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.

End-to-end (E2E) learning has recently been proposed to jointly design the modulator and symbol detector by using deep neural networks (DNNs). However, existing schemes lack sufficient capability to cancel multi-user interference (MUI) in uplink multi-user multiple-input multiple-output (MU-MIMO) systems. In this paper, we propose a graph neural network (GNN)-based E2E learning scheme that employs a GNN-based modulator to generate learned constellation points, and a GNN-based detector to cancel MUI. They are jointly optimized to minimize the symbol error rate (SER) performance loss. Simulation results demonstrate that the proposed E2E outperforms existing schemes with a predefined modulator. Specifically, it achieves an approximate 2 dB gain in a high MUI environment and surpasses even the maximum-likelihood (ML) detector in a low MUI condition.

This paper presents a robust yet efficient Grey Wolf Optimizer-Support Vector Machine algorithm, termed K-GWO-SVM, for the analysis of ECG signals in smart healthcare systems, aiming to improve classification accuracy and computational efficiency. The proposed model introduces three main contributions: (1) the use of GWO to automatically search for the optimal hyperparameters of SVM tailored to each dataset, (2) a mini-batch strategy guided by K-means clustering to improve the efficiency and convergence of GWO by selecting representative subsets of data, and (3) an enhanced regulation function integrated into GWO that prevents premature convergence by improving the balance between exploration and exploitation. A convergence study is conducted to demonstrate the influence of mini-batch size on both classification accuracy and computational efficiency, showing that using mini-batches as small as 10% of the training data significantly improves computational efficiency without compromising classification accuracy. The K-GWO-SVM framework is evaluated on two benchmark datasets: WESAD for emotion recognition and MIT-BIH Arrhythmia for cardiac classification. The proposed model achieves 99.02% accuracy on WESAD with over a 90% reduction in computational time (10% mini-batch), and 100% accuracy on MIT-BIH with over a 50% reduction in computational time (50% mini-batch), validating its effectiveness, robustness, and suitability for deployment in resource-constrained smart healthcare environments. • Robust yet efficient K-GWO-SVM algorithm is presented for ECG signal analysis. • A novel mini-batch technique is introduced to reduce computational complexity. • K-means defines centroids to form mini-batches for faster GWO convergence. • Convergence study demonstrates mini-batch size effects on accuracy and efficiency. • Dual validation on WESAD and MIT-BIH datasets proves clinical applicability.

This paper investigates the finite-blocklength performance of reconfigurable holographic surfaces (RHS) for ultra-reliable low-latency communication (URLLC). A physics-consistent RHS model is established, and the information-theoretic dispersion is derived in closed form using the Mellin transform method, we derive a closed-form probability density function of the RHS-induced channel gain. Then, we apply second-order Taylor expansion and saddle point approximation to obtain analytical expressions for the mutual information and unconditional information variance, which explicitly capture the amplitude-induced anisotropic variance. Finally, leveraging the Berry–Esseen theorem, the closed-form achievability and converse bounds are established which quantify the impact of RHS design parameters, blocklengthn, and average error probability ϵ on the rate. Analytical and simulation results demonstrate that RHS reduces the variance of the effective channel power by 35–50% in required blocklength compared with reconfigurable intelligent surfaces (RIS) under identical resource budgets. The findings identify RHS as a physically scalable and mathematically tractable architecture for short-packet 6G communication.

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.

The integration of the Internet of Things (IoT) with Federated Learning (FL) offers a transformative approach to addressing the challenges of massive data processing and privacy preservation in distributed systems. As a decentralized machine learning paradigm, FL enables model training on distributed datasets while safeguarding data privacy, making it well-suited for IoT applications. However, the performance of wireless FL systems is often constrained by limited communication resources and the mobility of participating clients, which can disrupt efficient model training and convergence. In this paper, we propose a novel mobility-aware FL scheduling strategy that leverages interpretable machine learning to enhance resource allocation in wireless networks. A more effective and fair resource allocation strategy can be achieved by dynamically adjusting the weight of the model quality and the communication quality of the training participants. We evaluate the proposed strategy against traditional scheduling methods in both single and multi-base station scenarios. Simulation results reveal that our approach significantly enhances overall learning efficiency by prioritizing high-value local models. Furthermore, for mobile clients, we identify an optimal range of average speed and participant numbers that maximizes the performance of wireless FL systems, offering practical insights for real-world deployments.

Wireless time-sensitive networking (WTSN) is essential for Industrial Internet of Things. We address the problem of minimizing time slots needed for WTSN transmissions while ensuring reliability subject to interference constraints—an NP-hard task. Existing semidefinite programming (SDP) methods can relax and solve the problem but suffer from high polynomial complexity. We propose a sparse interference graph-aided SDP (SIG-SDP) framework that exploits the interference’s sparsity arising from attenuated signals between distant user pairs. First, the framework utilizes the sparsity to establish the upper and lower bounds of the minimum number of slots and uses binary search to locate the minimum within the bounds. Here, for each searched slot number, the framework optimizes a positive semidefinite (PSD) matrix indicating how likely user pairs share the same slot, and the constraint feasibility with the optimized PSD matrix further refines the slot search range. Second, the framework designs a matrix multiplicative weights (MMW) algorithm that accelerates the optimization, achieved by only sparsely adjusting interfering user pairs’ elements in the PSD matrix while skipping the non-interfering pairs. We also design an online architecture to deploy the framework to adjust slot assignments based on real-time interference measurements. Simulations show that the SIG-SDP framework converges in near-linear complexity and is highly scalable to large networks. The framework minimizes the number of slots with up to 10 times faster computation and up to 100 times lower packet loss rates than compared methods. The online architecture demonstrates how the algorithm complexity impacts dynamic networks’ performance.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Atrial fibrillation (AF) is a prevalent cardiac arrhythmia that poses a serious threat to patients' cardiovascular health. Deep learning-based single-lead wearable ECG devices have been widely studied and shown satisfactory performance in early AF detection. However, most existing models increase complexity by employing deep networks and feature fusion techniques to enhance robustness and generalization. Despite several model compression techniques also have been applied to reduce model parameters by compromising certain aspects of performance. In this study, to strike a balance between model performance and complexity, we proposed a Piecewise Aggregate Approximation(PAA)-based lightweight feature fusion schema in the proposed KAN-based Network, with an improved connected layer from Kolmogorov-Arnold Network(KAN) to replace the fully connected layer. Our innovation leveraged the learnable nonlinear activation functions of the KAN Layer to enhance the complex high-dimensional representations at our network's output. Additionally, to reduce the number of parameters generated by feature fusion and the KAN Layer, we applied large-scale dimensionality reduction to the ECG signals using PAA as the secondary feature, allowing the lowdimensional features to represent part of the high-dimensional features through sequence-level fusion. Testing on the PhysioNet2017 dataset demonstrates that our method outperformed the benchmark, achieving a$\mathbf{2. 2 \%}$improvement in the F1 score for AF detection while reducing parameters by$13 \%$compared to traditional feature fusion.

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.

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.

Inter-carrier interference (ICI) caused by mobile reflectors significantly degrades the conventional orthogonal frequency division multiplexing (OFDM) performance in high-mobility environments. The orthogonal time frequency space (OTFS) modulation system effectively represents ICI in the delay-Doppler domain, thus significantly outperforming OFDM. Existing iterative and neural network (NN) based OTFS detectors suffer from high complex matrix operations and performance degradation in untrained environments, where the real wireless channel does not match the one used in the training, which often happens in real wireless networks. In this paper, we propose to embed the prior knowledge of interference extracted from the estimated channel state information (CSI) as a directed graph into a decoder untrained neural network (DUNN), namely graph-based DUNN (GDUNN). We then combine it with Bayesian parallel interference cancellation (BPIC) for OTFS symbol detection, resulting in GDUNN-BPIC. Simulation results show that the proposed GDUNN-BPIC outperforms state-of-the-art OTFS detectors under imperfect CSI.

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.

The O-RAN architecture standardized by the O-RAN Alliance does not support the latency requirement of real-time fifth-generation (5G) edge intelligence, typically at a msec level. In this paper, we proposed a deep reinforcement learning (DRL) packet scheduler framework to manage users with different quality of service (QoS) requirements. The DRL framework uses an advantage actor-critic (A2C) algorithm, referred to as a QoS-A2C scheduler. The developed QoS-A2C scheduler is then proposed as an O-RAN real-time App at the edge network. Simulation results show that the latency of the App is at$\mu\sec$level. It also improves the QoS satisfaction level by more than 50% compared to other DRL-based scheduler schemes.

This paper studies the early hybrid automatic repeat request (E-HARQ) in the 5G new radio (NR). The earliest ARQ feedback to the transmitter within 0.2 msec, suitable for ultra-reliable-low-latency (URLLC) services, happens when the feedback indicates retransmission or a new data request is sent before the decoding process. In this case, the feedback is based on predicting the decoding outcome of the codeword bits sent as symbols with a specific modulation and coding scheme (MCS). Existing state-of-the-art neural network-based E-HARQ predictor exploits log-likelihood-ratio (LLR), calculated by the symbol detector, to predict ARQ feedback. They also did not include MCS as an input, so individual predictors are needed for different MCSs. This paper proposes a single NN-based E-HARQ predictor for different MCSs. The predictor has a single hidden layer, and it uses the channel estimates, the MCS information, the redundancy versions, and the approximate probability distribution function of LLRs at the receiver as inputs to predict decoding outcomes. Simulation results show that the proposed predictor reduces the latency of existing NN-based E-HARQ predictors and traditional HARQ by 46% and 60%, respectively. Its complexity is shown to be, on average, 99% lower than other predictors.

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.

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.

In this paper, we establish a task-oriented cross-system design framework to minimize the required packet rate for timely and accurate modeling of a real-world robotic arm in the Metaverse, where sensing, communication, prediction, control, and rendering are considered. To optimize a scheduling policy and prediction horizons, we design a Constraint Proximal Policy Optimization (C-PPO) algorithm by integrating domain knowledge from relevant systems into the advanced reinforcement learning algorithm, Proximal Policy Optimization (PPO). Specifically, the Jacobian matrix for analyzing the motion of the robotic arm is included in the state of the C-PPO algorithm, and the Conditional Value-at-Risk (CVaR) of the state-value function characterizing the long-term modeling error is adopted in the constraint. Besides, the policy is represented by a two-branch neural network determining the scheduling policy and the prediction horizons, respectively. To evaluate our algorithm, we build a prototype including a real-world robotic arm and its digital model in the Metaverse. The experimental results indicate that domain knowledge helps to reduce the convergence time and the required packet rate by up to 50%, and the cross-system design framework outperforms a baseline framework in terms of the required packet rate and the tail distribution of the modeling error.

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.

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 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$L$.

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.

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 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).

Restricted access window (RAW) in Wi-Fi 802.11ah networks manages contention and interference by grouping users and allocating periodic time slots for each group’s transmissions. We will find the optimal user grouping decisions in RAW to maximize the network’s worst-case user throughput. We review existing user grouping approaches and highlight their performance limitations in the above problem. We propose formulating user grouping as a graph construction problem where vertices represent users and edge weights indicate the contention and interference. This formulation leverages the graph’s max cut to group users and optimizes edge weights to construct the optimal graph whose max cut yields the optimal grouping decisions. To achieve this optimal graph construction, we design an actor-critic graph representation learning (AC-GRL) algorithm. Specifically, the actor neural network (NN) is trained to estimate the optimal graph’s edge weights using path losses between users and access points. A graph cut procedure uses semidefinite programming to solve the max cut efficiently and return the grouping decisions for the given weights. The critic NN approximates user throughput achieved by the above-returned decisions and is used to improve the actor. Additionally, we present an architecture that uses the online-measured throughput and path losses to fine-tune the decisions in response to changes in user populations and their locations. Simulations show that our methods achieve$30\%\sim80\%$higher worst-case user throughput than the existing approaches and that the proposed architecture can further improve the worst-case user throughput by$5\%\sim30\%$while ensuring timely updates of grouping decisions.

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.

This paper considers opportunistic scheduler (OS) design using statistical channel state information (CSI). We apply max-weight schedulers (MWSs) to maximize a utility function of users’ average data rates. MWSs schedule the user with the highest weighted instantaneous data rate every time slot. Existing methods require hundreds of time slots to adjust the MWS’s weights according to the instantaneous CSI before finding the optimal weights that maximize the utility function. In contrast, our MWS design requires few slots for estimating the statistical CSI. Specifically, we formulate a weight optimization problem using the mean and variance of users’ signal-to-noise ratios (SNRs) to construct constraints bounding users’ feasible average rates. Here, the utility function is the formulated objective, and the MWS’s weights are optimization variables. We develop an iterative solver for the problem and prove that it finds the optimal weights. We also design an online architecture where the solver adaptively generates optimal weights for networks with varying mean and variance of the SNRs. Simulations show that our methods effectively require 4~10 times fewer slots to find the optimal weights and achieve$5\sim 15\%$better average rates than the existing methods.

In this paper, we consider a massive ultrareliable and low-latency communication (mURLLC) network with multiple antennas at each transmitter. We formulate a distributed beamforming and power control problem by minimizing the logarithm-based average utility of decoding error probability for the worst link over different network topologies and channels, where the policy is represented by a graph neural network (GNN). To reduce signaling overhead and computation delay for distributed inference, we first develop a GNN for mURLLC (G4U) framework, where the graph embedding of each node is updated according to its previous graph embedding. In addition, we represent the local message of each node by the amplitude and phase of its pilot signal, such that the graph convolution can be accomplished efficiently by broadcasting the pilot signals. To further reduce the overall latency, we propose the pipeline G4U (PG4U), where each node determines its policy solely based on the channel state information acquired in the previous frames. The feedforward neural networks in PG4U for graph convolution can be executed efficiently during data transmission. To train the GNNs in mURLLC where the decoding error probability is small, we develop a novel loss function based on the asymptotic expression of the GaussianQ-function. Simulation results show that G4U and PG4U are scalable to a different number of links. They can outperform the existing GNN and other policies significantly in terms of the QoS outage probability. Moreover, PG4U is suitable for mURLLC networks with short frame durations and highly correlated channels, while G4U is suitable for moderate frame durations with low channel correlation coefficients.

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.

This paper investigates the maximal achievable rate for a given maximal error probability, and blocklength for the reconfigurable intelligent surface (RIS) assisted multiple-input and multiple-output (MIMO) system. The result consists of a finite blocklength and finite alphabet constraints channel coding achievability and converse bounds based on the Berry-Esseen theorem, the Mellin transform and the closed-form expression of the mutual information and the unconditional variance. The numerical evaluation shows a fast speed of convergence to the maximal achievable rate as the blocklength increases and also proves that the channel variance is a sound measurement of the backoff from the maximal achievable rate due to finite blocklength.

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.

In this article, we consider a federated learning (FL) problem over wireless channel that takes into account the coding rate and packet transmission errors. Communication channels are modeled as packet erasure channels (PECs), where the probability of erasure is determined by block length, code rate, and signal-to-noise ratio (SNR). In spite of fluctuations in instantaneous loss of FL, we prove that the expectation of loss converges even in the presence of packet erasure. To mitigate the impact of packet erasure on FL performance, we suggest a paradigm in which the central node (CN) makes use of memory. In particular, we propose two schemes in which, in the event of packet erasure, the CN retains either the most recent local updates or the most recent global parameters. We investigate the impact of coding rate, SNR, and the CN memory on the convergence of FL. For both short- and long-packet communications, we examine a realistic scenario of a massive IoT under the assumption of error-prone transmissions. Our simulation results demonstrate that even a single memory unit has a considerable effect on the FL’s efficiency in erroneous communication.

This article analyzes the maximal achievable rate for a given blocklength and maximal error probability over a multiple-antenna ambient backscatter channel. The result consists of a finite blocklength channel coding achievability bound and a converse bound for the legacy system with finite alphabet constraints and multiple-input-multiple-output based on the Neyman–Pearson test, the Berry–Esseen theorem, and the Mellin transform. Then, we derive the closed-form expression of the mutual information and the information variance to reduce the complexity of the computation. By applying the low-complexity maximum-likelihood detection, the relation between the maximal error probability of the RF source signal and the average error probability of the tag symbol with respect to the blocklength is proposed. Finally, numerical evaluation of these bounds shows fast convergence to the maximal achievable rate as the blocklength increases and also proves that the information variance is an accurate measure of the backoff from the maximal achievable rate due to finite blocklength.

Emotion recognition or detection is broadly utilized in patient–doctor interactions for diseases, such as schizophrenia and autism and the most typical techniques are speech detection and facial recognition. However, features extracted from these behavior-based emotion recognitions are not reliable since humans can disguise their emotions. Recording voices or tracking facial expressions for a long term is also not efficient. Therefore, our aim is to find a reliable and efficient emotion recognition scheme, which can be used for nonbehavior-based emotion recognition in real time. This can be solved by implementing a single-channel electrocardiogram (ECG)-based emotion recognition scheme in a lightweight embedded system. However, existing schemes have relatively low accuracy. For instance, the accuracy is about 82.78% by using a least squares support vector machine (SVM). Therefore, we propose a reliable and efficient emotion recognition scheme—exploitative and explorative gray wolf optimizer-based SVM (X-GWO-SVM) for ECG-based emotion recognition. Two data sets, one raw self-collected iRealcare data set, and the widely used benchmark WESAD data set are used in the X-GWO-SVM algorithm for emotion recognition. Leave-single-subject-out cross-validation yields a mean accuracy of 93.37% for the iRealcare data set and a mean accuracy of 95.93% for the WESAD data set. This work demonstrates that the X-GWO-SVM algorithm can be used for emotion recognition and the algorithm exhibits superior performance in reliability compared to the use of other supervised machine learning methods in earlier works. It can be implemented in a lightweight embedded system, which is much more efficient than existing solutions based on deep neural networks.

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.

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.

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.

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.

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.

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.

Multiuser massive multiple-input multiple-output (MU-MIMO) systems can be used to meet high throughput requirements of 5G and beyond networks. In an uplink MU-MIMO system, a base station is serving a large number of users, leading to a strong multi-user interference (MUI). Designing a high performance detector in the presence of a strong MUI is a challenging problem. This work proposes a novel detector based on the concepts of expectation propagation (EP) and graph neural network, referred to as the GEPNet detector, addressing the limitation of the independent Gaussian approximation in EP. The simulation results show that the proposed GEPNet detector significantly outperforms the state-of-the-art MU-MIMO detectors in strong MUI scenarios with equal number of transmit and receive antennas.

IEEE 802.11ah targets long-range IoT applications where a large number of low power wireless sensor stations are connected to an access point (AP). Rate adaptation (RA) algorithm plays a prominent role in adaptively selecting an appropriate transmission rate in order to maximize throughput in that. Minstrel RA algorithm that uses a random sampling approach has been the defacto RA in industry due to its ability to adapt rate fast in a time-varying channel and non-dependency to the air-interface design used in 802.11ah as compared to other existing RA approaches. However, the throughput performance and convergence time of the Minstrel RA algorithm are still suboptimal due to its random sampling mechanism. In this paper, we propose to improve the Minstrel RA algorithm for the 802.11ah system by designing a shallow neural networks (SNNs) module that predicts a rate input for the random sampling mechanism in Minstrel. The SNNs module is implemented in the access point and consists of several SNNs. Our simulation results in the network simulator-3 (NS-3) show that our proposed algorithm is able to improve the conventional Minstrel algorithm’s convergence time and throughput by around 3.5 times and 40%.

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.

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.

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.

Multi-user multiple-input multiple-output (MU-MIMO) systems can be used to meet high throughput requirements of 5G and beyond networks. A base station serves many users in an uplink MU-MIMO system, leading to a substantial multi-user interference (MUI). Designing a high-performance detector for dealing with a strong MUI is challenging. This paper analyses the performance degradation caused by the posterior distribution approximation used in the state-of-the-art message passing (MP) detectors in the presence of high MUI. We develop a graph neural network based framework to fine-tune the MP detectors’ cavity distributions and thus improve the posterior distribution approximation in the MP detectors. We then propose two novel neural network based detectors which rely on the expectation propagation (EP) and Bayesian parallel interference cancellation (BPIC), referred to as the GEPNet and GPICNet detectors, respectively. The GEPNet detector maximizes detection performance, while GPICNet detector balances the performance and complexity. We provide proof of the permutation equivariance property, allowing the detectors to be trained only once, even in the systems with dynamic changes of the number of users. The simulation results show that the proposed GEPNet detector performance approaches maximum likelihood performance in various configurations and GPICNet detector doubles the multiplexing gain of BPIC detector.

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.

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.

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.

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.

This paper introduces an experimental inter-slice RAN controller to allow resource isolation between slices in 4G/5G networks. The inter-slice RAN controller differs from others in the literature in that it allows radio resources in each transmission time interval (TTI) to be used by different slices, dynamically adjusted according to slice feedback to the controller. The inter-slice controller also allows different scheduling strategies to be used for different slices. The proof-of-concept is then implemented by using Software Defined Radio (SDR) and srsLTE software suite. The experimental results show the effectiveness of the RAN inter-slice controller in effectively managing radio resources for different slices.

Recently, the orthogonal time frequency space (OTFS) modulation is proposed for 6G wireless system to deal with high Doppler spread. The high Doppler spread happens when the transmitted signal is reflected towards the receiver by fast moving objects (e.g. high speed cars), which causes inter-carrier interference (ICI). Recent state-of-the-art OTFS detectors fail to achieve an acceptable bit-error-rate (BER) performance as the number of mobile reflectors increases which in turn, results in high inter-carrier-interference (ICI). In this paper, we propose a novel detector for OTFS systems, referred to as the Bayesian based parallel interference and decision statistics combining (B-PIC-DSC) OTFS detector that can achieve a high BER performance, under high ICI environments. The B-PIC-DSC OTFS detector employs the PIC and DSC schemes to iteratively cancel the interference, and the Bayesian concept to take the probability measure into the consideration when refining the transmitted symbols. Our simulation results show that in contrast to the state-of-the-art OTFS detectors, the proposed detector is able to achieve a BER of less than 10−5, when SNR is over 14 dB, under high ICI environments.

Cell-free (CF) massive multiple-input multiple-output (M-MIMO) technology plays a prominent role in the beyond fifth-generation (5G) networks. However, designing a high performance CF M-MIMO detector is a challenging task due to the presence of pilot contamination which appears when the number of pilot sequences is smaller than the number of users. This work proposes a CF M-MIMO detector referred to as CF expectation propagation (CF-EP) that incorporates the pilot contamination when calculating the posterior belief. The simulation results show that the proposed detector achieves significant improvements in terms of the bit-error rate and sum spectral efficiency performances as compared to the ones of the state-of-the-art CF detectors.

The Industrial Internet-of-Thing (IIoT) has been considered as one of the most challenging application scenarios in future wireless networks. In this paper, we investigate how to improve the overall reliability of massive IIoT networks by optimizing user association. Specifically, the decoding error probability in the physical layer and the collision probability with grant-free random access are taken into account. We first propose a centralized optimization algorithm to achieve a good balance between decoding errors and collisions. To reduce computational complexity and communication overheads of the centralized optimization algorithm, a deep neural network (DNN) is trained offline in the central server and executed by each user in a distributed manner. Our results show that the communication overheads of the distributed DNN do not increase with the number of users, and the reliability achieved by the distributed DNN is close to the centralized optimization algorithm. In addition, the distributed DNN can reduce the packet loss probability by 40% when compared with an existing policy, where each user is connected to the base station with the highest signal-to-noise ratio.

In this article, we consider the massive nonorthogonal multiple access (NOMA) with a hybrid automatic repeat request (HARQ) for short packet communications. To reduce the latency, each user can perform one retransmission provided that the previous packet was not decoded successfully. The system performance is evaluated for both coordinated and uncoordinated transmissions. We first develop a Markov model (MM) to analyze the system dynamics and characterize the packet error rate (PER) and throughput of each user in the coordinated scenario. The power levels are then optimized for two scenarios, including the power constrained and reliability constrained scenarios. A simple yet efficient dynamic cell planning is also designed for the uncoordinated scenario. Numerical results show that both coordinated and uncoordinated NOMA-HARQ with a limited number of retransmissions can achieve the desired level of reliability with the guaranteed latency using a proper power control strategy. The results also show that NOMA-HARQ achieves a higher throughput compared to the orthogonal multiple access scheme with HARQ under the same average received power constraint at the base station.

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 develop a knowledge-assisted deep reinforcement learning (DRL) algorithm to design wireless schedulers in the fifth-generation (5G) cellular networks with time-sensitive traffic. Since the scheduling policy is a deterministic mapping from channel and queue states to scheduling actions, it can be optimized by using deep deterministic policy gradient (DDPG). We show that a straightforward implementation of DDPG converges slowly, has a poor quality-of-service (QoS) performance, and cannot be implemented in real-world 5G systems, which are non-stationary in general. To address these issues, we propose a theoretical DRL framework, where theoretical models from wireless communications are used to formulate a Markov decision process in DRL. To reduce the convergence time and improve the QoS of each user, we design a knowledge-assisted DDPG (K-DDPG) that exploits expert knowledge of the scheduler design problem, such as the knowledge of the QoS, the target scheduling policy, and the importance of each training sample, determined by the approximation error of the value function and the number of packet losses. Furthermore, we develop an architecture for online training and inference, where K-DDPG initializes the scheduler off-line and then fine-tunes the scheduler online to handle the mismatch between off-line simulations and non-stationary real-world systems. Simulation results show that our approach reduces the convergence time of DDPG significantly and achieves better QoS than existing schedulers (reducing 30% ~ 50% packet losses). Experimental results show that with off-line initialization, our approach achieves better initial QoS than random initialization and the online fine-tuning converges in few minutes.

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.

The stringent requirements on reliability and processing delay in the fifth-generation (5G) cellular networks introduce considerable challenges in the design of massive multiple-input-multiple-output (M-MIMO) receivers. The two main components of an M-MIMO receiver are a detector and a decoder. To improve the trade-off between reliability and complexity, a Bayesian concept has been considered as a promising approach that enhances classical detectors, e.g. minimum-mean-square-error detector. This work proposes an iterative M-MIMO detector based on a Bayesian framework, a parallel interference cancellation scheme, and a decision statistics combining concept. We then develop a high performance M-MIMO receiver, integrating the proposed detector with a low complexity sequential decoding for polar codes. Simulation results of the proposed detector show a significant performance gain compared to other low complexity detectors. Furthermore, the proposed M-MIMO receiver with sequential decoding ensures one order magnitude lower complexity compared to a receiver with stack successive cancellation decoding for polar codes from the 5G New Radio standard.

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 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.

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.

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.

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.

Much stringent reliability and processing latency requirements in ultra-reliable-low-latency-communication (URLLC) traffic make the design of linear massive multiple-input-multiple-output (M-MIMO) receivers becomes very challenging. Recently, Bayesian concept has been used to increase the detection reliability in minimum-mean-square-error (MMSE) linear receivers. However, the latency processing time is a major concern due to the exponential complexity of matrix inversion operations in MMSE schemes. This paper proposes an iterative M-MIMO receiver that is developed by using a Bayesian concept and a parallel interference cancellation (PIC) scheme, referred to as a linear Bayesian learning (LBL) receiver. PIC has a linear complexity as it uses a combination of maximum ratio combining (MRC) and decision statistic combining (DSC) schemes to avoid matrix inversion operations. Simulation results show that the bit-error-rate (BER) and latency processing performances of the proposed receiver outperform the ones of MMSE and best Bayesian-based receivers by minimum 2 dB and 19 times for various M-MIMO system configurations.

Pilot contamination (PC) interference causes an inaccurate user equipment's (UE) channel estimations and significant signal-to-interference ratio (SINR) degradations. To combat the PC effect and to maximize network spectral efficiency, pilot allocation can be combined with multi-Base Station (BS) association and then solved by using learning algorithm efficiently. However, current methods separate the pilot allocation and multi-BS association in the network. This results in suboptimal network spectral efficiency performance and can cause an outage where some UEs are not allocated pilots due to the limited availability of pilots at each BS. In this paper, we propose a multi-BS association and pilot allocation optimization via pursuit learning. Here, we design a parallel pursuit learning algorithm that decomposes the optimization function into smaller entities called learning automata. Each learning automaton computes the joint pilot allocation and BS association solution in parallel, by using the reward from the environment. Simulation results show that our scheme outperforms the existing schemes and does not cause an outage.

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.

The current Downlink Shared Channel (DLSCH) resource scheduler for cellular networks has the following features: 1) it is integrated with an evolved NodeB (eNB) and 2) uses proprietary interfaces. The first causes a temporary outage whenever the scheduler logic is reprogrammed to accommodate traffic profiles that have different requirements, while the latter prevents multi-vendor interoperability. In this paper, we propose a real-time vendor-neutral programmable DLSCH scheduler architecture. The scheduler and eNB are separated into two binary files that communicate via an agent. The agent uses standard interfaces to interpret information from/to different eNB vendors in real time. The proposed architecture is implemented on two open source 3rd Generation Partnership Project standard-compliant eNB stacks from the OAI and SRS. Experimental results show that the proposed architecture addresses the real time and proprietary challenges mentioned above.

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.

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.

The problem of designing polar-like codes for successive cancellation list (SCL) decoding algorithm is considered. A novel code design algorithm aimed to minimize both successive cancellation (SC) and maximum-likelihood (ML) decoding error probabilities is introduced. The algorithm performs optimization of a precoding matrix for the polarization transformation matrix to guarantee preselected minimum distance and the lowest possible SC decoding error probability to the resulting code. Constructed precoded polar codes are decoded as polar codes with dynamic frozen bits. Numerical results show that the proposed codes of lengths 32 and 64 significantly reduce the frame error rate (FER) compared to the original polar codes under the SCL decoding starting with the list size 4. The gain increases with the list size. The proposed codes have a much lower SC decoding error probability than extended Bose-Chaudhuri-Hocquenghem (e-BCH) codes and their polar subcodes without sacrificing the ML performance. In case of the code length 64, the constructed precoded polar codes demonstrate a FER reduction compared to the polar subcodes of e-BCH codes under the SCL decoding with the list size up to 16 and demonstrate a comparable FER starting with the list size 32.

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%.

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.

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).

Multi-tenant multi-antenna base stations (MBS) allow multiple vertical industries or operators, acting as tenants, to run their networks on a single BS. The current implementation assumes spectrum orthogonality to ensure tenant network isolation. We propose an MBS without spectrum orthogonality requirement. A new duality concept is developed to allocate precoding weights and transmit power for MBS so that the minimum isolation level from interference between tenants is maximised. An MBS prototype is built and the over-the-air experiments show it provides a higher minimum isolation level and network capacity than other known algorithms.

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.

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.

As cyber attacks to Industrial Internet of Things (IIoT) remain a major challenge, blockchain has emerged as a promising technology for IIoT security due to its decentralization and immutability characteristics. Existing blockchain designs, however, introduce high computational complexity and latency challenges which are unsuitable for IIoT. This paper proposes Xyreum, a new high-performance and scalable blockchain for enhanced IIoT security and privacy. Xyreum uses a Time-based Zero-Knowledge Proof of Knowledge (T-ZKPK) with authenticated encryption to perform Mutual Multi-Factor Authentication (MMFA). T-ZKPK properties are also used to support Key Establishment (KE) for securing transactions. Our approach for reaching consensus, which is a blockchain group decision-making process, is based on lightweight cryptographic algorithms. We evaluate our scheme with respect to security, privacy, and performance, and the results show that, compared with existing relevant blockchain solutions, our scheme is secure, privacy-preserving, and achieves a significant decrease in computation complexity and latency performance with high scalability. Furthermore, we explain how to use our scheme to strengthen the security of the REMME protocol, a blockchain-based security protocol deployed in several application domains.

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.

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 enable ultrareliable low-latency wireless communications required in the Industrial Internet of Things, in this paper we develop an energy-based modulation [i.e., non-negative pulse amplitude modulation (PAM)] constellation design framework for noncoherent detection in massive single-input multiple-output (SIMO) systems. We consider that one single-antenna transmitter communicates to a receiver with a large number of antennas over a Rayleigh fading channel, and the receiver decodes the transmitted information at the end of every symbol. For such an SIMO system with non-negative PAM modulation, we first propose a fast noncoherent maximum-likelihood decoding algorithm and derive a closed-form expression of its symbol error probability (SEP). We then enhance the system energy efficiency by finding the optimal PAM constellation that minimizes the exact SEP subject to a total signal power constraint for such a system with an arbitrary number of receiver antennas, signal-to-noise ratio (SNR), and constellation size. Furthermore, the closed-form upper and lower bounds on the optimal SEP are derived. Based on these bounds, the exact expression for coding gain of the dominant term of the SEP is presented for such an optimal massive SIMO system. We also present an asymptotic SEP expression at a high SNR regime and the approximate diversity gain of the system. Simulation results for the proposed optimal PAM constellation validate the theoretical analysis, and show that our presented optimal constellation attains significant performance gains over the currently available minimum-distance-based constellation systems.

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.

As one of the key enabling technologies of emerging smart societies and industries (i.e., industry 4.0), the Internet of Things (IoT) has evolved significantly in both the technologies and applications. It is estimated that more than 25 billion devices will be connected by wireless IoT networks by 2020. In addition to ubiquitous connectivity, many envisioned applications of the IoT, such as industrial automation, vehicle-to-everything (V2X) networks, smart grids, and remote surgery, will have stringent transmission latency and reliability requirements, which may not be supported by the existing systems. Thus, there is an urgent need for rethinking the entire communication protocol stack for wireless IoT networks.

As one of the key enabling technologies of emerging smart societies and industries (i.e., industry 4.0), the Internet of Things (IoT) has evolved significantly in both technologies and applications. It is estimated that more than 25 billion devices will be connected by wireless IoT networks by 2020. In addition to ubiquitous connectivity, many envisioned applications of IoT, such as industrial automation, vehicle-to-everything (V2X) networks, smart grids, and remote surgery, will have stringent transmission latency and reliability requirements, which may not be supported by existing systems. Thus, there is an urgent need for rethinking the entire communication protocol stack for wireless IoT networks. In this tutorial paper, we review the various application scenarios, fundamental performance limits, and potential technical solutions for high-reliability and low-latency (HRLL) wireless IoT networks. We discuss physical, MAC (medium access control), and network layers of wireless IoT networks, which all have significant impacts on latency and reliability. For the physical layer, we discuss the fundamental information-theoretic limits for HRLL communications, and then we also introduce a frame structure and preamble design for HRLL communications. Then practical channel codes with finite block length are reviewed. For the MAC layer, we first discuss optimized spectrum and power resource management schemes and then recently proposed grant-free schemes are discussed. For the network layer, we discuss the optimized network structure (traffic dispersion and network densification), the optimal traffic allocation schemes and network coding schemes to minimize latency.

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.

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.

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.

The Energy Internet is an advanced smart grid solution to increase energy efficiency by jointly operating multiple energy resources via the Internet. However, such an increasing integration of energy resources requires secure and efficient communication in the Energy Internet. To address such a requirement, we propose a new secure key bootstrapping protocol to support the integration and operation of energy resources. By using a universal composability model that provides a strong security notion for designing and analyzing cryptographic protocols, we define an ideal functionality that supports several cryptographic primitives used in this paper. Furthermore, we provide an ideal functionality for key bootstrapping and secure communication, which allows exchanged session keys to be used for secure communication in an ideal manner. We propose the first secure key bootstrapping protocol that enables a user to verify the identities of other users before key bootstrapping. We also present a secure communication protocol for unicast and multicast communications. The ideal functionalities help in the design and analysis of the proposed protocols. We perform some experiments to validate the performance of our protocols, and the results show that our protocols are superior to the existing related protocols and are suitable for the Energy Internet. As a proof of concept, we apply our functionalities to a practical key bootstrapping protocol, namely generic bootstrapping architecture.

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.

We consider the non-orthogonal multiple access (NOMA) design for a classical two-user multiple access channel (MAC) with finite-alphabet inputs. In contrast to the majority of existing NOMA schemes using continuous Gaussian distributed inputs, we consider practical quadrature amplitude modulation (QAM) constellations at both transmitters, whose sizes are not necessarily the same. By adjusting the scaling factors (i.e., instantaneous transmitting powers) of both users, we aim to maximize the minimum Euclidean distance of the received sum-constellation for a maximum likelihood (ML) receiver. The formulated problem is a mixed continuous-discrete optimization problem and in general it is nontrivial to resolve. By carefully examining the structure of the objective function, we discover that Farey sequence can be employed to tackle the formulated problem. However, the existing Farey sequence is not applicable when the constellation sizes of the two users are different. To address this challenge, we define a new type of Farey sequence, termed punched Farey sequence. Based on this new definition and its properties, we manage to attain a closed-form optimal solution to the original problem by first dividing the entire feasible region into a finite number of Farey intervals and then taking the maximum over all the subintervals. Finally, computer simulations are carried out to verify our theoretical analysis, and to demonstrate the advantages of the proposed NOMA over known orthogonal and non-orthogonal designs.

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.

Multi-tenant cellular networks for Internet-of-Things (IoT) services is an architecture in which a single wireless cellular network is tenanted by multiple large-scale IoT sectors such as energy and transportation. Thus, a challenging issue is how to efficiently allocate the spectrum resources to serve numerous IoT tenants with vastly different traffic load distributions. In this paper, we address this problem by proposing a spectrum sharing model, based on a queuing system, that can be used to model any spectrum sharing policy between various tenants and derive analytical expressions for the blocking probability and spectrum utilisation. The simulation and analytical results, generated by using a real-time traffic, match very well.

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.

Recent findings show that many mission critical Internet of Things (IoT) applications are exposed to increasing security risks. The complex and dynamic nature of the IoT and its applications also bring new types of security threats. To achieve end-to-end secure communication, IoT applications need key establishment schemes with integrated security fundamentals such as identification and authentication of IoT components, as well as integrity, confidentiality, availability and authenticity of data, to prevent security attacks from weakening and disrupting the communication. In this context, we present a new lightweight key establishment scheme that comprises a novel Identity-Based Credentials (IBC) mechanism and key establishment protocol. The IBC mechanism enables an IoT component to securely disclose a single identity for security support and privacy enhancement for key establishment. In this paper, we model IoT application attributes to develop our lightweight security and privacy enhancing key establishment scheme. The formal verification and analysis show that, compared to the existing schemes, our proposed scheme is resilient against more types of security attacks, and incurs lower computational and communication costs in IoT applications.

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.

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.

A social network is a social structure made up of a set of social actors and a set of dyadic ties between these actors. The actors form a number of communities. In communities, some actors want to transmit their information to all other actors. Each actor corresponds to a user equipment (UE). The UE of the actor which has information to be transmitted is also called the source, and the UEs of all other actors are called destinations. The information is transmitted from the source to destinations with the assistance of helpers, which can be small cell base stations (SCBSs). A novel information dissemination approach, namely expanded network coding with a finite buffer size (ENCFB), is proposed for the case when the buffer size of helpers is limited. The performance comparison of the uncoded information dissemination approach, the network coded approach and the ENCFB approach is conducted. Comparison results show that the ENCFB approach can significantly improve the performance of information dissemination when the buffer size is limited.

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.

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.

This paper focuses on the non-orthogonal multiple access (NOMA) design for a classical two-user multiple access channel (MAC) with finite-alphabet inputs. In contrast to most of the existing NOMA designs using continuous Gaussian input distributions, we consider practical quadrature amplitude modulation (QAM) constellations at both transmitters, the sizes of which are assumed to be not necessarily identical. We propose maximizing the minimum Euclidean distance of the received sum constellation with a maximum likelihood (ML) detector by adjusting the scaling factors (i.e., instantaneous transmitted powers and phases) of both users. The formulated problem is a mixed continuous-discrete optimization problem, which is nontrivial to resolve in general. By carefully observing the structure of the objective function, we define a new type of Farey sequence, termed punched Farey sequence to tackle the formulated problem. Based on this, we manage to achieve a closed-form optimal solution to the original problem by first dividing the entire feasible region into a finite number of Farey intervals and then taking the maximum over all possible intervals. The resulting sum constellation is proved to be a regular QAM constellation of a larger size, and hence, a simple quantization receiver can be implemented as the ML detector for the demodulation. Moreover, the superiority of NOMA over time-division multiple access in terms of minimum Euclidean distance is rigorously proved. We subsequently address how to extend our design framework intended for the two-user MAC to systems with multiple users and multiple antennas. Finally, simulation results are provided to verify our theoretical analysis and demonstrate the merits of the proposed NOMA over existing orthogonal and non-orthogonal designs.

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.

Multi-tenant BS (MBS) is a new architecture to improve network capacity in which a single BS is tenanted to multiple operators. MBS allows multiple tenants to transmit from a single BS by using their own spectrum resources, resulting in a closer proximity to UEs. In this paper, we propose a UEs to MBSs association scheme, executed in the centralised controller of wireless networks. Each UE can be associated with more than one MBS. We first formulate an optimisation problem that maximises the spectral efficiency of a MBS network with UEs to MBSs associations as its optimisation variables. To solve it, we develop an optimisation solver based on a pursuit learning technique where we model each optimisation variable as a learning automaton. The automata system is executed at the centralised controller where each automaton computes the UE to MBS association in parallel. This results in an extremely low computational complexity and high scalability as compared to other existing schemes. Furthermore, simulation results show that the MBS network has significantly higher spectral efficiency when compared to a single tenant BS network.

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.

This paper considers a user association (UA) design for the base station (BS) and subcarrier (SC) allocation where a BS allocates different number of SCs to different users associated to it in a downlink heterogeneous cellular network. In jointly optimising the UA and SC allocation, we propose to use the Sharpe Ratio as the utility function for the optimisation objective. The Sharpe ratio is defined as the ratio between the mean of user achievable rates to its standard deviation. With this objective, the achieved user rates will be closer to each other, leading to a fair network access. To reduce the computational complexity of the solution, a simplified method based on binary Belief Propagation (BP) algorithm is proposed. Simulation results show that the achievable user rates are doubled in comparison with other schemes. The low computational complexity of the proposed method is achieved through BP solver by reducing the edges of the factor graph.

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.

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.

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.

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, 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, a novel distributed caching strategy in mobile social networks based on device-to-device communications is proposed. The proposed approach combines the characters of social networks to handle some practical issues, e.g., the selfishness of users. In order to maximize the throughput of the whole system, a fast convergence learning automaton, called the discrete generalized pursuit algorithm is utilized. Incorporating with social characters, the algorithm not only optimizes the content placement problems in caching theory, but also satisfies the physical and social constraints appropriately. Simulation results show that, compared with other investigated caching strategies, the proposed algorithm has higher convergence speed and at the same time, it can reduce the transmission delay and improve the system throughput. Moreover, the proposed algorithm can get a better performance in higher density district.

Evidence indicates that downloading on-demand videos accounts for a dramatic increase in data traffic over cellular networks. Caching popular videos in the storage of small-cell base stations (SBS), namely, small-cell caching, is an efficient technology for reducing the transmission latency while mitigating the redundant transmissions of popular videos over back-haul channels. In this paper, we consider a commercialized small-cell caching system consisting of a network service provider (NSP), several video retailers (VRs), and mobile users (MUs). The NSP leases its SBSs to the VRs for the purpose of making profits, and the VRs, after storing popular videos in the rented SBSs, can provide faster local video transmissions to the MUs, thereby gaining more profits. We conceive this system within the framework of Stackelberg game by treating the SBSs as specific types of resources. We first model the MUs and SBSs as two independent Poisson point processes, and develop, via stochastic geometry theory, the probability of the specific event that an MU obtains the video of its choice directly from the memory of an SBS. Then, based on the probability derived, we formulate a Stackelberg game to jointly maximize the average profit of both the NSP and the VRs. In addition, we investigate the Stackelberg equilibrium by solving a non-convex optimization problem. With the aid of this game theoretic framework, we shed light on the relationship between four important factors: the optimal pricing of leasing an SBS, the SBSs allocation among the VRs, the storage size of the SBSs, and the popularity distribution of the VRs. Monte Carlo simulations show that our stochastic geometry-based analytical results closely match the empirical ones. Numerical results are also provided for quantifying the proposed game-theoretic framework by showing its efficiency on pricing and resource allocation.

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, we propose adaptive techniques for multiuser multiple-input and multiple-output (MU-MIMO) cellular communication systems, to solve the problem of energy efficient communications with heterogeneous delay-aware traffic. In order to minimize the total transmission power of the MU-MIMO, we investigate the relationship between the transmission power and the M-ary quadrature amplitude modulation (MQAM) constellation size and get the energy efficient modulation for each transmission stream based on the minimum mean square error (MMSE) receiver. Since the total power consumption is different for MU-MIMO and multiuser single input and multiple output (MU-SIMO), by exploiting the intrinsic relationship among the total power consumption model, and heterogeneous delay-aware services, we propose an adaptive transmission strategy, which is a switching between MU-MIMO and MU-SIMO. Simulations show that in order to maximize the energy efficiency and consider different Quality of Service (QoS) of delay for the users simultaneously, the users should adaptively choose the constellation size for each stream as well as the transmission mode.

Cloud-based small cell networks (C-SCNs) have recently been proposed as new wireless cellular architecture. In cloud-based networks, optimization of radio resources at the base station (BS) is moved to a cloud data center for centralized optimization. In the center, multiple processors referred to as the cloud computational unit (CCU) are used for the optimization. As the cell size and networks become, respectively, smaller and denser, the number of BSs to be optimized grows exponentially, resulting in high computational complexity and latency at CCUs. In this paper, we propose belief propagation-based power allocation schemes for C-SCNs that can be used for any network optimization objectives, such as energy consumption minimization at the data center and BSs, and spectral efficiency. The computation for the schemes is distributed across multiple processors and done in parallel, leading to very low latency and computational complexity with increasing number of BSs. We prove mathematically that the messages of the proposed algorithms converge to a fixed point and show via simulation that their performances in terms of spectral and energy efficiencies are close to an exhaustive search solution in finding the best configuration.

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.

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.

Wireless Network Virtualisation (WNV) is a promising approach to address the explosive traffic growth in future mobile networks. In the WNV, the physical spectrum, which is owned by the Network Operator (NO), is shared among multiple Virtual Operators (VOs) according to a predefined sharing policy between the NO and the VOs. In this paper, we develop a unified analytical framework for WNV that is based on a queuing system. By using this framework, we derive analytical expressions for various performance metrics, e.g., the blocking probability, and use them to evaluate the performance of the WNV under different sharing policies. The analytical and simulations results agree very well, confirming that the framework is accurate and showing its suitability to serve as a tool to design an efficient policy for sharing the physical spectrum in the WNV.

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 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 investigate the performance of a wireless sensor network, in which multiple groups of source nodes communicate with their respective destination nodes with the help of a common relay network. A network code division multiplexing (NCDM) scheme is proposed to remove the inter-session interference among multiple transmission sessions at each destination. We focus on analyzing the soft processing algorithm of the NCDM scheme. Based on the analysis results, a new code design criteria for the construction of the generator matrix is proposed. Simulation results show that by following the proposed code design criteria, the bit error ratio (BER) performance gap between the scheme we studied and the serial session scheme can be managed effectively. In serial session scheme, source nodes in a number of groups communicate with their respective destinations in a time division manner.

We investigate the spectrum sharing problem of a radio frequency (RF)-powered cognitive radio network, where a multi-antenna secondary user (SU) harvests energy from RF signals radiated by a primary user (PU) to boost its available energy before information transmission. In this paper, we consider that both the PU and SU are rational and self-interested. Based on whether the SU helps forward the PU's information, we develop two different operation modes for the considered network, termed as non-cooperative and cooperative modes. In the non-cooperative mode, the SU harvests energy from the PU and then use its available energy to transmit its own information without generating any interference to the primary link. In the cooperative mode, the PU employs the SU to relay its information by providing monetary incentives and the SU splits its energy for forwarding the PU's information as well as transmitting its own information. Optimization problems are respectively formulated for both operation modes, which constitute a Stackelberg game with the PU as a leader and the SU as a follower. We analyze the Stackelberg game by deriving solutions to the optimization problems and the Stackelberg Equilibrium (SE) is subsequently obtained. Simulation results show that the performance of the Stackelberg game can approach that of the centralized optimization scheme when the distance between the SU and its receiver is large enough.

In this paper, we propose a novel network coded non-binary low-density generator matrix (LDGM) code structure for a multi-access relay system, where multiple sources transmit lattice signals to a destination with the help of a relay. Specifically, we first develop a network coded non-binary LDGM code structure by jointly considering lattice-signal transmissions at the sources and the relay. Then we derive the achievable computation rate (ACR) for the proposed system and on that basis optimize the key parameters in the proposed structure to maximize the ACR. Furthermore, we optimize the network coded non-binary LDGM codes based on lattices to approach the ACR. Simulation results show that the optimal setting of the parameters is consistent with that obtained from our analysis and the proposed code structure outperforms the designed reference scheme.

A small cell networks (SCN) concept has been widely accepted as the most efficient method to increase cellular network capacity. As the cell size and networks become smaller and denser, respectively, inter-cell interference (ICI) at a user terminal equipment (UE), coming from the adjacent base station (BS) transmissions, grows considerably and becomes more complex to manage. In this paper, we develop a distributed cooperative downlink power allocation algorithm for SCN that maximises the network sum capacity, subject to the minimum received SINR requirements at the UEs. We first formulate the power optimisation problem, with BSs transmit powers as the variables to be optimised. A factor graph representation for ICI and Belief Propagation (BP) method for the power allocation optimisation are then developed. This optimisation representation allows each BS to cooperate by exchanging messages about the estimates of the sum capacity that can satisfy minimum SINR requirements at UEs. Each BS uses this information to optimise its transmit power allocation. To reduce the overhead information that needs to be exchanged by the BSs, we allow only a subset of BSs in the network, chosen randomly, to exchange messages. The simulation results show that the network sum capacity obtained by the proposed algorithm, with only 70% randomly chosen active BSs, is close to the one obtained by using a global optimal exhaustive search method and it outperforms the best existing scheme.

Heterogeneous cellular networks (HCNs) with embedded small cells are considered, where multiple mobile users wish to download network content of different popularity. By caching data into the small-cell base stations, we will design distributed caching optimization algorithms via belief propagation (BP) for minimizing the downloading latency. First, we derive the delay-minimization objective function and formulate an optimization problem. Then, we develop a framework for modeling the underlying HCN topology with the aid of a factor graph. Furthermore, a distributed BP algorithm is proposed based on the network's factor graph. Next, we prove that a fixed point of convergence exists for our distributed BP algorithm. In order to reduce the complexity of the BP, we propose a heuristic BP algorithm. Furthermore, we evaluate the average downloading performance of our HCN for different numbers and locations of the base stations and mobile users, with the aid of stochastic geometry theory. By modeling the nodes distributions using a Poisson point process, we develop the expressions of the average factor graph degree distribution, as well as an upper bound of the outage probability for random caching schemes. We also improve the performance of random caching. Our simulations show that 1) the proposed distributed BP algorithm has a near-optimal delay performance, approaching that of the high-complexity exhaustive search method; 2) the modified BP offers a good delay performance at low communication complexity; 3) both the average degree distribution and the outage upper bound analysis relying on stochastic geometry match well with our Monte-Carlo simulations; and 4) the optimization based on the upper bound provides both a better outage and a better delay performance than the benchmarks.

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 investigate the performance of a wireless relay network with multiple transmission sessions, in which multiple groups of source nodes communicate with their respective destination nodes via a shared wireless relay network. A multiple transmission session model with network code division multiplexing (NCDM) scheme is proposed to remove the inter-session interference at each destination. The fundamental idea of the NCDM scheme takes advantage of the property of G Θ HT= 0 of the low-density generator matrix (LDGM) codes. Based on the analysis of the NCDM scheme, we investigate the relationship among the equivalent received signal vector, the number of sessions and the column weight of the generator matrix. New code design criteria for the construction of the generator matrix is proposed. We further evaluate the multiple transmission session model with the proposed NCDM scheme in terms of throughput and complexity. Our evaluation demonstrates that the proposed scheme not only has a linear computational complexity, but also shows a similar error performance in the AWGN case and a considerable throughput improvement compared with its counterpart, which is referred to as a serial session scheme, where groups of source nodes communicate with their respective destinations in a time division manner.

The most challenging issue in Machine-to-Machine (M2M) last mile wireless access networks is the management of a large number of M2M devices that generate a vast amount of M2M traffic. The communications traffic for various M2M applications or services is classified as Fixed-Scheduling (FS) or Event-Driven (ED). The FS traffic is an operational traffic, which occurs on a periodic basis, such as reports on the measured data, sent by M2M sensor devices. The ED traffic, which is assumed to have a higher priority, is triggered by occurrence of specific events, such as the traffic generated by M2M devices, due to failure in the monitored systems. To date, we have not seen any traffic model for M2M last mile wireless access networks, which incorporates the different characteristics of ED and FS traffic. Thus, in this paper, we develop an analytical traffic model for M2M last mile wireless access networks based on a priority queuing system, which considers the combination of both periodic FS and random ED traffic. By using the proposed model, we derive expressions for the mean queuing delay and blocking probability of each traffic class. The derived analytical expressions are validated by simulations of a wireless network model and are shown to agree very well.

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.

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 threshold-based one-bit soft forwarding (TOB-SF) protocol for a multi-source relaying uplink system with network coding. In the TOB-SF protocol, the relay calculates the log-likelihood ratio (LLR) value of each network coded symbol, compares this LLR value with a pre-optimized threshold, and determines whether to transmit or keep silent. We first derive the bit error rate (BER) expression at the destination, based on which, we optimize the threshold to minimize the BER. Then we theoretically prove that the system can achieve the full diversity gain by using this threshold. Further, we optimize the power allocation at the relay to achieve a higher coding gain. Simulation results show that the proposed TOB-SF protocol outperforms other conventional relaying protocols in terms of error 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 first analyse bit error rate (BER) bounds of the distributed network coding (DNC) scheme based on the Luby-transform (LT) codes, which is a class of fountain codes, for wireless sensor networks (WSNs). Then we investigate the effect from two parameters of the degree distributions, i.e., the degree value and the proportion of odd degree, to the performance of the LT-based DNC scheme. Based on the analysis and investigation results, a degree distribution design criteria is proposed for the DNC scheme based on fountain codes over Rayleigh fading channels. We compare the performance of the DNC scheme based on fountain codes using degree distributions designed in this paper with other schemes given in the literature. The comparison results show that the degree distributions designed by using the proposed criteria have better performance.

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.

In this paper, we propose an uplink channel- and buffer-aware scheduling and resource allocation algorithm for a multi-cell LTE-A network that exploits the number of bits waiting for transmission in each user's buffer, referred to as buffer length, in addition to the wireless channel state information. The algorithm also takes into account the constraints imposed by the 3GPP standards on how the radio Resource Blocks (RBs) are allocated to the users in an LTE-A uplink deploying the Single Carrier Frequency Division Multiple Access (SCFDMA) scheme. These constraints state that a RB can only be assigned to one user and that all RBs assigned to the same user should be adjacent and have the same Modulation and Coding Scheme (MCS). Simulation results show that the proposed algorithm outperforms the existing schemes by at least 35% in terms of the system throughput.

The most challenging issue in Smart Grid (SG) communications networks is the management of a vast amount of SG traffic in the access network, which connects power substations to a large number of SG monitoring devices. In this paper, we develop an analytical traffic model for SG access networks based on a priority queuing system. The SG traffic in the access network is classified as Fixed-Scheduling (FS) or Event-Driven (ED). The FS traffic is an operational traffic, which occurs on a periodic basis, such as smart meter readings. The ED traffic, which is assumed to have a higher priority, occurs as a response to electricity supply conditions, such as demand response. To date, we have not seen any traffic model for SG access networks, which incorporates the different characteristics of ED and FS traffic. By using the proposed model, we derive expressions for the mean buffer length and queuing delay of each traffic. The derived analytical expressions are validated by a wireless network model with real-world traffic profiles from the Ausgrid Smart Grid Smart City project and shown to agree well with the simulations.

Small cell networks (SCN) concept has been widely accepted as the most efficient method to increase cellular network capacity. As the cell size and networks become smaller and denser, respectively, inter-cell interference (ICI) at a user terminal equipment (UE), coming from the adjacent base station (BS) transmissions to their respective UEs, grows considerably and becomes more complex to manage. In this paper, we developed a distributed cooperative downlink power allocation algorithm for SCN that maximises the number of BSs transmissions to UEs such that the received signal-to-interference-plus-noise ratio (SINR) at the UEs is greater than a minimum SINR threshold for wireless transmissions. We first formulate the BS power allocation problem with BS transmit power as binary variables to indicate whether the BS is on or off. A factor graph representation and Belief Propagation (BP) method based on a sum-product approach for power allocation optimisation are developed. This optimisation representation allows each BS to cooperate by exchanging messages about the probability distribution of the number of BS transmissions. BS then uses this information to optimise its own transmit power allocation. To reduce the overhead information that needs to be exchanged by the BSs, we allow only a subset of randomly chosen BSs in the network to exchange messages. The simulation results show that the number of BSs transmissions obtained by the proposed algorithm is on average 5% less than the one obtained by using a global optimal exhaustive search method.

In this paper, we propose a distributed network coding (DNC) scheme based on the Raptor codes for wireless relay networks (WRNs), where a group of source nodes communicate with a single sink through a common relay network in a multi-hop fashion. At the sink, a graph-based Raptor code is formed on the fly. After receiving a sufficient number of encoded packets, the sink begins to decode. The main contributions of this paper are the derivations of upper and lower bit error rate (BER) bounds for the proposed Raptor-based DNC scheme.

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.

A dynamic resource allocation algorithm is investigated for the orthogonal frequency division multiplexing access (OFMDA) system under the network architecture of High-Speed Railway (HSR). The mobile base station (MBS) on the top of the train forwards the signal received from the base station (BS) on the ground to the user equipments (UE) in the train. Due to the high mobility in the BS-MBS link, the inter-carrier interference (ICI) caused by the Doppler shift may degrade the system performance. In this paper, we aim to combat the influence of the ICI to improve the system capacity by means of the resource allocation, which includes subcarrier allocation, subcarrier pairing and power allocation. Simulation results demonstrate that the proposed resource allocation algorithm can improve the system performance dramatically.

In this paper we investigate novel soft mutual information forwarding (MIF) protocols in a two-way relay channel (TWRC), where two sources exchange information with the help of an intermediate relay. Based on the estimated signals from the two sources, the relay calculates the soft mutual information, and then broadcasts it to the two sources. In specific, we propose two MIF protocols, namely, network coded MIF (NC-MIF) and superposition coded MIF (SC-MIF), suitable to different channel conditions. The expressions derived for the received signal-to-noise ratio (SNR) at the sources reveal that if both source-to-relay channels are in good conditions, the NC-MIF outperforms the SC-MIF. Otherwise, the SC-MIF is superior to the NC-MIF. For the TWRC with varying channels, we further develop an adaptive scheme, which enables the dynamic switch between the two protocols, depending on the received SNR at the sources. Furthermore, the threshold that determines the switch of the protocols is developed as a close-form expression. Simulation results show that our adaptive scheme outperforms all the existing relaying protocols in the fading channels.

In many applications of wireless relay networks (WRNs), such as image and video systems, unequal error protection (UEP) among the transmission data from different source nodes is required. All the source nodes in a WRN with different protection requirements form multiple protection groups. In this paper, we focus on estimating the protection parameter, i.e., the protection weight, for each protection group. We first analyze the bit error rate (BER) upper bound of the UEP distributed fountain codes over Rayleigh fading channels for WRNs. Then based on the analysis results, we derive the approximate expression for the BER upper bound with protection weight as the variable. The derived approximate expression can be used to estimate the protection weights for the protection groups according to their various performance requirements. Finally, examples on the application of the approximate expression in estimating protection weights are given.

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 this paper, we propose a threshold-based one-bit soft forwarding (TOB-SF) protocol for a multi-source relaying system with network coding, where two sources communicate with the destination with the help of a relay. Specifically in the TOB-SF protocol, the relay calculates the log-likelihood ratio (LLR) value of each network coded symbol, compares this LLR value with a pre-optimized threshold, and determines whether to transmit or keep silent. We are interested in optimizing the TOB-SF protocol in fading channels, and consider both the uncoded and low-density parity check coded systems. In the uncoded system, we first derive the bit error rate (BER) expressions at the destination, based on which, we derive the optimal threshold. Then we theoretically prove that the system can achieve the full diversity gain by using this threshold. Further, we optimize the power allocation at the relay to achieve a higher coding gain. In the coded system, we first optimize the LLR threshold. Then we develop a methodology to track the BER evolution at the destination by using Gaussian approximations. Based on the BER evolution, we further optimize the power allocation at the relay which minimizes the system BER. Simulation results show that the proposed TOB-SF protocol outperforms other conventional relaying protocols in terms of error performance.

In this paper we propose a novel power adaptive network coding (PANC) for a non-orthogonal multiple-access relay channel (MARC), where two sources transmit their information simultaneously to the destination with the help of a relay. In contrast to the conventional XOR-based network coding (CXNC), the relay in PANC generates network coded symbols by considering the coefficients of the source-to-relay channels, and forwards each symbol with a pre-optimized power level. Specifically, by defining a symbol pair as two symbols from the two sources, we first derive the expression of symbol pair error rate (SPER) for the system. Noting that deriving the exact SPER are complex due to the irregularity of the decision regions caused by random channel coefficients, we propose a coordinate transform (CT) method on the received constellation to simplify the derivations of the SPER. Next, we obtain the optimal power level by decomposing it as a multiplication of a power scaling factor and a power adaptation factor. We prove that with the power scaling factor at the relay, our PANC scheme can achieve a full diversity gain, i.e., an order of two diversity gain, while the CXNC can achieve only an order of one diversity gain. In addition, we optimize the power adaptation factor at the relay to minimize the SPER at the destination by considering of the relationship between SPER and minimum Euclidean distance of the received constellation, resulting in an improved coding gain. Simulation results show that (1) the SPER derived based on our CT method can well approximate the exact SPER with a much lower complexity; (2) the PANC scheme with power adaptation optimizations and power scaling factor design can achieve a full diversity, and obtain a much higher coding gain than other network coding schemes.

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 wireless relay networks (WRNs), multiple source nodes communicate with a single destination node through a common relay network. Different source nodes may have different error protection or recovery time requirements. In this paper, we focus on unequal error protection (UEP) distributed network coding (DNC) design for WRNs. Specifically, we propose a continuous UEP method based on fountain codes with the objective of satisfying the UEP or unequal recovery time (URT) requirements. Then based upon this UEP method, we develop an adaptive UEP DNC scheme to realize adaptive UEP in WRNs. We analyze the properties of the proposed adaptive UEP DNC scheme and derive the upper and lower bit error rate (BER) bounds for it over Rayleigh fading channels under maximum-likelihood (ML) decoding. Simulation results show that the proposed adaptive UEP DNC scheme has desirable UEP and URT properties. The transmitted data from the source nodes in different protection groups can be recovered successfully under their performance requirements in a shorter time by using the adaptive UEP DNC scheme.

Wireless communication is notoriously lossy due to channel fading, interference and multi-path effects. This work investigates the reliability of all-to-all broadcast in lossy wireless networks where the reliability is measured by the probability that every node in the network receives or decodes the native packet of every other node. To improve the reliability, a novel network coding scheme, namely random neighbour network coding (RNNC) scheme is proposed, which is capable of adaptively generating encoding packets according to the packets received from lossy wireless channels. The network reliability is analysed theoretically and the optimal RNNC scheme that maximises the reliability of a given network is obtained. The theoretical analysis is validated using simulations and it is shown that RNNC can improve the network reliability significantly.

In this paper, we propose a new compressive sensing (CS) based channel estimation method for high mobility orthogonal frequency division multiplexing (OFDM) systems. The proposed scheme offers the benefits of orthogonal matching pursuit (OMP) and subspace pursuit (SP) estimation methods combined with an inter-carrier interference (ICI) cancellation process. The proposed CS based channel estimation scheme, referred to as the hybrid pursuit (HP) based channel estimation method, operates in an iterative, decision-directed fashion. Here, in each iteration, once the channel is estimated, data symbols are detected and used to calculate the estimate of ICI, caused by the Doppler spread. After that, the ICI term is subtracted from the received signals. The whole process is then repeated, iteratively. The simulation results assess the performance gains achieved by the proposed scheme over the best known channel estimation methods.

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 practical 1-bit soft forwarding protocol for a network-coded two-way relay channel. Different from the conventional estimate-and-forward (EF) protocol, the proposed protocol forwards 1-bit soft information at the relay. We employ the joint trellis coded quantization/modulation (TCQ/M) to implement 1-bit transmission of the soft information. Also, the codebooks in the TCQ are designed to be adaptive to the source-to-relay channel conditions so that the system can achieve the full diversity gain over fading channels. Specifically, in the low source-to-relay channel SNR region, we apply the TCQ/M to the soft information based on the codebook generated by the LloydMax quantizer. In the high source-to-relay channel SNR region, where the soft information is equivalent to its hard decision, we design the codebook by repeating the soft information. It has been shown that the proposed protocol outperforms both the amplify-and-forward (AF) and the decode-and-forward (DF) protocols over fading channels.

In this paper, we propose an iterative channel estimation and inter-carrier interference (ICI) cancellation method for highly mobile users in Long-Term-Evolution (LTE) systems. The proposed scheme estimates the wireless channel by using pilot symbols, estimates of the data symbols and Doppler spread information at the receiver. The wireless channel is expressed by a weighted time-domain channel interpolation, where the interpolation weights are designed based on the Doppler spread. The channel estimates are obtained by employing a least square (LS) method. A simplified parallel interference cancellation (PIC) scheme coupled with decision statistical combining (DSC) is used to cancel the ICI and to improve the data symbols detection. These data symbols are then utilized to refine the channel estimation further, iteratively. Simulation results are used to verify the effectiveness of the proposed method.

Inter-cell interference coordination (ICIC) and resource allocation problems are fundamental challenges for the design of wireless networks. In this paper, we propose a distributed network inter-cell control scheme, and introduce a Belief Propagation (BP) framework to solve the optimization problem. The goal is to maximize the sum rate of those Base Stations (BS). This new approach assumes that the inter-cell interference is a set of stochastic variables. Based on a set of prior distributions, it calculates the posterior distributions of the scheduling variables. The solution allocates PRBs to Mobile Stations (MS) in the cells, including optimization of the transmit powers in each subcarrier. Numerical results demonstrate that this algorithm achieves a good result in typically a couple of iterations.

In this paper we propose a novel network coded LDPC code design for a multiple-access relay channel (MARC). We first investigate the achievable rate region for the MARC. Then we propose a novel physical layer network coded (PNC) LDPC code structure, named PNC-LDPC code. Next, an iterative detection-and-decoding receiver is designed to deal with the multi-user interference at the destination. Based on the code structure and the iterative receiver, we optimize the degree distribution of the PNC-LDPC code to approach the system achievable rate by utilizing the extrinsic mutual information transfer (EXIT) chart. Simulations show that the performance of our PNC-LDPC code, with a code length of 10000, at the destination, is 1:5 dB away from the capacity.

In this work we determine the achievable rate in a multi-source relaying system with Gaussian phase-fading channels. In our system, M sources simultaneously transmit their messages to a common destination in M separate frequency bands with the help of a single relay (an M − 1 − 1 system). The achievable rates of both a separate processing scheme at the relay, and a network coding scheme at the relay, are considered. For the separate processing scheme, we propose an new constrained water-filling algorithm which determines the power allocation at the relay in order to obtain the achievable rate. For the network coding scheme we derive the achievable rate based on the use of a new Galois field rate-splitting theorem, and discuss why power allocation at the relay in this scheme can be set using a traditional water-filling algorithm. We show how our network coding scheme will always obtain higher achievable rates relative to those obtained from a separate processing scheme.

In this paper, we focus on the realization of multiple interpretations (MI) in multi-way relay channels (MWRC) with fading, where multiple sources communicate with each other with the help of a relay. We first propose a novel nested convolutional lattice codes (NCLC) over the finite field, which can achieve the MI for each source in two time slots. Then we derive a theoretical upper bound for the codeword error rate (WER) of the NCLC. We further optimize our NCLC by developing a code design criterion which minimizes the derived WER. In simulations, we construct a specific NCLC based on our code design criterion. Simulation results show that our code can realize MI for each source in two time slots, and validate the derived upper bound in the high normalized signal-to-effective-noise ratio (SENRnorm) region.

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 focus on network coding design for the wireless sensor networks (WSNs), where multiple source nodes communicate with a common destination node with the help of multiple relay nodes in a two-hop fashion. Specifically, we propose an unequal error protection (UEP) distributed network-channel coding (DNCC) scheme based on Luby-transform (LT) codes. We analyse three properties of the proposed UEP DNCC scheme, i.e. effective weights, turning points, and thresholds of the source nodes' number. Also, we derive the upper and lower bit error rate (BER) bounds for the proposed UEP DNCC scheme over Rayleigh fading channels under maximum-likelihood (ML) decoding. Based on the analysis, it is observed that the proposed UEP DNCC scheme can achieve all protection levels required when the number of source nodes is large enough. Simulation results show that our UEP DNCC scheme can provide desirable UEP to all source nodes.

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.

In this paper, we propose a distributed network coding (DNC) scheme based on the Raptor codes for wireless sensor networks (WSNs), where a group of sensor nodes, acting as source nodes, communicate with a single sink through some other sensor nodes, serving as relay nodes, in a multi-hop fashion. At the sink, a graph-based Raptor code is formed on the fly. After receiving a sufficient number of encoded packets, the sink begins to decode. The main contributions of this paper are the derivation of a bit error rate (BER) lower bound for the LT-based DNC scheme over Rayleigh fading channels under maximum-likelihood (ML) decoding, and the derivations of upper and lower BER bounds for the proposed Raptor-based DNC scheme on the basis of the derived BER bound of LT codes.

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.

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.

We consider a K-user multiple-input multiple-output (MIMO) relay channel, where each user sends K - 1 independent messages to the other K - 1 users via a common relay. All users and the relay are assumed to have multiple antennas. The communication happens in two time slots, the multiple access (MA) stage and the broadcast (BC) stage. A linear transmit beamforming and receive combining scheme is proposed based on signal subspace alignment and physical layer network coding. The optimization of the beamforming and combining vectors is addressed with an iterative suboptimal algorithm based on orthogonal projection optimization in the signal subspaces. Suboptimal power allocation is also considered to maximize the effective signal-to-noise ratios (SNRs). The bit error rate (BER) performance of the proposed scheme in various channel configurations is verified by simulation, which shows that the proposed scheme produces significant improvement over existing one.

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.

The new wireless standard, Long-Term-Evolution (LTE), needs to support high data rate orthogonal frequency division multiplexing (OFDM) transmission for highly mobile users. Due to users' mobility, the wireless channel becomes time-variant and frequency-selective. The symbol transmission is thus impaired by Doppler spread. As a consequence, the known channel estimation methods do not give satisfactory performances. In this paper, we propose an iterative channel estimation and intercarrier interference (ICI) cancellation method that estimates the wireless channel by utilizing pilot symbols, estimates of the data symbols and Doppler spread information at the receiver. The wireless channel is expressed by a weighted time-domain channel interpolation, where the interpolation weights are designed based on the Doppler spread and time-domain channel correlations. The channel estimates are obtained by employing a least square (LS) method. Once the channel is estimated, the ICI is cancelled by performing a zero-forcing technique. Data symbols are then estimated by a detector. The estimates of data symbols are used to refine the estimation of channel coefficients, iteratively. The simulation results show that the performance degradation of the proposed scheme, when users move at the speed of up to 324 Km/h, compared to a system when users are static and perfect channel state information (CSI) is available at the receiver, is minimal.

Multi-source multi-destination wireless relay network coded systems are investigated in this paper. To achieve multiple interpretations at different receivers, we employ nested codes in our proposed system. Besides, an opportunistic scheduling (OS) technique is adopted at the relay to maximize the system capacity. The proposed system model combines the merits of both nested codes and OS. First, we present the detailed coding process of the proposed scheme. Then, we derive the upper bounds on the bit error probability of the schemes with and without OS. Finally, we investigate good codes for our system and carry out simulations to validate the theoretical analysis.

The multiple-input multiple-output (MIMO) Y channel, where three users simultaneously exchange independent messages with each other via a single relay within two time slots, is considered in this paper. We first propose a cooperative network coding protocol, which is called denoise-demodulate-and-forward (DDF), with the design of transmit beamforming and combining schemes to increase network throughput. More importantly, we formulate an optimization problem by using the newly derived bit error rate (BER) expression of adaptive M-ary quadrature amplitude modulation (M-QAM). As the result, the modulation types for both time slots can be chosen to maximize the total throughput of the proposed system under the BER constraint. Performance evaluations show that the proposed scheme can significantly improve the total throughput by comparing to the existing MIMO Y channel and the solution of the optimization problem is validated.

We consider a K-user multiple-input multiple-output (MIMO) relay channel, where each user sends independent messages to the other K-1 users via a common relay in two time slots. All users and the relay are equipped with multiple antennas. In contrast to existing work, we consider systems with both multiplexing and diversity, where each user message contains multiple data streams and there are extra degrees of freedom to optimize the transmit beamforming matrices. We propose a novel iterative beamforming optimization algorithm based on orthogonal projection optimization with the signal subspace alignment. An optimal power allocation is also considered to maximize the system sum rate. The sum rate performance of the proposed scheme in various channel configurations is verified by simulations, which shows that the proposed scheme produces significant improvement over existing one.

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.

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 investigate a cooperative transmission method employing precoding, beamforming and power allocation for a multi-user multiple-input multiple-output (MU-MIMO) relay network. In the proposed scheme, the interference is canceled by using the combination of beamforming weights and Tomlinson-Harashima precoding (THP). To achieve symbol error rate (SER) fairness among different users and further improve the performance of MU-MIMO relay networks, we propose a low-complexity power allocation (LC-PA) that allocates power to each user so that the signal-to-interference-and-noise-ratio (SINR) for all users are equal. The simulation results show that the SER performance of the proposed scheme considerably outperforms other existing schemes under the same configuration.

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 work, we propose a low complexity semiblind channel estimation algorithm, referred to as the variance squared maximum likelihood (VSML) estimator, which employs only one training symbol in each channel estimation, to estimate general non-reciprocal flat-fading channels in amplify-and-forward (AF) two-way relay networks (TWRNs). We formulate a non-convex objective function and obtain closed-form channel estimates by minimizing its approximate expression. Theoretical analysis proves that the derived channel estimation is asymptotically optimal in large sample size scenarios. Monte-Carlo simulation results show that the VSML estimator outperforms the existing relaxed maximum likelihood (RML) estimator in terms of mean squared error (MSE) performance and remarkably reduces the computational complexity by completely avoiding the grid-search algorithm under M-ary phase-shift-keying (MPSK) modulation.

A random beamforming technique is proposed for broadcast channels in multiple antenna systems. In the proposed scheme, a random weight vector, corresponding to a random pattern, is imposed on each communication resource in time-frequency domain, with a resulting average of the random patterns on all resources to be isotropic. We proved that, the capacity of such a system is upper bounded by that of a single antenna system, on the premise of same power budget. The design criteria and detailed design of random patten sequence are presented. We propose a basic random beamforming and Alamouti enhanced scheme, and a structure of the transmitter and receiver. The performance of the proposed scheme is verified by numerical simulation. It is also observed that it is robust to calibration errors and failures of radio frequency chains.

In this study, exact closed-form expressions for the second-order statistics of the signal-to-noise ratio at a maximum ratio combiner (MRC) output for a Nakagami fading channel are derived. Using the joint characteristic function for the MRC output and its time derivative, the level crossing rate, average fading duration and autocorrelation function expressions are derived for the case of independent but unbalanced diversity branches, with unequal fading parameters and an arbitrary number of diversity branches. The analytical results are validated by simulations.

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 paper, a novel frequency domain multi-user detector is proposed for a time division-code division multiple access (TD-CDMA) up-link. Unlike conventional frequency domain detectors, the proposed detector first transforms the system matrix of TD-CDMA systems into a circulant matrix by cyclic truncation. It subsequently uses a new method to convert the circulant matrix into a frequency domain block diagonalized matrix through discrete Fourier transforms and permutations. Therefore, the proposed detector can utilize the channel frequency domain coherence to further decrease its computational complexity with a controlled performance loss. Moreover, a novel approach is proposed to calculate the frequency domain correlation matrix and matched filter. With the help of this novel approach, the proposed detector expresses significant complexity advantage over other frequency domain detectors for a real TD-CDMA system in a short-time-dispersive channel.

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.

Information theoretical study shows that multiple-input multiple-output (MIMO) systems have a much higher channel capacity than single-input single-output (SISO) systems, which can be characterized as a multiplexing gain. Adaptive modulation and coding (AMC) is proven to be an effective technique to approach the channel capacity by utilizing the channel state information (CSI) at the transmitter. In this paper, following an analysis of the multiplexing gain of a generic AMC MIMO system, we propose an adaptive coded MIMO system that achieves a near-full multiplexing gain as well as a robust BER performance in an outdated CSI environment. A comparison among various adaptive and non-adaptive MIMO systems reveals the proposed system as a good trade-off between the spectral efficiency, BER and system complexity.

We consider relay broadcast channels (RBCs) with multiple antennas at all nodes. A practical linear precoding, relaying and combining scheme is proposed. Under an overall power constraint, we derive the optimal power allocation solution in a closed form. A low complexity beamforming vector optimization algorithm is proposed to maximize the effective channel gains and improve the system performance. Simulation results are presented for various channel configurations, which show that the proposed cooperative beamforming algorithm achieves performance very close to that of the exhaustive search algorithm but with a much lower complexity, and the maximum diversity gain is always attained.

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.

This paper presents a novel spatial frequency domain packet scheduling and frequency domain equalization (FDE) algorithm for uplink Single Carrier (SC) Frequency Division Multiple Access (FDMA) multiuser MIMO systems. Our analysis model is confined to 3GPP uplink SC-FDMA transmission with Multi-user (MU) Spatial Division Multiplexing (SDM). The results show that the proposed MU-MIMO scheduler in conjunction with the new FDE singificantly increases the maximum achievable rate and improves the bit error rate (BER) performance for the system under consideration.

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.

We consider a multi-user (MU) multiple-input multiple-output (MIMO) channel where each node is equipped with multiple antennas. An orthogonal projection optimization (OPO) algorithm is proposed based on linear transmit beamforming and receive combining. The performance of the OPO algorithm is analyzed which discloses its convergence characteristics. The complexity of the OPO algorithm is also addressed and a computationally efficient algorithm is developed. The OPO algorithm is applied to both uplink and downlink MU MIMO systems, and simulation results show its excellent performance.

In this paper, we investigate transmission methods for a multi-user multiple-input multiple-output (MIMO) network that utilizes base stations (BS) cooperation. To eliminate the interference between users, iterative zero forcing- (ZF) and iterative Tomlinson Harashima precoding-based (THP) schemes are proposed. In the iterative ZF-based scheme, all interference is cancelled by using the transmit-receive weights. To reduce the complexity of iterative ZF scheme, in the iterative THP-based scheme, the interference is cancelled by using transmit-receive weights and the THP. To achieve symbol error rate (SER) fairness among different users and further improve the performance of multi-user MIMO relay systems, we develop optimal and sub-optimal power allocation (PA) methods that ensure signal-to-interference-and-noise-ratio (SINR) across all users are equal, under the power constraints at both BSs and relay station (RS). In the optimal PA scheme, the PA is done at both BSs and RS. In the sub-optimal scheme, to reduce the computational complexity of optimal PA significantly, the PA is done only at BSs, and at the RS a power scaling is performed to satisfy the RS power constraint. The simulation results show that by using optimal and sub-optimal PAs, the iterative ZF-based scheme outperforms the iterative THP-based scheme by an average of 0.4 dB at the cost of a four times higher complexity.

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, 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 letter derives mathematical expressions for the received signal-to-interference-plus-noise ratio (SINR) of uplink Single Carrier (SC) Frequency Division Multiple Access (FDMA) multiuser MIMO systems. An improved frequency domain receiver algorithm is derived for the studied systems, and is shown to be significantly superior to the conventional linear MMSE based receiver in terms of SINR and bit error rate (BER) performance.

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.

We consider relay broadcast channels (RBCs) with multiple antennas at all nodes. A practical linear preceding, relaying and combining scheme is proposed. Under an overall power constraint, we derive the optimal power allocation solution in a closed form. A low complexity beamforming vector optimization algorithm is proposed to maximize the effective channel gains and improve the system performance. Simulation results are presented for various channel configurations, which show that the proposed optimized beamforming algorithm achieves performance very close to that of the exhaustive search algorithm but with a much lower complexity, and the maximum diversity gain is always attained.

The LTE downlink multiuser Multiple Input Multiple Output (MIMO) systems are analyzed in this paper. Two Spatial Division Multiplexing (SDM) multiuser MIMO schemes are investigated: Single User (SU) and Multi-user (MU) MIMO schemes. The main contribution of this paper is the establishment of a mathematical model for the Signal to Interference plus Noise Ratio (SINR) distribution for multiuser SDM MIMO systems with frequency domain packet scheduler.

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.

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.

Multiple-input multiple-output (MIMO) technologies have the advantage of improving system throughput and error performance by exploiting the spatial diversity, owing to the multiple independent transmit-receive paths. A lot of efforts have been put into MIMO broadcast channels research and it is expected to be a core technology for 3G and 4G wireless systems. Generally, channel state information (CSI) is required at the transmitter, to fully exploit the diversity. In the case that channel reciprocity does not apply, limited feedback is one effective solution to get CSI at the transmitter but it increases system complexity and degrades the performance. In this paper, we propose a low complexity limited feedback scheme for MIMO broadcast channels. Non-cooperative receivers without full CSI and a feedback link with a limited rate are considered. The proposed scheme is based on a novel Grassmannian-based two-layer codebook, each layer having a small dimension, and a vector selection algorithm, to maximize the effective channel gain. A simulation is presented for the proposed scheme, showing that the performance is very close to the best known conventional high dimensional codebook, while the search complexity and memory requirement are reduced by several orders of magnitude.

We consider two-way relay channels (TWRCs) with multiple antennas at the relay node. Two stage communications are considered, where both sources transmit during the multiple access (MA) stage, and the relay transmits during the broadcast (BC) stage. An optimized network coding scheme is proposed. For the MA stage, a maximum likelihood algorithm is proposed to decode the XOR of the signals received from the two sources. For the BC stage, an optimized beamforming algorithm is proposed to maximize the product of the effective channel gains for the two sources. Both analytical and simulation results show that the proposed scheme achieves a full diversity gain and outperforms the amplify-and-forward (AF) scheme significantly, owing to its efficient use of the relay transmit power and matched excellent performance of the two stages.

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.

Mathematical expressions are derived for the received signal to interference plus noise ratio of uplink single carrier (SC) frequency division multiple access (FDMA) multi-user multiple-input multiple-output (MIMO) systems with spatial frequency domain packet scheduling. The scheduler is able to exploit the available multi-user diversity in time, frequency and spatial domains. Our analysis model is confined to 3GPP uplink SC-FDMA transmission in which we specifically investigate multi-user spatial divsion multiplexing MIMO schemes.

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.

In this paper, we propose and analyze three new transmit antenna selection schemes with reduced feedback rate requirement compared with the conventional scheme. In scheme 1, Ltavailable transmit antennas are divided as equally as possible into two groups with consecutive antennas. The best single antenna within each group is selected. In scheme 2, only the best one among Ltantennas is made known to the transmitter, and the other one is selected at random. In Scheme 3, Ltantennas are divided into multiple subsets each consisting of two adjacent antennas, and the best subset is selected. Bit error rate (BER) expressions for the proposed schemes with Alamouti code are derived for independent flat Rayleigh fading channels. It is found that all the three schemes achieve a full diversity order. The relative merit of each proposed scheme is delineated based on the trade-off between the asymptotic performance loss and feedback reduction, both relative to the conventional scheme. We conclude that Schemes 1 and 3 are more favorable for practical applications, and the appropriate application scenarios are also identified. The proposed schemes enrich the choices for antenna selection system design for various feedback channel bandwidths and different requirements for quality of service.

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.

In this paper, we present upper bounds on the symbol error probability for convolutional encoded continuous phase modulation (CPM) over rings with maximum likelihood sequence detection (MLSD). Both coded CPM schemes with and without feedback from the CPM encoder to the ring convolutional encoder are considered. The bounds are based on the transfer function technique. The paper contribution is in the development of the analytical upper bound on the symbol error probability for the investigated system. The bound can be used as a tool to analyze and design ring convolutional encoded CPM systems.

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.

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.

This paper presents the analysis of the average channel capacity and the SINR distribution for multiuser Multiple Input Multiple Output (MIMO) systems in combination with the base station based packet scheduler. The packet scheduler is used to exploit the available multiuser diversity in the time, frequency and spatial domains. The analysis model is carried out for 3 GPP LTE downlink transmission. Two Spatial Division Multiplexing (SDM) multiuser MIMO schemes in the context of LTE downlink transmission are investigated. They are Single User (SU) and Multi-user (MU) MIMO schemes. In general, the outage probability for systems using SU-MIMO scheme is larger than the one with MU-MIMO scheme. Compared with the systems without preceding, linear preceding can improve the outage probability. The paper contributions are the derivation of a mathematical expression of the SINR distribution and the average channel capacity for multiuser MIMO systems with a frequency domain packet scheduler.

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.

This paper analyzes the average capacity for a wireless network with joint opportunistic scheduling and wireless network coding. The capacity and the optimal power allocation scheme are derived for a multiuser fading broadcasting channel with perfect channel side information at the transmitter. The packets generated by the source nodes are encoded prior to the transmission to the relay node. The received encoded packets are mixed by XOR operation at the relay node and then broadcasted to the destination nodes. The encoder in the source nodes is designed in such a way that each destination node can give different interpretation of the received packets with their own side information. From the numerical and simulation results, we can see that the proposed simultaneous power and rate adaption for wireless network coding with opportunistic scheduling can significantly improve the average channel capacity.

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.

We consider relay broadcast channels (RBCs) with multiple antennas at all nodes. A practical precoding, relaying and combining scheme is proposed. Under an overall power constraint, we derive the optimal power allocation solution in a closed form. The conditions for an effective RBC scheme are identified with a qualitative analysis to the relationship between the relay gain and the relative strengths of the direct and relay links. Simulation results are shown for various channel configurations, which verify the analysis and conclude that the first hop in the relay chain is a determining factor to the overall performance of the RBC.

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.

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.

An adaptive turbo coded multiple-input multiple- output (MIMO) system with outdated channel state information (CSI) at the transmitter is proposed and investigated with a focus on the multiplexing gain and the bit error rate (BER) performance. By incorporating the rate-compatible punctured turbo codes (RCPTCs) into the adaptive modulation MIMO system, the adaptive turbo coded MIMO system is shown to achieve a near-full multiplexing gain as well as a robust BER performance against the CSI feedback delay.

The second-order statistics of the time varying signal to noise ratio at the output of a multiple-input-multiple-output(MIMO) system with maximal ratio combining (MRC) are analyzed. Exact closed-form expressions for the level crossing rate and the average fade duration are derived for the Ricean propagation and arbitrary channel matrix dimensions. Novel expressions are compared with the special case of MRC receive diversity. The analytical results are validated by simulation.

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.

This paper investigates the capacity of multiuser MIMO-MRC systems in spatially correlated environments. We present new capacity approximations which are shown to be accurate. The approximations are based on new simple expansions which we derive for the maximum eigenvalue of correlated Wishart matrices. We show that for a large number of users there is a capacity offset due to correlation. Through this, we show that correlation is beneficial for capacity. Our results are confirmed through comparison with Monte-Carlo simulations.

In this paper, we propose three different transmit antenna selection schemes with reduced feedback requirement compared with the conventional scheme. In Scheme 1, all the Lt available transmit antennas are divided as equally as possible into two groups. The best single antenna within each group is selected. In Scheme 2, only the best one among Lt antennas is made known to the transmitter, and the other one is selected at random. Scheme 3 is for even Lt, and Lt antennas are divided into multiple subsets each consisting of two adjacent antennas, among which the best subset is selected. Analytical performances of these three schemes with the Alamouti space-time block code (STBC) are derived for flat Rayleigh fading channels. The asymptotic signal-to-noise ratio (SNR) loss of each proposed scheme relative to the conventional transmit antenna selection scheme is quantified. Together with the reduction in feedback requirement, the relative merit of each proposed scheme is delineated. In general, all of the three schemes provide a good trade-off between error performance and feedback requirement. And the application scenario for each scheme is also identified. The results in this paper provide guidance for the design of transmit antenna selection systems with various feedback channel bandwidths, different requirements for quality of service, and specific antenna configuration.

An adaptive coded multiple-input multiple-output (MIMO) system with outdated channel state information (CSI) at the transmitter is proposed and investigated. By incorporating the rate-compatible punctured codes (RCPCs) and bit-interleaved coded modulation (BICM) into the adaptive MIMO system, the proposed adaptive RCPC-BICM MIMO system is shown to achieve both a near-full multiplexing gain and a robust BER against the CSI feedback delay. A comparison among various adaptive MIMO systems shows that the proposed system provides a good trade-off between the spectral efficiency, BER and system complexity.

An analytical method for the performance evaluation of adaptive multiple-input-multiple-output (MIMO) system employing maximal ratio combining (MRC) by a Markov model is developed. The finite state Markov channel model parameters were determined by a new exact expression for level crossing rate of the signal to noise ratio at the output of MIMO-MRC with imperfect channel state information, derived for the Rayleigh propagation and arbitrary channel matrix dimensions. The analytical results are validated by computer simulation.

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

In this paper, following an analysis on the effect of channel state information (CSI) feedback delay over the eigenmode transmission MIMO system, we incorporated a bit-interleaved coded modulation (BICM) technique into the previously proposed adaptive modulation multiple-input multiple-output (MIMO) system to combat the CSI imperfection. Under a perfect CSI assumption, the optimum adaptation policy for the adaptive BICM MIMO system was obtained and both analytical and simulation results were presented showing the robustness of this system to the CSI feedback delay. Based on that, a modified adaptation algorithm was proposed, which further improved the BER performance by trading off the achieved spectral efficiency in an adaptive way.

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.

In this paper, we propose a method to reduce the interference and increase the throughput of orthogonal frequency division multiplexing (OFDM) systems in co-working wireless local area networks (WLANs) by using joint adaptive multiple antennas (AMA) and adaptive modulation (AM) with acknowledgement (ACK) eigen-steering. The calculation of AMA and AM are performed at the receiver. The AMA is used to suppress interference and to maximise the signal to noise plus interference ratio (SNIR) by adjusting its weights dynamically. The improved SNIR is then used by AM as an input to allocate OFDM sub-carriers, power, and modulation mode subject to the constraints of power, discrete modulation, and the bit error rate (BER). The transmit weights, the allocation of power, and the allocation of sub-carriers are obtained at the transmitter using ACK eigen-steering. The derivations of AMA, AM, and ACK eigen-steering are shown. The performance of joint AMA and AM for various AMA configurations are evaluated through the simulations of BER and spectral efficiency (SE) against SIR. The simulation results for joint AMA-AM also show that joint AM and receive beamforming produce the most effective solution in co-working OFDM-WLANs

Accurate self-localization capability is highly desirable in wireless sensor networks. A major problem in wireless sensor network localization is the flip ambiguity, which introduces large errors in the location estimates. In this paper, we propose a two phase simulated annealing based localization (SAL) algorithm to address the issue. Simulated annealing (SA) is a technique for combinatorial optimization problems and it is robust against being trapped into local minima. In the first phase of our algorithm, simulated annealing is used to obtain an accurate estimate of location. Then a second phase of optimization is performed only on those nodes that are likely to have flip ambiguity problem. Based on the neighborhood information of nodes, those nodes likely to have affected by flip ambiguity are identified and moved to the correct position. The proposed scheme is tested using simulation on a sensor network of 200 nodes whose distance measurements are corrupted by Gaussian noise. Simulation results show that the proposed scheme gives accurate and consistent location estimates of the nodes and mitigate errors due to flip ambiguities.

It has been shown that distributed turbo coding (DTC) can approach the capacity of a wireless relay network. In the existing DTC schemes, it is usually assumed that error-free decoding is performed at a relay. We refer to this type of DTC schemes as perfect DTC. In this paper, we propose a novel DTC scheme. For the proposed scheme, instead of making a decision on the transmitted information symbols at the relay as in perfect DTC, we calculate and forward the corresponding soft information. We derive parity symbol soft estimates for the interleaved source information when only the a posteriori probabilities of the information symbols are known. The results show that the proposed scheme can effectively mitigate error propagation due to erroneous decoding at the relay. Simulation results also confirm that the proposed scheme approaches the outage probability bound of a distributed two-hop relay network at high signal-to-noise ratios

A novel full diversity space-time trellis code, referred to as an assembled space-time trellis code (ASTTC), is presented in this letter. For this scheme, space-time trellis coded signals are first linearly transformed, and then the transformed signals are coded by using the Alamouti space-time block code. A new design criterion is proposed. It is shown that the ASTTCs can achieve not only the full diversity order but also a significant coding gain determined by the minimum weighted code distance (MWCD). Based on this design criterion, a new 4-state code is derived by a systematic code search. Simulation results show that the new ASTTC is superior by about 1.7 dB to the TSC (TarokhSeshadri-Calderbank) code at the frame error rate (FER) of 1.0e-2 over quasi-static fading channels.

A novel multiuser transmission and detection scheme for high-speed asynchronous ultra-wideband (UWB) communication systems is proposed. Firstly, a block-spreading code-division multiple-access (BS-CDMA) scheme with zero correlation window (ZCW) is designed for application in asynchronous UWB communication systems with dense frequency selective fading propagation. As a result, multiple access interference can be completely removed by maintaining the spreading code orthogonality. Secondly, the UWB channel model is converted from a block Toeplitz matrix into block circulant matrix. Consequently, the inter-symbol interference is eliminated through the use of a fast Fourier transform (FFT) based minimum mean square error equalization. It is shown that the proposed multiuser transmission and detection scheme for UWB systems can deliver superior performance relative to the existing schemes, with low computation complexity

A novel multiuser detection scheme for asynchronous ultra wideband (UWB) impulse radio systems is proposed. A block-spreading code-division multiple-access (BS-CDMA) system with zero correlation window (ZCW) is designed and applied in UWB systems. It is shown that multiple access interference is completely removed by maintaining code orthogonality, even with asynchronous reception. Furthermore, the UWB channel can be modelled as a block circulant matrix by re-ordering the received sequence. With the advantage of the circulant matrix, a fast Fourier transform (FFT) based minimum mean square error equalization scheme is applied to eliminate the ISI to such an extent that the system performance approaches the AWGN channel. As the large number order of the UWB channel, the FFT based equalization scheme also dramatically decreases the computational complexity.

In sensor networks, the information obtained from sensors are meaningless without the location information. In this paper, we propose a simulated annealing based localization (SAL) scheme for wireless sensor networks. Simulated annealing (SA) is used to estimate the approximate solution to combinatorial optimization problems. The SAL scheme can bring the convergence out of the local minima in a controlled fashion. Simulation results show that this scheme gives accurate and consistent location estimates of the nodes.

In this paper, the error performance of the Alamouti scheme with transmit antenna selection is investigated in the context of imperfect subset selection. The asymptotic bit error performance is derived for binary phase-shift keying (BPSK) modulation in flat Rayleigh fading channels. It is shown that the transmit diversity order is equal to the larger ordinal number of the antenna within the selected antenna subset, while the smaller ordinal number only determines horizontal location of the error performance curve without an impact on asymptotic diversity order. Simulation results are provided to substantiate the theoretical analysis.

A space time trellis coding scheme with linear precoding (STTC-LP) for transmit beamforming is presented in this paper. The upper bounds on pairwise error probability (PEP) over quasi-static and fast fading channels are derived, and new design criteria are proposed. It is shown that STTC-LP can achieve not only the maximum received SNR but also a significant coding gain. Based on the design criteria the optimum 4-state codes are derived by the systematic code search. Simulation results show that the new 4-state STTC-LP is superior by about 1.5 dB and 4.0 dB at the FER of 1.0e-2 over quasi-static fading channels, and by about 8 dB and 10 dB at the BER of 1.0e-4 over fast fading channels, to the beamforming and Tarokh-Seshadri-Calderbank (TSC) codes, respectively.

In this paper, we propose a novel distributed turbo coding (DTC) scheme which can be used in a system where imperfect decoding occurs at a relay. For the proposed scheme, rather than making a decision on the transmitted information symbols at the relay as in the conventional DTC, we calculate and forward the corresponding soft information at the relay. For the proposed scheme, we derive the parity symbols soft estimates for the interleaved source information when only the a posteriori probabilities (APP) of the information symbols are known. Analytical results show that the performance of the proposed scheme, at high SNR, is limited by the ratio of the received SNR at the relay and that at the destination. In order to make the performance loss of the proposed scheme, compared to the perfect DTC as small as possible, the relay should be placed closer to the source than to the destination and/or make the transmit power from the source larger than that from the relay.

It is well known that an adaptive frequency domain receiver can dramatically reduce the detection computation complexity of a communication system compared to standard time domain receiver. In this paper, we propose a new low complexity frequency domain adaptive and iterative receiver for a layered space-time coded CDMA (LSTC-CDMA) system with no knowledge of channel state information (CSI), spreading sequences, and the fading coefficients, except a training sequence for each user. The proposed low complexity receiver consists of a normalized LMS feed forward filter and a feedback iterative parallel interference canceller in frequency domain. The proposed frequency domain receiver has a significant reduction in computation complexity and achieves the same performance compared to a time domain receiver. Our simulation results exhibit that the proposed adaptive iterative receiver can also approach the interference-free single user performance for a certain range of the signal to noise ratio (SNR).

In this paper, we first address the effects of the feedback delay on a variable-rate variable-power adaptive modulation MIMO system that is designed under a perfect channel state information (CSI) assumption. Closed-form expressions for the average bit error rate (BER) and average spectral efficiency (ASE) are derived. Based on that, simple but effective adaptive modulation designs using the outdated instantaneous CSI and time correlation coefficient are proposed based on the signal-to-interference and noise ratio (SINK) and BER analysis. Analytical and simulation results show that these designs provide good trade-offs between the ASE and BER in an adaptive way, and make the adaptive MIMO system much more robust to the CSI imperfection.

This letter presents an optimized space-time block code constructed by using multidimensional super-set partitioning (STBC-MDSP). The theoretical upper bound on the pairwise probability of STBC-MDSP is derived, and based on it, a code design criterion is proposed. It is shown that to optimize the performance of STBC-MDSP, one should maximize the sum square distance (SSD) in each level of super-set partitioning. Simulation results show that the proposed scheme is superior by about 2.5 and 1.5 dB to the Alamouti scheme and Tarokh-Seshadri-Calderbank (TSC) code with the same number of states, respectively, at the frame error rate (FER) of 10/sup -2/ over quasi-static fading channels.

The stochastic gradient-based least-mean-square (LMS) algorithm has a significant feature of simple implementations. Its applications in multiple-input-multiple-output (MIMO) layered space-time (LST) systems are considered in this paper. We derive the design criteria of the adaptive iterative/decision-feedback (DFB) LMS receivers in MIMO systems. It has been found that the algorithm converges in accordance with the minimum eigenvalue of the correlation matrices where received symbols and interference estimates are the entries of two correlation matrices respectively.

The performance of space time trellis codes (STTCs) can be further improved by utilizing the partial channel information and combining some precoding schemes at the transmitter. This paper presents a STTC scheme - hybrid STTC (HSTTC). For this scheme, the STTC signals are first linearly transformed and then transmitted through the single selected antenna. The upper bound on pairwise error probability (PEP) for HSTTC is derived. It is shown that by a proper code construction HSTTC can achieve not only a full spatial diversity order, but also a time diversity order, as well as a significant coding gain determined by the weighted square product distance (WSPD). A new design criterion for HSTTCs has been proposed and the optimum 4-state code, based on this criterion, has been found by systematic code search. Simulation results are provided for the quaternary phase-shift keying (QPSK) signal sets. They show that the proposed new code is superior by 9 dB and 11 dB to the conventional transmit antenna selection (TAS) and Tarokh-Seshadri-Calderbank codes (TSC), respectively, at a BER of 10e/sup -4/ over time-varying fading channels.

Without estimating the channel coefficients, a novel iterative singular vector estimation scheme has been proposed for a beamforming transmission and detection in a wireless multiple input and multiple output (MIMO) system.

Adaptive modulation (AM) schemes in multiinput multioutput (MIMO) systems are usually designed and analyzed under the assumption of perfect channel state information (CSI). In this paper, we consider the effect of CSI imperfection on the bit error rate (BER) and average spectral efficiency (ASE) performance. Approximate analytical expressions for BER and ASE are derived and verified by simulations. The thresholds for the CSI imperfection are identified, below which an ideally designed AM MIMO system can operate satisfactorily

We investigate the error performance of the Alamouti scheme with transmit antenna selection. The exact bit error rate (BER) is derived for binary phase-shift keying (BPSK) in flat Rayleigh fading channels. The analysis reveals that this scheme achieves a full diversity order at high SNRs, as if all the transmit antennas were used. Simulation results are provided to substantiate the analysis. It is shown that compared with conventional space-time block codes (STBCs), this scheme incurs much less SNR loss inherent to the transmit-diversity system. Therefore, the Alamouti scheme with transmit antenna selection provides a new general approach to the design of MIMO systems for high-data-rate downlink transmission with a high diversity order.

Layered space-time (LST) architectures can support efficient signal processing in multiple-input multiple-output (MIMO) systems. Current applications to LST systems are based on pilot-aid transmissions, with the problem of reducing spectral efficiency. In this paper, the new adaptive algorithm called multilayer recursive least square (RLS) is applied to an iterative receiver structure in LST systems. A multi-layer RLS algorithm is an efficient method to provide accurate interference cancellation and fast convergence. This multi-layer RLS adaptive iterative receiver considers joint detection/decoding and channel estimation to avoid the periodic insertion of pilot-tones and hence improves spectral efficiency. The proposed iterative adaptive receiver is compared with a lower bound non-adaptive MMSE receiver where the channel state information (CSI) is assumed to be perfectly known a priori at the receiver. The proposed receiver structure is suitable for MIMO systems in slow time variant channels. Simulation results are supplied to demonstrate the excellent performance of the proposed multi-layer RLS adaptive receiver over a conventional linear RLS receiver.

Distributed multi-hop communication systems have been introduced recently which allow the application of multiple-input-multiple-output (MIMO) capacity enhancement techniques over spatially separated relaying mobile terminals. They were shown to yield significant capacity gains over direct communication or non-distributed single-input-single-output (SlSO) relaying networks. The contribution of This work is the derivation of throughput-maximising resource allocation strategies in terms of fractional frame duration and transmission power. It is assumed that distributed terminals cooperate at each relaying stage and that the detection of packets is performed at each relaying stage.

We propose a layered space time (LST) transceiver in a multiuser code division multiple access (CDMA) mobile communication system. In particular, we examine a multiuser iterative receiver with joint detection, channel estimation and decoding in a frequency selective fading channel. The iterative parallel interference canceller (PIC) with statistics combining is used as the multiuser detection scheme and a least mean square (LMS) adaptive algorithm is applied to estimate the multiple input and multiple output (MIMO) frequency selective fading channel. Simulation results are presented to demonstrate the performance of this receiver. It is shown that the performance of the receiver with iterative channel estimation is close to that of the receiver with perfect channel state information (CSI).

Space-time trellis codes (STTCs) have been shown to efficiently use the transmit diversity to improve the error performance. In the existing STTCs, the transmit power is equally distributed across all the transmit antennas. However, this power allocation strategy is not optimum with regards to the error performance. We propose a new scheme, referred to as the space-time trellis codes with dynamic transmit power allocation (STTCs/DTPA), when partial channel state information (CSI) is available at the transmitter side. It is demonstrated that this scheme can achieve a full diversity order and have much better performance than general STTC schemes in error probability.

In this paper we investigate the error performance of a multiple-input-multiple-output (MIMO) scheme combining transmit antenna selection (TAS) and space-time trellis codes (STTCs), which is referred to as the TAS/STTC scheme. In this scheme, two transmit antennas, which maximize the total received signal power, are selected to transmit the full-rank baseline STTCs designed for two transmit antennas. An upper bound on the pairwise error probability (PEP) of this scheme is derived in quasistatic flat Rayleigh fading channels. It is shown that, as long as the baseline STTC has a full rank, a full diversity order can be achieved, as if all the transmit antennas were used. This scheme has a fixed low decoding complexity as for the baseline STTC and a full diversity order can be achieved with a small memory order. Therefore, the TAS/STTC scheme provides a new approach to the design of MIMO system achieving a high diversity order based on the existing STTCs designed for a small number of transmit antennas.

A space-time trellis code with linear transformation (STTC-LT) is presented. The upper bound on the pairwise error probability (PEP) is derived and a new design criterion is proposed. It is shown that the performance of STTC-LT over fast Rayleigh fading channels depends on the weighted square product distance (WSPD). Simulation results are provided for QPSK signal sets. They show that the proposed scheme is superior by about 8 dB and 10 dB to the conventional beamforming and Tarokh-Seshadri-Calderbank codes (TSC) at the bit error rate (BER) of 1.0e-4 over fast fading channels.

In commercial wideband code division multiple access (W-CDMA), the transmitted signal in the downlink channel is spread by orthogonal codes. However, frequency selective fading destroys the orthogonality, which causes multiple access interference (MAI). Although the RAKE receiver can exploit path diversity, it does not restore the orthogonality and suppress the MAI. As a result, CDMA becomes an interference limited system. The equalization followed by despreader can be adopted to restore the orthogonality and then to suppress the MAI, without significantly increasing the complexity. In this paper, we propose to apply a chip level decision feedback equalizer (DFE) with the Alamouti transmit diversity scheme to restore the orthogonality of the received spreading sequences. Theoretical and simulation results show significant performance gains compared to the chip level linear equalizer (LE) and the RAKE receiver.

The pairwise-error probability upper bounds of space-time codes (STCs) in independent Rician fading channels are derived. Based on the performance analysis, novel code design criteria for slow and fast Rayleigh fading channels are developed. It is found that, in fading channels, the STC design criteria depend on the value of the possible diversity gain of the system. In slow fading channels, when the diversity gain is smaller than four, the code error performance is dominated by the minimum rank and the minimum determinant of the codeword distance matrix. However, when the diversity gain is larger than, or equal to, four, the performance is dominated by the minimum squared Euclidean distance. Based on the proposed design criteria, new codes are designed and evaluated by simulation.

It has been established that the appropriate design parameters for space-time trellis code (STTC) in quasi-static flat Rayleigh fading channels are the rank and determinant criteria or the Euclidean distance criterion, depending on the value of the overall diversity gain. We propose two groups of new 4- and 8-PSK STTCs with two to four transmit antennas based on these two design criteria, respectively. Simulation results show that increasing the number of transmit antennas in general provide large performance improvement.

Multiple antenna systems have the potential to provide a high capacity wireless communication system. The spectral efficiency of space time trellis coding (STTC) is limited by the encoder structure. The layered space time (LST) architecture can overcome this problem. Three different LST schemes are presented. An improved iterative parallel interference canceller (PIC) method is applied at the receiver. A significant performance improvement is achieved compared to the standard PIC. Simulation results of three various layer structures are compared with low density parity check (LDPC) and convolutional codes as component codes.

This paper concentrates on developing and analyzing advanced space-time multiuser receivers for WCDMA forward link. Through exploring the multiuser interference and utilizing the space-time diversity, an optimum diversity receiver, called a constrained adaptive space-time diversity combiner (C-ASTDC), is proposed. To mitigate the disadvantages of the full-rank multiple antenna system, such as computational complexity and increased difficulty to accurately tune the algorithm, a reduced form of C-ASTDC is also proposed.

This paper concentrates on developing a complexity reduced adaptive space-time interference canceller for multiuser WCDMA forward link. Through exploring the multiuser interference and utilizing the space-time diversity, a sub-optimum diversity receiver, called a reduced-rank constrained adaptive space-time interference canceller (R-CASTIC), is proposed. The proposed scheme mitigates the disadvantages of the full-rank multiple antenna system, such as computational complexity and increased difficulty to accurately tune the algorithm. R-CASTIC performs similar to the full-rank receiver within 1 dB at the BER of 10/sup -3/ in less than 60% of total cell capacity, with only an eighth of the complexity of a full-rank receiver.

A code-matched interleaver design for turbo codes in which a particular interleaver is constructed to match the code weight distribution is proposed. The design method is based on the code distance spectrum. The low weight paths in the code trellis which give large contributions to the error probability in the signal-to-noise ratio region of interest for practical communication systems are eliminated so that they do not appear in the overall code trellis after interleaving. The proposed interleaver improves the code error performance at moderate to high signal-to-noise ratio and considerably increases the asymptotic slope of the error probability curves.

This paper presents a maximum-likelihood decoding (MLD) and a suboptimum decoding algorithm for Reed-Solomon (RS) codes. The proposed algorithms are based on the algebraic structure of the binary images of RS codes. Theoretical bounds on the performance are derived and shown to be consistent with simulation results. The proposed suboptimum algorithm achieves near-MLD performance with significantly lower decoding complexity. It is also shown that the proposed suboptimum, algorithm has better performance compared with generalized minimum distance decoding, while the proposed MLD algorithm has significantly lower decoding complexity than the well-known Vardy-Be'ery (1991) MLD algorithm.

The performance of parallel and serial concatenated codes on frequency-nonselective fading channels is considered. The analytical average upper bounds of the code performance over Rician channels with independent fading are derived. Furthermore, the log-likelihood ratios and extrinsic information for maximum a posteriori (MAP) probability and soft-output Viterbi algorithm (SOVA) decoding methods on fading channels are developed. The derived upper bounds are evaluated and compared to the simulated bit-error rates over independent fading channels. The performance of parallel and serial codes with MAP and SOVA iterative decoding methods, with and without channel state information, is evaluated by simulation over independent and correlated fading channels. It is shown that, on correlated fading channels, the serial concatenated codes perform better than parallel concatenated codes. Furthermore, it has been demonstrated that the SOVA decoder has almost the same performance as the MAP decoder if ideal channel state information is used on correlated Rayleigh fading channels.

This paper presents the design of space-time turbo trellis coded modulation (ST turbo TCM). We introduce new recursive space-time trellis coded modulation (STTC) which outperform feedforward STTC proposed by Tarokh, Seshadri and Calderbank (see Trans. Inform. Theory, vol.44, no.2, p.744-65, 1998) and by Baro, Bauch and Hansmann (see IEEE Trans. Commun. Letters, vol.4, no.1, p.20-22, 2000) . A substantial improvement in performance can be obtained by constructing parallel concatenation of recursive STTC and making use of iterative decoding. The new recursive STTCs can be used directly in this scheme. ST turbo TCM outperforms the best known STTC by about 2 dB on slow fading channels and by up to 8 dB on fast fading channels.

A layered space-time architecture has been proposed to realize the tremendous increase in capacity which is offered by rich scattering wireless channels in comparison with a single transmit and single receive antenna case. We analyze the performance of horizontally (HLST) and diagonally (DLST) layered space-time architectures on slow and fast Rayleigh fading channels. Based on the analysis, we derive the code design criteria for the constituent codes for each architecture.

It has been established that the appropriate criteria for space-time trellis coded modulation (STTCM) design on slow Rayleigh fading channels are maximization of the minimum rank and the minimum determinant of the distance matrices. We show here that when STTCM is used in systems with a large product of the numbers of the transmit and the receive antennas (>3), the multiple fading subchannels between individual transmit and receive antenna pairs converge to an additive white Gaussian noise (AWGN) channel and the design of codes with maximum coding gain is governed by the minimum trace of the distance matrices, or the minimum Euclidean distance between any two codewords over all transmit antennas. A number of new 4 and 8-PSK codes based on the proposed design criterion were constructed and shown to be superior to other known codes.

Novel code design criteria for space-time codes on slow Rayleigh fading channels are presented. It is shown that the code performance is dominated by the minimum rank r and the minimum determinant of the codeword distance matrix when the product of the minimum rank r and the number of receive antennas n/sub R/ is less than 4. However, when r/spl middot/n/sub R/ is greater than or equal to 4, the code error performance is dominated by the minimum trace of the codeword distance matrix. Furthermore, we present recursive spacetime trellis coded modulation (STTC) which outperforms feedforward STTC on slow and fast fading channels. A substantial increase in performance can be obtained by constructing spacetime turbo trellis coded modulation (ST turbo TCM) which consists of concatenated recursive STTC, decoded by iterative decoding algorithm. The proposed recursive STTC are used as constituent codes in this scheme. The proposed ST turbo TCM significantly outperforms the best known STTC on both slow and fast fading channels.

We propose novel space-time multistage and iterative receiver structures and examine their application in code division multiple access (CDMA) mobile communication systems. In particular we derive an expression for weighting coefficients in parallel interference cancellers (PICs) in a system with a large number of users, where decision statistics bias is pronounced. We further examine the parameters in this expression and show how to obtain a practical partial cancellation method that allows on-line estimation of the weighting coefficients. In the proposed multistage PIC, the coefficients are calculated by using only the variances of the detector outputs. We also examine an iterative PIC and observe that this receiver has similar limitations as the multistage PIC. The application of the novel parallel interference cancellation strategy in the iterative receiver structure results in a spectacular system capacity improvement with a negligible complexity increase relative to the standard iterative receiver. The performance of the proposed receivers is further enhanced by receiver adaptive array antennas and space-time processing.

In this paper, we consider coded asynchronous multiuser signals in an additive white Gaussian noise channel. Since optimum joint multiuser detection (MUD) and forward error correction (FEC) decoding is characterized with a very high computational complexity, we consider disjoint MUD and FEC decoding. The optimum disjoint multiuser detector is the soft-output maximum a posteriori detector that provides sequences of a posteriori probabilities to the corresponding FEC decoders. It involves backward and forward recursions resulting in high complexity and processing delay. In this paper, we consider several suboptimum soft output disjoint multiuser detectors that involve only forward recursions and have reduced complexity and delay.

A new algorithm for optimal design of joint source-channel coding over coded communication systems is presented. The source coder and decoder (codec) based on the vector quantization (VQ) technique are optimized for the coded system by minimizing the end-to-end system distortion. The channel codec is realized with a turbo code. The proposed algorithm is derived based on a discrete-input analog-output additive white Gaussian noise (AWGN) channel model. Maximum a posteriori (MAP) are used for soft-output channel decoding. The soft output signals are applied directly to the designed optimal source decoder for source decoding. Applications of the proposed algorithm for wireless low-bit-rate speech transmission is also given. Simulation results show that, the proposed algorithm can objectively gain 4-5 dB improvement of decoded source SNR (DSSNR) for a first-order Gauss-Markov source, and subjectively achieve 2-3 mean-opinion scores (MOS) enhancement for the decoded speech quality relative to the traditional system with hard decoding outputs, especially over the low SNR channels.

The performance of turbo codes on frequency non-selective slow Rician fading channels is considered. The analytical average upper bounds of turbo code performance over the channels with independent fading are derived. Furthermore, we developed a decoding metric for fading channels and log-likelihood ratios and extrinsic information for maximum a posteriori (MAP) and soft output Viterbi algorithm (SOVA) decoding methods. Finally, the derived upper bounds are evaluated and the bit error rates for turbo codes over independent and correlated fading channels are simulated. The performance of the MAP and SOVA iterative decoding methods, with and without channel state information is discussed. It is shown that the SOVA decoder has almost the same performance as the MAP decoder on correlated fading channels if ideal channel state information is used.

The impact of the distance spectrum and interleaver structure on the bit error probability of turbo codes is considered. A new turbo code design method for Gaussian channels is presented. The proposed method combines a search for good component codes with interleaver design. The optimal distance spectrum is used as the design criterion to construct good turbo component codes at low signal-to-noise ratios (SNRs). In addition, an interleaver design method is proposed. This design improves the code performance at high SNR. Search for good component codes at low SNR is combined with a code matched interleaver design. This results in new turbo codes with a superior error performance relative to the best known codes at both low and high SNR. The performance is verified by both analysis and simulation.

A review of adaptive detection techniques for direct-sequence code division multiple access (CDMA) signals is given. The goal is to improve CDMA system performance and capacity by reducing interference between users. The techniques considered are implementations of multiuser receivers, for which background material is given. Adaptive algorithms improve the feasibility of such receivers. Three main forms of receivers are considered. The minimum mean square error (MMSE) receiver is described and its performance illustrated. Numerous adaptive algorithms can be used to implement the MMSE receiver, including blind techniques, which eliminate the need for training sequences. The adaptive decorrelator can be used to eliminate interference from known interferers, though it is prone to noise enhancement. Multistage and successive interference cancellation techniques reduce interference by cancellation of one detected signal from another. Practical problems and some open research topics are mentioned. These typically relate to the convergence rate and tracking performance of the adaptive algorithm.