each client by maximizing the expected AoI drop of the network in each time slot (i.e., minimizing the expected network-wide AoI of the next slot). The problem is shown to be non-convex, we manage to solve it by approximating it as a convex optimization problem. Simulation results validate the tightness of the adopted approximation. Specifically, the performance of the adaptive NOMA/OMA scheme by solving the convex optimization is shown to be close to that of the max-weight policy solved by exhaustive search. Besides, the adaptive NOMA/OMA scheme achieves significant performance improvement compared to the OMA scheme, especially when the number of clients in the network is large and the transmission SNR is high.

In this paper, we consider a millimeter wave multiple-input single-output tracking system, where the time-varying angle of departure (AoD) is assumed to change following a discrete state Markov process. Depending on whether the associated AoD transition function is available or not, we propose two different training beam sequence design approaches. Specifically, in the case when the AoD transition function is available, we leverage the maximum a posteriori criterion to estimate the updated AoD in each beam tracking period. Since it is infeasible to derive an explicit expression for the resultant estimation error rate, we turn to its upper bound, which possesses a closed-form expression and is therefore used as the objective function to optimize the training beam sequence. Considering the complicated objective function and the unit modulus constraints imposed by the analog phase shifters, we resort to a particle swarm algorithm to solve the formulated optimization problem. In the case when the AoD transition function is unavailable, we turn to the maximum likelihood criterion for AoD estimation. To cope with the unknown AoD transition function, we reformulate the beam tracking problem as a partially observable Markov decision process problem and develop an actor-critic reinforcement learning framework to obtain an efficient training beam sequence design. Numerical results demonstrate superiorities of the proposed training beam sequence design approaches for both two cases.

Wireless virtual reality (VR) systems are able to provide users with immersive experiences, and require low latency and high data rate. To meet these conflicting requirements with limited radio resources, edge intelligence is a promising architecture. It exploits the edge server co-located at the base station to predict the field of view (FoV) of the next VR video segment, pre-render the three-dimensional video within the predicted FoV, and transmit it to the user in advance. Since the prediction is not error-free, the predicted FoV may not cover the actual FoV requested by the user, and hence may result in video quality loss. To address this issue, we first formulate a constrained partially observable Markov decision process problem to optimize the redundant range of the FoV according to the head motion prediction and the redundant range for the previous video segment. Then, we develop a constrained deep reinforcement learning algorithm to minimize the video quality loss ratio subject to the latency constraint. Simulation results show that the proposed algorithm outperforms the existing methods in terms of video quality loss ratio (from 6.9% to 4.9%) and latency (from 0.72 s to 0.63 s).

The rapid development of 5G and Internet-of-Things catalyzes ever-increasing computation-intensive and delay-sensitive applications demanding ubiquitous computation services. Integrating mobile edge computing (MEC) in the ultra-dense network (UDN) is a key enabler to meet the service demand by allowing smart devices to perform uninterrupted task offloading via densely deployed MEC servers. In this paper, we take a user-oriented approach to minimize a long-term delay for a given task duration under a price budget constraint. To address this problem, we develop a novel contextual sleeping bandit learning (CSBL) algorithm, which integrates context information and sleeping bandit theory to handle the fast changing environment and leverages Lyapunov optimization to deal with the price budget. We derive the upper bound of learning regret and provide a rigorous proof that CSBL asymptotically approaches the Oracle algorithm within bounded deviations for finite task duration. Simulation results illustrate that CSBL significantly outperforms existing algorithms.

Unmanned aerial vehicles (UAVs) have been used in millimeter-wave (mmWave) networks as relays to assist remote or blocked communication nodes. In this paper, we perform secrecy analysis for UAV-based mmWave relaying networks, where a cooperative jamming scheme is proposed via utilizing the destination and an external UAV to cooperatively disrupt the eavesdroppers at the two stages of relaying, respectively. Considering the probability of line-of-sight (LoS) between the UAV and ground nodes, the three-dimensional (3D) antenna gain, and the Nakagami-m small-scale fading model, closed-form SOP of the network is obtained by employing the Gauss-Chebyshev quadrature. Simulation results are presented to validate the theoretical expressions of SOP and to show the effectiveness of the proposed scheme.

Short length channel coding and low complexity decoding is essential for 5G ultra-reliable low latency communications (uRLLC). In this paper, an efficient ordered statistics decoder (E-OSD) scheme is proposed for finite length non-binary Raptor code (NBRC) towards uRLLC. The segmentation and discarding rules of test error patterns, and the stop criteria are designed for the proposed E-OSD scheme to reduce the decoding complexity. A block error rate (BLER) upper bound of the NBRC under OSD is derived to estimate the number of NBRC symbols required for achieving the desired BLER performance. Simulation results show that the complexity of the proposed E-OSD scheme is greatly reduced compared to the existing OSD schemes, and it can achieve the BLER lower than 10−5in the finite length regime (<256 bits), satisfying the requirements of uRLLC.

This article considers a cooperative Internet-of-Things (IoT) system with a source aiming to transmit randomly generated status updates to a designated destination as timely as possible under the help of a relay. We adopt a recently proposed concept, the Age of Information (AoI), to characterize the timeliness of the status updates. In the considered system, delivering the status updates via the one-hop direct link will have a shorter transmission time at the cost of incurring a higher error probability, while the delivery of status updates through the two-hop relay link could be more reliable at the cost of suffering longer transmission time. Thus, it is important to design the relaying protocol of the considered system for optimizing the information freshness. Considering the limited capabilities of IoT devices, we propose two low-complexity Age-oriented Relaying (AoR) protocols, i.e., the source-prioritized AoR (SP-AoR) protocol and the relay-prioritized AoR (RP-AoR) protocol, to reduce the AoI of the considered system. Specifically, in the SP-AoR protocol, the relay opportunistically replaces the source to retransmit the successfully received status updates that have not been correctly delivered to the destination, but the retransmission at the relay can be preempted by the arrival of a new status update at the source. Differently, in the RP-AoR protocol, once the relay replaces the source to retransmit the status updates that have not been successfully received by the destination, the retransmission at the relay will not be preempted by new status update arrivals at the source. By carefully analyzing the evolution of the instantaneous AoI, we derive closed-form expressions of the average AoI for both the proposed AoR protocols. We further optimize the generation probability of the status updates at the source in both protocols. Simulation results validate our theoretical analysis and demonstrate that the two proposed protocols outperform each other under various system parameters. Moreover, the protocol with better performance can achieve near-optimal performance compared with the optimal scheduling policy attained by applying the Markov decision process (MDP) tool.

Wireless networked control systems (WNCSs) provide a key enabling technique for Industrial Internet of Things (IIoT). However, in the literature of WNCSs, most of the research focuses on the control perspective and has considered oversimplified models of wireless communications that do not capture the key parameters of a practical wireless communication system, such as latency, data rate, and reliability. In this article, we focus on a WNCS, where a controller transmits quantized and encoded control codewords to a remote actuator through a wireless channel, and adopt a detailed model of the wireless communication system, which jointly considers the interrelated communication parameters. We derive the stability region of the WNCS. If and only if the tuple of the communication parameters lies in the region, the average cost function, i.e., a performance metric of the WNCS, is bounded. We further obtain a necessary and sufficient condition under which the stability region is n -bounded, where n is the control codeword blocklength. We also analyze the average cost function of the WNCS. Such analysis is nontrivial because the finite-bit control-signal quantizer introduces a nonlinear and discontinuous quantization function that makes the performance analysis very difficult. We derive tight upper and lower bounds on the average cost function in terms of latency, data rate, and reliability. Our analytical results provide important insights into the design of the optimal parameters to minimize the average cost within the stability region.

The fifth generation (5G) of mobile standards is expected to provide ultrareliability and low-latency communications (URLLC) for various applications and services, such as online gaming, wireless industrial control, augmented reality, and self driving cars. Meeting the contradictory requirements of URLLC, i.e., ultrareliability and low latency, is considered to be very challenging, especially in bandwidth-limited scenarios. Most communication strategies rely on the hybrid automatic repeat request (HARQ) to improve reliability at the expense of increased packet latency due to the retransmission of failing packets. To guarantee high reliability and very low latency simultaneously, we enhance the HARQ retransmission mechanism to achieve reliability with guaranteed packet-level latency and in-time delivery. The proposed nonorthogonal HARQ (N-HARQ) utilizes nonorthogonal sharing of time slots for conducting retransmission. The reliability and delay analysis of the proposed N-HARQ in the finite block length (FBL) regime shows very high performance gain in packet delivery delay over conventional HARQ in both additive white Gaussian noise (AWGN) and Rayleigh fading channels. We also propose an optimization framework to further enhance the performance of N-HARQ for single and multiple retransmission cases.

In this paper, we investigate the age minimization problem for a two-hop relay system, under a resource constraint on the average number of forwarding operations at the relay. We first design an optimal policy by modelling the considered scheduling problem as a constrained Markov decision process (CMDP) problem. Based on the observed multi-threshold structure of the optimal policy, we then devise a low-complexity double threshold relaying (DTR) policy with only two thresholds, one for relay's AoI and the other one for the age gain between destination and relay. We derive approximate closed-form expressions of the average AoI at the destination, and the average number of forwarding operations at the relay for the DTR policy, by modelling the tangled evolution of age at relay and destination as a Markov chain (MC). Numerical results validate all the theoretical analysis, and show that the low-complexity DTR policy can achieve near optimal performance compared with the optimal CMDP-based policy. Moreover, the relay should always consider the threshold for its local age to maintain a low age at the destination. When the resource constraint is relatively tight, it further needs to consider the threshold on the age gain to ensure that only those packets that can decrease destination's age dramatically will be forwarded.

Envisioned as a promising component of the future wireless Internet-of-Things (IoT) networks, the non-orthogonal multiple access (NOMA) technique can support massive connectivity with a significantly increased spectral efficiency. Cooperative NOMA is able to further improve the communication reliability of users under poor channel conditions. However, the conventional system design suffers from several inherent limitations and is not optimized from the bit error rate (BER) perspective. In this article, we develop a novel deep cooperative NOMA scheme, drawing upon the recent advances in deep learning (DL). We develop a novel hybrid-cascaded deep neural network (DNN) architecture such that the entire system can be optimized in a holistic manner. On this basis, we construct multiple loss functions to quantify the BER performance and propose a novel multi-task oriented two-stage training method to solve the end-to-end training problem in a self-supervised manner. The learning mechanism of each DNN module is then analyzed based on information theory, offering insights into the explainable DNN architecture and its corresponding training method. We also adapt the proposed scheme to handle the power allocation (PA) mismatch between training and inference and incorporate it with channel coding to combat signal deterioration. Simulation results verify its advantages over orthogonal multiple access (OMA) and the conventional cooperative NOMA scheme in various scenarios.

As one of the key communication scenarios in the fifth-generation and also the sixth-generation (6G) mobile communication networks, ultrareliable and low-latency communications (URLLCs) will be central for the development of various emerging mission-critical applications. State-of-the-art mobile communication systems do not fulfill the end-to-end delay and overall reliability requirements of URLLCs. In particular, a holistic framework that takes into account latency, reliability, availability, scalability, and decision-making under uncertainty is lacking. Driven by recent breakthroughs in deep neural networks, deep learning algorithms have been considered as promising ways of developing enabling technologies for URLLCs in future 6G networks. This tutorial illustrates how domain knowledge (models, analytical tools, and optimization frameworks) of communications and networking can be integrated into different kinds of deep learning algorithms for URLLCs. We first provide some background of URLLCs and review promising network architectures and deep learning frameworks for 6G. To better illustrate how to improve learning algorithms with domain knowledge, we revisit model-based analytical tools and cross-layer optimization frameworks for URLLCs. Following this, we examine the potential of applying supervised/unsupervised deep learning and deep reinforcement learning in URLLCs and summarize related open problems. Finally, we provide simulation and experimental results to validate the effectiveness of different learning algorithms and discuss future directions.

We study closed-form interference-exploitation precoding for multi-level modulations in the downlink of multi-user multiple-input single-output (MU-MISO) systems. We consider two distinct cases: first, when the number of served users is not larger than the number of transmit antennas at the base station (BS), we mathematically derive the optimal precoding structure based on the Karush-Kuhn-Tucker (KKT) conditions. By formulating the dual problem, the precoding problem is transformed into a pre-scaling operation using quadratic programming (QP) optimization. We further consider the case where the number of served users is larger than the number of transmit antennas at the BS. By employing the pseudo inverse, we show that the optimal solution of the pre-scaling vector is equivalent to a linear combination of the right singular vectors corresponding to zero singular values, and derive the equivalent QP formulation. We also present the condition under which multiplexing more streams than the number of transmit antennas is achievable. For both considered scenarios, we propose a modified iterative algorithm to obtain the optimal precoding matrix, as well as a sub-optimal closed-form precoder. Numerical results validate our derivations on the optimal precoding structures for multi-level modulations, and demonstrate the superiority of interference-exploitation precoding for both scenarios.

A novel method to recursively construct a set of precoded polar codes of various rates and short-to-moderate lengths is presented. The proposed code design method minimizes the successive cancellation (SC) decoding error probability estimate under three constraints. The first constraint is the minimum distance requirement to improve the maximum-likelihood (ML) performance of the resulting code and therefore the performance under the SC list (SCL) decoding. The other two constraints introduce preselected supercode and subcode, where the supercode ensures fast computation of the minimum distance and the subcode ensures reduction of the search space size. The supercode is given by the Plotkin sum of shorter codes, which are nested to simplify computation of low-weight codewords. These low-weight codewords are needed to satisfy the minimum distance constraint. The simulation results indicate that the proposed precoded polar codes of lengths 128 and 256 provide a better frame error rate (FER) than polar codes with CRC and e-BCH polar subcodes under the SCL decoding algorithm with the list size$8-128$.

In this paper, a novel training beam sequence design for multiuser millimeter wave tracking systems is proposed. For each receiver, a single-path channel model is firstly investigated, where we introduce a maximum a posteriori (MAP) criterion to estimate the time-varying angle of departure (AoD), followed by an extended Kalman filter to update the stale complex path gain. We then employ training beam sequence design to minimize the estimated AoD’s average mean squared error (AMSE), which however has no explicit expression. We firstly derive a closed-form upper bound for the AMSE and then simplify this upper bound into a tractable form, based on which a nonlinear optimization problem (NLP) is formulated. By solving this NLP optimally using its corresponding Karush-Kuhn-Tucker conditions, we obtain an efficient training beam sequence. The proposed MAP criterion and its associated training beam sequence design are further extended to multi-path scenarios, where a joint estimation of the multiple paths is firstly discussed, followed by a sequential estimation as a low-complexity alternative. Numerical results demonstrate the superiority of our proposed scheme over the existing benchmark methods, especially in the case when the receivers’ channels change rapidly.

In this paper, we propose a low-latency virtual reality (VR) delivery system where an unmanned aerial vehicle (UAV) base station (U-BS) is deployed to deliver VR content from a cloud server to multiple ground VR users. Each VR input data requested by the VR users can be either projected at the U-BS before transmission or processed locally at each user. Popular VR input data is cached at the U-BS to further reduce backhaul latency from the cloud server. For this system, we design a low-complexity iterative algorithm to minimize the maximum communications and computing latency among all VR users subject to the computing, caching and transmit power constraints, which is guaranteed to converge. Numerical results indicate that our proposed algorithm can achieve a lower latency compared to other benchmark schemes. Moreover, we observe that the maximum latency mainly comes from communication latency when the bandwidth resource is limited, while it is dominated by computing latency when computing capacity is low. In addition, we find that caching is helpful to reduce latency.

The combination of pervasive edge computing and blockchain technologies opens up significant possibilities for industrial Internet of Things (IIoT) applications, but there are several critical limitations regarding efficient storage and rapid response for large-scale low-delay IIoT scenarios. To address these limitations, in this article we propose a hierarchical edge-cloud blockchain called LayerChain. Specifically, to promote scalability, we design a layered structure to hierarchically store the blockchain data in multiple distributed clouds and edge nodes. Next, we propose a node classification method to accommodate differences between the edge nodes when deploying the blockchain. Moreover, to mitigate lengthy delays during block propagation, we propose a tree-based clustering algorithm where blocks are propagated through different clusters with a compressed tree depth. Simulation results show that our LayerChain efficiently reduces the system's resource requirements and block propagation time, making it well-suited for large-scale low-delay IIoT applications.

This special section aims at soliciting original research and practical contributions from both industry and academia to advance the IIoT, including network modeling and architecture, AI algorithms for various layers, intelligent resource management, big data driven edge systems, orchestration of edge, and cloud servers. Through a rigorous peer-review process, nine articles have been accepted. In the following, we summarize the accepted articles in this editorial.

This paper revisits the ordered statistics decoding (OSD). It provides a comprehensive analysis of the OSD algorithm by characterizing the statistical properties, evolution and the distribution of the Hamming distance and weighted Hamming distance from codeword estimates to the received sequence in the reprocessing stages of the OSD algorithm. We prove that the Hamming distance and weighted Hamming distance distributions can be characterized as mixture models capturing the decoding error probability and code weight enumerator. Simulation and numerical results show that our proposed statistical approaches can accurately describe the distance distributions. Based on these distributions and with the aim to reduce the decoding complexity, several techniques, including stopping rules and discarding rules, are proposed, and their decoding error performance and complexity are accordingly analyzed. Simulation results for decoding various eBCH codes demonstrate that the proposed techniques can significantly reduce the decoding complexity with a negligible loss in the decoding error performance.

Mobile computing network is envisioned as a powerful framework to support the growing computation-intensive applications in the era of the Internet of Things (IoT). In this paper, we exploit the potential of a multi-layer network via a two-dimensional (2-D) task offloading scheme, which enables horizontal cooperations among the edge nodes. To minimize the average task offloading delay for all the mobile users, we formulate a mixed non-linear programming (MINLP) by jointly optimizing the 2-D offloading decisions and communication/computational resource allocation. To address this very challenging problem, we exploit the unique algorithmic structure of the optimal branch-and-bound (B&B) algorithm, and propose a novel Gaussian process imitation learning (GPIL) method to learn how to discover the shortcut for node searching in the B&B enumeration tree and significantly accelerate the B&B algorithm. When the network key parameters change, we further propose a novel recursive GPIL (RGPIL) method to agilely adapt to the new scenario with a fast policy update, where the new posterior distribution can be recursively updated based on a few new training data. Our simulation results show that the proposed method can achieve a near optimal solution with a significantly reduced complexity (e.g., a reduction of 98.7% in the number of searched nodes for a typical case). On this basis, the advantage of 2-D offloading scheme over the conventional schemes is also verified.

In this work, we develop a contract theory framework to tackle the allocations of heterogeneous baseband processing units (BBUs) in cloud radio access network. We first model a monopoly market by viewing the BBUs as a kind of resource. The infrastructure provider (InP), as the monopolist, owns all the heterogeneous BBUs of different processing abilities and maintaining costs, and leases them to multiple mobile network operators (MNOs) to gain profit. At the same time, the MNOs intend to rent reasonable amount of BBUs to provide services to their mobile clients. Then we propose a contract theory framework, in which contract items are optimized to maximize the InP’s utility, while maintain the welfare of the MNOs. We design the optimal contracts with complete and asymmetric information on the MNOs. Our contract design achieves the near optimum solution to heterogeneous computational resource allocation even under the information asymmetric case. Our derivations indicate that the optimal contracts with asymmetric information achieve a lower utility for the InP than the ones with complete information and the utility reduction is higher when the BBUs are heterogeneous rather than homogeneous. Numerical results demonstrate that, the InP having heterogeneous BBUs can achieve a higher utility relative to having homogeneous BBUs, which is more profitable and realistic for the InP. Moreover, we regard Stackelberg game theoretic approach as a comparison, and show that our method is more realistic.

To accommodate diverse Quality-of-Service (QoS) requirements in 5th generation cellular networks, base stations need real-time optimization of radio resources in time-varying network conditions. This brings high computing overheads and long processing delays. In this work, we develop a deep learning framework to approximate the optimal resource allocation policy that minimizes the total power consumption of a base station by optimizing bandwidth and transmit power allocation. We find that a fully-connected neural network (NN) cannot fully guarantee the QoS requirements due to the approximation errors and quantization errors of the numbers of subcarriers. To tackle this problem, we propose a cascaded structure of NNs, where the first NN approximates the optimal bandwidth allocation, and the second NN outputs the transmit power required to satisfy the QoS requirement with given bandwidth allocation. Considering that the distribution of wireless channels and the types of services in the wireless networks are non-stationary, we apply deep transfer learning to update NNs in non-stationary wireless networks. Simulation results validate that the cascaded NNs outperform the fully connected NN in terms of QoS guarantee. In addition, deep transfer learning can reduce the number of training samples required to train the NNs remarkably.

Employing unmanned aerial vehicles (UAVs) in millimeter-wave (mmWave) networks as relays has emerged as an appealing solution to assist remote or blocked communication nodes. In this case, the network security becomes a great challenge due to the presence of malicious eavesdroppers. In this paper, we perform a secrecy analysis for a UAV-based mmWave relaying network. We first investigate the relaying scheme without jamming where the UAV decodes and forwards the information from the source to the destination with malicious eavesdropping. Furthermore, to enhance the secrecy performance, we propose a cooperative jamming scheme via utilizing the destination and an external UAV to cooperatively disrupt the eavesdroppers at the two stages of relaying, respectively. Using the probability of line-of-sight (LoS) between the UAV and ground nodes, the three-dimensional (3D) antenna gain, and the Nakagami-m small-scale fading model, the secrecy outage probability (SOP) of the two schemes with and without jamming is analyzed. Closed-form expressions for the SOP of the two schemes are obtained by employing the Gauss-Chebyshev quadrature. Simulation results are presented to validate the theoretical expressions of SOP and to show the effectiveness of the proposed schemes.

This paper considers a cooperative status update system with a source aiming to send randomly generated status updates to a designated destination as timely as possible with the help of a relay. We adopt a recently proposed concept, the age of information (AoI), to characterize the timeliness of the status updates. We propose a new age-oriented opportunistic relaying (AoR) protocol to reduce the AoI of the considered system. Specifically, the relay opportunistically replaces the source to retransmit the successfully received status updates that have not been correctly delivered to the destination, but the retransmission at the relay can be preempted by the arrival of a new status update at the source. By carefully analyzing the evolution of the AoI, we derive a closed-form expression of the average AoI for the proposed AoR protocol. We further minimize the average AoI by optimizing the generation probability of the status updates at the source. Simulation results validate our theoretical analysis and demonstrate that the average AoI performance of the proposed AoR protocol is superior to that of the non-cooperative system.

Analyzing network vulnerability, especially connectivity vulnerability, is vital for network security planning. Traditionally, network vulnerability analysis methods separate the studies of global connectivity vulnerability and critical area vulnerability, and thus ignore joint failure of network connectivity and critical-area integrity that may cause grave damage to a network. To this end, this paper proposes a prioritizing critical area approach for connectivity analysis to identify the corresponding vulnerable elements. Specifically, we consider the worst-case scenario of a network and aim at finding the minimum disruption-cost set of elements whose removal not only severely damages network connectivity but also disrupts the critical-area integrity. Since the above optimization problem is NP-hard, a heuristic algorithm based on spectral partitioning is developed to solve it. Simulation results validate the effectiveness of our proposed scheme in accurately identifying the vulnerable elements in critical areas to prevent significant loss in the overall network connectivity and performance.

In this paper, we consider a multiuser multiple-input multiple-output (MIMO) downlink communication system with simultaneous wireless information and power transfer (SWIPT). In particular, we focus on a realistic and efficient multi-receiver multi-eavesdropper MIMO SWIPT system, in which the channel state information (CSI) of each legitimate receiver and energy receiver (i.e., potential eavesdropper) is partially known to the transmitter. Based on this, we propose a robust artificial noise (AN)-aided secure transmission scheme for the system, where the channel uncertainties are modeled by the worst-case model. In the proposed scheme, we aim to maximize the worst-case achievable secrecy rate under the transmit power constraint and the energy harvesting (EH) constraint, by jointly optimizing the transmit precoding matrix and the AN covariance matrix. We utilize the S-Procedure and Taylor series approximation to transform the non-convex problem. Then, we apply the interior point method to tackle the transformed convex problem, obtaining the approximate optimal matrices and the corresponding maximum worst-case secrecy rate. Simulation results show that our proposed scheme achieves significant performance improvements in terms of convergence and the worst-case achievable secrecy rate.

In this paper, we focus on 1-bit precoding for large-scale antenna systems in the downlink based on the concept of constructive interference (CI). By formulating the optimization problem that aims to maximize the CI effect subject to the 1-bit constraint on the transmit signals, we mathematically prove that, when relaxing the 1-bit constraint, the majority of the obtained transmit signals already satisfy the 1-bit constraint. Based on this important observation, we propose a 1-bit precoding method via a partial branch-and-bound (P-BB) approach, where the BB procedure is only performed for the entries that do not comply with the 1-bit constraint. The proposed P-BB enables the use of the BB framework in large-scale antenna scenarios, which was not applicable due to its prohibitive complexity. Numerical results demonstrate a near-optimal error rate performance for the proposed 1-bit precoding algorithm.

The cooperative relay network is a type of multi-terminal communication system. We present in this paper a Neural Network (NN)-based autoencoder (AE) approach to optimize its design. This approach implements a classical three-node cooperative system as one AE model, and uses a two-stage scheme to train this model and minimize the designed losses. We demonstrate that this approach shows performance close to the best baseline in decode-and-forward (DF), and outperforms the best baseline in amplify-and-forward (AF), over a wide range of signal-to-noise-ratio (SNR) values. It is also shown that training at a list of mixed SNR values can improve the error performance compared to training at a fixed SNR value. Moreover, to verify the robustness of the trained AE model, we test it under the effect of impulse-noise.

Mobile-edge computing (MEC) is a promising technology to support computation-intensive and delay-sensitive applications at smart devices by offloading their local tasks to the network edge. In this paper, we propose a novel index based real-time task offloading policy for an asynchronous large-scale MEC system. We first formulate the policy design as a restless multi-armed bandit (RMAB) to capture the stochasticity and criticality in tasks. Based on the Whittle index theory, we then rigorously establish the indexability of our RMAB and derive a closed-form solution, making it scalable to the number of users and extremely simple to implement in practice. Simulation results show that the propose policy can achieve a significant performance improvement in term of the accumulative reward and completion ratio, compared with some existing policies.

In this paper, a non-orthogonal hybrid automatic re-peat request (N-HARQ) packet transmission strategy is proposed for ultra-reliable delay sensitive communications. As opposed to conventional HARQ, where retransmission of the failing packet is provided in new time slots, in the proposed scheme, retransmission of the packet is served together with the next arriving packet. Using N-HARQ, we avoid the queuing delay due to retransmission and reduce the packet arrival delay. We consider the short block length regime and analyze the error rate, throughput and delay performance of N-HARQ using the Markov model. Simulation results show that the proposed scheme achieves superior performance in providing packet arrival delay guarantee in comparison to its baseline orthogonal HARQ (O-HARQ).

This paper considers a wireless network with a base station (BS) conducting timely transmission to two clients in a slotted manner via hybrid non-orthogonal multiple access (NOMA)/orthogonal multiple access (OMA). Specifically, the BS is able to adaptively switch between NOMA and OMA for the downlink transmission to minimize the information freshness, characterized by Age of Information (AoI), of the network. If the BS chooses OMA, it can only serve one client within a time slot and should decide which client to serve; if the BS chooses NOMA, it can serve both clients simultaneously and should decide the power allocated to each client. To minimize the weighted sum of expected AoI of the network, we formulate a Markov Decision Process (MDP) problem and develop an optimal policy for the BS to decide whether to use NOMA or OMA for each downlink transmission based on the instantaneous AoI of both clients. We prove the existence of optimal stationary and deterministic policy, and perform action elimination to reduce the action space for lower computation complexity. The optimal policy is shown to have a switching-type property with obvious decision switching boundaries. A suboptimal policy with lower computation complexity is also devised, which can achieve near-optimal performance according to our simulation results. The performance of different policies under different system settings is compared and analyzed in numerical results to provide useful insights for practical system designs.

Mobile traffic prediction opens a promising avenue to demand-aware large-scale resource allocation with a significant improvement in the spectral efficiency. Various long-term prediction methods have been proposed in the literature. However, when considering the stringent requirement of the real-time and efficient radio resource allocation for future wireless communications, developing short-term prediction methods with high prediction accuracy is more desirable. In this paper, we exploit spatiotemporal correlations among the mobile traffic data and propose a novel machine learning-based short-term prediction method, referred to as spatiotemporal Gaussian Process Kalman filter (ST-GPKL) method, which includes two phases: the model selection and inference. The function of the model selection is to fine-tune the hyperparameters of the designed kernel function, while that of the inference incorporates the Kalman filter to predict the future mobile data traffic. Compared with the conventional methods, the proposed one can significantly improve the prediction accuracy, resulting in much higher efficiency in large-scale resource allocation.

In this paper, we focus on the constructive interference (CI) precoding for the scenario when the number of streams simultaneously transmitted by the base station (BS) is larger than that of transmit antennas at the BS, and derive the optimal precoding structure by employing the pseudo inverse. We show that the optimal pre-scaling vector in IE precoding is equal to a linear combination of the right singular vectors that correspond to zero singular values of the coefficient matrix. By formulating the dual problem, we further show that the optimal precoding matrix can be expressed as a function of the dual variables in a closed form, and an equivalent quadratic programming (QP) formulation is derived for computational complexity reduction. Numerical results validate our analysis and demonstrate significant performance improvements for interference exploitation precoding in the considered scenario.

Achieving ultra-reliable, low-latency and secure communications is essential for realizing the industrial Internet of Things (IIoT). Non-coherent massive multiple-input multiple-output (MIMO) has recently been proposed as a promising methodology to fulfill ultra-reliable and low-latency requirements. In addition, physical layer authentication (PLA) technology is particularly suitable for IIoT communications thanks to its low-latency attribute. A PLA method for non-coherent massive single-input multiple-output (SIMO) IIoT communication systems is proposed in this paper. Specifically, we first determine the optimal embedding of the authentication information (tag) in the message information. We then optimize the power allocation between message and tag signal to characterize the trade-off between message and tag error performance. Numerical results show that the proposed PLA is more accurate then traditional methods adopting the uniform tag when the communication reliability remains at the same level. The proposed PLA method can be effectively applied to the non-coherent system.

In this paper, a dynamic-hybrid automatic repeat request (D-HARQ) scheme with guaranteed delay performance is proposed. As opposed to the conventional HARQ that the maximum number of re-transmissions, L, is fixed, in the proposed scheme packets can be re-transmitted more times given that the previous packet was received with less than L re-transmissions. The dynamic of the proposed scheme is analyzed using the Markov model. For delay sensitive applications, the proposed scheme shows a superior performance in terms of packet error rate compared with the conventional HARQ and Fixed retransmission schemes when the channel state information is not available at the transmitter. We further show that D-HARQ achieves a higher throughput compared with the conventional HARQ and fixed re-transmission schemes under the same reliability constraint.

This article considers a wireless networked control system (WNCS) consisting of a dynamic system to be controlled (i.e., a plant), a sensor, an actuator, and a remote controller for mission-critical Industrial Internet of Things (IIoT) applications. A WNCS has two types of wireless transmissions, i.e., the sensor's measurement transmission to the controller and the controller's command transmission to the actuator. In the literature of WNCSs, the controllers are commonly assumed to work in a full-duplex (FD) mode by default, i.e., being able to simultaneously receive the sensor's information and transmit its own command to the actuator. In this article, we consider a practical half-duplex (HD) controller, which introduces a novel transmission-scheduling problem for WNCSs. A frequent scheduling of sensor's transmission results in a better estimation of plant states at the controller and thus a higher quality of control command, but it leads to a less frequent/timely control of the plant. Therefore, considering the overall control performance of the plant in terms of its average cost function, there exists a fundamental tradeoff between the sensor's and the controller's transmissions. We formulate a new problem to optimize the transmission-scheduling policy for minimizing the long-term average cost function. We derive the necessary and sufficient condition of the existence of a stationary and deterministic optimal policy that results in a bounded average cost in terms of the transmission reliabilities of the sensor-to-controller and controller-to-actuator channels. Also, we derive an easy-to-compute suboptimal policy, which notably reduces the average cost of the plant compared to a naive alternative-scheduling policy.

Wireless networked control systems for the Industrial Internet of Things (IIoT) require low-latency communication techniques that are very reliable and resilient. In this article, we investigate a coding-free control method to achieve ultralow latency communications in single-controller-multiplant networked control systems for both slow- and fast-fading channels. We formulate a power allocation problem to optimize the sum cost functions of multiple plants, subject to the plant stabilization condition and the controller's power limit. Although the optimization problem is a nonconvex one, we derive a closed-form solution, which indicates that the optimal power allocation policy for stabilizing the plants with different channel conditions is reminiscent of the channel-inversion policy. We numerically compare the performance of the proposed coding-free control method and the conventional coding-based control methods in terms of the control performance (i.e., the cost function) of a plant, which shows that the coding-free method is superior in a practical range of signal-to-noise ratios.

Mobile crowdsourcing is a promising strategy for trusted data collection in Internet-of-Things (IoT) applications. In this article, we propose a new fog-blockchain distributed approach for crowdsourcing reputation management to prevent user's privacy leakage, malicious users' participation, and reputation tampering in wireless IoT systems. To protect the user's privacy, we design a cross-layer privacy protection model to separate the user's identity and tasks flexibly by means of a hierarchical structure based on fog computing. Moreover, considering the multiconstraint requirement of crowdsourcing tasks, we present a multifactor reputation evaluation method to accurately identify malicious users. Furthermore, to solve the multi-identity problem of users on multiple fog nodes, we propose an adaptive fog-blockchain reputation storage method, which efficiently reduces the system resource consumption by analyzing the adaptive classification of fog nodes. Exhaustive experimental simulation results validate the security and efficiency of our proposed reputation management system.

Reconfigurable intelligent surfaces (RISs) have drawn considerable attention from the research community recently. RISs create favorable propagation conditions by controlling the phase shifts of reflected waves at the surface, thereby enhancing wireless transmissions. In this paper, we study a downlink multi-user system where the transmission from a multi-antenna base station (BS) to various users is achieved by an RIS reflecting the incident signals of the BS towards the users. Unlike most existing works, we consider the practical case where only a limited number of discrete phase shifts can be realized by a finite-sized RIS. A hybrid beamforming scheme is proposed and the sum-rate maximization problem is formulated. Specifically, continuous digital beamforming and discrete RIS-based analog beamforming are performed at the BS and the RIS, respectively, and an iterative algorithm is designed to solve this problem. Both theoretical analysis and numerical validations show that the RIS-based system can achieve good sum-rate performance by setting a reasonable size of the RIS and a small number of discrete phase shifts.

Using radio-frequency (RF) sensing techniques for human posture recognition has attracted growing interest due to its advantages of pervasiveness, contact-free observation, and privacy protection. Conventional RF sensing techniques are constrained by their radio environments, which limit the number of transmission channels to carry multi-dimensional information about human postures. Instead of passively adapting to the environment, in this paper, we design an RF sensing system for posture recognition based on reconfigurable intelligent surfaces (RISs). The proposed system can actively customize the environments to provide desirable propagation properties and diverse transmission channels. However, achieving high recognition accuracy requires the optimization of RIS configuration, which is a challenging problem. To tackle this challenge, we formulate the optimization problem, decompose it into two subproblems, and propose algorithms to solve them. Based on the developed algorithms, we implement the system and carry out practical experiments. Both simulation and experimental results verify the effectiveness of the designed algorithms and system. Compared to the random configuration and non-configurable environment cases, the designed system can greatly improve the recognition accuracy.

As distributed energy resources (DERs) are widely deployed, DC packetized power microgrids have been considered as a promising solution to incorporate DERs effectively. In this paper, we consider a DC packetized power microgrid, where the energy is dispatched in the form of power packets with the assistance of a power router. However, the benefits of the microgrid can only be realized when energy subscribers (ESs) equipped with DERs actively participate in the energy market. Therefore, peer-to-peer (P2P) energy trading is necessary in the DC packetized power microgrid to encourage the usage of DERs. Different from P2P energy trading in AC microgrids, the dispatching capability of the router needs to be considered in DC microgrids, which will complicate the trading problem. To tackle this challenge, we formulate the P2P trading problem as an auction game, in which the demander ESs submit bids to compete for power packets, and a controller decides the energy allocation and power packet scheduling. Analysis of the proposed scheme is provided, and its effectiveness is validated through simulation.

We consider a wireless powered two-way relaying system consisting of two energy constrained single antenna sources and one multi-antenna relay with constant power supply. The time division protocol is adopted, where the relay first acts as the energy source and employs energy beamforming to charge the two sources, and then switches its role as a relay to help forward the information to the sources. To maintain user fairness, we aim to maximize the minimum rate of two sources, by jointly optimizing the energy beamforming vector, time splitting factor and relay transformation matrix. To further reduce the complexity of the optimal algorithm, we propose an alternating optimization method, where closed-form expressions for the energy beamforming and time splitting factor are obtained. To gain more insights, we propose a simple suboptimal design and analyze the outage probability and the average rate when the relay applies simple energy beamforming and transformation matrix. The analysis shows that the system can achieve a diversity order of $\frac {N}{3}$ for a system with a relay of $N$ antennas. Numerical results show that the performance of the proposed low-complexity alternating optimization method approaches the optimal algorithm over the entire SNR range, but has a significantly low complexity, and the proposed suboptimal design can also achieves a decent performance especially in small $N$ regime.

Emerging recently as a novel concept in communication system design, end-to-end learning introduces deep neural networks (NNs) to represent the transmitter and receiver functions. Consequently, the whole system can be interpreted as an autoencoder (AE), which can be optimized from a holistic approach through a data-driven training method. Until now, the AE technique is mainly developed for point-to-point communication scenarios. In this paper, we aim to develop a novel NN-based AE scheme for relay-assisted cooperative communication systems. Specifically, three NN components are constructed to learn the behavior of the transmitter, relay node, and receiver, respectively. As the conventional end-to-end training is inapplicable, a novel two-stage training approach is proposed to indirectly solve the end-to-end training problem. The implicit approximations involved are analytically expressed based on information theory, offering insights on the achievable performance with the proposed training method. The proposed AE model eliminates the need for channel state information and noise variance of any link, and is adaptive to the variation in the input block length. Simulation results verify its advantages over the conventional decode-and-forward (DF) and amplify-and-forward (AF) schemes in various scenarios.

We consider computation-offloading for Internet-of-things (IoT) applications in multiple-input-multiple-output (MIMO) cloud-radio-access-network (C-RAN). Specifically, the computational tasks of the IoT devices (IoTDs) are offloaded to a MIMO C-RAN, where a MIMO radio resource head (RRH) is connected to a baseband unit (BBU) through a capacity-limited fronthaul link, facilitated by the spatial filtering and uniform scalar quantization. We formulate a computation-offloading optimization problem to minimize the total transmit power of the IoTDs while satisfying the latency requirement of the computational tasks. To obtain a feasible solution for the non-convex problem, firstly the spatial filtering matrix is locally optimized at the MIMO RRH. Subsequently, leveraging the alternating optimization framework for joint optimization on the residual variables at the BBU, the baseband combiner, the optimal resource allocation and the number of quantization bits are obtained through the minimum-mean-squared-error (MMSE) metric, the successive inner convexification method and the line-search method, respectively. As a low-complexity approach, we apply a supervised deep learning (DL) method, which learns from the solutions obtained with our proposed algorithm. In addition, the deep transfer learning is adopted to adjust the neural network in dynamic IoT systems. Numerical results validate the effectiveness of the proposed optimization algorithm and the learning based methods.

Conventional networks are based on layered protocols with intensive cross-layer interactions and complex signal processing at every node, making it difficult to meet the ultra-low latency requirement of mission critical applications in future communication systems. In this paper, we address this issue by proposing the concept of network template, which allows data to flow through it at the transmission symbol level, with minimal node processing. This is achieved by carefully calibrating the inter-connecting links among the nodes and pre-calculating the routing/network coding actions for each node, according to a set of preconfigured flows. In this paper, we focus on the minimum cost network template design to minimize the connections within the template, while ensuring that all the pre-defined configurations are feasible with the guaranteed throughput, latency and reliability. We show that the minimum cost network template design problem is difficult to solve optimally in general. We thus propose an efficient greedy algorithm to find a close-to-optimal solution. Simulation results show that the construction cost of the templates obtained by the proposed algorithm is very close to a lower bound. Furthermore, the construction cost increases only slightly with the number of pre-defined configurations, which confirms the flexibility of the network template design.

In this paper, we propose a secure unmanned aerial vehicle (UAV) mobile edge computing (MEC) system where multiple ground users offload large computing tasks to a nearby legitimate UAV in the presence of multiple eavesdropping UAVs with imperfect locations. To enhance security, jamming signals are transmitted from both the full-duplex legitimate UAV and non-offloading ground users. For this system, we design a low-complexity iterative algorithm to maximize the minimum secrecy capacity subject to latency, minimum offloading and total power constraints. Specifically, we jointly optimize the UAV location, users' transmit power, UAV jamming power, offloading ratio, UAV computing capacity, and offloading user association. Numerical results show that our proposed algorithm significantly outperforms baseline strategies over a wide range of UAV self-interference (SI) efficiencies, locations and packet sizes of ground users. Furthermore, we show that there exists a fundamental tradeoff between the security and latency of UAV-enabled MEC systems which depends on the UAV SI efficiency and total UAV power constraints.

Achieving ultra-reliable, low-latency and secure communications is essential for realizing the industrial Internet of Things (IIoT). Non-coherent massive multiple-input multiple-output (MIMO) is one of promising techniques to fulfill ultra-reliable and low-latency requirements. In addition, physical layer authentication (PLA) technology is particularly suitable for secure IIoT communications thanks to its low-latency attribute. A PLA method for non-coherent massive single-input multiple-output (SIMO) IIoT communication systems is proposed in this paper. This method realizes PLA by embedding an authentication signal (tag) into a message signal, referred to as “message-based tag embedding”. It is different from traditional PLA methods utilizing uniform power tags. We design the optimal tag embedding and optimize the power allocation between the message and tag signals to characterize the trade-off between the message and tag error performance. Numerical results show that the proposed message-based tag embedding PLA method is more accurate than the traditional uniform tag embedding method which has an unavoidable tag error floor close to 10%.

The new paradigm of wirelessly powered two-tier heterogeneous networks (HetNets) is considered in this paper. Specifically, the femtocell base station (FBS) is powered by a power beacon (PB) and transmits confidential information to a legitimate femtocell user (FU) in the presence of a potential eavesdropper (EVE) and a macro base station (MBS). In this scenario, we investigate the secrecy beamforming design under three different levels of FBS-EVE channel state information (CSI), namely, the perfect, imperfect and completely unknown FBS-EVE CSI. Firstly, given the perfect global CSI at the FBS, the PB energy covariance matrix, the FBS information covariance matrix and the time splitting factor are jointly optimized aiming for perfect secrecy rate maximization. Upon assuming the imperfect FBS-EVE CSI, the worst-case and outage-constrained SRM problems corresponding to deterministic and statistical CSI errors are investigated, respectively. Furthermore, considering the more realistic case of unknown FBS-EVE CSI, the artificial noise (AN) aided secrecy beamforming design is studied. Our analysis reveals that for all above cases both the optimal PB energy and FBS information secrecy beamformings are of rank-1. Moreover, for all considered cases of FBS-EVE CSI, the closed-form PB energy beamforming solutions are available when the cross-tier interference constraint is inactive. Numerical simulation results demonstrate the secrecy performance advantages of all proposed secrecy beamforming designs compared to the adopted baseline algorithms.

Ultra-reliable and low-latency communications (URLLC) are considered as one of three new application scenarios in the fifth generation cellular networks. In this work, we aim to reduce the user experienced delay through prediction and communication co-design, where each mobile device predicts its future states and sends them to a data center in advance. Since predictions are not error-free, we consider prediction errors and packet losses in communications when evaluating the reliability of the system. Then, we formulate an optimization problem that maximizes the number of URLLC services supported by the system by optimizing time and frequency resources and the prediction horizon. Simulation results verify the effectiveness of the proposed method, and show that the tradeoff between user experienced delay and reliability can be improved significantly via prediction and communication co-design. Furthermore, we carried out an experiment on the remote control in a virtual factory, and validated our concept on prediction and communication co-design with the practical mobility data generated by a real tactile device.

Real-time remote estimation is critical for mission-critical applications including industrial automation, smart grid and tactile Internet. In this paper, we propose a hybrid automatic repeat request (HARQ)-based real-time remote estimation framework for linear time-invariant (LTI) dynamic systems. Considering the estimation quality of such a system, there is a fundamental tradeoff between the reliability and freshness of the sensor's measurement transmission. We formulate a new problem to optimize the sensor's online transmission control policy for static and Markov fading channels, which depends on both the current estimation quality of the remote estimator and the current number of retransmissions of the sensor, so as to minimize the long-term remote estimation mean squared error (MSE). This problem is non-trivial. In particular, it is challenging to derive the condition in terms of the communication channel quality and the LTI system parameters, to ensure a bounded long-term estimation MSE. We derive a sufficient condition of the existence of a stationary and deterministic optimal policy that stabilizes the remote estimation system and minimizes the MSE. Also, we prove that the optimal policy has a switching structure, and accordingly derive a low-complexity suboptimal policy. Numerical results show that the proposed optimal policy significantly improves the performance of the remote estimation system compared to the conventional non-HARQ policy.

In this paper, we focus on 1-bit precoding approaches for downlink massive multiple-input multiple-output (MIMO) systems, where we exploit the concept of constructive interference (CI). For both PSK and QAM signaling, we firstly formulate the optimization problem that maximizes the CI effect subject to the requirement of the 1-bit transmit signals. We then mathematically prove that, when employing the CI formulation and relaxing the 1-bit constraint, the majority of the transmit signals already satisfy the 1-bit formulation. Building upon this important observation, we propose a 1-bit precoding approach that further improves the performance of the conventional 1-bit CI precoding via a partial branch-and-bound (P-BB) process, where the BB procedure is performed only for the entries that do not comply with the 1-bit requirement. This operation allows a significant complexity reduction compared to the fully-BB (F-BB) process, and enables the BB framework to be applicable to the complex massive MIMO scenarios. We further develop an alternative 1-bit scheme through an `Ordered Partial Sequential Update' (OPSU) process that allows an additional complexity reduction. Numerical results show that both proposed 1-bit precoding methods exhibit a significant signal-to-noise ratio (SNR) gain for the error rate performance, especially for higher-order modulations.

For future Internet-of-Things based Big Data applications, data collection from ubiquitous smart sensors with limited spectrum bandwidth is very challenging. On the other hand, to interpret the meaning behind the collected data, it is also challenging for an edge fusion center running computing tasks over large data sets with a limited computation capacity. To tackle these challenges, by exploiting the superposition property of multiple-access channel and the functional decomposition, the recently proposed technique, over-the-air computation (AirComp), enables an effective joint data collection and computation from concurrent sensor transmissions. In this paper, we focus on a single-antenna AirComp system consisting of K sensors and one receiver. We consider an optimization problem to minimize the computation mean-squared error (MSE) of the K sensors' signals at the receiver by optimizing the transmitting-receiving (Tx-Rx) policy, under the peak power constraint of each sensor. Although the problem is not convex, we derive the computation-optimal policy in closed form. Also, we comprehensively investigate the ergodic performance of the AirComp system, and the scaling laws of the average computation MSE (ACM) and the average power consumption (APC) of different Tx-Rx policies with respect to K. For the computation-optimal policy, we show that the policy has a vanishing ACM and a vanishing APC with the increasing K.

In this paper, we propose the hybrid-precoding design that alleviates the performance loss caused by beam-misalignment in the mmWave multi-user communication systems. To this end, we firstly design the beam-misalignment aware fully-digital precoders for two distinct scenarios. First, for a base-station (BS) with full estimated channel-state-information (CSI), the minimum-mean-squared-error metric incorporating the `error-statistics' of the beam-misalignment error is used to analytically derive a closed-form expression for the fully-digital precoder, which maximizes the array gain while suppressing the inter-user interference for each user-equipment (UE). Second, for a BS which can only acquire partial estimated CSI, a min-max non-convex optimization is considered to obtain the fully-digital precoder, which minimizes the maximum loss in the array gains of the expected beam-misalignment `error-range' over the UEs while cancelling the inter-user interference. Subsequently, we propose the hybrid-precoding design that approximates the fully-digital designs based on the gradient-projection method, which is mathematically proven to converge to an approximate local solution with further reduced complexity compared to the state-of-the-art algorithms. Finally, the proposed hybrid-precoding design is further extended to the wideband mmWave communication systems. Numerical results show that the proposed hybrid-precoding design can effectively alleviate the performance degradation incurred by the beam-misalignment.

This article considers a cognitive radio-based IoT monitoring system, consisting of an IoT device that aims to update its measurement to a destination using cognitive radio technique. Specifically, the IoT device as a secondary user (SIoT), seeks and exploits the spectrum opportunities of the licensed band vacated by its primary user (PU) to deliver status updates without causing visible effects to the licensed operation. In this context, the SIoT should carefully make use of the licensed band and schedule when to transmit to maintain the timeliness of the status update. The timeliness of the status update characterizes how the destination knows the latest information of the SIoT. We adopt a recent metric, Age of Information (AoI), to characterize the timeliness of the status update of the SIoT. We aim to minimize the long-term average AoI of the SIoT while satisfying the collision constraint imposed by the PU by formulating a constrained Markov decision process (CMDP) problem. We first prove the existence of optimal stationary policy of the CMDP problem. The optimal stationary policy (termed age-optimal policy) is shown to be a randomized simple policy that randomizes between two deterministic policies with a fixed probability. We prove that the two deterministic policies have a threshold structure and further derive the closed-form expression of average AoI and collision probability for the deterministic threshold-structured policy by conducting Markov Chain analysis. The analytical expression offers an efficient way to calculate the threshold and randomization probability to form the age-optimal policy. For comparison, we also consider the throughput maximization policy (termed throughput-optimal policy) and analyze the average AoI performance under the throughput-optimal policy in the considered system. Numerical simulations show the superiority of the derived age-optimal policy over the throughput-optimal policy. We also unveil the impacts of various system parameters on the corresponding optimal policy and the resultant average AoI.

In this paper, we consider the packet-level forward error correction (FEC) code design, without feedback or with delayed feedback, for achieving the minimum end-to-end latency, i.e., the latency between the time that packet is generated at the source and its in-order delivery to the application layer of the destination. We first show that the minimum-latency FEC design problem can be modeled as a partially observable Markov decision process (POMDP), and hence the optimal code construction can be obtained by solving the corresponding POMDP. However, solving the POMDP optimally is in general difficult unless its state and action space is very small. To this end, we propose an efficient heuristic algorithm, namely the majority vote policy, for obtaining a high quality approximate solution. We also derive the tight lower and upper bounds of the optimal state values of this POMDP, based on which a more sophisticated D-step search algorithm can be implemented for obtaining near-optimal solutions. The simulation results show that the proposed code designs via solving the POMDP, either with the majority vote policy or the D-step search algorithm, strictly outperform the existing schemes, for both cases, without or with only delayed feedback.

The sparse code multiple access (SCMA) is a promising candidate for bandwidth-efficient next generation wireless communications, since it can support more users than the number of resource elements. On the same note, faster-than-Nyquist (FTN) signaling can also be used to improve the spectral efficiency. Hence in this paper, we consider a combined uplink FTN-SCMA system in which the data symbols corresponding to a user are further packed using FTN signaling. As a result, a higher spectral efficiency is achieved at the cost of introducing intentional inter-symbol interference (ISI). To perform joint channel estimation and detection, we design a low complexity iterative receiver based on the factor graph framework. In addition, to reduce the signaling overhead and transmission latency of our SCMA system, we intrinsically amalgamate it with grant-free scheme. Consequently, the active and inactive users should be distinguished. To address this problem, we extend the aforementioned receiver and develop a new algorithm for jointly estimating the channel state information, detecting the user activity and for performs data detection. In order to further reduce the complexity, an energy minimization based approximation is employed for restricting the user state to Gaussian. Finally, a hybrid message passing algorithm is conceived. Our Simulation results show that the FTN-SCMA system relying on the proposed receiver design has a higher throughput than conventional SCMA scheme at a negligible performance loss.

This paper considers a wireless networked control system (WNCS) consisting of a dynamic system to be controlled (i.e., a plant), a sensor, an actuator and a remote controller for mission-critical Industrial Internet of Things (IIoT) applications. A WNCS has two types of wireless transmissions, i.e., the sensor's measurement transmission to the controller and the controller's command transmission to the actuator. In the literature of WNCSs, the controllers are commonly assumed to work in a full-duplex mode by default, i.e., can simultaneously receive the sensor's information and transmit its own command to the actuator. In this work, we consider a practical half- duplex controller, which introduces a novel transmission-scheduling problem for WNCSs. A frequent schedule of the sensor's transmission results in a better estimation of the plant states at the controller and thus a higher quality of the control command, but it leads to a less frequent/timely control of the plant. Therefore, considering the overall control performance of the plant, i.e., the average cost function of the plant, there exists a fundamental tradeoff between the sensor's and controller's transmission. We formulate a new problem to optimize the transmission-scheduling policy so as to minimize the long-term average cost function. We derive the necessary and sufficient condition of the existence of a stationary and deterministic optimal policy that results in a bounded average cost in terms of the transmission reliability of the sensor- to- controller and controller-to-actuator channels. Also, we derive an easy-to-compute suboptimal policy, which notably reduces the average cost of the plant compared to a naive alternative-scheduling policy.

This paper focuses on the signal design in a Gaussian amplify-and-forward (AF) relay system with superposition coding (SC) being applied at the relay, which allows the source and relay to transmit their own information within two time slots. Practical quadrature amplitude modulation (QAM) constellations are adopted at the source and relay. To improve the system error performance, we optimize the weight coefficients adopted at the source and relay to maximize the minimum Euclidean distance of the received composite constellation, subject to their individual average power constraints. The formulated optimization problem is shown to be a mixed continuous-discrete one that is non-trivial to resolve in general. By resorting to the punched Farey sequence, we manage to obtain the optimal solution to the formulated problem by first partitioning the entire feasible region into a finite number of sub-intervals and then taking the maximum over all the possible sub intervals. Simulation results are provided to demonstrate the superior performance of our proposed design based on SC over that using conventional time division multiple access (TDMA).

Wireless networked control systems for Industrial Internet of Things (IIoT) require low latency communication techniques. In this paper, we investigate a coding-free control method to achieve ultra-low latency communications in single-controller-multi-plant networked control systems. We formulate a power allocation problem to optimize the sum cost functions of multiple plants, subject to the plant stabilization condition and the controller's power limit. Although the optimization problem is a non-convex one, we derive a closed-form solution, which indicates that the optimal power allocation policy for stabilizing the plants with different channel conditions is reminiscent of the channel-inversion policy. Also, we numerically compare the performance of the proposed coding-free control method and the conventional coding-based control methods in terms of the cost function of a plant, which shows that the coding-free method is superior in a practical range of SNRs.

The upcoming fifth-generation (5G) cellular networks and beyond are expected to support various new application scenarios with diverse quality-of-service requirements. In this work, we study how to allocate the transmit power and the number of subcarriers in a 5G New Radio system with delay-tolerant, delay-sensitive, and ultra- reliable and low-latency services. We first formulate an optimization framework with different kinds of services, and then propose a low- complexity algorithm to maximize the number of users that can be supported in this system. In addition, we study the optimality conditions of the proposed algorithm. The theoretical analysis shows that the conditions hold for delay-tolerant and delay-sensitive services. For ultra-reliable and low-latency services, we prove that the conditions hold when the number of antennas at the base station is large. Numerical results validate that the conditions hold even when the number of antennas is small. Simulation results show that compared with an existing method, our algorithm can support more users with a lower computing complexity.

In this paper, we consider a point-to-point wireless communication system. The source monitors a physical process and generates status update packets according to a Poisson process. The packets are transmitted to the destination by using finite blocklength coding to update the status (e.g., temperature, speed, position) of the monitored process. In some applications, such as real-time monitoring and tracking, the timeliness of the status updates is critical since the users are interested in the latest condition of the process. The timeliness of the status updates can be reflected by a recently proposed metric, termed the age of information (AoI). We focus on the packet management policies for the considered system. Specifically, the preemption and discard of status updates are important to decrease the AoI. For example, it is meaningless to transmit stale status updates when a new status update is generated. We propose three packet management schemes in the transmission between the source and the destination, namely non-preemption (NP), preemption (PR) and retransmission (RT) schemes. We derive closed-form expressions of the average AoI for the proposed three schemes. Based on the derived analytical expressions of the average AoI, we further minimize the average AoI by optimizing the packet blocklength for each status update. Simulation results are provided to validate our theoretical analysis, which further show that the proposed schemes can outperform each other for different system setups, and the proposed schemes considerably outperform the existing ones without packet management at medium to high generation rate.

Opportunistic spectrum access (OSA) is envisioned to support the spectrum demand of future- generation wireless networks. In practice, primary channels are usually correlated and network dynamics is unknown a-priori. This entails a great challenge on sensing policy design, and conventional model-based methods are generally inapplicable. In this paper, we propose a novel Gaussian process reinforcement learning (GPRL) based model-free solution to enable the fast sensing policy optimization in OSA. In essence, Gaussian process is embedded in RL framework as a Q-function approximator to efficiently utilize the past learning experience. A novel kernel function is first tailor designed to measure spectrum data correlation. Then a covariance-based exploration strategy is developed to strike a better trade-off between the exploration and exploitation in RL. Our simulation results show that the proposed GPRL can obtain a near-optimal policy with significantly reduced learning period compared with deep reinforcement learning.

In this paper, we propose an efficient reliability based segmentation-discarding decoding (SDD) algorithm for short block-length codes. A novel segmentation- discarding technique is proposed along with the stopping rule to significantly reduce the decoding complexity without a significant performance degradation compared to ordered statistics decoding (OSD). In the proposed decoder, the list of test error patterns (TEPs) is divided into several segments according to carefully selected boundaries and every segment is checked separately during the reprocessing stage. Decoding is performed under the constraint of the discarding rule and stopping rule. Simulations results for different codes show that our proposed algorithm can significantly reduce the decoding complexity compared to the existing OSD algorithms in literature.

As distributed energy resources (DERs) are widely deployed, the DC packetized power microgrid is a promising solution to incorporate DERs effectively and steadily. In this paper, we consider a DC microgrid, where the energy is dispatched by a power router in the form of power packets. Since energy subscribers (ESs) with DERs in the microgrid can generate surplus electricity, the peer-to-peer (P2P) energy trading is an effective way in order to balance the energy. Different from the P2P trading in AC smart grids, the dispatching capability of the router in the DC microgrid needs to be considered, which will make the trading problem more complicated. To tackle this challenge, we formulate the P2P trading problem as an auction game, where the demander ESs submit bids to compete for power packets, and a controller decides the energy allocation and power packet scheduling. The effectiveness of the proposed scheme is validated through simulations.

In this paper, we investigate the interference exploitation precoding for multi-level modulations in the downlink multi-antenna systems. We mathematically derive the optimal precoding structures based on the Karush-Kuhn-Tucker (KKT) conditions. Furthermore, by formulating the dual problem, the precoding problem for multi-level modulations can be transformed into a pre-scaling operation using quadratic programming (QP) optimization. Compared to the original second-order cone programming (SOCP) formulation, this transformation that finally leads to a QP optimization allows a considerable complexity reduction. Simulation results validate our derivations on the optimal precoding structure, and demonstrate significant performance improvements for interference exploitation precoding over traditional precoding methods for multi-level modulations.

In this paper, we study the cooperative beamforming schemes for multi-cell multi-user full-dimensional (FD) massive multiple-input multiple-out (MIMO) networks. In the considered network, base stations (BSs) work together to direct their beams to user equipments (UEs) such that inter-cell interference is minimized and each UE is assigned a beam from one BS by user association. We propose to maximize the network capacity by jointly optimizing the beamforming vectors and the user association factors (UAFs), which is further transformed into an optimization on UAFs only by maximizing a lower bound of the signal-to-leakage-and-noise ratio (SLNR). To solve the optimization on UAFs, we propose a belief propagation (BP) based algorithm to obtain the UAFs at the UE level in a parallel manner. Simulation results show that the proposed cooperative beamforming method significantly outperforms the benchmarks in the literature.

Ultra-reliable and low-latency communications (URLLC) are considered as one of three key application scenarios in the 5th generation (5G) communication systems. It is very challenging to satisfy the ultra-low end-to-end (E2E) delay requirement, especially in long distance communication scenarios since the delay in backhauls and core networks could be tens of milliseconds. In this work, we aim to reduce the latency experienced by users through prediction and communication co-design, where the transmitter predicts it's future states and sends them to the receiver in advance. Considering that the prediction is not error free, we take into consideration prediction errors and the packet loss in communications when analyzing the reliability of the system. Then, we formulate an optimization problem that minimizes the required bandwidth to satisfy the E2E delay and reliability requirements of URLLC. With the proposed method, it is possible for users to achieve zero latency experience when the prediction time equals to the communication delay. Simulation results verify the effectiveness of the proposed method, and show that the tradeoffs among bandwidth, reliability, and latency can be fundamentally improved via prediction and communication co-design. The results are further evaluated by using practical motion data acquired from experiments of a real hardware device.

Real-time remote estimation is critical for mission-critical applications including industrial automation, smart grid, and the tactile Internet. In this paper, we propose a hybrid automatic repeat request (HARQ)-based real-time remote estimation framework for linear time-invariant (LTI) dynamic systems. Considering the estimation quality of such a system, there is a fundamental tradeoff between the reliability and freshness of the sensor's measurement transmission. When a failed transmission occurs, the sensor can either retransmit the previous old measurement such that the receiver can obtain a more reliable old measurement, or transmit a new but less reliable measurement. To design the optimal decision, we formulate a new problem to optimize the sensor's online decision policy, i.e., to retransmit or not, depending on both the current estimation quality of the remote estimator and the current number of retransmissions of the sensor, so as to minimize the long-term remote estimation mean-squared error (MSE). This problem is non-trivial. In particular, it is not clear what the condition is in terms of the communication channel quality and the LTI system parameters, to ensure that the long-term estimation MSE can be bounded. We give a sufficient condition of the existence of a stationary and deterministic optimal policy that stabilizes the remote estimation system and minimizes the MSE. Also, we prove that the optimal policy has a switching structure, and derive a low-complexity suboptimal policy. Our numerical results show that the proposed optimal policy notably improves the performance of the remote estimation system compared to the conventional non-HARQ policy.

Multi-parameter cognition in a cognitive radio network provides a potential avenue to more efficient spectrum usage. In this paper, we propose a two-stage spectrum sharing strategy, where the primary user operates with multiple transmit power levels. Different from the conventional approaches, our method does not require any prior knowledge of the primary transmitter (PT) power characteristics. In the first stage, we use a conditionally conjugate Dirichlet process Gaussian mixture model to capture the multi-level power characteristics inherent in the PT signals, and design a Bayesian inference method to infer the model parameters. In the second stage, we propose a secondary transmitter (ST) prediction-transmission method based on reinforcement learning, which adapts to the PT power variation and strike an excellent tradeoff between the secondary network throughput and the interference to the primary network. The simulation results show the effectiveness of the proposed strategy.

In this paper, we propose a fast beam tracking strategy for mobile millimeter-wave systems, where the temporal variations of the angle of departure (AoD) are considered and modeled as a discrete Markov process. In contrast to most existing works that rely on the slow-fading assumption, we consider a more practical scenario in which the AoD can vary rapidly due to blockage and other environmental obstructions. In this case, the use of narrow training beams becomes inefficient, and therefore we propose to employ multiple radio-frequency chains generating wide beams to reduce the training time. By optimizing the selected training beams, we aim to minimize the average tracking error probability (ATEP). However, since the exact expression for ATEP is difficult to obtain, we derive its upper bound in a closed form, and aim to minimize this upper bound instead. The associated training beam sequence design problem is transformed into the construction of a bipartite graph that does not contain cycles of length 4, which is implemented with the progressive edge-growth algorithm. Numerical results demonstrate significant gains of the proposed beam tracking strategy over the existing benchmark methods.

The DC power packet transmission contributes to a reliable integration of distributed energy resources (DERs) in power grids and reduces the load fluctuation. In this paper, we consider a DC packetized-power microgrid for the integration of DERs and propose a power packet dispatching protocol to regulate the peer to peer power interchange within the microgrid. We formulate the joint subscriber matching and energy allocation problem to optimize the subscribers' benefits, which is proved to be NP-hard. Based on the matching theory, we associate the problem equivalent to a many-to-many matching problem and design a two-sided matching algorithm to solve it. We then design a graph coloring based algorithm to schedule the energy transmissions of the matched energy subscribers. Simulation results validate the effectiveness of the proposed protocol in achieving a steady and efficient microgrid power dispatching.

This paper considers an Industrial Internet of Thing (IIoT) system with a source monitoring a dynamic process with randomly generated status updates. The status updates are sent to an designated destination in a real-time manner over an unreliable link. The source is subject to a practical constraint of limited average transmission power. Thus, the system should carefully schedule when to transmit a fresh status update or retransmit the stale one. To characterize the performance of timely status update, we adopt a recent concept, Age of Information (AoI), as the performance metric. We aim to minimize the long-term average AoI under the limited average transmission power at the source, by formulating a constrained Markov Decision Process (CMDP) problem. To address the formulated CMDP, we recast it into an unconstrained Markov Decision Process (MDP) through Lagrangian relaxation. We prove the existence of optimal stationary policy of the original CMDP, which is a randomized mixture of two deterministic stationary policies of the unconstrained MDP. We also explore the characteristics of the problem to reduce the action space of each state to significantly reduce the computation complexity. We further prove the threshold structure of the optimal deterministic policy for the unconstrained MDP. Simulation results show the proposed optimal policy achieves lower average AoI compared with random policy, especially when the system suffers from stricter resource constraint. Besides, the influence of status generation probability and transmission failure rate on optimal policy and the resultant average AoI as well as the impact of average transmission power on the minimal average AoI are unveiled.

In this paper, we derive the distribution of the Hamming distance at 0-reprocessing of the ordered statistics decoding (OSD). With the assumption of decoding a random linear block code, we first find the distribution of the number of errors in any partition of the ordered channel output sequence. Then the distribution of the Hamming distance after 0-reprocessing is derived by a mixture model of two random variables. Based on the proposed statistical approach, we outline the design of high-efficiency OSD algorithm. Simulation and numerical results show that our proposed statistical approaches accurately describe the Hamming distance distributions in OSD decoding process.

Different from the existing isotropic path loss model, in this paper, we develop an anisotropic path loss model for the fifth generation (5G) multi-input multi-output (MIMO) fractal cellular networks, in which the coverage boundary has the fractal characteristics including the self-similarity and the detailed structure at arbitrarily small scales of the angle domain. Based on the real-world measurement data collected from the Zhangjiang Road in Shanghai, China, we analytically derive the coverage probability, the area spectral efficiency (ASE), and the sum rate for the fractal small cell networks, with the assumption that the path loss exponent follows the Gamma distribution. Simulation results indicate that compared with the conventional isotropic path loss model, the coverage probability under the anisotropic path loss model has been overestimated in small cell networks. With the anisotropic path loss model, the ASE with MIMO technologies is higher than that with single-input single-output (SISO) technologies in the low signal to interference ratio (SIR) regions and is lower than that with SISO technologies in the high SIR regions. Therefore, the coverage model of small cell network needs to be rethought by taking into account the fractal characteristic in wireless channels.

Analog fountain code (AFC) is a seamless rate adaptation strategy and was recently shown to closely approach the channel capacity without the channel state information at the transmitter side. The code was mainly designed for large block lengths and therefore the design for short packet communications which is one of the communication scenarios in 5G ultra-reliable and low latency communications (URLLC), is still not clear. We tackle this problem in this work and particularly focus on the design of the precoder which is shown to significantly affects the overall performance of the AFC in the short block length regime. We consider BCH codes as the precoder, and determine the rate of the code in order to maximize the realized rate under a given block error rate constraint. Simulation is performed to investigate the effect of choices for BCH precoder on the overall performance of BCH-AFC in terms of reliability and latency. We show that by selecting the appropriate BCH code as the AFC precoder, AFC performs closely to the normal approximation benchmark. We further proposed a threshold-based decoder to reduce the decoding complexity which is of significant importance in URLLC scenarios.

To satisfy the requirement of the fifth generation (5G) mobile communications that offers an ultra high data rate of 100Mbps to 1Gbps anytime and anywhere, the coordinated multipoint (CoMP) technique is proposed to mitigate intercell interference to improve the coverage of high data rate services, cell-edge throughput, and system capacity. However, the joint transmission (JT) CoMP technique is difficult to be applied in practice due to the critical time synchronization for multiple coordination links and the bottleneck of backhaul capacity and radio resource at each small cell base stations (SBSs). Moreover, since the coordination SBSs in the conditional scheme are entirely separate from each other, different time of arrivals at the user cause the severe time synchronization problem. The anisotropic propagation environment in the urban scenario makes the implementation condition even worse. To tackle these issues, we propose a novel JT-CoMP scheme with the anisotropic path loss model to minimize the network backhaul traffic subject to the constraints on the radio resource and the differences in time of arrivals. Simulation results demonstrate that the proposed distance-resource-limited CoMP scheme can obtain the maximum achievable rate with the minimum network backhaul traffic, compared with existing schemes.

One of focuses in green communication studies is the energy efficiency (EE) of massive multiple-input multiple-output (MIMO) systems. Although the massive MIMO technology can improve the spectral efficiency (SE) of cellular networks by configuring a large number of antennas at base stations (BSs), the energy consumption of radio frequency (RF) chains increases dramatically. The increment of energy consumption is caused by the increase of RF chain number to match the antenna number in massive MIMO communication systems. To overcome this problem, a generalized spatial modulation (GSM) solution is presented to simultaneously reduce the number of RF chains and maintain the SE of massive MIMO communication systems. A EE model is proposed to estimate the transmission and computation power of massive MIMO communication systems with GSM. Simulation results demonstrate that the EE of massive MIMO communication systems with GSM outperforms the massive MIMO communication systems without GSM. Besides, the computation power consumed by massive MIMO communication systems with GSM is effectively reduced.

We consider an Internet of Things (IoT) monitoring system, in which an IoT device monitors a physical process and transmits randomly generated status updates to its associated access point (AP) as timely as possible. The timeliness of the status updates is characterized by a recently introduced metric, termed the age of information (AoI), which is defined as the time elapsed since the generation of the last successfully received status update. The channel between the IoT device and the AP is considered to be error-prone and thus the status updates suffer from packet loss. Assuming that the AP provides no feedback to the IoT device, we adopt a practical truncated automatic repeat request (TARQ) scheme: the IoT device keeps transmitting the current status update repeatedly until the maximum allowable transmission times is reached or a new status update is generated. We characterize the inherent age-energy tradeoff for the considered IoT monitoring system. Specifically, a larger value of the maximum allowable transmission times reduces the average AoI, at the cost of incurring higher average energy consumption at the IoT device. Based on the evolution of AoI, we derive the closed-form expressions of the average AoI, the average peak AoI, and the average energy consumption. We then minimize the average AoI by optimizing the transmit power of the IoT device and the maximum allowable transmission times under an average transmit power constraint. Simulations validate the theoretical analysis and reveal that under the same average transmit power constraint, the adopted TARQ scheme achieves a lower average AoI than the classical ARQ scheme that allows an infinite number of retransmission times.

We consider a cognitive radio-based Internet-of-Things (CR-IoT) network consisting of one primary IoT (PIoT) system and one secondary IoT (SIoT) system. The IoT devices of both the PIoT and the SIoT, respectively, monitor one physical process and send randomly generated status updates to their associated access points (APs). The timeliness of the status updates is important as the systems are interested in the latest condition (e.g., temperature, speed, and position) of the IoT device. In this context, two natural questions arise: 1) how to characterize the timeliness of the status updates in CR-IoT systems? 2) which scheme, overlay or underlay, is better in terms of the timeliness of the status updates? To answer these two questions, we adopt a new performance metric, named the age of information (AoI). We analyze the average peak AoI of the PIoT and the SIoT for overlay and underlay schemes, respectively. Simple asymptotic expressions of the average peak AoI are also derived when the PIoT operates at high signal-to-noise ratio (SNR). Based on the asymptotic expressions, we characterize a critical generation rate of the PIoT system, which can determine the superiority of overlay and underlay schemes in terms of the average peak AoI of the SIoT. Numerical results validate the theoretical analysis and uncover that the overlay and underlay schemes can outperform each other in terms of the average peak AoI of the SIoT for different system setups.

An increasing number of low-power Internet of Things (IoT) devices will be widely deployed in the near future. Considering the short-range communication of low-power devices, multihop transmissions will become an important transmission mechanism in IoT networks. It is a crucial for low-power devices to transmit data over long distances via multihop in a low-delay and reliable way. The small-world characteristics of networks indicate that the network has an advantage of a small average shortest-path length (ASL) and a high average clustering coefficient (ACC). In this article, a new IoT routing mechanism considering small-world characteristics is proposed to reduce the delay and improve the reliability. The ASL and ACC are derived for the performance analysis of small-world characteristics in IoT networks based on Cayley graphs. Besides, the reliability and delay models are proposed for small-world IoT based on Cayley graphs (SWITCH). The simulation results demonstrate that SWITCH has lower delay and better reliability than that of conventional nearest neighboring routing (NNR). Moreover, the maximum delay of SWITCH is reduced by 50.6% compared with that by NNR.

The Internet of Things (IoT) provides enormous connections of devices and sensors with different applications. It is an enabling technology for smart city, intelligent transportation systems, environmental monitoring, security surveillance, smart homes, satellite and space information network, ocean monitoring, and unmanned border awareness systems, just to name a few. IoT as a high-density network will take the burden of massive data generated by different kinds of terminals and sensors. Dramatic growth in IoT has created a shortage in the available radio spectrum. Wireless communications services in IoT such as cellular phones, tablets, and wireless Internet access have to compete with existing users in radar, government and military communications, environmental monitoring, and other IoT applications. The strategy to increase the efficiency of spectrum sharing among the enormous users in IoT. Besides, the IoT applications demand more and better functionality and performance from new electronic devices; these demands translate into greater energy consumption demands. The gap between energy storage and demand continues to grow and the battery technologies for energy storage are not expected to increase tremendously in the coming years. Furthermore, reducing signal transmission power can lessen interference among devices in IoT. Energy-efficient protocols and network architectures will further reduce the number of transmissions and extend the battery life of IoT devices (IoTDs). Therefore, it is essential to pursue fundamental research on new components, techniques, and architectures to achieve energy-efficient sensing, communications, and networking in a shared spectrum environment for IoT.

In this paper, we establish a cross-layer framework for optimizing user association, packet offloading rates, and bandwidth allocation for mission-critical Internet-of-Things (MC-IoT) services with short packets in mobile edge computing (MEC) systems, where enhanced mobile broadband (eMBB) services with long packets are considered as background services. To reduce communication delay, the fifth generation new radio is adopted in radio access networks. To avoid long queueing delay for short packets from MC-IoT, processor-sharing (PS) servers are deployed at MEC systems, where the service rate of the server is equally allocated to all the packets in the buffer. We derive the distribution of latency experienced by short packets in closed form, and minimize the overall packet loss probability subject to the end-to-end delay requirement. To solve the nonconvex optimization problem, we propose an algorithm that converges to a near optimal solution when the throughput of eMBB services is much higher than MC-IoT services, and extend it into more general scenarios. Furthermore, we derive the optimal solutions in two asymptotic cases: communication or computing is the bottleneck of reliability. The simulation and numerical results validate our analysis and show that the PS server outperforms first-come-first-serve servers.

This paper deals with spectrum sensing for cognitive radio-based Internet of Things (IoT) systems and their coexistence with Long Term Evolution (LTE) systems. Due to the sparsity of the covariance matrix of IoT/LTE signals, we reveal that the likelihood ratio test approximates to energy detection (ED) at low signal to noise ratio. However, the noise (power) uncertainty can degrade the performance of ED severely, especially when low-cost IoT devices are employed for spectrum sensing. To tackle this issue, we derive the relationship among noise power, total power, and autocorrelation coefficient of received signals, and propose an unbiased estimator of noise power without the knowledge of the presence/absence of IoT/LTE signals. We then design a new ED with multiple estimates of noise power from historical and current sensing data, and analyze its theoretical performance. Numerical results are provided to verify the theoretical results and demonstrate the superior performance of the proposed detector. It is shown that, by exploiting sufficient historical sensing data, the performance of the proposed ED can closely approach that of the ideal ED.

In this paper, we examine new plan pricing schemes for multimedia applications that offload computing-intensive tasks to computing servers incurring both communication and computing costs. Pricing schemes offered to users include: 1) a pay-as-you-go payment for usage of communication or computing resources, 2) an upfront data (resp. computing) plan for unlimited usage of communication (resp. computing) resources during a period, and 3) an upfront combo plan for unlimited usage of both communication and computing resources during a period. We aim to solve an online plan reservation problem: the amount of resources needed by a task is only known when it arrives, i.e., the future is unknown. However, even if the resource usage of future tasks is known in advance, the plan reservation problem is NP-hard and thus challenging. To tackle this problem, we propose a randomized online reservation (ROR) scheme to reserve plans probabilistically, where the probability is determined by the recent usage of resources. The performance gap (competitive ratio) between our proposed scheme and the optimal solution is analyzed and derived in closed-form, and this gap is proved to be the minimum among all online algorithms which do not know the usage of future tasks. Trace-driven simulations verify the cost advantage of ROR and characterize how different prices of plans influence users' plan reservation strategies.

In this paper, we consider a massive grant-free non-orthogonal multiple access (GF-NOMA) scheme, where devices have strict latency requirements and no retransmission opportunities are available. Each device chooses a pilot sequence from a predetermined set as its signature and transmits its selected pilot and data simultaneously. A collision occurs when two or more devices choose the same pilot sequence. Existing GF-NOMA schemes assume that a collision of at least one pair of users entails a collision for all simultaneously transmitting users, which is sub-optimal in terms of individual outage and system throughput. For that, we propose a novel framework, where collisions are treated as interference to the remaining received signals. With the aid of Poisson point processes and ordered statistics, we derive simplified expressions that can well approximate the outage probability and throughput of the system for both successive joint decoding (SJD) and successive interference cancellation (SIC). Numerical results verify the accuracy of our analytical expressions. For low data rate transmissions, results show that the performance of SIC is close to that of SJD in terms of outage probability, for packet arrival rates up to 10 packets per slot. However, SJD can achieve almost double the throughput of SIC and is, thus, far more superior.

Caching the most popular contents at the wireless network edge such as small-cell base stations (SBSs) is a smart way of reducing duplicated content transmissions and offloading the mobile data traffic in the network backhaul. Currently, most small-cell caching strategies are conceived, designed, and optimized based on the global content request probability (GCRP), with very limited consideration of the individual content request probability (ICRP) reflecting personal preferences. To enable more efficient wireless caching, in this paper, we propose a novel localized deterministic caching framework, drawing upon the recent advances in recommendation systems based on machine learning techniques. By introducing the concept of the rating matrix, we first propose a new Bayesian learning method to predict personal preferences and estimate the ICRP. This crucial information is then incorporated into our caching strategy for maximizing the system throughput, or equivalently, minimizing the download latency, where a deterministic caching algorithm based on reinforcement learning is proposed to optimize the content placement. To this end, we extend the framework to enable device-to-device (D2D) connections to further reduce the download delay, and also design a feedback mechanism to improve the accuracy in the ICRP estimation. Our simulation results verified that with the estimated ICRP and the proposed caching strategy, the proposed framework can significantly outperform the existing methods in terms of hit rate and system throughput.

In this paper, we establish a framework for enabling ultra-reliable and low-latency communications in the control and non-payload communications (CNPC) links of the unmanned aerial vehicle (UAV) communication systems. We first derive the available range of the CNPC links between UAVs and a ground control station. The available range is defined as the maximal horizontal communication distance within which the round-trip delay and the overall packet loss probability can be ensured with a required probability. To exploit the macro-diversity gain of the distributed multi-antenna systems (DAS) and the array gain of the centralized multi-antenna systems (CAS), we consider a modified DAS (M-DAS), where the ground control station is equipped with the distributed access points (APs), and each AP can have multiple antennas. We then show that the available range can be maximized by judiciously optimizing the altitude of UAVs, the duration of the uplink and downlink phases, and the antenna configuration. To solve the non-convex problem, we propose an algorithm that can converge to the optimal solution in DAS and CAS, and then extend it into more general M-DAS. The simulation and numerical results validate our analysis and show that the available range of M-DAS can be significantly larger than those of the DAS and CAS.

Passive localization based on time of arrival (TOA) measurements is investigated, where the transmitted signal is reflected by a passive target and then received at several distributed receivers. After collecting all measurements at receivers, we can determine the target location. The aim of this paper is to provide a unified factor graph-based framework for passive localization in wireless sensor networks based on TOA measurements. Relying on the linearization of range measurements, we construct a Forney-style factor graph model and conceive the corresponding Gaussian message passing algorithm to obtain the target location. It is shown that the factor graph can be readily modified for handling challenging scenarios such as uncertain receiver positions and link failures. Moreover, a distributed localization method based on consensus-aided operation is proposed for a large-scale resource constrained network operating without a fusion center. Furthermore, we derive the Cramér-Rao bound (CRB) to evaluate the performance of the proposed algorithm. Our simulation results verify the efficiency of the proposed unified approach and of its distributed implementation.

This paper considers a passive localization scenario relying on a single transmitter, several receivers, and multiple moving targets to be located. The so-called “passive” targets equipped with RFID reflectors are capable of reflecting the signals from the transmitter to the receivers. Existing approaches assume that the transmitter and receivers are synchronous or quasi-synchronous, which is not always realistic in practical scenarios. Hence, an asynchronous wireless network is considered, where different clock offsets are assumed at different receivers. We propose a centralized expectation-maximization-based passive localization method for asynchronous receivers (EMpLaR) by treating the clock offsets as hidden variables. Thereby, the proposed algorithm makes use of Taylor expansions to arrive at a closed-form maximization. Furthermore, to improve the robustness to link failures and to reduce the energy consumption, we propose a distributed localization approach based on average consensus formulation to locate the target at each receiver. By applying a quadratic polynomial approximation of the function on which consensus has to be reached, both the computational complexity and the communications overhead are significantly reduced. The Cramér-Rao bound of the target location is derived as a benchmark of our proposed algorithms. Our simulation results show that the proposed centralized and distributed EMpLaR algorithms match the Cramér-Rao bound and significantly improve the localization performance compared with the conventional methods.

A promising solution to address the spectrum shortage in 5G cellular systems is the deployment of millimeter wave (mmWave) heterogeneous networks (HetNets). However, a key challenge for mmWave HetNets is to manage the user mobility and handovers among mmWave small cells exploiting highly directional antennas and conventional microwave macro cells. In this paper, we propose a new efficient handover decision algorithm based on a Markov Decision Process (MDP) to optimize the overall service experience of users in mmWave HetNets. By utilizing user's mobility information (velocity and location), the proposed algorithm avoids excessive handovers and effectively tackles beamforming misalignments, and signal blockages in mmWave small cells. To improve the computational efficiency of the MDP, we apply the action elimination method by exploiting unique handover properties of mmWave HetNets. While maintaining optimality, theoretical analysis shows that our proposed handover decision algorithm can reduce the computational complexity by 0.25M|Bs| + 0.25M/(M - 1) times, where M is the number of base stations and |Bs| is the number of beamwidth options for mmWave beamforming. Mobility-trace driven numerical results demonstrate the optimality of our proposed algorithm compared with other benchmark schemes and its low computational complexity over the traditional approach.

In this paper, we propose a terrestrial-satellite network (TSN) architecture to integrate the ultra-dense low earth orbit (LEO) networks and the terrestrial networks to achieve efficient data offloading. In TSN, each ground user can access the network over C-band via a macro cell, a traditional small cell, or a LEO-backhauled small cell (LSC). Each LSC is then scheduled to upload the received data via multiple satellites over Ka-band. We aim to maximize the sum data rate and the number of accessed users while satisfying the varying backhaul capacity constraints jointly determined by the LEO satellite-based backhaul links. The optimization problem is then decomposed into two closely connected subproblems and solved by our proposed matching algorithms. The simulation results show that the integrated network significantly outperforms the non-integrated ones in terms of the sum data rate. The influence of the traffic load and LEO constellation on the system performance is also discussed.

In this paper, we consider a mobile edge computing system with both ultra-reliable and low-latency communications services and delay tolerant services. We aim to minimize the normalized energy consumption, defined as the energy consumption per bit, by optimizing user association, resource allocation, and offloading probabilities subject to the quality-of-service requirements. The user association is managed by the mobility management entity (MME), while resource allocation and offloading probabilities are determined by each access point (AP). We propose a deep learning (DL) architecture, where a digital twin of the real network environment is used to train the DL algorithm off-line at a central server. From the pre-trained deep neural network (DNN), the MME can obtain user association scheme in a real-time manner. Considering that the real networks are not static, the digital twin monitors the variation of real networks and updates the DNN accordingly. For a given user association scheme, we propose an optimization algorithm to find the optimal resource allocation and offloading probabilities at each AP. The simulation results show that our method can achieve lower normalized energy consumption with less computation complexity compared with an existing method and approach to the performance of the global optimal solution.

In this paper, we aim to find the global optimal resource allocation for ultra-reliable and low-latency communications (URLLC), where the blocklength of channel codes is short. The achievable rate in the short blocklength regime is neither convex nor concave in bandwidth and transmit power. Thus, a non-convex constraint is inevitable in optimizing resource allocation for URLLC. We first consider a general resource allocation problem with constraints on the transmission delay and decoding error probability, and prove that a global optimal solution can be found in a convex subset of the original feasible region. Then, we illustrate how to find the global optimal solution for an example problem, where the energy efficiency (EE) is maximized by optimizing antenna configuration, bandwidth allocation, and power control under the latency and reliability constraints. To improve the battery life of devices and EE of communication systems, both uplink and downlink resources are optimized. The simulation and numerical results validate the analysis and show that the circuit power is dominated by the total power consumption when the average inter-arrival time between packets is much larger than the required delay bound. Therefore, optimizing antenna configuration and bandwidth allocation without power control leads to minor EE loss.

In this paper, we propose a codebook-based beam tracking strategy for mobile millimeter-wave (mmWave) systems, where the temporal variation of the angle of departure (AoD) is considered. A closed-form upper bound of the average tracking error probability (ATEP) is derived and further optimized. We first consider a slow-varying scenario where narrow training beams implemented by single radio-frequency (RF) chain are employed. We show that the ATEP can be reduced by optimizing the power allocation strategy over these training beams, which is formulated and transformed into a second-order cone programming. The fast-varying scenario is further considered where the use of narrow training beams becomes inefficient due to the rapid variations of AoD. In order to reduce the training time, multiple RF chains generating wide beams are employed to track the AoD's variations, and the associated beam pattern design problem is shown to be a 0 - 1 nonlinear optimization problem (NLP). A sequential quadratic programming method is used to solve this binary NLP. To reduce the complexity, a progressive edge-growth algorithm is further introduced by associating the binary NLP with a bipartite graph. Numerical results demonstrate significant gains of the proposed beam tracking strategy over existing benchmarks for both scenarios.

In this paper, we propose a multi-layer grant-free non-orthogonal multiple access scheme for short packet transmissions. In every time slot, users choose a layer randomly and independently of other users. Each layer corresponds to a code book, and users in each layer utilize the same code book and perform power control such that they have the same received power level at the Access Point (AP). Users of the same layer also choose the same pilot sequence that is transmitted with their code word, together with all users from other layers. The AP uses the pilot sequences to detect the layers selected and estimate the number of users in each layer. Using this information, the AP first separates the codewords corresponding to the different layers. Then, users of the same layer are decoded jointly. Based on this, we formulate an optimization problem to find the power levels that maximize the reliability of the system, i.e., the probability that an arbitrary user is decoded successfully, subject to some power and decoding complexity constraints. The problem is found to be non-linear and very complex. We propose some approximations that allow us to use mixed-integer linear programming (MILP). Numerical results show that the multi-layer setup can support a larger load with higher power efficiency for the same reliability and decoding complexity requirements.

In this paper, we propose a terrestrial-satellite network (TSN) architecture to integrate the ultra- dense low earth orbit (LEO) networks and the terrestrial networks for data offloading. In TSN, each user can access the network over C-band via a macro cell, a traditional small cell, or a LEO- backhauled small cell (LSC). Each LSC is scheduled to upload the received data via multiple satellites over Ka-band. We aim to maximize the sum data rate while satisfying the varying backhaul capacity constraints jointly determined by the LEO satellite based backhaul links. The optimization problem is then solved by our proposed matching algorithm. Simulation results show that the integrated network significantly outperforms the non-integrated one in terms of the sum data rate.

With the large penetration of distributed energy resources, DC power packet transmission is a potential technique to achieve efficient peer to peer (P2P) power dispatching. In this paper, a multi-router local area packetized power network (LAPPN) is considered, where multiple power routers are employed to dispatch power packets among demander and supplier energy subscribers (ESs) connected to different routers. To achieve the efficient P2P power transmission in the LAPPN, a power packet dispatching protocol is developed, in which the routes of power packets are optimized first to maximize the power packets utilization efficiency. Then the transmission schedule is determined by allowing different power packets to transmit on different power channels concurrently to meet the variety of urgency requirements of demander ESs. Simulation results demonstrate the effectiveness of the proposed LAPPN power dispatching protocols in achieving high power packet utilization.

Spectrum sensing in heterogeneous networks is very challenging as it usually requires a large number of static secondary users (SUs) to capture the global spectrum states. In this paper, we tackle the spectrum sensing in heterogeneous networks from a new perspective. We exploit the mobility of multiple SUs to simultaneously collect spatial-temporal spectrum sensing data. Then, we propose a new non-parametric Bayesian learning model, referred to as beta process hidden Markov model to capture the spatio-temporal correlation in the collected spectrum data. Finally, Bayesian inference is carried out to establish the global spectrum picture. Simulation results show that the proposed algorithm can achieve a significant spectrum sensing performance improvement in terms of receiver operating characteristic curve and detection accuracy compared with other existing spectrum sensing algorithm.

In this paper, we propose a trellis coded modulation (TCM) based non-orthogonal multiple access (NOMA) scheme. Different from those in the traditional code-domain NOMA, the incoming bit streams of multiple layers are jointly coded and mapped to the codewords so as to improve the coding gain of the system. Based on the multi- dimensional TCM techniques, additional coding gain from the error control coding can be achieved without any bandwidth extension. New design criteria are provided and a novel set partitioning algorithm is proposed for multi-dimensional signal set labeling. To achieve the trade-off between the BER performance and complexity, a suboptimal two- layer Viterbi algorithm is proposed for joint decoding. Simulation results show that our proposed TCM-based NOMA scheme performs significantly better than the traditional code- domain NOMA in terms of the BER performance.

In this paper, we investigate a multiuser MIMO system consisting of one multiple-antenna access point (AP) and multiple single-antenna users under short-packet communication scenarios. Two fundamental schemes, i.e., time division multiple access (TDMA) and zero-forcing beamforming (ZFB), are considered for the users to receive individual information from the AP in the downlink. To analyze the performance of TDMA and ZFB in finite blocklength regime, we derive approximate closed-form expressions of the block error rate (BLER) for each user for both schemes. Simple asymptotic expressions at high signal-to- noise ratio (SNR) are also derived. Based on the derived expressions, we then formulate sum-BLER minimization problems in terms of blocklength allocation to each user in TDMA scheme and power allocation in ZFB scheme. We resolve the formulated problems by performing their convexity analysis. We finally validate our theoretical analysis and compare the performance of TDMA and ZFB schemes by simulation results, which show that TDMA and ZFB can outperform each other in different system setups.

In this paper, we propose an ambient backscatter multiple-access system, in which a receiver (Rx) simultaneously detects the information sent from an active transmitter (Tx) and a passive Tag. Specifically, the information-carrying signal sent by the Tx arrives at the Rx through two wireless channels: one is the direct Tx-Rx channel, and the other is the backscatter channel, i.e., the Tx- Tag-Rx channel, which further carries the Tag's information due to the multiplicative backscatter operation at the Tag. The proposed multiple-access scheme introduces a new channel model named as the multiplicative multiple-access channel (M-MAC), which has not been addressed before. We study the achievable rate region and the capacity region of the M-MAC, and prove that the achievable rate region of the M-MAC is strictly larger than that of the conventional time-sharing one (i.e., the M- MAC capacity region is strictly convex) in many cases, including the high SNR case and the typical case that the direct channel is much stronger than the backscatter channel. Moreover, the numerical results have also validated this phenomenon under a practical range of SNR and channel conditions. The proposed multiple-access scheme is an attractive technique to improve the throughput of ambient backscatter communication systems.

In this paper, we consider the design of training beam sequence for sparse millimeter wave (mmWave) multiple-input multiple-output (MIMO) tracking systems. We use Markov random walks to model the temporal variations of the beam steering angle of arrival (AoA) and angle of departure (AoD), respectively. By exploiting the MIMO virtual channel representation, the AoA/AoD tracking problem is equivalent to choosing a set of directional training beams to find the nonzero elements in a two-dimensional virtual channel matrix. Furthermore, in contrast to existing work that used each transmitting-receiving beam pair once only, we consider a more general case such that each beam pair might be adopted more than once in the tracking procedure. As the number of repetitions of each transmitting-receiving beam pair can only be integer, the training beam sequence design problem is then formulated as an integer nonlinear programming (INLP) problem. To resolve the formulated INLP problem, we derive a tractable lower bound of the successful tracking probability and then decompose it into a set of convex INLP subproblems, which are solved by implementing an iterative branch-and-bound (BB) method. Numerical results show that our proposed iterative BB algorithm significantly outperforms the benchmark schemes and achieves near-optimal tracking performance.

In practical communication and computation systems, errors occur predominantly in adjacent positions rather than in a random manner. In this paper, we develop a stabilizer formalism for quantum burst error correction codes (QBECC) to combat such error patterns in the quantum regime. Our contributions are as follows. Firstly, we derive an upper bound for the correctable burst errors of QBECCs, the quantum Reiger bound (QRB). Secondly, we propose two constructions of QBECCs: one by heuristic computer search and the other by concatenating two quantum tensor product codes (QTPCs). We obtain several new QBECCs with better parameters than existing codes with the same coding length. Moreover, some of the constructed codes can saturate the quantum Reiger bounds. Finally, we perform numerical experiments for our constructed codes over Markovian correlated depolarizing quantum memory channels, and show that QBECCs indeed outperform standard QECCs in this scenario.

In this paper, we propose an effective read-voltage optimization method for multi-level-cell (MLC) NAND flash memory to improve the performance of error correcting codes (ECCs) with finite blocklength. Specifically, we first obtain the maximal channel coding rate achievable at a given blocklength and error probability of quantized channel. Based on this finite-blocklength channel-coding rate (FCR), we convert the optimization problem into minimizing the error probability instead of the channel coding rate. Then, we develop a cross iterative search (CIS) method and the genetic algorithm to solve this optimization problem. In our simulations, for a well-designed LDPC code, our read-voltage optimization method improves program-and-erase (PE) endurance up to about 900 and 600 cycles against the maximizing the mutual information (MMI) and entropy-based optimization methods, respectively, at a frame-error-rate (FER) of 2×10-4.

Base station (BS) cooperation has been regarded as an effective solution to improve network coverage and throughput in next-generation wireless systems. However, it also introduces more complicated handoff patterns, which may potentially degrade user performance. In this work, we aim to theoretically quantify users' handoff performance in disjoint-clustered cooperative wireless networks. It is a challenging task due to spatial randomness of network topologies. We propose a stochastic geometric model on user mobility, and use it to derive a theoretical expression for the handoff rate experienced by an active user with arbitrary movement trajectory. As a study on the application of the handoff rate analysis, we furthermore characterize the average downlink user data rate under a common non-coherent joint-transmission scheme, which is then used to derive a tradeoff between handoff and data rates in a network with an optimal cooperative cluster size. Finally, extensive simulations are conducted to validate our analysis.

As a promising wireless network virtualization technology, virtualized base station (BS) has been proposed to tackle the problem of low-efficient utilization of BS's computing resources, e.g., baseband processing units (BPU). In this paper, we design a novel scheme to achieve the efficient BPU allocation based on a contract-theoretic approach. To achieve this, we consider the BPUs as a kind of trading resources. We establish a monopoly market, where the infrastructure provider (InP) is the monopolist owning all the BPUs, and multiple mobile network operators (MNOs) intend to rent BPUs from the InP for processing their baseband signals. In such a market, the InP offers a set of quantity-price contract items to the MNOs based on statistical information of their types, and at the same time, the MNOs are stimulated to accept the offers for the purpose of making profit. We propose the optimal contract design to maximize the InP's profit, as well as develop an incentive mechanism to guarantee each MNO choosing a proper contract item. Numerical results validate the effectiveness of our incentive mechanism for BPU resource allocation.

Radio frequency energy harvesting is a promising technology to charge unattended Internet of Things (IoT) lowpower devices remotely. To enable this, in future IoT system, besides the traditional data access points (DAPs) for collecting data, energy access points (EAPs) should be deployed to charge IoT devices to maintain their sustainable operations. Practically, the DAPs and EAPs may be operated by different operators, and the DAPs thus need to provide effective incentives to motivate the surrounding EAPs to charge their associated IoT devices. Different from existing incentive schemes, we consider a practical scenario with asymmetric information, where the DAP is not aware of the channel conditions and energy costs of the EAPs. We first extend the existing Stackelberg game-based approach with complete information to the asymmetric information scenario, where the expected utility of the DAP is defined and maximized. To deal with asymmetric information more efficiently, we then develop a contract theory-based framework, where the optimal contract is derived to maximize the DAP's expected utility as well as the social welfare. Simulations show that information asymmetry leads to severe performance degradation for the Stackelberg game-based framework, while the proposed contract theory-based approach using asymmetric information outperforms the Stackelberg game-based method with complete information. This reveals that the performance of the considered system depends largely on the market structure (i.e., whether the EAPs are allowed to optimize their received power at the IoT devices with full freedom or not) than on the information availability (i.e., the complete or asymmetric information).

This paper develops and evaluates an accumulate-then-transmit framework for multiuser scheduling in a full-duplex (FD) wireless-powered Internet-of-Things (IoT) system, consisting of multiple energy harvesting (EH) IoT devices (IoDs) and one FD hybrid access point (HAP). All IoDs have no embedded energy supply and thus need to perform EH before transmitting their data to the HAP. Thanks to its FD capability, the HAP can simultaneously receive data uplink and broadcast energy-bearing signals downlink to charge IoDs. The instantaneous channel information is assumed unavailable throughout this paper. To maximize the system average throughput, we design a new throughput-oriented scheduling scheme, in which a single IoD with the maximum weighted residual energy is selected to transmit information to the HAP, while the other IoDs harvest and accumulate energy from the signals broadcast by the HAP. However, similar to most of the existing throughput-oriented schemes, the proposed throughout-oriented scheme also leads to unfair interuser throughput because IoDs with better channel performance will be granted more transmission opportunities. To strike a balance between the system throughput and user fairness, we then propose a fairness-oriented scheduling scheme based on the normalized accumulated energy. To evaluate the system performance, we model the dynamic charging/discharging processes of each IoD as a finite-state Markov chain. Analytical expressions of the system outage probability and average throughput are derived over Rician fading channels for both proposed schemes. Simulation results validate the performance analysis and demonstrate the performance superiority of both proposed schemes over the existing schemes.

The Tactile Internet that will enable humans to remotely control objects in real time by tactile sense has recently drawn significant attention from both academic and industrial communities. Ensuring ultra-reliable and low-latency communications with limited bandwidth is crucial for Tactile Internet. Recent studies found that the packet arrival processes in Tactile Internet are very bursty. This observation enables us to design a spectrally efficient resource management protocol to meet the stringent delay and reliability requirements while minimizing the bandwidth usage. In this paper, both model-based and data-driven unsupervised learning methods are applied in classifying the packet arrival process of each user into high or low traffic states, so that we can design efficient bandwidth reservation schemes accordingly. However, when the traffic-state classification is inaccurate, it is very challenging to satisfy the ultra-high reliability requirement. To tackle this problem, we formulate an optimization problem to minimize the reserved bandwidth subject to the delay and reliability requirements by taking into account the classification errors. Simulation results show that the proposed methods can save 40%-70% bandwidth compared with the conventional method that is not aware of burstiness, while guaranteeing the delay and reliability requirements. Our results are further validated by the practical packet arrival processes acquired from experiments using a real tactile hardware device.

This letter investigates an amplify-and-forward two-way relay network (TWRN) for short-packet communications. We consider a classical three-node TWRN consisting of two sources and one relay. Both two time slots (2TS) scheme and three time slots (3TS) scheme are studied under the finite blocklength regime. We derive approximate closed-form expressions of sum-block error rate (BLER) for both schemes. Simple asymptotic expressions for sum-BLER at high signal-to-noise ratio (SNR) are also derived. Based on the asymptotic expressions, we analytically compare the sum-BLER performance of 2TS and 3TS schemes, and attain an expression of critical blocklength, which can determine the performance superiority of 2TS and 3TS in terms of sum-BLER. Extensive simulations are provided to validate our theoretical analysis. Our results discover that 3TS scheme is more suitable for a system with higher differences between the average SNR of both links, and relatively lower requirements on data rate and latency.

This letter investigates the proactive eavesdropping for multiple suspicious links either through interfering or assisting the links. Considering the power constraint at eavesdropper, our objective is to maximize weighted sum eavesdropping rate of multiple suspicious links via jointly optimizing their intervention strategies (jamming or relaying) and the corresponding transmit power at eavesdropper. The formulated problem is shown to be a mixed-integer nonlinear programming (MINLP) problem, which is NP-hard in general. By identifying the separable structure of the formulated problem, we decouple the complex MINLP problem into two subproblems: 1) a jamming subproblem; and 2) a relaying subproblem. These two subproblems are then solved by further recasting them into a combinational problem and a typical concave optimization problem, respectively. Numerical simulations show that our proposed approach can achieve higher eavesdropping rate than conventional eavesdropping approaches.

Generalized spatial modulation (GSM) activates 'it out of Nt (1 ≤ 'it <; Nt) available transmit antennas, and information is conveyed through 'it modulated symbols as well as the index of the 'it activated antennas. GSM strikes an attractive tradeoff between spectrum efficiency and energy efficiency. Linear precoding that exploits channel state information at the transmitter enhances the system error performance. For GSM with 'it = 1 (the traditional SM), the existing precoding methods suffer from high computational complexity. On the other hand, GSM precoding for 'it ≥ 2 is not thoroughly investigated in the open literature. In this paper, we develop a unified precoding design for GSM systems, which universally works for all 'it values. Based on the maximum minimum Euclidean distance criterion, we find that the precoding design can be formulated as a large-scale nonconvex quadratically constrained quadratic program problem. Then, we transform this challenging problem into a sequence of unconstrained subproblems by leveraging augmented Lagrangian and dual ascent techniques. These subproblems can be solved in an iterative manner efficiently. Numerical results show that the proposed method can substantially improve the system error performance relative to the GSM without precoding and features extremely fast convergence rate with a very low computational complexity. I'idex Terms-

This paper is concerned with the channel estimation problem in multi-user millimeter wave wireless systems with large antenna arrays. We develop a novel simultaneous-estimation with iterative fountain training (SWIFT) framework, in which multiple users estimate their channels at the same time and the required number of channel measurements is adapted to various channel conditions of different users. To achieve this, we represent the beam direction estimation process by a graph, referred to as the beam-on-graph, and associate the channel estimation process with a code-on-graph decoding problem. Specifically, the base station (BS) and each user measure the channel with a series of random combinations of transmit/receive beamforming vectors until the channel estimate converges. As the proposed SWIFT does not adapt the BS's beams to any single user, we are able to estimate all user channels, simultaneously. Simulation results show that SWIFT can significantly outperform the existing random beamforming-based approaches, which use a predetermined number of measurements, over a wide range of signal-to-noise ratios and channel coherence time. Furthermore, by utilizing the users' order in terms of completing their channel estimation, our SWIFT framework can infer the sequence of users' channel quality and perform effective user scheduling to achieve superior performance.

Ultra-reliable and low-latency communications (URLLC) have stringent requirements on quality-of-service and network availability. Due to path loss and shadowing, it is very challenging to guarantee the stringent requirements of URLLC with satisfactory communication range. In this paper, we first provide a quantitative definition of network availability in the short blocklength regime: the probability that the reliability and latency requirements can be satisfied when the blocklength of channel codes is short. Then, we establish a framework to maximize the available range, defined as the maximal communication distance subject to the network availability requirement, by exploiting multi-connectivity. The basic idea is using both device-to-device (D2D) and cellular links to transmit each packet. The practical setup with correlated shadowing between D2D and cellular links is considered. Besides, since processing delay for decoding packets cannot be ignored in URLLC, its impacts on the available range are studied. By comparing the available ranges of different transmission modes, we obtained some useful insights on how to choose transmission modes. Simulation and numerical results validate our analysis and show that multi-connectivity can improve the available ranges of D2D and cellular links remarkably.

In this paper, we consider a novel ambient backscatter multiple-access system, where a receiver (Rx) simultaneously detects the signals transmitted from an active transmitter (Tx) and a backscatter tag. Specifically, the information-carrying signal sent by the Tx arrives at the Rx through two wireless channels: the direct channel from the Tx to the Rx and the backscatter channel from the Tx to the tag and then to the Rx. The received signal from the backscatter channel also carries the tag's information because of the multiplicative backscatter operation at the tag. This multiple-access system introduces a new channel model referred to as backscatter multiplicative multiple-access channel (BM-MAC). We analyze the achievable rate region of the BM-MAC and prove that its region is strictly larger than that of the conventional time-division multiple-access scheme in many cases, including, e.g., the high SNR regime and the case when the direct channel is much stronger than the backscatter channel. Hence, the multiplicative multiple-access scheme is an attractive technique to improve the throughput for ambient backscatter communication systems. Moreover, we analyze the detection error rates for coherent and noncoherent modulation schemes adopted by the Tx and the tag, respectively, in both synchronous and asynchronous scenarios, which further bring interesting insights for practical system design.

The rapid development of mobile communications requires even higher spectral efficiency. Non-orthogonal multiple access (NOMA) has emerged as a promising technology to further increase the access efficiency of wireless networks. Among several NOMA schemes, it has been shown that sparse code multiple access (SCMA) is able to achieve better performance. In this paper, we consider a downlink MIMO-SCMA system over frequency selective fading channels. For optimal detection, the complexity increases exponentially with the product of the number of users, the number of antennas and the channel length. To tackle this challenge, we propose near optimal low-complexity iterative receivers based on factor graph. By introducing auxiliary variables, a stretched factor graph is constructed and a hybrid belief propagation (BP) and expectation propagation (EP) receiver, named stretch-BP-EP, is proposed. Considering the convergence problem of BP algorithm on loopy factor graph, we convexify the Bethe free energy and propose a convergence-guaranteed BP-EP receiver, named conv-BP-EP. We further consider cooperative network and propose two distributed cooperative detection schemes to exploit the diversity gain, namely, belief consensus-based algorithm and the Bregman alternative direction method of multipliers (ADMM)-based method. Simulation results verify the superior performance of the proposed conv-BP-EP receiver compared with other methods. The two proposed distributed cooperative detection schemes can improve the bit error rate performance by exploiting the diversity gain. Moreover, Bregman ADMM method outperforms the belief consensus-based algorithm in noisy inter-user links.

In this paper, we consider a dense vehicular communication network where each vehicle broadcasts its safety information to its neighborhood in each transmission period. Such applications require low latency and high reliability, and thus, we exploit non-orthogonal multiple access to reduce the latency and to improve the packet reception probability. In the proposed scheme, the BS performs semi-persistent scheduling and allocates time-frequency resources in a non-orthogonal manner while the vehicles autonomously perform distributed power control. We formulate the centralized scheduling and resource allocation problem as a multi-dimensional stable roommate matching problem and develop a novel rotation matching algorithm to solve it. Simulation results show that the proposed scheme outperforms the traditional orthogonal multiple access scheme in terms of the latency and reliability.

By enabling wireless devices to be charged wirelessly and remotely, radio frequency energy harvesting (RFEH) has become a promising technology to power the unattended Internet of Things (IoT) low-power devices. To enable this, in future IoT networks, besides the conventional data access points (DAPs) responsible for collecting data from IoT devices, energy access points (EAPs) should be deployed to transfer radio frequency (RF) energy to IoT devices to maintain their sustainable operations. In practice, the DAPs and EAPs may be operated by different operators and a DAP should provide certain incentives to motivate the surrounding EAPs to charge its associated IoT device(s) to assist its data collection. Motivated by this, in this paper we develop a contract theory-based incentive mechanism for the energy trading in RFEH assisted IoT systems. The necessary and sufficient condition for the feasibility of the formulated contract is analyzed. The optimal contract is derived to maximize the DAP's expected utility as well as the social welfare. Simulation results demonstrate the feasibility and effectiveness of the proposed incentive mechanism.

This paper develops a novel channel estimation approach for multi-user millimeter wave (mmWave) wireless systems with large antenna arrays. By exploiting the inherent mmWave channel sparsity, we propose a novel simultaneous-estimation with iterative fountain training (SWIFT) framework, in which the average number of channel measurements is adapted to various channel conditions. To this end, the base station (BS) and each user continue to measure the channel with a random subset of transmit/receive beamforming directions until the channel estimate converges. We formulate the channel estimation process as a compressed sensing problem and apply a sparse estimation approach to recover the virtual channel information. As SWIFT does not adapt the BS's transmitting beams to any single user, we are able to estimate all user channels simultaneously. Simulation results show that SWIFT can significantly outperform existing random-beamforming based approaches that use a fixed number of measurements, over a range of signal-to-noise ratios.

Conventional microwave bands are expected to encounter bandwidth shortage due to the ever-increasing demand for high speed wireless services. The use of millimeter wave (MMW) spectrum has been regarded as one of the promising solutions to support data traffic demands in future cellular networks. MMW cellular networks are envisioned to be densely deployed to attain acceptable coverage and rate. In dense networks, intercell interference emerges as the main factor of degrading the coverage and capacity. As such, coordination of base stations (BSs) should be implemented to improve the system performance. In this paper, we aim to quantify the performance of BS coordination via disjoint clustering in dense MMW cellular networks. Using tools from stochastic geometry, the coverage probability and area spectral efficiency are derived by incorporating the key features of MMW systems, i.e., blockage and directional antenna. Simulation results are provided to validate the accuracy of the analytical results under various system parameters and demonstrate the performance superiority of BS coordination via disjoint clustering over the non-coordinated case. The results suggest that the optimal cluster size to achieve the maximum area spectral efficiency (ASE) increases as the blockage factor, which is determined by the density and the average size of the buildings, decreases.

This paper considers the joint antenna selection (AS) problem for a classical two-user non-orthogonal multiple access (NOMA) network where both the base station and users are equipped with multiple antennas. Since the exhaustive-search-based optimal AS scheme is computationally prohibitive when the number of antennas is large, two computationally efficient joint AS algorithms, namely max-min-max AS (AIA-AS) and max-max-max AS (A3-AS), are proposed to maximize the system sum-rate. The asymptotic closed-form expressions for the average sum-rates for both AIA-AS and A3-AS are derived in the high signal-to-noise ratio (SNR) regime, respectively. Numerical results demonstrate that both AIA-AS and A3-AS can yield significant performance gains over comparable schemes. Furthermore, AIA-AS can provide better user fairness, while the A3-AS scheme can achieve the near-optimal sum-rate performance.

This paper proposes and analyzes a new full-duplex (FD) cooperative cognitive radio network with wireless energy harvesting (EH). We consider that the secondary receiver is equipped with a FD radio and acts as a FD hybrid access point (HAP), which aims to collect information from its associated EH secondary transmitter (ST) and relay the signals. The ST is assumed to be equipped with an EH unit and a rechargeable battery such that it can harvest and accumulate energy from radio frequency (RF) signals transmitted by the primary transmitter (PT) and the HAP. We develop a novel cooperative spectrum sharing (CSS) protocol for the considered system. In the proposed protocol, thanks to its FD capability, the HAP can receive the PT's signals and transmit energy-bearing signals to charge the ST simultaneously, or forward the PT's signals and receive the ST's signals at the same time. We derive analytical expressions for the achievable throughput of both primary and secondary links by characterizing the dynamic charging/discharging behaviors of the ST battery as a finite-state Markov chain. We present numerical results to validate our theoretical analysis and demonstrate the merits of the proposed protocol over its non-cooperative counterpart.

Many uplink grant-free non-orthogonal multiple access (NOMA) schemes have been recently proposed to solve the massive access problem of M2M communications. However, little has been done on characterizing the performance bounds for such systems. In this paper, we take a step forward in this direction. We consider an uncoordinated NOMA scheme where devices have strict latencies and no retransmission opportunities are available. Devices choose pilot sequences from a predetermined set uniformly at random. Then, each device encodes its data using the pilot as the signature and transmits its selected pilot and data simultaneously with the rest of the devices. A collision occurs when two or more devices choose the same pilot sequence. Collisions are regarded as interference to the remaining set of transmitting devices. We first show that this interference can be well-approximated by a PPP. Then, we derive the average system throughput under joint decoding and massive access for a Rayleigh fading and path loss channel model. Our numerical results verify the accuracy of our derived analytical expressions. Finally, we investigate the impact of finite block lengths on the system throughput, i.e., when the decoding error probability is strictly non-zero. We show that we can support more than 10 packets per slot when the pilot sequences are large enough.

Wideband spectrum sensing for cognitive radio is highly challenging because it needs to locate multiple active spectrum subbands (channels) across a large bandwidth. The high-speed Nyquist sampling involved is either technically infeasible or very expensive in implementation. In this paper, we draw on the recent development in Bayesian machine learning, and propose a new high-resolution wideband spectrum sensing method, referred to as sub-Nyquist assisted matrix sparse Bayesian learning (M-SBL). We first use multicoset sampling to significantly reduce the sampling rate. Then we develop a M-SBL method that carries out Bayesian inference from received spectrum data to learn and iteratively reconstruct a latent variable, whose significant peaks can be used to locate multiple active spectrum subbands. Simulation results indicate that the proposed method significantly outperforms conventional ones in sensing accuracy, especially at low signal-to-noise ratios or with a small number of cosets.

In this paper, we investigate linear precoding for spatial modulation (SM) over multiple-input-multiple-output (MIMO) fading channels. With channel state information avail- able at the transmitter, our focus is to maximize the minimum Eu- clidean distance among all candidates of SM symbols. We prove that the precoder design is a large-scale non-convex quadratically constrained quadratic program (QCQP) problem. However, the conventional methods, such as semi- definite relaxation and it- erative concave-convex process, cannot tackle this challenging problem effectively or efficiently. To address this issue, we leverage augmented Lagrangian and dual ascent techniques, and transform the original large-scale non-convex QCQP problem into a sequence of subproblems. These subproblems can be solved in an iterative manner efficiently. Numerical results show that the proposed method can significantly improve the system error performance relative to the SM without precoding, and features extremely fast convergence rate with very low computational complexity.

In this paper, we study a wireless-powered two-way relay system, in which both wireless-powered sources exchange information through a multi-antenna relay. Both sources are assumed to have no embedded energy supply and thus first need to harvest energy from the radio frequency signals broadcasted by the relay before exchanging their information via the relay. We aim to maximize the sum throughput of both sources by jointly optimizing the time switching duration, the energy beamforming vector and the precoding matrix at the relay. The formulated problem is non-convex and hard to solve in its original form. Motivated by this, we simplify the problem by reducing the number of variables and by decomposing the precoding matrix into a transmit vector and a receive vector. We then propose bisection search, 1-D search and iterative algorithms to optimize each variable. Numerical results show that our proposed scheme can achieve higher throughput than the conventional scheme without optimization on beamforming vector and precoding matrix at the relay.

This paper investigates the optimal resource allocation of a downlink non-orthogonal multiple access (NOMA) system consisting of one base station and multiple users. Unlike existing short-term NOMA designs that focused on the resource allocation for only the current transmission timeslot, we aim to maximize a long-term network utility by jointly optimizing the data rate control at the network layer and the power allocation among multiple users at the physical layer, subject to practical constraints on both the short-term and long-term power consumptions. To solve this problem, we leverage the recently developed Lyapunov optimization framework to convert the original long-term optimization problem into a series of online rate control and power allocation problems in each timeslot. The power allocation problem, however, is shown to be non-convex in nature and thus cannot be solved with a standard method. However, we explore two structures of the optimal solution and develop a dynamic programming-based power allocation algorithm, which can derive a globally optimal solution, with a polynomial computational complexity. Extensive simulation results are provided to evaluate the performance of the proposed joint rate control and power allocation framework for NOMA systems, which demonstrate that the proposed NOMA design can significantly outperform multiple benchmark schemes, including orthogonal multiple access schemes with optimal power allocation and NOMA schemes with non-optimal power allocation, in terms of average throughput and data delay.

In this paper, we consider a dense vehicular communication network where each vehicle broadcasts its safety information to its neighborhood in each transmission period. Such applications require low latency and high reliability, and thus, we exploit non-orthogonal multiple access to reduce the access latency and to improve the packet reception probability. In the proposed two-fold scheme, the BS performs semi-persistent scheduling and allocates time-frequency resources in a nonorthogonal manner while the vehicles autonomously perform distributed power control with iterative signaling control. We formulate the centralized scheduling and resource allocation problem as equivalent to a multi-dimensional stable roommate matching problem, in which the users and time/frequency resources are considered as disjoint sets of objects to be matched with each other. We then develop a novel rotation matching algorithm, which converges to an L-rotation stable matching after a limited number of iterations. Simulation results show that the proposed scheme outperforms the traditional orthogonal multiple access scheme in terms of the access latency and reliability.

Millimeter wave (mmWave) communications have recently attracted large research interest, since the huge available bandwidth can potentially lead to the rates of multiple gigabit per second per user. Though mmWave can be readily used in stationary scenarios, such as indoor hotspots or backhaul, it is challenging to use mmWave in mobile networks, where the transmitting/receiving nodes may be moving, channels may have a complicated structure, and the coordination among multiple nodes is difficult. To fully exploit the high potential rates of mmWave in mobile networks, lots of technical problems must be addressed. This paper presents a comprehensive survey of mmWave communications for future mobile networks (5G and beyond). We first summarize the recent channel measurement campaigns and modeling results. Then, we discuss in detail recent progresses in multiple input multiple output transceiver design for mmWave communications. After that, we provide an overview of the solution for multiple access and backhauling, followed by the analysis of coverage and connectivity. Finally, the progresses in the standardization and deployment of mmWave for mobile networks are discussed.

For the potential of providing rates of multiple Giga-bps in a single channel, millimeter wave (mmWave) communications have recently attracted substantial research interest. While mmWave technology is already being used in stationary scenarios such as indoor hotspots or backhaul, it is challenging to use mmWave frequencies in mobile networks, where transmitting/receiving nodes may be moving, channels may have a complicated structure, and the coordination among multiple nodes is difficult. To fully exploit the high potential rates of mmWave in mobile networks, many significant technical challenges must be tackled. The main objective of this IEEE JSAC Special Issue on “Millimeter wave communications for future mobile networks” is to collect the most recent technical advances in mmWave for future mobile networks. The response from the community to the call has been overwhelming. We received 96 submissions with a call period short than 4 months. Many of the submissions are from the most well known research groups in the field. After a strict review process, we decided to accept 38 papers, which will be published in two issues. The papers were selected based on the technical relevance and merits. Unfortunately, due to space limitations, a number of interesting papers were not selected, despite the merits that they had. We sincerely hope those papers can find other publishing venues.

We propose a novel random access (RA) scheme with the quality of service (QoS) guarantees for machine-to-machine (M2M) communications. We consider a slotted uncoordinated data transmission period during which machine type communication (MTC) devices transmit over the same radio channel. Based on the latency requirements, MTC devices are divided into groups of different sizes, and the transmission frame is divided into sub-frames of different lengths. In each sub-frame, each group is assigned an access probability based on which an MTC device decides to transmit replicas of its packet or remain silent. The base station employs successive interference cancellation to recover all the superposed packets. We derive the closed-form expressions for the average probability of device resolution for each group, and we use these expressions to design the access probabilities. The accuracy of the expressions is validated through Monte Carlo simulations. We show that the designed access probabilities can guarantee the QoS requirements with high reliability and high energy efficiency. Finally, we show that RA can outperform standard coordinated access schemes as well as some of the recently proposed M2M access schemes for cellular networks.

In this paper, we study the user pairing in a downlink non-orthogonal multiple access (NOMA) network, where the base station allocates the power to the pairwise users within the cluster. In the considered NOMA network, a user with poor channel condition is paired with a user with good channel condition, when both their rate requirements are satisfied. Specifically, the quality of service for weak users can be guaranteed, since the transmit power allocated to strong users is constrained following the concept of cognitive radio. A distributed matching algorithm is proposed in the downlink NOMA network, aiming to optimize the user pairing and power allocation between weak users and strong users, subject to the users' targeted rate requirements. Our results show that the proposed algorithm outperforms the conventional orthogonal multiple access scheme and approaches the performance of the centralized algorithm, despite its low complexity. In order to improve the system's throughput, we design a practical adaptive turbo trellis coded modulation scheme for the considered network, which adaptively adjusts the code rate and the modulation mode based on the instantaneous channel conditions. The joint design work leads to significant mutual benefits for all the users as well as the improved system throughput.

In this paper, we study an X-duplex relay system with one source, one amplify-and-forward relay, and one destination, where the relay is equipped with a shared antenna and two radio frequency (RF) chains used for transmission or reception. X-duplex relay can adaptively configure the connection between its RF chains and antenna to operate in either half-duplex (HD) or full-duplex (FD) mode, according to the instantaneous channel conditions. We first derive the distribution of the signal to interference plus noise ratio, based on which we then analyze the outage probability, average symbol error rate (SER), and average sum rate. We also investigate the X-duplex relay with power allocation and derive the lower bound and upper bound of the corresponding outage probability. Both analytical and simulated results show that the X-duplex relay achieves a better performance over pure FD and HD schemes in terms of SER, outage probability and average sum rate, and the performance floor caused by the residual self interference can be eliminated using flexible RF chain configurations.

Explosive demand for wireless internet services has posed critical challenges for wireless networks due to their limited capacity. To tackle this hurdle, wireless Internet service providers (WISPs) take the smart data pricing to manage data traffic loads. Meanwhile, from the users' perspective, it is also reasonable and desired to employ mobile data traffic management under the pricing policies of WISPs to improve the economic efficiency of data consumption. In this paper, we introduce a concept of cost efficiency (CE) for user's mobile data management, defined as the ratio of user's mobile data consumption benefits and its expense. We propose an integrated CE-based data traffic management scheme, including long-term data demand planning, short-term data traffic pre-scheduling, and real-time data traffic management. The real-time data traffic management algorithm is proposed to coordinate user's data consumption to tailor to the pre-scheduled data traffic profile. Numerical results demonstrate the effectiveness of CE framework in indicating and motivating mobile user's data consumption behavior. The proposed management scheme can effectively motivate the user to adjust its data consumption profile to obtain the optimal data consumption CE.

One of the fundamental problems in cryptography is the generation of a common secret key between two legitimate parties to prevent eavesdropping. In this paper, we propose an information-theoretic secret key generation (SKG) method for time division duplexing (TDD)-based orthogonal frequency-division multiplexing (OFDM) systems over multipath fading channels. By exploring physical layer properties of the wireless medium, i.e., the reciprocity, randomness, and privacy features of the radio channel, an SKG method is proposed to maximize the number of secret bits given a target secret key disagreement ratio (SKDR). In the proposed SKG method, the phase information of the estimated channel state information (CSI) is distilled for SKG, and a special guard band (GB) scheme is designed to achieve the target SKDR with a small phase information loss. The proposed GB consists of both the amplitude GB (AGB) and phase GB (PGB), where the AGB is determined by the average signal-to-interference plus noise ratio (SINR), whereas the PGB adapts itself to the instantaneous SINR and thus incurs a smaller phase information loss in the higher SINR region. Analyses show that this GB scheme trades off a small loss of channel phase information for a better SKDR performance, and achieves a much larger number of quantization levels for a given SKDR due to the fact that the PGB decreases quickly as the SINR increases. Based on the performance analysis on the SKDR, the average secret key length, the phase information loss percentage (PILP), and the optimal GB and quantization level of the adaptive quantizor are derived for a given target SKDR. Both analytical and simulation results are presented to demonstrate the superiority of the proposed scheme for TDD-OFDM systems over frequency-selective fading channels.

In this paper, we study the resource allocation problem for a single-cell non-orthogonal multiple access (NOMA) relay network where an OFDM amplify-and-forward relay allocates the spectrum and power resources to the source-destination (SD) pairs. We aim to optimize the resource allocation to maximize the average sum-rate. The optimal approach requires an exhaustive search, leading to an NP-hard problem. To solve this problem, we propose two efficient many-to-many two-sided SD pair-subchannel matching algorithms, in which the SD pairs and sub-channels are considered as two sets of players chasing their own interests. The proposed algorithms can provide a sub-optimal solution to this resource allocation problem in affordable time. Both the static matching algorithm and the dynamic matching algorithm converge to a pair-wise stable matching after a limited number of iterations. Simulation results show that the capacity of both proposed algorithms in the NOMA scheme significantly outperforms the conventional orthogonal multiple access scheme. The proposed matching algorithms in NOMA scheme also achieve a better user-fairness performance than the conventional orthogonal multiple access.

This paper investigates a wireless-powered two-way relay network (WP-TWRN), in which two sources exchange information with the aid of one amplify-and-forward (AF) relay. Contrary to the conventional two-way relay networks, we consider the scenario that the AF relay has no embedded energy supply, and it is equipped with an energy harvesting unit and rechargeable battery. As such, it can accumulate the energy harvested from both sources' signals before helping forwarding their information. In this paper, we develop a power splitting-based energy accumulation (PS-EA) scheme for the considered WP-TWRN. To determine whether the relay has accumulated sufficient energy, we set a predefined energy threshold for the relay. When the accumulated energy reaches the threshold, relay splits the received signal power into two parts, one for energy harvesting and the other for information forwarding. If the stored energy at the relay is below the threshold, all the received signal power will be accumulated at the relay's battery. By modeling the finite-capacity battery of relay as a finite-state Markov Chain (MC), we derive a closed-form expression for the system throughput of the proposed PS-EA scheme over Nakagami-m fading channels. Numerical results validate our theoretical analysis and show that the proposed PS-EA scheme outperforms the conventional time switching- based energy accumulation (TS-EA) scheme and the existing power splitting schemes without energy accumulation.

In this paper, we consider the channel estimation problem in millimeter wave (mmWave) wireless systems with large antenna arrays. By exploiting the inherent sparse nature of the mmWave channel, we develop a novel rate-adaptive channel estimation (RACE) algorithm, which can adaptively adjust the number of required channel measurements based on an expected probability of estimation error (PEE). To this end, we design a maximum likelihood (ML) estimator to optimally extract the path information and the associated probability of error from the increasing number of channel measurements. Based on the ML estimator, the algorithm is able to measure the channel using a variable number of beam patterns until the receiver believes that the estimated direction is correct. This is in contrast to the existing mmWave channel estimation algorithms, in which the number of measurements is typically fixed. Simulation results show that the proposed algorithm can significantly reduce the number of channel estimation measurements while still retaining a high level of accuracy, compared to existing multi-stage channel estimation algorithms.

This paper investigates a wireless energy harvesting cooperative network (WEHCN) consisting of a source, a decode-and-forward (DF) relay and a destination. We consider the relay as an energy harvesting (EH) node equipped with EH circuit and a rechargeable battery. Moreover, the direct link between source and destination is assumed to exist. The relay can thus harvest and accumulate energy from radio-frequency signals ejected by the source and assist its information transmission opportunistically. We develop an incremental accumulate-then-forward (IATF) relaying protocol for the considered WEHCN. In the IATF protocol, the source sends its information to destination via the direct link and requests the relay to cooperate only when it is necessary such that the relay has more chances to accumulate the harvested energy. By modeling the charging/discharging behaviors of the relay battery as a finite-state Markov chain, we derive a closed-form expression for the outage probability of the proposed IATF. Numerical results validate our theoretical analysis and show that the IATF scheme can significantly outperform the direct transmission scheme without cooperation.

In this paper, we analyze Luby Transform (LT) codes with unequal recovery time (URT-LT) and intermediate feedback. Different sets of information symbols within a message block are allocated different priorities, where the higher prioritized sets are to be recovered in a shorter time. We divide the encoding process into stages where in each stage different sets are allocated different selection probabilities. A stage ends when one of the sets' recovery times expires. We incorporate an intermediate feedback that notifies the transmitter of the recovered information symbols at the end of each stage. These recovered symbols are then excluded from future encoding. We use the AND-OR tree analysis to find the optimal selection probabilities that guarantee the sets are recovered within the required time, with a relatively small error probability. We compare the scheme to the case where the sets are encoded and transmitted separately.

In this paper, we study the codebook-based resource allocation problem for an uplink sparse code multiple access (SCMA) network. The base station (BS) assigns to each user a set of subcarriers corresponding to a specific codebook, and each user performs power control over multiple subcarriers. We aim to optimize the subcarrier assignment and power allocation to maximize the total sum-rate. To solve the above problem, we formulate it as a many-to-many two-sided matching problem with externalities. A novel swap-matching algorithm is then proposed in which the users and the subcarriers are considered as two sets of players, and every two users can cooperate to swap their matches so as to improve each other's profits. The algorithm converges to a pair-wise stable matching after a limited number of iterations. Simulation results show that the proposed algorithm greatly outperforms the orthogonal multiple access scheme and a random allocation scheme.

This paper investigates a wireless-powered cooperative network (WPCN) consisting of one source-destination pair and multiple decode-and-forward (DF) relays. We consider that the DF relays are wireless-powered such that they rely only on the harvested energy from the source to perform information forwarding. Furthermore, these relays are equipped with separate energy and information receivers as well as energy storage to accumulate the harvested energy. In this paper, we develop an energy threshold based multi-relay selection (ETMRS) scheme for the considered WPCN, in which each relay determines to switch between energy harvesting and information forwarding modes in a fully distributed manner. By modeling the charging/discharging of the discrete-level battery at each relay as a finite state Markov Chain (MC), we derive a closed-form expression for the system outage probability of the proposed ETMRS scheme over independent but not necessarily identical Rayleigh fading channels. Numerical results validate our theoretical analysis and show that the proposed ETMRS scheme can outperform the existing single-relay selection scheme, especially when the source's transmission rate is relatively high.

In this paper, a novel X-Duplex relay scheme with one source, one amplify-and-forward (AF) relay and one destination is proposed. The relay is equipped with a shared antenna and two radio frequency (RF) chains used for transmission or reception. The proposed scheme can be reduced to either full-duplex (FD) or half-duplex (HD) with different RF chain configurations. In the proposed scheme, relay adaptively configures the connection between its RF chains and the antenna to optimise the end-to-end system performance according to the instantaneous channel conditions. In this paper, we analyze the system overall performances based on the distribution of the signal to interference plus noise ratio (SINR) of the hybrid mode, including outage probability and average sum rate. Monte-Carlo simulations are used to validate the analytical expressions. Results show that the X-Duplex relay achieves a lower outage probability and a higher average sum rate compared to FD and HD schemes.

Featured by centralized processing and cloud-based infrastructure, cloud radio access network (C-RAN) has emerged as a promising solution to handle the data proliferation in future wireless networks. However, the attractive capacity enhancement brought by large-scale centralized processing comes along with increased computing resource requirement in the baseband unit (BBU) pool. Thus, computing resource as another dimension of manageable resource needs to be considered in resource allocation and C-RAN system design. In this paper, we first characterize the relationship between PHY transmission characteristics and the required computing resource in the BBU pool. Based on this, we propose a feasible algorithm to maximize the network sum-rate under limited computing resource constraint, which is a binary-integer non-linear programming (BINLP) problem with non-convex constraints in nature. Numerical results show the significant impact of computing resource on both user-RRH association strategy and the achievable sum-rate performance.

In this paper, we study the resource allocation problem for a single-cell non-orthogonal multiple access (NOMA) relay network where an OFDM amplify-and-forward (AF) relay allocates the spectrum and power resources to the source-destination (SD) pairs. We aim to optimize the spectrum and power resource allocation to maximize the total sum-rate. This is a very complicated problem and the optimal approach requires an exhaustive search, leading to a NP hard problem. To solve this problem, we propose an efficient many-to-many two sided SD pair-subchannel matching algorithm in which the SD pairs and sub-channels are considered as two sets of rational and selfish players chasing their own interests. The algorithm converges to a pair-wise stable matching after a limited number of iterations with a low complexity compared with the optimal solution. Simulation results show that the sum-rate of the proposed algorithm approaches the performance of the optimal exhaustive search and significantly outperforms the conventional orthogonal multiple access scheme, in terms of the total sum-rate and number of accessed SD pairs.

To support multi-hop device-to-device (D2D) transmission, this paper proposes a novel wireless architecture, device-to-device software defined networks (D2D-SDN). In D2D-SDN, mobile devices are not only terminals but also can act as wireless switches to forward packets for others based on the routing and scheduling instructions from controllers. Under the proposed D2D-SDN architecture, we test the routing and adaptive resource allocation by jointly exploiting network information collection, and network abstraction. We also developed a testbed based on USRP to conduct experiments and demonstrate the feasibility and superiority of our proposed D2D-SDN.

In this paper, we investigate the impact of feedback in LT codes to guarantee unequal recovery time (URT) for different message segments. We analyze the URT-LT codes using the AND-OR tree for two scenarios: complete and partial feedback. We derive the necessary conditions for these two feedback schemes to achieve the required recovery time. We validate the analysis by simulation and highlight the cases where feedback is advantageous.

In this paper, the authors study a half‐duplex two‐way relay channel with correlated sources exchanging bidirectional information. In the case, when both sources have the knowledge of correlation statistics, a source compression with physical‐layer network coding scheme is proposed to perform the distributed compression at each source node. When only the relay has the knowledge of correlation statistics, the authors propose a relay compression with physical‐layer network coding scheme to compress the bidirectional messages at the relay. The closed‐form block error rate expressions of both schemes are derived and verified through simulations. It is shown that the proposed schemes achieve considerable improvements in both error performance and throughput compared with the conventional non‐compression scheme in correlated two‐way relay networks.

In this letter, we develop a low-complexity transceiver design, referred to as semirandom beam pairing, for sparse multipath massive multiple-input-multiple-output (MIMO) channels. By exploring a sparse representation of the MIMO channel in the virtual angular domain, we generate a set of transmit-receive beam pairs in a semirandom way to support the simultaneous transmission of multiple data streams. These data streams can be easily separated at the receiver via a successive interference cancelation technique, and the power allocation among them are optimized based on the classical waterfilling principle. The achieved degree of freedom (DoF) and capacity of the proposed approach are analyzed. Simulation results show that, compared to the conventional singular value decomposition-based method, the proposed transceiver design can achieve near-optimal DoF and capacity with a significantly lower computational complexity.

In this paper, the resource allocation and scheduling problem for a full-duplex (FD) orthogonal frequency-division multiple-access network is studied where an FD base station simultaneously communicates with multiple pairs of uplink (UL) and downlink (DL) half-duplex (HD) users bidirectionally. In this paper, we aim to maximize the network sum-rate through joint UL and DL user pairing, OFDM subchannel assignment, and power allocation. We formulate the problem as a non-convex optimization problem. The optimal algorithm requires an exhaustive search, which will become prohibitively complicated as the numbers of users and subchannels increase. To tackle this complex problem more efficiently, we formulate the user-pairing and subchannel allocation problem as a three-sided matching problem, and propose a novel low-complexity near-optimal matching algorithm. The algorithm is analyzed, and we prove that it converges to a stable matching. Simulation results show that the FD scheme can significantly improve the spectrum efficiency compared with the HD scheme. The proposed algorithm performs very close to the optimal algorithm, and significantly outperforms other resource allocation schemes.

In this paper, we study the resource allocation and user scheduling problem for a downlink non-orthogonal multiple access network where the base station allocates spectrum and power resources to a set of users. We aim to jointly optimize the sub-channel assignment and power allocation to maximize the weighted total sum-rate while taking into account user fairness. We formulate the sub-channel allocation problem as equivalent to a many-to-many two-sided user-subchannel matching game in which the set of users and sub-channels are considered as two sets of players pursuing their own interests. We then propose a matching algorithm, which converges to a two-side exchange stable matching after a limited number of iterations. A joint solution is thus provided to solve the sub-channel assignment and power allocation problems iteratively. Simulation results show that the proposed algorithm greatly outperforms the orthogonal multiple access scheme and a previous non-orthogonal multiple access scheme.

In this paper, we investigate the optimization of non-uniform linear antenna arrays (NULAs) for millimeterwave (mmWave) line-of-sight (LoS) multiple-input multipleoutput (MIMO) channels. Our focus is on the maximization of the system effective multiplexing gain (EMG), by optimizing the individual antenna positions in the transmit/receive NULAs. Here, the EMG is defined as the number of signal streams that are practically supported by the channel at a finite signal-to-noise ratio. We first derive analytical expressions for the asymptotic channel eigenvalues with arbitrarily deployed NULAs when, asymptotically, the end-to-end distance is sufficiently large compared with the aperture sizes of the transmit/receive NULAs. Based on the derived expressions, we prove that the asymptotically optimal NULA deployment that maximizes the achievable EMG should follow the groupwise Fekete-point distribution. Specifically, the antennas should be physically grouped into K separate ULAs with the minimum feasible antenna spacing within each ULA, where K is the target EMG to be achieved; in addition, the centers of these K ULAs follow the Fekete-point distribution. We numerically verify the asymptotic optimality of such an NULA deployment and extend it to a groupwise projected arch-type NULA deployment, which provides a more practical option for mmWave LoS MIMO systems with realistic nonasymptotic configurations. Numerical examples are provided to demonstrate a significant capacity gain of the optimized NULAs over traditional ULAs.

We revisit random multiple access (RMAC) for wireless systems with successive interference cancellation (SIC) employed at the access point (AP). We consider an asymptotically large number of users that transmit over a large number of orthogonal sub-channels. In each transmission block, each user chooses a degree d, where d is a random variable that follows a predefined degree distribution Ω(x). Then, users transmit in d sub-channels chosen uniformly at random. Specifically, we consider signal to interference and noise ratio (SINR) based RMAC where it is assumed that a user's information can be recovered successfully at a given iteration of the SIC process when its updated SINR is above a predetermined threshold. In this paper, we develop a generalized analytical framework based on the codes-on-graph representation to track the evolution of error probabilities in each iteration of the SIC process. We compare our approach to the conventional RMAC employing SIC which assumes that only clean, interference-free transmissions can be recovered successfully. This clean packet model relies on having time slots with a single user's transmission at each iteration of the SIC process. It was shown to be analogous to the iterative recovery process of codes-on-graph for the binary erasure channel (BEC), thus, allowing the direct application of the AND-OR tree analysis. We show that the clean packet model is a special case of our more generalized tree-based analytical framework. Our numerical results show that our model can support more users under the same power requirements.

In this paper, we study the resource allocation and scheduling problem for a downlink non- orthogonal multiple access (NOMA) network where the base station (BS) allocates the spectrum resources and power to the set of users. We aim to optimize the sub-channel assignment and power allocation to achieve a balance between the number of scheduled users and total sum-rate maximization. To solve the above problem, we propose a many-to-many two-sided user-subchannel matching algorithm in which the set of users and sub-channels are considered as two sets of players pursuing their own interests. The algorithm converges to a pair-wise stable matching after a limited number of iterations. Simulation results show that the proposed algorithm can approach the performance of the upper bound and greatly outperforms the OFDMA scheme.

This paper investigates a wireless-powered communication network (WPCN) setup with one multi- antenna access point (AP) and one single-antenna source. It is assumed that the AP is connected to an external power supply, while the source does not have an embedded energy supply. But the source could harvest energy from radio frequency (RF) signals sent by the AP and store it for future information transmission. We develop a discrete time-switching (DTS) protocol for the considered WPCN. In the proposed protocol, either energy harvesting (EH) or information transmission (IT) operation is performed during each transmission block. Specifically, based on the channel state information (CSI) between source and AP, the source can determine the minimum energy required for an outage-free IT operation. If the residual energy of the source is sufficient, the source will start the IT phase. Otherwise, EH phase is invoked and the source accumulates the harvested energy. To characterize the performance of the proposed protocol, we adopt a discrete Markov chain (MC) to model the energy accumulation process at the source battery. A closed-form expression for the average throughput of the DTS protocol is derived. Numerical results validate our theoretical analysis and show that the proposed DTS protocol considerably outperforms the existing harvest-then-transmit protocol when the battery capacity at the source is large.

This paper studies a power beacon-assisted wireless-powered communication network, consisting of one hybrid access point (AP), one information source, and multiple power beacons (PBs). The source has no embedded power supply, and thus, has to harvest RF energy from the AP in the downlink before transmitting its information to the AP in the uplink. The PBs are deployed to help the AP charge the source in the downlink. However, in practice, the AP and PBs may belong to different operators. Thus, incentives are needed for the PBs to assist the AP during DL energy transfer phase, which is referred to as “energy trading”. We formulate this energy trading process as a Stackelberg game, in which the AP is a leader and the PBs are the followers. We then derive the Stackelberg equilibrium of the formulated game. Numerical results show that the proposed scheme can achieve better performance as either the number of the PBs or the value of the gain per unit throughput increase, and as the distance between source and PBs decreases.

In this paper, we consider a wireless-powered cooperative communication network, which consists of one hybrid access point (AP), one source and one relay to assist information transmission. Unlike conventional cooperative networks, the source and relay are assumed to have no embedded energy supplies in the considered system. Hence, they need to first harvest energy from the radio-frequency (RF) signals radiated by the AP in the downlink (DL) before information transmission in the uplink (UL). Inspired by the recently proposed harvest-then-transmit (HTT) and harvest-then-cooperate (HTC) protocols, we develop a new adaptive transmission (AT) protocol. In the proposed protocol, at the beginning of each transmission block, the AP charges the source. AP and source then perform channel estimation to acquire the channel state information (CSI) between them. Based on the CSI estimate, the AP adaptively chooses the source to perform UL information transmission either directly or cooperatively with the relay. We derive an approximate closed-form expression for the average throughput of the proposed AT protocol over Nakagami-m fading channels. The analysis is then verified by Monte Carlo simulations. Results show that the proposed AT protocol considerably outperforms both the HTT and HTC protocols.

This paper is concerned with the channel estimation problem in millimetre wave (MMW) wireless systems with large antenna arrays. By exploiting the sparse nature of the MMW channel, we present an efficient estimation algorithm based on a novel overlapped beam pattern design. The performance of the algorithm is analyzed and an upper bound on the probability of channel estimation failure is derived. Results show that the algorithm can significantly reduce the number of required measurements in channel estimation (e.g., by 225% when a single overlap is used) when compared to the existing channel estimation algorithm based on non-overlapped beam patterns.

In this paper, we investigate the optimal resource allocation in a power beacon-assisted wireless-powered communication network (PB-WPCN), which consists of a set of hybrid access point (AP)-source pairs and a power beacon (PB). We assume that all sources have no embedded power supply. Thus, each source first harvests energy from the signals broadcast by its associated AP and/or the PB in the downlink (DL) and then uses the harvested energy to transmit its information to the AP in the uplink (UL). The PB is deployed to assist the APs during the DL wireless energy transfer (WET) phase. We formulate an optimization problem for the considered network, in which the DL WET time of each AP-source pair and the energy allocation of the PB are jointly optimized to maximize the weighted sum-throughput of all AP-source pairs in the UL. We also propose a waterfilling-based algorithm to solve the formulated problem in a distributed manner. Numerical results are performed to validate the convergence of the proposed algorithm and demonstrate the impacts of various system parameters.

In this paper we propose a novel distributed caching scheme in Heterogeneous Cellular Networks (HCN). We are interested in optimizing the content placement in order to minimize the downloading latency. We achieve this in a decentralized manner, based on a game of independent learning automata (LA). First, we propose a faster-converging discrete generalist pursuit algorithm (DGPA) for a single LA based on the concept of conditional inaction (CI), referred to as CI-DGPA. Then we develop a framework for a game of LA based on CIDGPA defining the information exchange between learners and the environment. Within this framework, we design a reward function that approaches the performance of a greedy algorithm and show that a smart partition of the search space can double the game convergence speed, thereby halving the overhead due to signalling. Simulations show that our scheme can approach the greedy algorithm with a very small performance gap while providing a much lower computational complexity.

In this paper, we study the open-challenging problem of antenna selection (AS) at both the relay and source nodes in MIMO two-way relay networks (TWRNs). Two near-optimal algorithms, namely the joint relay-source AS (JRSAS) and the separated relay-source AS (SRSAS), are proposed in a greedy manner. Specially, JRSAS selects antennas at both the relay and source nodes simultaneously in each AS step to maximize the increment of the system throughput. In order to further reduce the AS computational complexity, SRSAS performs AS at the relay and source nodes in two separate stages. Numerical results show that both JRSAS and SRSAS can approach the optimal exhaustive search (ES) AS algorithm but the computational complexity has been significantly reduced.

In this paper, we study a full-duplex wireless-powered communication network (FD-WPCN), which consists of one full-duplex (FD) hybrid access-point (H-AP) and one FD user. The H-AP and user are both equipped with two antennas, one for downlink wireless energy transfer (WET) from the H-AP to user and the other for uplink wireless information transfer (WIT) from the user to H-AP, where WET and WIT are performed simultaneously through the same frequency band. We consider the scenario that the role of each antenna (i.e., transmission or reception) is not predefined and propose an antenna pair selection (APS) scheme to improve the performance by optimally configuring the transmit and receive antennas at each node. The closed-form expressions for outage probability and probability density function (PDF) of the received signal-to-noise ratio (SNR) at the H-AP are derived. Based on the PDF, we then calculate the closed-form expressions of ergodic capacity, SNR moments and symbol error rate (SER). Finally, we verify the analytical results through Monte Carlo simulations.

In this paper, we investigate optimal resource allocation in a power beacon-assisted wireless-powered communication network (PB-WPCN), which consists of a set of hybrid access point (AP)-source pairs and a power beacon (PB). Each source, which has no embedded power supply, first harvests energy from its associated AP and/or the PB in the downlink (DL) and then uses the harvested energy to transmit information to its AP in the uplink (UL). We consider both cooperative and non-cooperative scenarios based on whether the PB is cooperative with the APs or not. For the cooperative scenario, we formulate a social welfare maximization problem to maximize the weighted sum-throughput of all AP-source pairs, which is subsequently solved by a water-filling based distributed algorithm. In the non-cooperative scenario, all the APs and the PB are assumed to be rational and self-interested such that incentives from each AP are needed for the PB to provide wireless charging service. We then formulate an auction game and propose an auction based distributed algorithm by considering the PB as the auctioneer and the APs as the bidders. Finally, numerical results are performed to validate the convergence of both the proposed algorithms and demonstrate the impacts of various system parameters.

In this paper, we consider simultaneous wireless information and power transfer (SWIPT) in relay interference channels, where multiple source-destination pairs communicate through their dedicated energy harvesting relays. Each relay needs to split its received signal from sources into two streams: one for information forwarding and the other for energy harvesting. We develop a distributed power splitting framework using game theory to derive a profile of power splitting ratios for all relays that can achieve a good network-wide performance. Specifically, non-cooperative games are respectively formulated for pure amplify-and-forward (AF) and decode-and-forward (DF) networks, in which each link is modeled as a strategic player who aims to maximize its own achievable rate. The existence and uniqueness for the Nash equilibriums (NEs) of the formulated games are analyzed and a distributed algorithm with provable convergence to achieve the NEs is also developed. Subsequently, the developed framework is extended to the more general network setting with mixed AF and DF relays. All the theoretical analyses are validated by extensive numerical results. Simulation results show that the proposed game-theoretical approach can achieve a near-optimal network-wide performance on average, especially for the scenarios with relatively low and moderate interference.

Future machine-to-machine (M2M) communications need to support a massive number of devices communicating with each other with little or no human intervention. Random access techniques were originally proposed to enable M2M multiple access, but suffer from severe congestion and access delay in an M2M system with a large number of devices. In this paper, we propose a novel multiple access scheme for M2M communications based on the capacity-approaching analog fountain code to efficiently minimize the access delay and satisfy the delay requirement for each device. This is achieved by allowing M2M devices to transmit at the same time on the same channel in an optimal probabilistic manner based on their individual delay requirements. Simulation results show that the proposed scheme achieves a near optimal rate performance and at the same time guarantees the delay requirements of the devices. We further propose a simple random access strategy and characterize the required overhead. Simulation results show that the proposed approach significantly outperforms the existing random access schemes currently used in long term evolution advanced (LTE-A) standard in terms of the access delay.

In this paper, we propose a joint relay and transmit/ receive (Tx/Rx) antenna mode selection scheme (RAMS) in the general full-duplex (FD) relay networks consisting of one source, one destination, and N FD amplify-and-forward (AF) relays. Each FD relay is equipped with two antennas, one for receiving and the other for transmitting. In the proposed scheme, each antenna of the FD relay is able to transmit/receive the signal. Each relay adaptively selects its Tx antenna and Rx antenna based on the instantaneous channel conditions, and the optimal single relay with the optimal Tx/Rx antenna configuration is selected to maximize the end-to-end signal to interference and noise ratio (SINR) of the FD relay system. The performance of the proposed scheme is analyzed. The closed-form expressions of the outage probability, average symbol error rate, and the ergodic capacity are derived. The analytical results are verified by the simulations. To reduce the error floor and capacity ceiling caused by the self-loop interference in FD relay, we propose a RAMS scheme with adaptive power allocation (RAMS-PA). We provide an upper bound and a lower bound of the end-to-end SINR for RAMS-PA scheme, and prove that the error floor can be removed in the RAMS-PA scheme. Results show that the proposed scheme achieves an extra spatial diversity in the medium SNR region due to the FD antenna selection at the relay nodes and considerably improve the system performance compared to the conventional FD relay selection scheme with fixed relay Tx and Rx antennas.

In this paper, we consider a point to point full duplex (FD) MIMO communication system. We assume that each node is equipped with an arbitrary number of antennas which can be used for transmission or reception. With FD radios, bidirectional information exchange between two nodes can be achieved at the same time. In this paper, we design bidirectional link selection schemes by selecting a pair of transmit and receive antenna at both ends for communications in each direction to maximize the weighted sum rate or minimize the weighted sum symbol error rate (SER). The optimal selection schemes require exhaustive search, so they are highly complex. To tackle this problem, we propose a Serial-Max selection algorithm, which approaches the exhaustive search methods with much lower complexity. In the Serial-Max method, the antenna pairs with maximum “obtainable SINR” at both ends are selected in a two-step serial way. The performance of the proposed Serial-Max method is analyzed, and the closed-form expressions of the average weighted sum rate and the weighted sum SER are derived. The analysis is validated by simulations. Both analytical and simulation results show that as the number of antennas increases, the Serial-Max method approaches the performance of the exhaustive-search schemes in terms of sum rate and sum SER.

In this paper, we focus on the sparse event detection (SED) problem in wireless sensor networks (WSNs), where a set of sensor nodes are placed in the field of interest to capture sparse active event sources. The SED problem in WSNs is represented from a coding theory perspective by using capacity approaching analog fountain codes (AFCs) and further solved with a standard belief propagation (BP) decoding algorithm. We show that the sensing process in WSNs produces an equivalent analog fountain code at the sink node, with code parameters determined based on the sensing capability of sensor nodes and channel gains. We analyze the probability of false detection of the proposed approach and show it to be negligible. Simulation results show that the proposed approach, namely sparse event detection with AFC (SED-AFC), achieves a significantly higher probability of correct detection (PCD) compared to existing literature at various SNR values, with a negligible probability of false detection (PFD). Moreover, we show that the minimum sampling ratio in high SNRs for the proposed scheme reaches the lower bound which is mainly characterized by the sensing coverage of the sensors and field dimensions.

This paper studies the joint transmit and receive antennas selection (JTRAS) in bidirectional MIMO communication systems consisting of two full duplex (FD) nodes. We assume that each node is equipped with N antennas which can be used for transmission or reception. For this bidirectional FD system, we select one transmit antenna and one receive antenna from all the possible antenna configurations at each node to achieve the minimum sum symbol-error-rate (Min-SER). The optimal Min-SER based selections is performed by exhaustively searching, so that it is difficult to analyze and has a high complexity. To tackle this problem, we propose a low-complexity near-optimal Serial-Max method which selects the antenna pairs with maximum SINR in a two-step serial way. The performance of the proposed Serial-Max method is analyzed, and the closed-form expression of the average sum SER is derived. The analysis is validated by simulations. It is shown that the diversity order of the Serial-Max method is (N - 1)2with perfect self-interference cancelation, or zero with residual self interference. Both analytical and simulation results show that as N increases, the Serial-Max method approaches the optimal performance in terms of average sum SER.

Traditional cellular networks play an essential role in the evolution of machine-to-machine (M2M) networks, due to their ubiquitous network coverage. However, M2M communications have some unique characteristics, which are different from the conventional human to human communications, such as the massive number of connected machine type devices (MTD) and stringent QoS requirements. This requires an innovative design of cellular access networks in order to accommodate these requirements. In this paper, we consider the design of M2M communications by using the existing cellular network infrastructure. Since the MTDs do not have their own radio resources, they send their data through the cellular users' (CU) sub-channels. We propose a distributed algorithm that matches a group of MTDs with a particular CU that aims earning more profit by sharing its resources. Then the MTDs in each group access the sub-channels of their matched CU in a TDMA manner. We prove that the proposed algorithm results in a stable matching after a small number of iterations. It is shown that the weighted sum data rate of the MTDs in the proposed algorithm approaches that of the centralized algorithm as the price step-number is sufficiently small, with a significantly lower overhead than the centralized approach.

In this paper, we propose a practical transmission scheme using polar coding for the half-duplex degraded relay-eavesdropper channel. We prove that the proposed scheme can achieve the maximum perfect secrecy rate under the decode-and-forward (DF) strategy. Our proposed scheme provides an approach for ensuring both reliable and secure transmission over the relay-eavesdropper channel while enjoying practically feasible encoding/decoding complexity.

In this paper, we propose a novel estimate-and-forward (EF) transmission protocol, and combine it with the essence of compressive sensing (CS) for a network consisting of two correlated sources, one relay and one destination. Compared with the conventional estimate-and-forward (EF) protocol, in our protocol, correlation is exploited in calculating the soft symbols at the relay. Then we transform the network coded soft symbol vector into a sparse vector, which is suitable for compression by using CS. We analyze that the soft symbols in the proposed protocol are more suitable than those in the EF protocol for CS. Simulations show that our protocol can achieve as good bit error rate performance as the uncompressed EF protocol with reduced transmission time at the relay, thus improving the system throughput performance.

In this paper, we propose a novel rateless code with unequal error protection (UEP) property based on recently proposed analog fountain codes (AFCs). AFCs have been originally designed and optimized to approach the capacity of a Gaussian channel in a wide range of signal to noise ratios (SNRs). In this paper, we are particularly interested in the UEP property of AFC codes, providing different levels of error protection for various sets of information symbols. In the proposed AFC code with UEP property (AFC-UEP), the whole block of information symbols is partitioned into several parts, where each part requires a certain level of error protection. Each part is then assigned with a selection probability and a code degree, which are optimized based on the error probability analysis of the AFC code to satisfy the required error protection levels of all information parts. Simulation results show that the proposed scheme can effectively provide an unequal error protection for different sets of information symbols. Moreover, various error protection requirements can be simply achieved by optimizing the code degree and the selection probability of each part; thus, achieving the desired level of error protection for each part.

In this paper, we study the point-to-point E-band millimeter wave wireless channel with uniform linear antenna arrays (ULAs) deployed at both link ends and present an analytical approach to characterize the channel behavior. We first derive explicit expressions for some channel eigenvalues at certain discrete system settings. The asymptotic behavior and the effective multiplexing distance (EMD) of the E-band channel are then investigated, where the latter is defined as the end-to-end distance at which the channel can support a certain number of spatially independent streams at finite signal-to-noise ratios (SNRs). We analytically show that the EMD for a given number of parallel signal transmissions is mainly determined by the product of the aperture sizes of the transmit and receive ULAs. This finding provides useful insights into the design of practical multi-gigabits wireless communication systems over E-band.

In this paper, we propose a joint relay and antenna selection scheme in general full-duplex (FD) relay networks with one source, one destination and N FD amplify-and-forward (AF) relays. Each FD relay is equipped with two antennas, one for receiving and one for transmitting. We consider a joint antenna and relay selection scheme to optimize the end-to-end error performance. In the proposed scheme, each relay adaptively selects the transmit antenna and receive antenna based on the instantaneous channel conditions, and the optimal single relay with the optimal Tx/Rx antenna configuration is selected to optimize the end-to-end performance of the system transmission. This is in contrast to the conventional pure FD relay selection, where the Tx and Rx FD antenna of each relay are fixed. The proposed scheme achieves an extra space diversity due to the antenna selection at the relay nodes, and considerably improves the system performance compared to the conventional FD relay selection. Furthermore, closed-form expressions for the outage probability and average symbol error rate (SER) are derived. The analytical results are verified by the computer simulations. Results show that the proposed scheme outperforms the conventional full-duplex relay selection scheme with fixed relay Tx and Rx antennas.

In this paper, we consider simultaneous wireless information and power transfer (SWIPT) in relay interference channels, where multiple source-destination pairs communicate through their dedicated energy harvesting relays. Game theory is applied to design a distributed power splitting scheme for the considered system. Particularly, a non-cooperative game is formulated, in which each link is modeled as a strategic player who aims to maximize its own achievable rate. The existence and uniqueness of the Nash equilibrium (NE) for the formulated game is analyzed. A distributed algorithm is proposed based on the best response functions to achieve the NE. Numerical results show that the proposed game-theoretical approach can achieve a near-optimal network-wide performance.

In this paper, we propose a novel rateless multiple access scheme based on the recently proposed capacity-approaching analog fountain code (AFC). We show that the multiple access process will create an equivalent analog fountain code, referred to as the multiple access analog fountain code (MA-AFC), at the destination. Thus, the standard belief propagation (BP) decoder can be effectively used to jointly decode all the users. We further analyze the asymptotic performance of the BP decoder by using a density evolution approach and show that the average log-likelihood ratio (LLR) of each user's information symbol is proportional to its transmit signal to noise ratio (SNR), when all the users utilize the same AFC code. Simulation results show that the proposed scheme can approach the sum-rate capacity of the Gaussian multiple access channel in a wide range of signal to noise ratios.

In this paper, we consider a wireless-powered cooperative communication network, which consists of a hybrid access-point (AP), a hybrid relay, and an information source. In contrast to the conventional cooperative networks, the source in the considered network is assumed to have no embedded energy supply. Thus, it first needs to harvest energy from the signals broadcast by the AP and/or relay, which have constant power supply, in the downlink (DL) before transmitting the information to the AP in the uplink (UL). The hybrid relay can not only help to forward information in the UL but also charge the source with wireless energy transfer in the DL. Considering different possible operations of the hybrid relay, we propose two cooperative protocols for the considered network. We jointly optimize the time and power allocation for DL energy transfer and UL information transmission to maximize the system throughput of the proposed protocols. Numerical results are presented to compare the performance of the proposed protocols and illustrate the impacts of system parameters.

A pragmatic distributed algorithm (PDA) is proposed for supporting the efficient spectral access of multiple Primary Users (PUs) and Cognitive Users (CUs) in cooperative Cognitive Radio (CR) networks. The CUs may serve as relay nodes for relaying the signal received from the PUs to their destinations, while both the PUs' and the CUs' minimum rate requirements are satisfied. The key idea of our PDA is that the PUs negotiate with the CUs concerning the specific amount of relaying and transmission time, whilst reducing the required transmission power or increasing the transmission rate of the PU. Our results show that the cooperative spectral access based on our PDA reaches an equilibrium, when it is repeated for a sufficiently long duration. These benefits are achieved, because the PUs are motivated to cooperate by the incentive of achieving a higher PU rate, whilst non-cooperation can be discouraged with the aid of a limited-duration punishment.

Machine-to-machine (M2M) communications have emerged as a flourishing technology for next-generation communications, and are undergoing rapid development while inspiring numerous applications. However, unique features of M2M communications, such as the massive number of machine type devices (MTD) and delay sensitive applications require specific considerations. To enhance the communication efficiency with delay sensitive short messages, a key strategy is to utilize data aggregation. To facilitate an efficient distributed data aggregation among MTDs with different urgency levels, we propose a game theoretic mechanism based on the coalitional game. Through the proposed algorithm, MTDs autonomously collaborate and self-organize into disjoint independent and stable coalitions, and send their data through a coalition head known as the aggregator. Within each coalition, the utility of the users is defined in such a way that maximum cooperation is compelled. Finally, we discuss the stability of the resulting network structure, and analyse the performance of the proposed scheme.

Deployment of low-power low-cost small access points such as femtocell access points (FAPs) constitute an attractive solution for improving the existing macrocell access points' (MAPs) capacity, reliability, and coverage. However, FAP deployment faces challenging problems such as interference management and the lack of flexible frameworks for deploying the FAPs by the service providers (SPs). In this paper, we propose a novel solution that jointly associates the user equipment (TIE) to the APs, and allocates the FAPs to the SPs such that the the total satisfaction of the TIEs in an uplink OFDMA network is maximized. We model the competitive behaviors among the TIEs, FAPs, and SPs as a dynamic matching game and we propose distributed algorithms to find the optimal TIE association and FAP allocations. We show analytically that the matching algorithms converge to the optimal and stable outcomes after a small number of iterations. Simulations and analytical results show the proposed mechanism is arbitrarily close to the global optimal solution by setting a design parameter (step number) but has significantly reduced overhead and complexity over the centralized algorithm.

In a two-way relay network (TWRN), the optimal joint relay selection (RS) and network coding (NC) (O-RS-NC) scheme, which searches all the relay combinations to select a best relay subset, requires high computational complexity and significant amount of feedback. To address this issue, two joint RS and NC (RS-NC) schemes, referred to as a joint single RS and NC (S-RS-NC) and a joint dual RS and NC (D-RS-NC), are proposed based on decode-and-forward (DF) protocol for error-prone TWRNs. Specifically, for the S-RS-NC scheme, a single relay is selected to minimize the sum bit error rate (BER) of the TWRN. The S-RS-NC scheme is simple to implement, but it suffers from a relatively large signal-to-noise ratio (SNR) loss compared to the O-RS-NC scheme. To reduce the SNR loss, a D-RS-NC scheme is proposed. In the D-RS-NC scheme, one or two relays are selected to minimize the sum BER of the network. Because the source and relay transmission powers (ESand ER) have different impacts on the equivalent SNR of the TWRN, the RS criterion is designed based on different ratios of ESand ER. BER lower-bounds for these schemes are derived and verified by simulations to be tight asymptotically. Both analytical and simulation results show that the proposed RS-NC schemes are superior to the conventional RS without NC scheme when 2ES>ER. In most practical applications, for example, a wireless sensor network, all the nodes transmit at the same power, where the proposed RS-NC schemes perform better than the conventional RS without NC scheme.

This paper studies the fundamental characteristics of point-to-point E-band channels with uniform linear antenna arrays (ULAs) deployed at both the transmitter and receiver. We model the channels as line-of-sight (LoS) multiple-input multiple-output (MIMO) ones and focus on the channel eigenvalue characterization when theRayleigh distance criterioncannot be fulfilled due to limited physical sizes of the transmitter and receiver. We first derive explicit expressions for some channel eigenvalues at certain discrete system settings. Asymptotic analyses are then developed when the antenna numbers at the transmitter and receiver or the distance between them goes to infinity. Based on these analytical results, the maximum eigenvalue and theeffective multiplexing distance(EMD) of the E-band channel are investigated, where EMD is defined as the end-to-end distance at which the channel can support a certain number of simultaneous spatial streams at a given signal-to-noise ratio (SNR). We analytically show that the EMD for a given number of parallel signal transmissions is mainly determined by the product of the aperture sizes of the transmit and receive ULAs. Numerical results are provided to validate the analyses.

In this paper, we investigate a new polynomial pricing function based demand side management scheme for future smart grid, where the consumers are charged based on their instantaneous load. A non-cooperative game is formulated, where each consumer aims to minimize their total energy cost based on the given pricing function. By casting this game in the variational inequality framework, we present sufficient conditions for the uniqueness of the optimal solution. We then propose a distributed and simultaneous algorithm to achieve the optimal solution. Sufficient conditions for the geometrical convergence of our algorithm are also provided and proved. Numerical results reveal that our proposed algorithm converges very quickly, and is effective in encouraging consumers to shift their energy usage from peak to non-peak times.

In this paper, we propose a novel multihop transmission scheme using selective network coding (NC) and differential modulation (SNC-DM) for two-way relay networks (TWRNs) when neither the source nodes nor the relay nodes know the channel state information (CSI). We first develop a bidirectional transmission scheme using NC where the information exchange in a two-way multihop relay network with the arbitrary number of hops can be completed in four transmission phases. As a result, the maximum achievable throughput does not decrease as the number of hops increases. To overcome the error propagation in the multihop transmission with decode-and-forward (DF) protocol in wireless fading channels, a selective NC scheme is proposed. In addition, we apply differential modulation in the proposed scheme to avoid channel estimation in the multihop networks. The performance of the proposed scheme is analyzed, and a closed-form frame error rate (FER) expression is derived. It is shown that the proposed scheme achieves significant improvements in both FER performance and network throughput compared to the conventional multihop DF scheme in TWRNs. The analytical results are verified through numerical simulations.

In this paper, we propose an analog rateless code to achieve high spectral-efficient adaptive transmission and increase the system throughput in AWGN channels. In the proposed analog rateless coding scheme, each coded symbol is generated from a number of information bits that are selected uniformly at random and multiplied by some real values obtained randomly from a predetermined probability distribution function, called weight distribution. The analog rateless codes can be described by a weighted bipartite graph. However, unlike the conventional bipartite graph, where the combining coefficients are the binary symbols, the combining coefficients in the weighted bipartite graph of analog rateless codes are real numbers selected from a finite set. As a result, the conventional sum-product decoder cannot be directly applied. We have developed a simple decoding algorithm, called 2-Sum verification decoder, for the proposed analog rateless codes. Its performance is evaluated by using Sum-Or tree analysis. The code degree and weight distributions are optimized to maximize the error recovery probability of the 2-Sum verification decoder. Simulation results shows the proposed code can approach the channel capacity within one bit across a wide range of SNRs.

In this paper, we propose a novel rateless coding scheme for dynamic decode-and-forward (DDF) relaying in wireless relay networks. The proposed rateless code is developed to overcome the high error floor problem caused by the Luby Transform (LT) code-based encoding scheme in Raptor codes. The improvement is achieved by providing each symbol with approximately equal protection in the belief propagation (BP) decoding, and by designing a special full rank parity-check matrix in the encoding process. Simulation results show that the proposed scheme considerably outperforms the existing rateless coding scheme in wireless relay networks with DDF relaying protocols.

In this paper, we consider a bidirectional cellular relay network with distributed relays where a single base station exchanges information with multiple independent users through multiple single-antenna relays. We design the transceivers at the base station, the relays, and the users. The related optimization problems are generally non-convex and difficult to solve. In this paper, we propose a unified framework to design the transceiver algorithms based on two criteria, i.e. weighted sum MSE minimization and sum rate maximization. Specifically, we show that the sum rate maximization problem can be converted into an iterative weighted sum MSE minimization problem. Low-complexity iterative algorithms are developed for both weighted sum MSE minimization and sum rate maximization optimization problems. However, the convergence points of the proposed iterative algorithms are sensitive to the initial conditions, especially in the high signal-to-noise ratio (SNR) regime. For this reason, we further derive the high-SNR asymptotically optimal solutions and use them as the initials for the proposed iterative algorithms. Simulation results show that the proposed scheme can approximately double the system throughput, compared to the conventional four-stage transmission schemes.

In this letter, we evaluate the energy efficiency of a wireless network-coded cooperative system where multiple nodes cooperatively transmit their information to a common destination. The energy consumption of the transmission and reception circuits is taken into account. It is shown that network coding can provide significant energy savings compared to direct transmission and traditional cooperation techniques. We consider a Nakagami- m fading model, so that the influence of a line-of-sight is also investigated. The optimal number of users that minimizes the energy consumption is obtained analytically, and confirmed by numerical results. Using this number to organize the nodes in clusters can provide considerable energy savings and decrease the network encoding/decoding complexity.

In this paper, we propose a new distributed coding structure with a soft input soft output (SISO) relay encoder for error-prone parallel relay channels. We refer to it as the distributed soft coding (DISC). In the proposed scheme, each relay first uses the received noisy signals to calculate the soft bit estimate (SBE) of the source symbols. A simple SISO encoder is developed to encode the SBEs of source symbols based on a constituent code generator matrix. The SISO encoder outputs at different relays are then forwarded to the destination and form a distributed codeword. The performance of the proposed scheme is analyzed. It is shown that its performance is determined by the generator sequence weight (GSW) of the relay constituent codes, where the GSW of a constituent code is defined as the number of ones in its generator sequence. A new coding design criterion for optimally assigning the constituent codes to all the relays is proposed based on the analysis. Results show that the proposed DISC can effectively circumvent the error propagation due to the decoding errors in the conventional detect and forward (DF) with relay re-encoding and bring considerable coding gains, compared to the conventional soft information relaying.

In this paper, we propose a new rateless coded cooperation scheme for a general multi-user cooperative wireless system. We develop cooperation methods based on Raptor codes with the assumption that the channels face erasure with specific erasure probabilities and transmitters have no channel state information. A fully coded cooperation (FCC) and a partially coded cooperation (PCC) strategy are developed to maximize the average system throughput. Both PCC and FCC schemes have been analyzed through AND-OR tree analysis and a linear programming optimization problem is then formulated to find the optimum degree distribution for each scheme. Simulation results show that optimized degree distributions can bring considerable throughput gains compared to existing degree distributions which are designed for point-to-point binary erasure channels. It is also shown that the PCC scheme outperforms the FCC scheme in terms of average system throughput.

In this paper, we propose a physical-layer rateless code for wireless channels. A novel rateless encoding scheme is developed to overcome the high error floor problem caused by the low-density generator matrix (LDGM)-like encoding scheme in conventional rateless codes. This is achieved by providing each symbol with approximately equal protection in the encoding process. An extrinsic information transfer (EXIT) chart based optimization approach is proposed to obtain a robust check node degree distribution, which can achieve near-capacity performances for a wide range of signal to noise ratios (SNR). Simulation results show that, under the same channel conditions and transmission overheads, the bit-error-rate (BER) performance of the proposed scheme considerably outperforms the existing rateless codes in additive white Gaussian noise (AWGN) channels, particularly at low BER regions.

We consider the use of physical-layer security in a wireless communication system where multiple jamming nodes assist multiple source-destination nodes in combating unwanted eavesdropping from a single eavesdropper. In particular, we propose a distributed algorithm that matches each source-destination pair with a particular jammer. Our algorithm caters for three channel state information (CSI) assumptions: global CSI, local CSI, and local CSI without the eavesdropper channel. We prove that our algorithm has many desirable properties. First, the outcome of the proposed algorithm results in a stable matching, which is important if the source and jamming nodes are selfish. Second, the secrecy rate of the proposed algorithm converges to the secrecy rate of a centralized optimal solution, if the price step-number is sufficiently small. Third, our algorithm converges only after a small number of iterations, and its overhead is relatively small. Fourth, our algorithm has a significantly lower complexity than a centralized optimal approach.

In this paper, we consider general two-way relay networks (TWRNs) with two source and N relay nodes when neither the source nodes nor the relay nodes have access to channel-state information (CSI). A distributed differential space time coding with analog network coding (DDSTC-ANC) scheme is proposed. A simple blind estimation and a differential signal detector are developed to recover the desired signal at each source. The pairwise error probability (PEP) and block error rate (BLER) of the DDSTC-ANC scheme are analyzed. Exact and simplified PEP expressions are derived, which can be used for power allocation between the source and relay nodes. The analytical results are verified through simulations.

This paper proposes a distributed multiple-access transmission scheme, designed to support the transmission of a small number of active nodes to a central base station when the total number of nodes is large. To achieve this, we propose the integration of multiple antenna technologies with compressed sensing algorithms. This integration is made possible by new precoding weight designs which utilize the multiple transmit antennas to provide a power-gain. We demonstrate that the use of these multiple transmit antennas increases the received signal power, thus enhancing the performance of the compressed sensing algorithm. We also propose the use of multiple receive antennas, which we show reduces the delay. Finally, we compare our distributed scheme with a carrier sense multiple-access scheme, and show that our scheme achieves a lower average delay for even a small number of active nodes.

This paper presents a new rateless coded cooperation (CC) scheme for the two-user cooperative multiple access channel (CMAC), where two users cooperatively communicate with a common destination. We consider two rateless CC strategies, a fully coded cooperation (FCC) scheme used in the conventional rateless cooperative schemes and a new partially coded cooperation (PCC) scheme. In FCC, each user starts coded cooperation process only after the whole block of the other user's information symbols are fully recovered. In contrast, in PCC, each user starts cooperation as soon as it receives a fraction of new message sent from the other user. The degree distribution for the PCC scheme is designed to maximize the overall system throughput. Simulation results show that the proposed PCC scheme achieves a considerably higher throughput than the conventional scheme in various scenarios.

In this paper, we analyse the performance of distributed network-channel coding (DNCC) with multiple destinations executing a code nulling (MDCN) process. By analysing the formulation deduced from DNCC with the MDCN process, we find that some unstable zero elements and additional noise are generated after right multiplying the parity-check matrix. These unstable zero elements and additional noise are the reasons to degrade BER performance in the two groups of source nodes and two destination nodes (TSTD) network model. Theoretical bit error ratio (BER) curves are drawn according to the calculated equivalent received signal-to-noise ratio (SNR). The analysis results are consistent with the theoretical curves. A design principle for the generator matrix of the DNCC scheme is proposed to solve the BER performance degradation problem. Simulation results show that the problem caused by the MDCN process can be managed effectively and the BER performance can be improved significantly by using the proposed design principle.

We propose distributed matching algorithms for an uplink communication network comprised of multiple femtocell access points (FAPs), multiple wireless operators (WOs) which own multiple macrocell access points (MAPs) and multiple final users (FUs) subscribed to these WOs. In particular, we propose two algorithms: the first algorithm matches the FAPs with the final users (FUs), the resultant matchings of which are then used for the second matching to match the FAPs with the WOs. The key idea behind the proposed algorithms is that the FAPs aid the transmission of the FUs belonging to the WOs by (i) increasing the WO's coverage and (ii) reducing the traffic load of the WO's macrocell access points (MAPs), and in exchange, the WOs provide monetary compensation to the FAPs. We prove that both of the proposed algorithms converge to a group stable matching. Numerical analysis also reveal that the proposed distributed algorithms achieves a performance close to a centralized method, with much less overhead.

In a wireless multi-hop relay network, the optimal routing scheme with exhaustive path search entails high computational complexity and large storage requirement, and is impractical for a large number of hops. In this paper, we propose a suboptimal path selection scheme, based on amplify-and-forward (AF) protocol, that has outage performance close to the optimal routing scheme, but with much less complexity. The proposed scheme draws on the analogy between the node distribution of a commonly used relay network model and the trellis of a convolutional code, and applies the Viterbi algorithm in selecting a path to maximize the end-to-end signal-to-noise ratio (SNR). In specific, the relay network topology is first mapped to the trellis diagram of a convolutional code. In the trellis, the branch metric is defined as the inverse of the instantaneous SNR of the channel connecting two relays in two adjacent clusters. Consequently, the path metric is equal to the inverse of the equivalent SNR of the path. Then, the sliding window Viterbi algorithm is used to select a path from the source to the destination. Simulation results show that when the window size is five times the total encoder memory or more, the proposed routing scheme achieves near optimal outage performance. The proposed scheme has a polynomial complexity and low communication overhead. Therefore, it is very efficient for relay networks with a large number of hops.

In this paper, we revisit the BCJR soft-input soft-output (SISO) maximum a posteriori probability (MAP) decoding process of rate-1 recursive convolutional (RC) codes. From this we establish some interesting duality properties between encoding and decoding of RC codes. We observe that the forward and backward BCJR decoders can be simply represented by their dual SISO channel encoders using shift registers in the complex field. Similarly, the bidirectional MAP decoding can be implemented by linearly combining the outputs of the dual SISO encoders of the respective forward and backward decoders.

In this paper, we propose a distributed rateless coding (DRC) scheme for a two-user cooperative system. In DRC, the overall transmission is divided into two phases, a broadcast phase and a cooperation phase. In the broadcast phase, each user keeps transmitting its rateless coded symbols to the other user and the destination until its message has been successfully decoded by the destination or the other user. In the cooperation phase, each user encodes both users' messages by using a rateless code and transmits them to the destination. A linear programming optimization problem is then formulated to find the optimal degree distribution for the proposed distributed rateless code. The performance of the proposed code is analyzed and validated by simulations.

In wireless communications, the physical layer security is an emerging security research area that explores the possibilities of achieving high secrecy data transmission between source-destination nodes, while malicious eavesdroppers will obtain zero information from the source nodes. In this paper we consider enhancing the security level of a network comprising of multiple source-destination pairs, multiple friendly jammers and a malicious eavesdropper. We propose a distributed algorithm to facilitate secure data transmission from each source to its corresponding destination, through the help of friendly jammers. The key idea behind the proposed algorithm is that the friendly jammers help the source nodes to increase their secrecy rates and in exchange, the source nodes provide monetary compensation to the friendly jammers. We prove that after a limited number of iterations, the proposed algorithm converges to an optimal stable matching. Numerical analysis also reveals that the distributed algorithm can achieve a performance comparable to an optimal centralized solution, but with a significantly less overhead and complexity.

In 3GPP Long Term Evolution (LTE) networks, the frequency reuse schemes such as fractional frequency reuse (FFR) and soft frequency reuse (SFR) are used to improve system capacity. The allocation of transmit power and subcarriers to each cell in these schemes are fixed prior to network deployment. This limits the potential performance of these frequency reuse schemes. In this paper, we propose to improve the capacity of SFR scheme by jointly optimizing subcarrier and power allocation in multi-cell LTE networks. An iterative algorithm that can adaptively vary the number of major subcarriers and adjust the transmit power for each cell according to wireless traffic loads is proposed. Simulation results show that the proposed algorithm outperforms the existing Reuse 1, FFR and static SFR schemes in both system throughput and cell edge user performance.

The hybrid relay selection (HRS) scheme, which adaptively chooses amplify-and-forward (AF) and decode-and-forward (DF) protocols based on the decoding results at the relay, is very effective to achieve robust performance in wireless relay networks. This paper analyzes the frame error rate (FER) of the HRS scheme in general wireless relay networks without and with utilizing error control coding at the source node. We first develop an improved signal-to-noise ratio (SNR) threshold-based FER approximation model. Then, we derive an analytical average FER expression as well as a high SNR asymptotic expression for the HRS scheme and generalize to other relaying schemes. Simulation results exhibit an excellent agreement with the theoretical analysis, which validates the derived FER expressions.

In this paper, we design interference free transceivers for multi-user two-way relay systems, where a multi-antenna base station (BS) simultaneously exchanges information with multiple single-antenna users via a multi-antenna amplify-and-forward relay station (RS). To offer a performance benchmark and provide useful insight into the transceiver structure, we employ alternating optimization to find optimal transceivers at the BS and RS that maximizes the bidirectional sum rate. We then propose a low complexity scheme, where the BS transceiver is the zero-forcing precoder and detector, and the RS transceiver is designed to balance the uplink and downlink sum rates. Simulation results demonstrate that the proposed scheme is superior to the existing zero forcing and signal alignment schemes, and the performance gap between the proposed scheme and the alternating optimization is minor.

In this paper, we study a multi-user multi-antenna two-way relay system, where a multi-antenna base station (BS) exchanges uplink and downlink signals with multiple users via a multi-antenna amplify-and-forward relay station (RS). We jointly design the BS and RS transceivers, aiming to maximize the system bidirectional sum rate under inter-user interference free constraint. Since the optimization problem is non-convex, we employ alternating optimization algorithm to design the transmit and receive weighting matrices at the BS and RS. Simulation results show that the proposed solution offers higher bidirectional sum rate than existing schemes.

We propose a distributed spectrum access algorithm for cognitive radio relay networks with multiple primary users (PU) and multiple secondary users (SU). The key idea behind the proposed algorithm is that the PUs negotiate with the SUs on the amount of time the SUs are either (i) allowed spectrum access, or (ii) cooperatively relaying the PU's data, such that both the PUs' and the SUs' minimum sum-rate requirement are satisfied. We prove that the proposed algorithm will result in a stable matching and is weak Pareto optimal. Numerical analysis also reveal that the distributed algorithm can achieve a performance comparable to an optimal centralized solution, but with significantly less overhead and complexity.

Inter-cell interference coming from multiple base stations (BS) in adjacent cells limits the capacity of wireless cellular networks. In this paper, we exploit the uplink-downlink duality principle and BS cooperation to design linear and nonlinear downlink inter-cell interference management techniques for multi-user multiple-input-multiple-output (MIMO) systems. The proposed linear algorithm can effectively effectively eliminate the inter-cell interference and achieves bit-error-rate (BER) fairness among different users. The simulation results show that the BER performance of the proposed schemes outperforms existing cooperative transmission schemes and approaches an interference free performance under the same configuration.

In this paper, we use extrinsic information transfer (EXIT) chart to design irregular low density generator matrix (LDGM) codes to form distributed network-channel codes in a wireless sensor network. We formulate the code design and code search as a linear programming (LP) problem. We consider a real-time wireless network with randomly changeable fading channels, resulting in link failures and time varying network topology. In forming such a dynamic network, the connected number of source nodes at each relay needs to satisfy previously obtained degree distributions. At the same time, the channel quality of the data links connecting the source nodes and relay nodes has to be considered. We propose the optimal relaying selection scheme to present such a solution. Simulation results of the proposed irregular codes show that a considerable performance improvement can be achieved in the waterfall region compared with the existing codes.

In this work, we propose an adaptive distributed network-channel coding for a cooperative multiple access channel where M users cooperatively communicate with a common base station. The scheme is based on the recently proposed generalized dynamic-network codes (GDNC), in which the network code design that maximizes the diversity order was recognized as equivalent to the design of linear block codes over a nonbinary finite field under the Hamming metric. The aim here is to increase the system average code rate without reducing its diversity order, making use of a small quantity of feedback. The average rate and the diversity order are obtained analytically, and computer simulations are shown to agree with the analytical results.

In this paper, we consider a two-way relay network (TWRN) employing amplify and forward (AF) relaying protocol and design a near optimal AF scheme to minimize the sum bit error rate (BER) of two end users. For the considered TWRN, in the first two time slots, each user respectively broadcasts its message to both the relay and the other user. Then, the relay linearly combines two received signals and broadcasts the combined signal to the two users. Closed-form near optimal combining coefficients at the relay are derived to minimize the sum BER of two users. Results show that the AF scheme with the proposed combining coefficients performs very closely to the scheme with optimal combining coefficients obtained by computer exhaustive search. It is also shown that the proposed AF scheme brings considerable gains to the system compared to the conventional AF with equal gain combining.

In this paper, we investigate two different multi-hop bi-directional relay transmission schemes based on amplify-and-forward (AF) protocol and analogue network coding (ANC). In the first scheme, referred to as the AF-ANC-Central scheme, AF is employed at each of the intermediate nodes, while ANC is only utilized at the central relay. In the second scheme, referred to as the AF-ANC-Even scheme, the even relays perform ANC and the odd relays only perform subtraction and AF. For reference purpose, the AF-No-ANC scheme is also considered, where the intermediate relays only perform AF and no ANC is employed. Bit error rate (BER) lower bounds of these three schemes are obtained, and are verified by Monte Carlo simulations to be asymptotically tight ones at high signal-to-noise ratios (SNRs). It is shown that the combination of AF and ANC is able to significantly improve system throughput when compared with the AF-No-ANC scheme. The BER performance of the AF-ANC- Central and AF-No-ANC schemes are of the same at high SNR region under the same transmission powers and system configuration, but the AF-ANC-Central scheme is able to double the system throughput. The AF-ANC-Even is able to further increase the system throughput with an SNR loss upper bounded by 1.76 dB when compared to the AF-ANC-Central and AF-No-ANC schemes.

In this contribution, we investigate Near-Capacity Multi-user Network-coding (NCMN) based systems using an Irregular Convolutional Code, a Unity-Rate Code and M-ary Phase-Shift Keying. In the NCMN based systems, we consider a multiuser network in which the users cooperatively transmit independent information to a common base station (BS). Extrinsic Information Transfer (EXIT) charts were used for designing the proposed NCMN scheme. The design principles presented in this contribution can be extended to a vast range of NCMN based systems using arbitrary channel coding schemes.

In this contribution, Near-capacity Non-coherent Cooperative Network-coding aided Multi-user (NNCNM) systems are designed with the aid of Extrinsic Information Transfer (EXIT) charts for the sake of approaching the Differential Discrete-input Continuous-output Memoryless Channel (D-DCMC)-based capacity. The proposed sub-frame-based network coding solution allows the system to significantly mitigate the effects of large-scale fading on each frame. Hence, NNCNM systems operating in large-scale fading environments are capable of approaching the D-DCMC capacity of the less hostile single link channel incurring the small-scale fading, but no shadow fading.

In this letter, we propose a piecewise-and-forward (PF) relaying protocol for wireless relay networks. In PF, the received signal at each relay is compared to an adaptive threshold. If the amplitude of the received signal is above the threshold, the relay will decode the signal, otherwise, the relay will forward the received signal after linear processing. An optimal maximum likelihood detector is developed at the destination for the proposed PF relaying protocol. Simulation results show that the PF protocol outperforms the existing amplify-and-forward, decode-and-forward and estimate-and-forward relaying protocols, and the gain increases as the number of relays increases.

In this work, we propose and analyze a construction of adaptive network codes for a multiple access network under independent block-fading assumption. We aim to increase the system average transmission rate of the recently proposed generalized dynamic-network codes (GDNC) without reducing its diversity order, making use of a small amount of information fed back by the base station. The average rate and the diversity order are obtained analytically, and computer simulations of the diversity order are shown to agree with the analytical results.

Distributed coding has been shown to be an efficient scheme to improve the performance of wireless relay networks. In this paper we propose an analog distributed channel coding (ADC) scheme. In the proposed scheme, the intermediate relays directly process the received analog noisy signals without making the hard estimation and performs analog channel encoding of the noisy soft estimates of the transmitted symbols, so that the processed signals forwarded by all relays can form an analog distributed convolutional codeword. Assuming the source packet is encoded by a channel code, the ADC scheme can form serial concatenated codes and iterative decoding can be applied at the destination. We compare our proposed scheme with other existing schemes under Additive White Gaussian Noise (AWGN) and fast Rayleigh fading channel and validate the simulation results with the aid of Extrinsic Information Transfer (EXIT) chart analysis. Simulation results show that the proposed ADC scheme can effectively overcome the error propagation due to the erroneous decoding at the relay in the conventional decode-forward (DF) scheme and provide considerable coding gains, thus considerably outperforming the conventional soft information relaying protocols. The coding gains increase as the number of state in the relay encoder increases.

In this paper, we design transceivers in a multi-user multi-antenna two-way relay system, where a single multi-antenna base station exchanges information with multiple users via a single multi-antenna relay station. We consider the half-duplex amplify-and-forward relay protocol. We aim to maximize the bidirectional sum rate under the constraint of no interference among different users. Suboptimal solutions to the problem that respectively maximizing the uplink and downlink rates are derived. We then introduce a threshold to balance the uplink and downlink rates so as to maximize the bidirectional sum rate. Simulation results show that the proposed scheme achieves considerably higher bidirectional sum rate than existing schemes.

In this paper, we analyze the bit error rate (BER) performance of a single relay selection with network coding (S-RS-NC) scheme in two-way relay channels. In this scheme, two source nodes first broadcast their information to the relays sequentially. A single relay which optimizes the system performance is selected. The selected relay decodes the received signals from two sources, performs network coding on two symbol estimates and then forwards them to two source nodes. Equivalent signal-to-noise-ratio (SNR) of the whole source-relay-destination link is analyzed and its high SNR approximation is derived. Closed form BER expressions are then derived and the results are verified through Monte-Carlo simulations which show that the derived analytical expressions offer a tight bound for the average BER. It is shown that this S-RS-NC scheme can achieve a full diversity order as if all relays were used. Simulation results show that the S-RS-NC scheme can bring considerable gains over the all-participation relaying scheme when the source transmission power is larger than or equal to the relay transmission power. However, when the relay transmission power is greater than the source power, the single relay selection scheme could be inferior to the all-participation relaying scheme. This is quite different from the conventional relay selection scheme in one way relay network, where the relay selection always outperforms the all participation relaying scheme.

In this work, we propose and analyze a generalized construction of distributed network codes for a network consisting of M users sending different information to a common base station through independent block fading channels. The aim is to increase the diversity order of the system without reducing its code rate. The proposed scheme, called generalized dynamic-network codes (GDNC), is a generalization of the dynamic-network codes (DNC) recently proposed by Xiao and Skoglund. The design of the network codes that maximizes the diversity order is recognized as equivalent to the design of linear block codes over a nonbinary finite field under the Hamming metric. The proposed scheme offers a much better tradeoff between rate and diversity order. An outage probability analysis showing the improved performance is carried out, and computer simulations results are shown to agree with the analytical results.

For a multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system operating in high mobility scenarios, channel estimation becomes a challenging issue, due to fast channel variation and severe inter-carrier interference (ICI). In this paper, we propose a novel pilot-aided iterative receiver, based on pilot symbols and iterative soft-estimate of data symbols. The channel is estimated by time-domain interpolation and least-square (LS) methods. Soft-estimate for data symbols are obtained by a maximum-a-posteriori (MAP) decoder and improved subsequently. The simulation results show that the performance of the proposed iterative receiver outperforms the existing schemes. The performance degradation of the proposed receiver structure when users move at speed of up to 324Km/h compared to the performance of a perfect CSI system with a zero Doppler shift is shown to be very marginal.

In this paper, we consider a multi-access relay interference channel (MARIC), where multiple groups of source nodes communicate with multiple respective destinations, respectively, through a common multi-hop relay network. A joint distributed network-channel coding (DNCC) scheme is proposed to explore both network and channel coding gains. In DNCC, each relay performs a linear network coding and a graph code is formed at each destination. However, multiple groups of source nodes interfere with each other at each destination as each code graph contains bits sent from all other groups of source nodes. To eliminate the inter-group interference, DNCC employs a code-nulling process, so that the graph code at each destination is only the code of its own group of source nodes and does not contain the bits from other group of source nodes. This converts a MARIC into multiple independent multi-access relay channels (MARCs), for each of which a group of source odes communicate with a single destination. Furthermore, for the systematic graph code, such as low density generate matrix (LDGM) code, with respect to each group of source nodes, the LDGM code formed in each decomposed MARC is essentially the same as that formed in the original MARIC. This significantly relax the system design as we can design the distributed LDGM code for each group of source nodes independently as if other group of source nodes does not exist.

Adaptive network coded cooperation (ANCC) has been proposed as an effective scheme to combine network and channel coding for cooperative wireless networks by matching network-on-graph to code-on-graph. Since the real network consists of randomly faded channels, the link failure and topology change are unavoidable. In this paper, we investigate the performance of ANCC in a real wireless sensor network (WSN). Different network codes are constructed to match these instantaneous network topologies. By designing a proper network code through optimizing source node degrees, a significant performance improvement is achieved. In addition, we consider the noisy channel between the source and relay, and propose a soft information relaying based ANCC scheme. Simulation results show that the proposed scheme considerably improves the system performance compared to the hard-decision relaying ANCC scheme.

QoS provisioning in wireless mesh networks has been to known as a challenging issue. In a distributed scheduling based wireless mesh backhaul network, conventional connection-based QoS provision mechanism requires considerable amount of overhead for per-link QoS signaling, thus cannot support high speed real time traffic. In this paper,we propose a DiffServ-like QoS provision mechanism for IEEE 802.16 mesh networks with directional antennas to increase network capacity and enable real time traffic. We develop a distributed scheduling algorithm based on our proposed scheme. Simulation results demonstrate that directional antennas can achieve higher network capacity compared with omni-antennas, and our QoS scheme can effectively guarantee QoS requirements of real time traffic.

In this paper, we consider a two hop cognitive radio network where there are multiple primary and secondary users. We propose the design of antenna weights at the source and relay based on zero forcing principles. These antenna weights are designed to cancel the interference among the secondary users while maintaining fairness, and to ensure interference free signals at each primary user. To analyze the performance of our proposed design, we derive new exact expressions for the signal to interference and noise ratio at each secondary user, which are subsequently used to analyze the error performance.

In this paper, we consider a general bi-directional relay network with two sources and N relays when neither the source nodes nor the relays know the channel state information (CSI). A bi-directional relay selection scheme is proposed using differential analog network coding (BRS-DANC), and a simple linear detector is given to recover the received signals. In the proposed scheme, we provide an optimal and a sub-optimal methods to select the relay node from a single source by minimizing the average symbol error rate (SER). The performance of the proposed BRS-DANC scheme is analyzed, and a simple asymptotic SER expression is derived. It is shown that the SER performance of the proposed differential scheme is about 3 dB away from that of the coherent detection scheme.

In this paper, the performance of a dual-hop Amplify-and-Forward (AF) multi-antenna relay network, with end-to-end (e2e) best antenna selection, is investigated. To investigate the performance of this system, we first derive the exact outage probability in closed-form. It is then used to obtain expressions for e2e SNR moments and the average symbol/bit error rate (SER/BER) valid for a large class of practical modulation schemes. A simple and accurate BER approximation is also derived to quantify the performance at high SNR. Our analytical results, show that the e2e antenna pair selection scheme achieves the same diversity order as for the case where all antennas are used. To further confirm the validity of our analysis, Monte Carlo simulation results are also presented.

The power allocation problem is addressed for time division duplex (TDD) LTE-A uplink systems in this paper. Due to the IDFT de-spreading in LTE-A uplink, the channel frequency responses in an IDFT de-spreading block will be tangled together. After analyzing the equivalent signal to interference plus noise ratio (SINR) in the time domain, a Truncated Squared norm of channel equalization Coefficients based Power Allocation (TSCPA) method is proposed to improve the final SINR performance after the IDFT de-spreading block. The proposed TSC-PA algorithm is verified for the clustered DFT-s-OFDM system in eigen-model block diagonalization multi-user MIMO uplink environment by simulations. The results demonstrate that the proposed TSC-PA algorithm can further improve the system block error rate (BLER) performance by selecting a proper truncation threshold.

In this paper, we study a decode-and-forward two-way relaying network. We propose an opportunistic two-way relaying (O-TR) scheme based on joint network coding and opportunistic relaying. In the proposed scheme, one single "best relay" is selected by MaxMin criterion to perform network coding on two decoded symbols sent from two sources, and then to broadcast the network-coded symbols back to the two sources. The performance of the proposed scheme is analyzed, and verified through Monte Carlo simulations. Results show that the proposed scheme achieves a better performance compared to the fully-distributed space-time two-way relaying (FDST-TR), which has been identified as the best decode-and-forward two-way relaying method so far.

This paper investigates the performance of practical physical-layer network coding (PNC) schemes for two-way relay channels. We first consider a network consisting of two source nodes and a single relay node, which is used to aid communication between the two source nodes. For this scenario, we investigate transmission over two, three or four time slots. We show that the two time slot PNC scheme offers a higher maximum sum-rate, but a lower sum-bit error rate (BER) than the four time slot transmission scheme for a number of practical scenarios. We also show that the three time slot PNC scheme offers a good compromise between the two and four time slot transmission schemes, and also achieves the best maximum sum-rate and/or sum-BER in certain practical scenarios. To facilitate comparison, we derive new closed-form expressions for the outage probability, maximum sum-rate and sum-BER. We also consider an opportunistic relaying scheme for a network with multiple relay nodes, where a single relay is chosen to maximize either the maximum sum-rate or minimize the sum-BER. Our results indicate that the opportunistic relaying scheme can significantly improve system performance, compared to a single relay network.

Interference among multiple base stations that co-exist in the same location limits the capacity of wireless networks. In this paper, we propose a method to design a spectrally efficient cooperative downlink transmission scheme employing precoding and beamforming for multi-user multiple-input-multiple-output (MIMO) systems. The algorithm eliminates the interference and achieves symbol error rate (SER) fairness among different users. To eliminate the interference, Tomlinson Harashima precoding (THP) is used to cancel part of the interference while the transmit-receive antenna weights are chosen to cancel the remaining interference. A new novel iterative method is applied to generate the transmit-receive antenna weights. To achieve SER fairness among different users and further improve the performance of multi-user MIMO systems, we develop algorithms that provide equal signal-to-interference-plus-noise-ratio (SINR) across all users. The users are also ordered so that the minimum SINR for each user is maximized. The simulation results show that the proposed scheme considerably outperforms existing cooperative transmission schemes in terms of the SER performance and complexity and approaches an interference free performance under the same configuration.

This paper investigates the performance of practical physical layer network coding (PLNC) schemes for two-way relay channels. We consider a network consisting of two source nodes and a single relay node, which is used to aid communication between the two source nodes. For this scenario, we investigate various transmission schemes, where transmission takes place over two, three or four time slots. We show that the two time slot PLNC scheme offers a higher maximum sum-rate, but a lower sum-bit error rate (BER) than the four time slot transmission scheme for a number of practical scenarios. We also investigate a three time slot PLNC scheme, which we show offers a good compromise between the two time slot PLNC and four time slot transmission schemes, and also achieves the best maximum sum-rate and/or sum-BER in certain practical scenarios. To facilitate comparison, we derive new closed-form expressions for the outage probability, maximum sum-rate and sum-BER.

Interference among multiple base stations that co-exist in the same location limits the capacity of wireless networks. In this paper, we propose a nonlinear multi-user MIMO cooperative downlink transmission scheme. The algorithm eliminates the interference and achieves symbol error rate (SER) fairness among different users. To eliminate the interference, Tomlinson Harashima precoding (THP) is used to cancel part of the interference while the transmit-receive antenna weights cancel the remaining part. The uplink-downlink duality principle is used to calculate transmit-receive antenna weights. The proposed scheme is then extended to work when the receiver does not have complete Channel State Informations (CSIs). The simulation results show that the proposed schemes considerably outperform existing cooperative transmission schemes in terms of SER performance and approach an interference free performance under the same configuration.

In this paper, we consider two hop networks with multiple source, relay and destination nodes. In particular, we investigate systems with different processing capabilities at the relay and destination nodes. We derive new closed-form outage probability expressions when zero forcing processing is used at the i) relay, ii) relay and destination and iii) destination nodes only. Our results indicate significant decreases in outage probability for certain node configurations. We confirm our results through comparison with Monte Carlo simulations.

In this contribution, we propose a distributed turbo trellis coded modulation (DTTCM) scheme for cooperative communications. The DTTCM scheme is designed based on its decoding convergence with the aid of non-binary extrinsic information transfer (EXIT) charts. The source node transmits TTCM symbols to both the relay and the destination nodes during the first transmission period. The relay performs TTCM decoding and re-encodes the information bits using a recursive systematic convolutional (RSC) code regardless whether the relay can decode correctly or not. Only the parity bits are transmitted from the relay node to the destination node during the second transmission period. The resultant symbols transmitted from the source and relay nodes can be viewed as the coded symbols of a three-component parallel-concatenated TTCM scheme. At the destination node, a novel three-component TTCM decoding is performed. It is shown that the performance of the DTTCM matches exactly the EXIT chart analysis. It also performs very closely to its idealised counterpart that assumes perfect decoding at the relay.

For a multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system, time-varying multipath fading of channel destroys the orthogonality among subcarriers and leads to serious intercarrier interference (ICI). The system performance degrades more severely as normalized Doppler frequency increases. In order to mitigate the effect of time-varying fading, a low-complexity iterative receiver with joint ICI cancellation and pilot-assisted channel estimation is proposed. The initial channel state information (CSI) is estimated by performing time-domain interpolation and least-square (LS) method on the received pilot symbols. The soft outputs are obtained from the decoders after low-complexity linear minimum mean-square error (LC-LMMSE) detection. In the following stages, the soft outputs are feedback to update the CSI estimation. Furthermore, a ¿linear statistics combining¿ (LSC) technique is used to improve the performance of the proposed equalizer by combining the outputs of LC-LMMSE and parallel interference canceler (PIC) with weighting coefficients estimated by maximizing the signal to interference-plus-noise ratio (SINR) at the output of LSC. The complexity of system is significantly reduced by restricting the interference to neighboring subcarriers and employing the LC-LMMSE by limiting the frequency-domain CSI into diagonal region. The simulation results show that the proposed iterative receiver with estimated CSI approaches the ICI-free bound even at very high mobility scenarios.

In this paper, we consider a general two-hop relay network and propose a distributed turbo coding with selective relaying (DTC-SR) scheme to improve the performance of relayed transmission. In the proposed scheme, each relay adaptively selects an amplify and forward (AAF) or a decode and forward (DAF) protocol based on whether it can decode correctly or not. Among all the relays, a single relay, which has the maximum destination SNR, is selected for transmission. If the selected relay uses the DAF protocol, it decodes the received signals, interleaves, re-encodes and forwards them to the destination. At the destination, the signals directly transmitted from the source and that from the selected relay form a distributed turbo code (DTC). If the selected relay uses the AAF protocol, it just simply amplifies the received signal. The destination then combines the signals transmitted from the source and the relay. Simulation results show that the DTC-SR can take advantages of both distributed turbo coding and relay selection, providing not only a considerable SNR gain contributed from the relay selection, but also a coding gain contributed from the distributed turbo coding. And these gains increase as the number of relay increases.

In this paper, we consider a two hop Amplify and Forward (AF) multiple-input multiple-output (MIMO) relay network with antenna correlation, where beamforming is performed at the source and destination. This network consists of a single relay which is used to amplify and forward the signal from the source to the destination. The source and destination are both equipped with multiple antennas, which are correlated in space, while the relay has a single antenna. In this paper, we derive closed form expressions for the outage probability and probability density function of the received signal-to-noise ratio at the destination. We also present exact symbol error rate expressions for the two hop AF MIMO relay network, and show that the full spatial diversity order can be achieved.

In this paper, we propose a hybrid relay selection (HRS) scheme in a general cooperative network using differential modulation. In the HRS scheme, when the destination decodes successfully, the relay nodes will remain silent. Otherwise, optimal relay node has to be determined to make an additional transmission. In this process, all the relays are divided into two groups, referred to as an amplify-and-forward (AAF) relay group and a decode-and-forward (DAF) relay group depending on whether they can decode correctly or not. The relay, which has the maximum signal-to-noise ratio (SNR) at the destination, will be selected from both AAF and DAF relay groups. Simulation results show that the proposed relay selection scheme significantly outperforms the conventional AAF selection in terms of both frame error rate (FER) and throughput, and these performance gains considerably grow as the number of relay nodes increases.

In this paper, the performance of an uncoded multiple-input-multiple-output (MIMO) scheme combining single transmit antenna selection and receiver maximal-ratio combining (the TAS/MRC scheme) is investigated for independent flat Nakagami-m fading channels with arbitrary real-valued m. The outage probability is first derived. Then the error rate expressions are attained from two different approaches. First, based on the observation of the instantaneous channel gain, the binary phase-shift keying (BPSK) asymptotic bit error rate (BER) expression is derived, and the exact BER expression is obtained as an infinite series, which converges for reasonably large signal-to-noise ratios (SNRs). Then the exact symbol error rate (SER) expressions are attained as a multiple infinite sum based on the moment generating function (MGF) method for M-ary phase-shift keying (M-PSK) and quadrature amplitude modulation (M-QAM). The asymptotic SER expressions reveal a diversity order equal to the product of the m parameter, the number of transmit antennas and the number of receive antennas. Theoretical analysis is verified by simulation.

Interference among multiple base stations that coexist in the same location limits the capacity of wireless networks. In this paper, we propose a method to design a spectrally efficient cooperative downlink transmission scheme employing precoding and beamforming. The algorithm eliminates interference and achieves symbol error rate (SER) fairness among different users. To eliminate the interference, Tomlinson Harashima precoding (THP) is used to cancel part of the interference while the transmit-receive antenna weights are chosen to cancel the remaining interference. A novel iterative method is applied to generate the transmit-receive antenna weights. The convergence behaviour of the iterative process is investigated. To achieve SER fairness among different users and improve the performance of the system, we develop algorithms that provide equal signal to-interference-plus-noise-ratios (SINR) across all users under both per base station (BS) and total BSs power constraints. Per BS and total BSs power constraints are constraints where the power for each BS and all BSs is limited to a particular value. The simulation results show that the proposed scheme outperforms existing cooperative transmission schemes in terms of the SER performance and complexity and closely approaches an interference free performance under the same configuration.

The performance of beamforming with antenna correlation in a two hop amplify and forward (AF) multiple input multiple-output (MIMO) relay network is analyzed. This network consists of a single relay which is used to amplify and forward the signal from the source to the destination. The source and destination are both equipped with multiple antennas, which are correlated in space, while the relay has a single antenna. In this paper, we derive new closed form expressions for the outage probability and probability density function of the received signal-to-noise ratio (SNR) at the destination. We also present exact symbol error rate expressions for the two hop AF MIMO relay network, and show that the full spatial diversity order can be achieved. Our results also indicate that spatial correlation is detrimental to the outage probability and symbol error rate at high SNR, and beneficial at low SNR.

In this paper, we propose a novel distributed spacetime trellis code (DSTTC) structure, and analyze its error performance in both slow and quasi-slow Rayleigh fading channels. The protocol adopted is decode-and-forward (DAF) with a single relay between the source and destination. Both scenarios with perfect and imperfect decoding at the relay are investigated. For imperfect decoding at the relay node, we consider an equivalent one-hop link model for the source-relay-destination path, and use it to modify the maximum likelihood detection metric by taking into account the equivalent signal-to-noise ratio (SNR) of the link model. The upper bounds of pairwise error probability (PEP) are derived for slow and quasi-slow Rayleigh fading channels, and the DSTTC design criteria are formulated accordingly. Based on the proposed design criteria, new DSTTCs are constructed by computer search. Simulation results demonstrate the superiority of the designed codes.

In this paper, we consider a multiple-relay network. We propose differential modulation at each communication node and selective combining method at the receiver. As both differential modulation and the selective combining do not require the full channel state information, the system design is greatly simplified without compromising much of the system performance. The average BER for this multiple-relay system with estimation-and- forward signaling protocol at the relay nodes is derived. In the analysis, we take into account the effect of imperfect estimation at the relay nodes, by replacing each of the source-relay-destination links with an equivalent relay-destination link. From the performance analysis, we conclude that this multiple-relay system with selective combiner can achieve full diversity. The analysis is also verified by simulation results.

The interference among multiple access points (APs) that co-exist in the same location, limits the capacity of co-working wireless local area networks (WLANs). In this paper, we propose a practical cooperative transmission scheme to mitigate the interference in co-working WLANs. In particular, we combine Tomlinson Harashima precoding (THP), joint transmit-receive beamforming based on SINR (signal-to-interference-plus-noise-ratio) maximization, and an adaptive precoding order to eliminate co-working interference and achieve bit error rate (BER) fairness among different users. We consider the design of the system when partial channel state information (CSI) (where each user only knows its own CSI) and full CSI (where each user knows CSI of all users) are available at the receiver respectively. We prove analytically and by simulation that the performance of our proposed scheme will not be degraded under partial CSI. The simulation results show that the proposed scheme considerably outperforms both the existing non-cooperative and cooperative transmission schemes and is only 2 dB away from an interference-free channel under the same configuration.

The performance of beamforming in a two hop amplify and forward (AF) relay network is analyzed. This network consists of a single relay which is used to amplify and forward the signal from the source to the destination. The source and destination are both equipped with multiple antennas while the relay has a single antenna. In this paper, we derive closed form expressions for the outage probability and probability density function of the received SNR. We also present exact symbol error rate expressions for the two hop AF relay network and show that full spatial diversity order, which corresponds to the minimum number of antennas at the source and destination, can be achieved. Our analytical results are confirmed through comparison with Monte Carlo simulations.

Distributed turbo coding (DTC) has been shown to be an effective coding scheme to approach the capacity of a wireless relay network. However, most of existing DTC schemes only consider a relay network with single relay node and assume that relay can perform an error free decoding, which we refer to as a perfect DTC scheme. In this paper, we consider a general 2-hop relay network with an arbitrary number of relays and design the DTC for such a network when taking into account imperfect decoding at each relay. We propose a generalized distributed turbo coding (GDTC) scheme with hybrid relaying protocol for such relay networks. In each transmission, based on whether relays can decode correctly or not, each relay is included into one of two relay groups, referred to as a decode and forward (DAF) relay group and an amplify and forward (AAF) relay group. Each relay in the DAF relay group decodes the received signals from the source, interleaves, re-encodes and forwards it to the destination, while each relay in the AAF relay group amplifies the received signals and forwards it to the destination. At the destination, all signals transmitted from the relays in the DAF relay group are combined into one signal and that in the AAF relay group are combined into another signal. These two signals form a generalized DTC codeword. Theoretical analysis and simulation results show that the proposed GDTC scheme benefits from a significant coding gain contributed from the DTC relay group compared to the distributed coding with pure AAF relaying and simultaneously overcome the detrimental effects of error propagation due to the imperfect decoding at relays in the conventional DTC schemes. It also approaches the perfect DTC as the signal to noise ratio (SNR) increases.

Distributed coding has been shown to be an effective scheme to explore cooperative spatial diversity in wireless relay networks. To date, the distributed coding schemes employ two major relaying protocols, decode and forward (DAF) and amplify and forward (AAF). They suffer from a disadvantage of either noise amplification or error propagation. In this paper, we propose a simple adaptive relaying protocol (ARP) for general relay networks. For the proposed approach, all relays are included into one of two relay groups, referred to as a DAF relay group and an AAF relay group. All relays, which decode correctly, are included in the DAF relay group, and other relays, which could not decode correctly, are included in the AAF relay group. Performance analysis of the proposed adaptive relaying protocol is carried out, and compared with other relaying protocols. It is shown that the proposed adaptive relaying protocol benefits from a significant coding gain contributed from the DAF relay group compared to a pure AAF relay protocol and simultaneously circumvent the detrimental effects of error propagation due to the imperfect decoding at relays in a DAF relay protocol, thus always outperforming both the AAF and DAF relaying protocols in all SNR regions.

Hybrid ARQ (HARQ) has been shown to be an effective transmission strategy to improve the performance and capacity of relayed transmission in cooperative wireless networks. In contrast to the conventional HARQ, the retransmitted packets in cooperative systems do not need to come from the original source but could instead be sent by relays that overhear the transmission. This paper proposes an improved HARQ scheme with an adaptive relaying protocol (ARP). The proposed HARQ- ARP scheme combines the retransmission mechanisms (repetition coding and incremental redundancy), the distributed turbo coding (DTC) and the adaptive relaying strategy. They can effectively avoid the problem of both error propagation and noise amplification encountered in current cooperative communication systems. A performance analysis is carried out for the proposed scheme. The analysis and simulation results show that the proposed HARQ scheme can achieve a superior frame error rate (FER) to the existing HARQ relaying methods in all signal to noise ratio (SNR) regions.

In MIMO-OFDM systems, the time-varying fading of channel can destroy the orthogonality of subcarriers. This causes serious intercarrier interference (ICI), thus leading to significant system performance degradation, which becomes more severe as the normalized Doppler frequency increases. In this paper, we propose a low-complexity iterative receiver with joint frequency-domain ICI cancellation and pilot-assisted channel estimation to minimize the effect of time-varying channel. At the first stage of receiver, the interference between adjacent sub-carriers is subtracted from received OFDM symbols. The parallel interference cancellation (PIC) detection with decision statistics combining (DSC) is then performed to suppress the interference from different antennas. By restricting the interference to the limited number of neighboring subcarriers, the computational complexity of our proposed receiver can be significantly reduced. Furthermore, channel parameters estimated with pilot symbols placed in equispaced groups on FFT grid are used to improve the system performance during iteration. Simulation results show that the proposed MIMO-OFDM iterative receiver can effectively mitigate the effect of ICI and approach the ICI-free performance over time-varying frequency-selective fading channels.

Bandwidth efficient parallel-concatenated Turbo Trellis Coded Modulation (TTCM) schemes were designed for communicating over uncorrelated Rayleigh fading channels. A symbol-based union bound was derived for analysing the error floor of the proposed TTCM schemes. A pair of In-phase (I) and Quadrature-phase (Q) interleavers were employed for interleaving the I and Q components of the TTCM coded symbols, in order to attain an increased diversity gain. The decoding convergence of the IQ-TTCM schemes was analysed using symbol-based EXtrinsic Information Transfer (EXIT) charts. The best TTCM component codes were selected with the aid of both the symbolbased union bound and non-binary EXIT charts, for designing capacity-approaching IQ-TTCM schemes in the context of 8 PSK, 16 QAM, 32 QAM and 64 QAM modulation schemes.

Space-time trellis codes (STTCs) have been shown to efficiently use transmit diversity to improve error performance. In existing STTCs, the transmit power is equally distributed across all transmit antennas. In fact, when feedback information is available at the transmitter, the equal power allocation strategy is not optimum regarding the error performance. In this paper, we propose a new scheme, referred to as space-time trellis codes with dynamic transmit power allocation (STTCs/DTPA), when partial channel state information (CSI) is available at the transmitter. A closed-form pairwise error probability (PEP) upper bound is derived. The optimum power allocation scheme and the code design criteria, based on the PEP upper bound, are proposed. Theoretical and simulation results show that the proposed scheme performs much better in terms of error probability than the general open-loop STTCs and other closed- loop transmit diversity schemes. For example, the new 16-state STTCs/DTPA scheme is 5.6 dB better than the standard 16-state Chen/Yuan/Vucetic code for three transmit antennas.

In this paper, we propose an improved relay selection scheme based on a hybrid relaying protocol (RS-HRP). In the proposed scheme, all the relays are included into two groups, referred to as an amplify and forward (AAF) and a decode and forward (DAF) relay groups. The relays which decode successfully are included in the DAF group and the rest of relays, which fail to decode correctly, are included in the AAF group. The best relay, which maximizes the destination SNR, will be selected from all relays in both AAF and DAF relay groups. If it is selected from the AAF group, it will amplify the received signal while if it is selected from the DAF group, it will decode the received signals and re-encode. Results show that the proposed relay selection scheme significantly outperforms the conventional AAF selection scheme and this performance gain considerably grows as the number of relays increases. It also approaches the perfect DAF relay selection as the SNR increases.

We consider a 2-hop wireless relay network. We explore transmit power allocation among the source and relays to maximize the received SNR at the destination. We consider two relaying protocols, "amplify and forward" (AAF) and "decode and forward" (DAF) and calculate the respective power allocations for both uncoded and coded system. For a 2- hop relay system with one relay node, we derive a closed-form power allocation solution and based on it we propose a relay active condition. If and only if the fading channel coefficients satisfy this condition, the relay transmits the signals to the destination; otherwise, the relay will stay in an idle state. For a system with more than one relay nodes, general closed-form power allocation solutions based on the extact SNR expression are not tractable, so we calculate a SNR upper bound and derive a sub-optimum power allocation solution based on this bound. The simulation results show that for a 2-hop relay channel, the proposed adaptive power allocation (APA) scheme can bring the system a considerable SNR gains compared to the equal power allocation scheme. This gain will be further monotonically increased as the number of relays increases.

Most of existing distributed coding schemes assume a perfect detection at relays. In this paper, we propose a distributed space time trellis coding (DSTTC) scheme by taking into account the imperfect detections at relays. We propose a detection metric to address the effect of decoding errors. In particular, we consider an equivalent one-hop link model for the source-relay-destination path and use it to modify the maximum likelihood detection metric by taking into account the equivalent signal to noise ratio (SNR) of the link model. The upper bound of the pairwise error probability (PEP) is then derived for both slow and quasi-slow Rayleigh fading channels, and the design criteria for the distributed space-time trellis codes are formulated accordingly. Based on the proposed design criteria, the optimal QPSK 4-state, 8-state, 16 -state and 32- state DSTTCs are constructed. The results are validated by computer simulations.

Bandwidth efficient parallel-concatenated turbo trellis coded modulation (TTCM) schemes were designed for communicating over uncorrelated Rayleigh fading channels. A symbol-based union bound was derived for analysing the error floor of the proposed TTCM schemes. A pair of in-phase (I) and quadrature-phase (Q) interleavers were employed for interleaving the I and Q components of the TTCM coded symbols, in order to attain an increased diversity gain. The decoding convergence of the IQ-TTCM schemes was analysed using symbol-based extrinsic information transfer (EXIT) charts. The best TTCM component codes were selected with the aid of both the symbol-based union bound and non-binary EXIT charts for the sake of designing capacity-approaching IQ-TTCM schemes in the context of 8 PSK, 16 QAM and 32 QAM signal sets. It will be shown that our TTCM design is capable of approaching the channel capacity within 0.5 dB at a throughput of 4 bit/s/Hz, when communicating over uncorrelated Rayleigh fading channels using 32 QAM.

The discovery of turbo codes and the subsequent rediscovery of low-density parity-check (LDPC) codes represent major milestones in the field of channel coding. Recent advances in the design and theory of turbo codes and their relationship to LDPC codes are discussed. Several new interleaver designs for turbo codes are presented which illustrate the important role that the interleaver plays in these codes. The relationship between turbo codes and LDPC codes is explored via an explicit formulation of the parity-check matrix of a turbo code, and simulation results are given for sum product decoding of a turbo code.

This paper presents the performance analysis and code design for differential space-time trellis code (DSTTC) when no channel state information (CSI) is available at neither the transmitter nor receiver. Upper bounds on the pairwise error probability of DSTTC over fast fading and quasi-static fading channels are derived and new design criteria are proposed based on these bounds. It is shown that the performance of DSTTC is determined by the minimum weighted square product distance (WSPD) over independent fast fading channels, and by the minimum cross correlation distance (CCD) over quasi-static fading channels. New DSTTCs are found by a systematic code search. Simulation results show that under the same spectral efficiency the proposed coding scheme has a superior performance and lower complexity compared to other existing differential space time coding schemes

In this paper, we consider a 2-hop wireless diversity relay network. We explore transmit power allocation among the source and relays to maximize the received signal to noise ratio (SNR) at the destination. We consider two relay protocols, "amplify and forward" (AAF) and "decode and forward" (DAF) and design the respective power allocations for both uneeded and coded systems. For a 2-hop relay system with one relay node, we derive a closed-form power allocation solution and, based on it, we propose a relay activation condition. If and only if the fading channel coefficients satisfy this condition, the relay transmits the signals to the destination; otherwise, the relay will stay in the idle state. For a system with more than one relay node, general closed-form power allocation solutions based on an exact SNR expression are difficult to derive; we hence, calculate a SNR upper bound and derive a sub-optimum power allocation solution based on this bound. The simulation results show that for a 2-hop diversity relay channel with one relay node the proposed adaptive power allocation (APA) scheme yields about 1- 2 dB SNR gains compared to the equal power allocation. This SNR gain increases monotonically as the number of relays increases

Space-time turbo trellis coding (STTuTC) has been shown to provide a significant performance improvement over comparable space-time trellis coding (STTC) schemes due to an increase in coding gain. In conventional STTuTC schemes equal power is allocated to each transmit antenna. From information theory we know that if partial or full channel state information (CSI) is available at the transmitter, the system performance can be further improved. In this paper we consider the design of a STTuTC scheme where the transmitter has knowledge of the order of channel quality for each transmit antenna. For the proposed scheme, the transmitter allocates the transmit power to each antenna based on this information. A closed-form expression for the pair-wise error probability (PEP) upper bound over quasi-static Rayleigh fading channels is derived. The optimum power allocation coefficients are calculated based on this PEP bound. Simulation results show a significant frame error rate (FER) performance improvement for the proposed scheme with optimal power allocation compared to the conventional STTuTC scheme with equal power allocation.