ABSTRACT This study proposes a federated split learning framework for large language models (FedsLLM) integrated with rate‐splitting multiple access (RSMA), aimed at enhancing the efficiency and privacy of LLM training in wireless communication systems. By leveraging low‐rank adaptation (LoRA) to distribute computational loads and a fluid antenna system to dynamically optimize channel capacity, the framework effectively reduces training latency through joint optimization of learning accuracy and communication resources. Experimental results demonstrate that the proposed framework significantly outperforms traditional time‐division multiple access including time division multiple access, frequency division multiple access (FDMA), enhanced bandwidth FDMA, and fairness‐enhanced FDMA across multiple metrics: at a transmit power of 20 dBm, RSMA reduces task completion time by 8.3%; under 20 MHz bandwidth, it achieves a 25% performance improvement; and even with a data volume of 900 Kbits, it maintains a 12% advantage. The adopted alternating optimization algorithm converges rapidly, reaching 95% of the optimal value within only 5 iterations, substantially outperforming the fixed‐point method. Overall, FedsLLM‐RSMA effectively addresses privacy, computational and communication bottlenecks in distributed LLM training. Compared to TDMA, it reduces total training latency by 28% and improves communication efficiency by 35%, while achieving higher model accuracy and faster convergence. This work provides a viable pathway for efficient and scalable deployment of LLMs in 6G networks.

In this paper, we investigate an uplink multi-modal probabilistic semantic communication (PSCom) system based on probability graph in the satellite scenario. The system consists of both: semantic computation and traditional communication. Firstly, at the user end, the transmitted data is compressed based on the probabilistic graph. Then, the compressed data is transmitted to the satellite, which uses the same probabilistic graph to recover the received data. In the considered model, this paper addresses an optimization problem for multi-modal multi-user semantic communication across multiple time slots. An optimization problems formulated aiming to minimize the total energy consumption of the PSCom system, with satisfying the transmission time, transmission power, transmission bandwidth, local computation frequency, and transmission data requirements. To solve this problem, the Lyapunov drift-plus-penalty function based on online optimization is first used to transform the multi-slot problem into a stochastic single-slot problem, thereby converting the optimization problem into a trade-off between system energy consumption and queue length. Subsequently, an alternating algorithm is proposed to iteratively optimize, semantic compression rate, local computation frequency, transmission bandwidth, transmission power, and time allocation variables. Finally, simulation experiments demonstrates the effectiveness of the proposed algorithm.

This paper aims to develop an effective uplink access scheduling strategy for massive Internet-of-Things (IoT) networks. To better reap the benefits of uplink resources, the BS has to adjust the uplink resources relies on the network states available at the BS. However, in massive IoT networks, the acquisition of network states, including traffic arrivals, channel conditions, and energy supply rate, are typically obtained through in-band feedback from devices. Therefore, the network states available at the BS are differently outdated across devices, as the staleness depends on the time elapsed since each device was last scheduled. This motivates us to develop a proactive scheduling scheme that enables the BS to schedule uplink access under differently outdated states information. To combat the performance loss caused by the outdated states information, we propose a novel primal-dual online learning framework. This framework leverages mini-batch gradient descent for dual updates and employs Online Convex Optimization for proactive primal updates, which effectively predicting current network states based on outdated knowledge. We evaluate the performance of the proposed proactive scheduling scheme against the offline optimum, which is optimized using prior knowledge of network states. The performance analysis shows that the proactive scheme asymptotically approaches to the offline optimum. Simulation results further validate the effectiveness of the proposed algorithm by comparing to other benchmarks.

Multimodal learning emerges as a promising solution for high-precision localization, a cornerstone of 6G integrated sensing and communications (ISAC), by integrating measurements from different data sources. Yet its real-world deployment remains challenging because(i)the quality and relevance of different modalities fluctuate with frequency, noise, and antenna heterogeneity and(ii)spatial and fingerprint ambiguities under non-line-of-sight (NLOS) propagation obscure the mapping between channel measurements and positions. To overcome these challenges, we propose a spatial-context-aware dynamicfusion architecture built on the mixture-of-experts (SCADF-MoE) backbone. We first construct a million-scale comprehensive ray-tracing dataset measuring synchronized angle, distance, gain, and channel across diverse carrier frequencies, antenna geometries, and noise levels. A three-stage pre-processing pipeline then clusters neighboring points into short trajectories, enriching data samples with spatial context information. The resulting sequences are fed into SCADF-MoE: first, multimodal soft MoE blocks with learnable routing matrices dynamically fuse heterogeneous inputs according to their modality relevance in different environmental contexts; second, a modality-task MoE formulates position estimation as a multi-objective problem, simultaneously predicting coordinates of neighboring points to leverage their shared spatial correlations. Additionally, we introduce a regularization loss that enforces expert diversity and mitigates gradient conflicts during multi-task optimization. Simulations across three environments (dense-urban, suburban, canyon) and three heterogeneity dimensions (frequency, noise, antenna) demonstrate that SCADF-MoE achieves consistent sub-meter accuracy in all conditions, reducing overall MSE by 63%, and cuts unseen-NLOS error by 55% compared to state-of-the-art methods. To the best of our knowledge, this is the first work that leverages large-scale multimodal MoEs for high-precision ISAC localization.

In this paper, we propose an energy efficient wireless communication system based on fluid antenna relay (FAR) to solve the problem of non-line-of-sight (NLoS) links caused by blockages with considering the physical properties. Driven by the demand for the sixth generation (6G) communication, fluid antenna systems (FASs) have become a key technology due to their flexibility in dynamically adjusting antenna positions. Existing research on FAS primarily focuses on line-of-sight (LoS) communication scenarios, and neglects the situations where only NLoS links exist. To address the issues posted by NLoS communication, we design an FAR-assisted communication system combined with amplify-and-forward (AF) protocol. In order to alleviate the high energy consumption introduced by AF protocol while ensuring communication quality, we formulate an energy efficiency (EE) maximization problem. By optimizing the positions of the fluid antennas (FAs) on both sides of the FAR, we achieve controllable phase shifts of the signals transmitting through the blockage which causes the NLoS link. Besides, we establish a channel model that jointly considers the blockage-through matrix, large-scale fading, and small-scale fading. To maximize the EE of the system, we jointly optimize the FAR position, FA positions, power control, and beamforming design under given constraints, and propose an iterative algorithm to solve this formulated optimization problem. Simulation results show that the proposed algorithm outperforms the traditional schemes in terms of EE, achieving up to 23.39% and 39.94% higher EE than the conventional reconfigurable intelligent surface (RIS) scheme and traditional AF relay scheme, respectively.

The communication performance of traditional two-dimensional (2D) antenna arrays is approaching its theoretical limit under constraints of physical size and hardware costs, thus failing to meet the escalating demands of wireless communications. While double-layer three-dimensional (3D) antenna arrays presents a breakthrough for overcoming this bottleneck by exploiting the additional degrees of freedom, its implementation is hindered by several challenges, notably the issues of codebook design. In this paper, we propose a novel codebook scheme tailored for 3D antenna array structures. Specifically, an angle-distance-aware codebook for 3D antenna arrays is designed to cater to both near-field and far-field scenarios by minimizing inter-beam interference, with proven asymptotic orthogonality. Furthermore, evanescent codewords for both regions are effectively eliminated to improve codebook construction efficiency. Simulation results illustrate the superior performance of the proposed codebook over 2D baselines, with a 29% and 12% narrower angular and distance beamwidth ofh=λ, and a 27% gain in spectral efficiency ofh=0.5λ, owing to the vertical dimension. Moreover, practical mutual coupling that manifests as beam deviations and broadening is analyzed to establish a basis for future work.

The rapid evolution of the forthcoming sixth-generation (6G) wireless network necessitates seamless integration of artificial intelligence (AI) with wireless communications to support emerging intelligent applications that demand both efficient communication and robust learning performance. This dual requirement calls for a unified framework of integrated learning and communication (ILAC), where AI enhances communication through intelligent signal processing and resource management, while wireless networks facilitate AI model deployment by enabling efficient and reliable data exchanges. However, achieving this integration presents significant challenges in practice. Communication constraints, such as limited bandwidth and fluctuating channels, hinder learning accuracy and convergence. Simultaneously, AI-driven learning dynamics, including model updates and task-driven inference, introduce excessive burdens on communication, necessitating flexible context-aware transmission strategies. This paper provides a comprehensive overview of ILAC design and optimization strategies. We establish corresponding foundational principles, covering system architectures and presenting a unified optimization formulation that closely links learning performance with communication efficiency. We then review recent advancements in ILAC from the strategic perspectives of model and data distributions, computational complexity, and communication overhead. Despite considerable progress, existing ILAC approaches still suffer from high communication overhead, unstable convergence, and scalability challenges. To address these issues, we propose an enhanced ILAC framework with large AI models leveraging hyperdimensional computing (HDC). In particular, utilizing large AI models improves generalization capabilities under dynamic task and network conditions, while HDC provides lightweight high-dimensional representations that reduce both communication and learning costs. Finally, we present a case study on a cost-to-performance optimization problem, where task assignments, model size selection, bandwidth allocation, and transmission power control are jointly optimized, aiming at improving both communication efficiency and inference accuracy with reduced computational cost. Leveraging the Dinkelbach and alternating optimization algorithms, we offer a practical and effective solution to achieve an optimal balance between learning performance and communication constraints.

Current congestion control algorithms ignore the application-layer delay, where the untransmitted data waiting at source nodes degrades the delay performance of services. Moreover, differentiated priorities are necessary for the application services with various delay requirements, especially for mission-critical services. Different from the existing works only considering the network delay, in this paper, by adding the flow queueing delay at source nodes, we formulate the network utility maximization (NUM) problem with additional deterministic service delay constraints. We propose a general TCP-based two-timescale congestion window control (TCWC) framework with delay-aware priority to enhance traditional algorithms. Specifically, to handle the obstacle of new delay constraints, we transform them to the time-average stability of virtual queues. By solving the new NUM problem via Lyapunov optimization, we design a short-term congestion window adjustment strategy in each time slot. To further guarantee the service delay, we apply extreme value theory (EVT) to evaluate the priorities of different flows, and determine the long-term control of window update rates. We deploy the proposed framework in three classic algorithms including NewReno, Vegas and DCTCP. In addition, simulation results show that our TCWC framework can significantly reduce the average service delay and provide deterministic guarantees compared with time-aware TCP congestion control algorithms such as TIMELY and BBRv2.

Wireless federated analytics face two critical challenges: data privacy and communication efficiency, since the local data may contain sensitive information and the users may be equipped with limited communication capability. Existing methods often adopt a direct combination of privacy-preservation schemes and compression mechanisms but overlook the privacy amplification effect from errors introduced in compression and wireless communication. With such consideration, a Differentially Private Quadrature Amplitude Modulation (DP-QAM) scheme, which leverages privacy amplification from both compression and noisy wireless channels, is proposed. The privacy guarantee is established in terms of the emergingf-DP, and the trade-off between privacy, communication cost, and accuracy in terms of mean square error (MSE) is characterized in the fundamental use cases of distributed mean estimation and frequency estimation, which outperforms the state-of-the-art methods. Moreover, the advantage of the proposed method over the classic Gaussian mechanism is further demonstrated from a rate-distortion perspective. Finally, extensive simulation results validate the effectiveness of the proposed mechanism.

Point clouds have gained prominence across numerous applications due to their ability to accurately represent 3D objects and scenes. However, efficiently compressing unstructured, high-precision point cloud data remains a significant challenge. In this paper, we propose NeRC ${}^{\textbf {3}}$ , a novel point cloud compression framework that leverages implicit neural representations (INRs) to encode both geometry and attributes of dense point clouds. Our approach employs two coordinate-based neural networks: one maps spatial coordinates to voxel occupancy, while the other maps occupied voxels to their attributes, thereby implicitly representing the geometry and attributes of a voxelized point cloud. The encoder quantizes and compresses network parameters alongside auxiliary information required for reconstruction, while the decoder reconstructs the original point cloud by inputting voxel coordinates into the neural networks. Furthermore, we extend our method to dynamic point cloud compression through techniques that reduce temporal redundancy, including a 4D spatio-temporal representation termed 4D-NeRC ${}^{\textbf {3}}$ . Experimental results validate the effectiveness of our approach: For static point clouds, NeRC ${}^{\textbf {3}}$ outperforms octree-based G-PCC standard and existing INR-based methods. For dynamic point clouds, 4D-NeRC ${}^{\textbf {3}}$ achieves superior geometry compression performance compared to the latest G-PCC and V-PCC standards, while matching state-of-the-art learning-based methods. It also demonstrates competitive performance in joint geometry and attribute compression.

As wireless communication applications evolve from traditional multipath environments to high-mobility scenarios like unmanned aerial vehicles, multiplexing techniques have advanced accordingly. Traditional single-carrier frequency-domain equalization (SC-FDE) and orthogonal frequency-division multiplexing (OFDM) have given way to emerging orthogonal timefrequency space (OTFS) and affine frequency-division multiplexing (AFDM). These approaches exploit specific channel structures—e.g., Toeplitz-structured multipath channel matrix for OFDM and SC-FDE or doubly selective channels for OTFS and AFDM—to diagonalize or sparsify the effective channel, thereby enabling low-complexity detection. However, their reliance on these structures significantly limits their robustness in dynamic, real-world environments. To address these challenges, this paper studies a random multiplexing technique that is decoupled from the physical channels, thereby enabling its application to arbitrary norm-bounded and spectrally convergent channel matrices. Random multiplexing achieves statistical fading-channel ergodicity for transmitted signals by constructing an equivalent input-isotropic channel matrix in the random transform domain. It guarantees the asymptotic replica MAP bit-error rate (BER) optimality of AMP-type detectors for linear systems with arbitrary norm-bounded, spectrally convergent channel matrices and signaling configurations, under the unique fixed point assumption. A low-complexity cross-domain memory AMP (CD-MAMP) detector is considered for random multiplexing systems, leveraging the sparsity of the time-domain channel and the input isotropy of the equivalent channel. Optimal power allocations are derived to minimize the replica MAP BER and maximize the replica constrained capacity of random multiplexing systems, respectively. The optimal coding principle and replica constrained-capacity optimality of CD-MAMP detector are investigated for random multiplexing systems. Additionally, the versatility of random multiplexing in diverse wireless applications is explored. Numerical results are presented to validate the theoretical findings.

This paper addresses the challenge of achieving security in semantic communication (SemCom) over a wiretap channel, where a legitimate receiver coexists with an eavesdropper experiencing a poorer channel condition. Despite previous efforts to secure SemCom against eavesdroppers, guarantee of approximately zero information leakage remains an open issue. In this work, we propose a secure SemCom approach based on superposition code, aiming to provide quantifiable and controllable security for digital SemCom systems. The proposed method employs a double-layered constellation map, where semantic information is associated with satellite constellation points and cloud center constellation points are randomly selected. By carefully allocating power between these two layers of constellation, we ensure that the symbol error probability (SEP) of the eavesdropper when decoding satellite constellation points is nearly equivalent to random guessing, while maintaining a low SEP for the legitimate receiver to successfully decode the semantic information. Simulation results demonstrate that the peak signal-to-noise ratio (PSNR) and mean squared error (MSE) of the eavesdropper's reconstructed data, under the proposed method, can range from decoding Gaussian-distributed random noise to approaching the variance of the data. This validates the effectiveness of our method in nearly achieving the experimental upper bound of security for digital SemCom systems when both eavesdroppers and legitimate users utilize identical decoding schemes. Furthermore, the proposed method consistently outperforms benchmark techniques, showcasing superior data security and robustness against eavesdropping. The implementation code is publicly available at:https://github.com/1weixuanchen/A-Superposition-Code-Based-Semantic-Communication.

Semantic communication emphasizes the transmission of meaning rather than raw symbols. It offers a promising solution to alleviate network congestion and improve transmission efficiency. In this paper, we propose a wireless image communication framework that employs probability graphs as shared semantic knowledge base among distributed users. High-level image semantics are represented via scene graphs, and a two-stage compression algorithm is devised to remove predictable components based on learned conditional and co-occurrence probabilities. At the transmitter, the algorithm filters redundant relations and entity pairs, while at the receiver, semantic recovery leverages the same probability graphs to reconstruct omitted information. For further research, we also put forward a multi-round semantic compression algorithm with its theoretical performance analysis. Simulation results demonstrate that our semantic-aware scheme achieves superior transmission throughput and satiable semantic alignment, validating the efficacy of leveraging high-level semantics for image communication.

Orthogonal delay-Doppler division multiplexing (ODDM) modulation has recently been regarded as a promising technology to provide reliable communications in high-mobility situations. Accurate and low-complexity channel estimation is one of the most critical challenges for massive multiple input multiple output (MIMO) ODDM systems, mainly due to the extremely large antenna arrays and high-mobility environments. To overcome these challenges, this paper addresses the issue of channel estimation in downlink massive MIMO-ODDM systems and proposes a low-complexity algorithm based on memory approximate message passing (MAMP) to estimate the channel state information (CSI). Specifically, we first establish the effective channel model of the massive MIMO-ODDM systems, where the magnitudes of the elements in the equivalent channel vector follow a Bernoulli-Gaussian distribution. Further, as the number of antennas grows, the elements in the equivalent coefficient matrix tend to become completely random. Leveraging these characteristics, we utilize the MAMP method to determine the gains, delays, and Doppler effects of the multi-path channel, while the channel angles are estimated through the discrete Fourier transform method. Finally, numerical results show that the proposed channel estimation algorithm approaches the Bayesian optimal results when the number of antennas tends to infinity and improves the channel estimation accuracy by about 30% compared with the existing algorithms in terms of the normalized mean square error.

In this paper, we incorporate physical knowledge into learning-based high-precision target sensing using the multi-view channel state information (CSI) between multiple base stations (BSs) and user equipment (UEs). Such kind of multi-view sensing problem can be naturally cast into a conditional generation framework. To this end, we design a bipartite neural network architecture, the first part of which uses an elaborately designed encoder to fuse the latent target features embedded in the multi-view CSI, and then the second uses them as conditioning inputs of a powerful generative model to guide the target’s reconstruction. Specifically, the encoder is designed to capture the physical correlation between the CSI and the target, and also be adaptive to the numbers and positions of BS-UE pairs. Therein the view-specific nature of CSI is assimilated by introducing a spatial positional embedding scheme, which exploits the structure of electromagnetic(EM)-wave propagation channels. Finally, a conditional diffusion model with a weighted loss is employed to generate the target’s point cloud from the fused features. Extensive numerical results demonstrate that the proposed generative multi-view (Gen-MV) sensing framework exhibits excellent flexibility and significant performance improvement on the reconstruction quality of target’s shape and EM properties.

In this paper, we explore the integration of communication and synthetic aperture radar (SAR)-based remote sensing in low Earth orbit (LEO) satellite systems to provide real-time SAR imaging and information transmission. Considering the high-mobility characteristics of satellite channels and limited processing capabilities of satellite payloads, we propose an integrated communication and remote sensing architecture based on an orthogonal delay-Doppler division multiplexing (ODDM) signal waveform. Both communication and SAR imaging functionalities are achieved with an integrated transceiver onboard the LEO satellite, utilizing the same waveform and radio frequency (RF) front-end. Based on such an architecture, we propose a transmission protocol compatible with the 5G NR standard using downlink pilots for joint channel estimation and SAR imaging. Furthermore, we design a unified signal processing framework for the integrated satellite receiver to simultaneously achieve high-performance channel sensing, low-complexity channel equalization and interference-free SAR imaging. Finally, the performance of the proposed integrated system is demonstrated through comprehensive analysis and extensive simulations in the sub-6 GHz band. Moreover, a software-defined radio (SDR) prototype is presented to validate its effectiveness for real-time SAR imaging and information transmission in satellite direct-connect user equipment (UE) scenarios within the millimeter-wave (mmWave) band.

Low Earth orbit (LEO) satellite Internet of Things (IoT) has been identified as one of the important components of the sixth-generation (6G) non-terrestrial networks (NTN) to provide ubiquitous connectivity. Due to the low orbit altitude and high mobility, a massive number of satellites are required to form a global continuous coverage constellation, leading to a high construction cost. To this end, this paper proposes a LEO satellite IoT constellation design algorithm with the goal of minimizing the total cost while satisfying quality of service (QoS) requirements in terms of coverage ratio and communication quality. Specifically, with a novel fitness function and efficient algorithm’s operators, the proposed algorithm converges more quickly and achieves lower constellation construction cost compared to baseline algorithms under the same QoS requirements. Theoretical analysis proves the global and fast convergence of the proposed algorithm due to a novel fitness function. Finally, extensive simulation results confirm the effectiveness of the proposed algorithm in LEO satellite IoT constellation design.

This paper investigates the energy efficiency (EE) of the fluid antenna relay (FAR)-assisted wireless communication systems in non-line-of-sight (NLoS) scenarios. Unlike conventional fixed-position antenna systems, the FAR dynamically adjusts the spatial positions of fluid antennas (FAs), enabling efficient signal transmission through blockages. By integrating the amplify-and-forward (AF) protocol, the proposed FAR architecture amplifies and forwards signals while controlling phase shifts via FA reconfiguration. An optimization problem is formulated to maximize the system EE under given constraints. The problem is decomposed into three sub-problems including large-scale fading optimization, small-scale fading optimization, and joint power control and beamforming design optimization. These subproblems are solved iteratively with successive convex approximation (SCA) and Dinkelbach methods. Numerical simulation results demonstrate that the proposed algorithm significantly outperforms the existing STAR-RIS and AF relay schemes, improving EE of the system by up to 29.92% and 45.04%, respectively. The work in this paper bridges the research gap in FAS research with NLoS challenges and provides a framework for future FAR-enabled wireless communication systems.

This paper investigates the energy efficiency maximization problem in a resource-constrained visible light communication (VLC)-based probabilistic semantic communication (PSCom) system. In the considered model, light-emitting diode (LED) transmitters perform semantic compression, reducing data size at the cost of computation overhead. The compressed semantic information is transmitted to the users for semantic inference based on a shared knowledge base, which requires regular updates to maintain synchronization. Rate splitting multiple access (RSMA) is used to transmit both knowledge base and information data simultaneously. The goal is to maximize the energy efficiency of the system through optimizing transmit beamforming, direct current (DC) bias, rate allocation, and semantic compression ratio, considering both communication and computation costs. An alternating optimization algorithm, utilizing successive convex approximation and Dinkelbach method, is proposed to solve the problem. Simulation results validate the effectiveness of the proposed approach.

In this paper, we propose a novel high-dimensional channel acquisition framework in multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. It incorporates the user's approximate position to infer large-scale features such as multipath structure, performs real-time coarse estimation to capture rapidly varying features such as phase information, and finally fuses them to represent the complete channel. This framework effectively circumvents the impractical requirement for wavelength-level position accuracy in existing position-to-channel prediction methods, while incorporating spatial features that incur no additional pilot overhead to enhance channel representation. To implement this framework, we employ deep learning techniques and propose two specialized neural networks. First, we design a position-aided complex-domain multilayer perceptron mixer (CMixer) network that processes position coordinate and coarse estimate as two information flows, using convolutional interaction for cross-modal fusion. Furthermore, we propose an enhanced attention-based spatial channel deduction network (ASCDNet). This network replaces approximate position coordinates with sampled spatial neighborhood channels, a priori achieving modal alignment of input to improve fusion efficiency. Comprehensive experimental evaluations demonstrate the superior accuracy and strong robustness of the proposed methods. For instance, leveraging meter-level precision position coordinate, ASCDNet achieves 7.93~9.54 dB gains in normalized channel error and enables up to 64% pilot reduction compared to state-of-the-art estimation method.

Generalization from a specific scenario to the whole network is of particular importance in wireless artificial intelligence, which usually calls for scaling up in training data and brings unaffordable costs of both data collection and model training. Motivated by the fact that local similarity among wireless scenarios probably means certain similarity in wireless channel characteristics, collecting data from representative scenarios may be an efficient way to scale down the training dataset. In this paper, we propose a proactive approach for scenario diversity assessment so as to choose the most representative scenarios for training data collection. Specifically, this approach first utilizes publicly available coarse environmental maps to extract the geometric features, then combines these with electromagnetic signal propagation models to synthesize electromagnetic characteristic distributions associated with the scenario. By employing Wasserstein distance to quantify the similarity between electromagnetic characteristic distributions across different scenarios, scenario-granularity clustering is achieved for selecting representative scenarios to construct a global dataset with sufficient information for all scenarios. Experimental results demonstrate that the proposed approach can achieve a Pearson correlation coefficient above 0.8 w.r.t. cross-scenario generalization performance, and it further improves the final multi-scenario generalization, surpassing existing methods while requiring no pre-measurement data.

In integrated sensing and communication (ISAC) systems, effectively processing multi-view data from multiple transmitter-receiver pairs is critical for high-quality environment reconstruction. This paper proposes a generative learning method with multi-view channel feature aggregation for solving multi-user equipment (UE) multi-base station (BS) collaborative sensing problem. Specifically, based on the revealed similarities between different BS-UE transmission links, we design a flexible channel state information encoder shared among various transmitter-receiver views to learn scatterer features. Then, a concise aggregation mechanism is introduced for multi-view feature fusion. Subsequently, considering the intrinsic probabilistic properties of the scatterer point cloud, we employ a diffusion model to decode the aggregated feature for the final environment reconstruction. These designs enables the proposed scheme to structurally adapt to the dynamic changes in the positions and numbers of BSs and UEs, ultimately achieving high-quality multi-view collaborative sensing. Extensive numerical experiments validate the effectiveness and desirable properties of the proposed method.

The demand for accurate sensing in the complex environment, such as urban areas and indoor spaces, is critical for future wireless networks, where scattered signals become essential for sensing occluded targets under non-line-of-sight (NLOS) conditions. To reduce the demand for beam-sweeping and geometric assumptions, we propose a bistatic NLOS sensing technique that fully exploits the scattered signals. By modeling the scattering channel responses and leveraging sparsity-driven compressed sensing, our method achieves robust environmental reconstruction to estimate target positions, shapes, and orientations. The proposed algorithm is applicable for estimating the parameters of first-order and second-order scattering targets. Experimental results demonstrate its superiority in occluded target sensing and environmental mapping, thus offering an efficient solution for NLOS sensing in complex scenarios.

Performance measurement is one of the key enablers towards the realization of future Smart Grids (SGs). However, balancing the trade-off among measurement completeness, accuracy, and timeliness under constrained resources, while providing targeted support for communication-intensive areas consisting of critical infrastructure remains a challenge. In this paper, we propose a hybrid network Performance Measurement (PM) framework for deterministic SGs in two steps. 1) To obtain PM information for the whole network without incurring overhead, passive measurement with missing data imputation is tailored for meeting the divergence of packet loss types of large-scale SGs based on the proposed Temporal-Spatial based Distinguishable Generative Adversarial Imputation Network (STD-GAIN), which explores and exploits both the spatial-temporal correlation and higher-order local correlation for refined imputation performance. 2) Regarding the circumstance that the reliability of the imputed data does not meet the requirement, active measurement is conducted at a finer granularity, i.e., along a specified path, where All-Pair Shortest Path (APSP) model is utilized to find the best paths. Finally, the simulation results show that the proposed framework could achieve complete, accurate and real-time network performance measurement for SGs.

Delivering high-quality 360-degree VR video challenges the communication system on low latency and high quality transmission, which can be resolved by tile-based transmission technology. However, existing works do not take into account unexpected events and consider field-of-view prediction and bandwidth allocation separately, resulting in the inability to match dynamic transmission conditions and further resulting in significant performance loss. This paper proposes a multimodal fusion-based framework to enhance viewpoint prediction which is jointly optimized with adaptive streaming. The proposed approach presents a neural network model to predict the viewpoint of the user based on multimodal fusion technique, which takes unexpected events into consideration for refined accuracy. After the prediction, we assign weights to the tiles based on the results and further allocate bandwidth and rate to optimize the user experience. Experimental results on real-world datasets demonstrate that the proposed method outperforms existing approaches, significantly enhancing user experience by 16% on average in adaptive VR streaming.

Large language models (LLMs) face significant challenges in specialized domains like telecommunication (Tele-com) due to technical complexity, specialized terminology, and rapidly evolving knowledge. Traditional methods, such as scaling model parameters or retraining on domain-specific corpora, are computationally expensive and yield diminishing returns, while existing approaches like retrieval-augmented generation, mixture of experts, and fine-tuning struggle with accuracy, efficiency, and coordination. To address this issue, we propose Telecom mixture of models (TeleMoM), a consensus-driven ensemble framework that integrates multiple LLMs for enhanced decision-making in Telecom. TeleMoM employs a two-stage process: proponent models generate justified responses, and an adjudicator finalizes decisions, supported by a quality-checking mechanism. This approach leverages strengths of diverse models to improve accuracy, reduce biases, and handle domain-specific complexities effectively. Evaluation results demonstrate that TeleMoM achieves a 9.7% increase in answer accuracy, highlighting its effectiveness in Telecom applications.

In this paper, we investigate the integration of communication and synthetic aperture radar (SAR)-based remote sensing in low Earth orbit (LEO) satellite systems. To address the high-mobility characteristic of LEO satellites, we propose an integrated system architecture based on an orthogonal delay-Doppler division multiplexing (ODDM) signal waveform. Specifically, we provide a wireless frame compatible with the 5G NR standard for signal sharing and design a unified channel sensing scheme that utilizes shared ODDM signals for both channel estimation in communication and interference-free range reconstruction in SAR imaging. Finally, numerical simulation results confirm the effectiveness of the proposed scheme.

Low earth orbit (LEO) satellite Internet of Things (IoT) has been recognized as a pivotal element within the realm of sixth-generation (6 G) non-terrestrial networks (NTN), aimed at delivering ubiquitous connectivity. Due to the low orbit altitude and fast movement speed, a massive number of satellites are needed to form a satellite constellation, resulting in substantial construction costs. To this end, this paper proposes a LEO satellite IoT constellation design algorithm with the goal of minimizing the total cost while satisfying quality of service (QoS) requirements in terms of coverage ratio and communication quality. Simulation results validate the efficiency of the proposed algorithm in LEO satellite IoT constellation.

Millimeter-wave (mmWave) radar in beamforming mode offers high-resolution sensing with an enhanced signal-to-noise ratio (SNR). However, in sparse antenna arrays, the effectiveness of beamforming is limited by sidelobe and grating lobe interference, which degrades angular resolution and imaging performance. Traditional algorithms often discard sidelobe and grating lobe echoes as noise or interference, missing valuable information for improved resolution. In this paper, we propose a novel high-resolution imaging algorithm that integrates sidelobe and grating lobe effects into the data processing. By developing an enhanced frequency-modulated continuous wave (FMCW) radar beamforming signal model, we jointly process signals from all beam directions, including both the main lobe and non-main lobe reflections, using a least-squares (LS) approach. This method improves angular resolution, target discrimination, and noise suppression. Simulation results of range-angle map, chamfer distance, and success rate of distinguishing adjacent points demonstrate that our approach outperforms conventional beamforming (CBF) methods and canonical polyadic decomposition (CPD) methods, improving resolution and reducing sidelobe and grating lobe artifacts, offering a promising solution for high-resolution mmWave radar imaging in sparse array configurations.

The prevalence of computational-intensive tasks stimulate the emergence of Computing Power Networks (CPN), where Software Defined Network (SDN) is utilized for flexible control. Considering the coexistence of in-execution tasks in the network and the arrival of emergency tasks, pre-migration is essential for reserving computing resources in advance, while it results in extra transmission cost in data plane and control cost in control plane. In this paper, we propose an analytical framework of task pre-migration policy in the SDN-based CPN to reduce system cost while meeting the delay budget. Specially, we consider the delay in SDN control plane induced by pre-migration flows, which involves$\ell_{0}-\mathbf{norm}$and causes the problem non-convex and discontinuous. To overcome the challenges, we propose an iterative approximation algorithm to handle$\ell_{0}-\mathbf{norm}$constraints, which transforms the original problem into a continuous function. Then we propose a dynamic programming based algorithm to minimize the approximation error, aiming at delay guarantee. The superiority of the proposed algorithm is verified through simulations, especially for heavy task offloading.

Abstract A ‐user cyclic shift‐aided serial concatenated code (SCC) is proposed over a Gaussian multiple access channel (MAC). In this code, a cyclic shift‐aided SCC, which integrates a regular repeat‐accumulate code with rate‐ and cyclic shift spreading (CSS), is employed for each user. Here, CSS consists of a length‐ bit spreading, a same chip‐level interleaver and a user‐specific cyclic shifter. This change avoids repeated complex interleaver generation operation and the main objective is to replace the random interleaver. At the receiver, iterative decoding is considered. For the design and optimization of the proposed code, a fixed point analysis (FPA) is developed to obtain the optimal and that achieves the maximum sum rate. Then, an analytic expression of collision probability is derived as a function of and . Finally, the advantage is analysed by comparing the generation time of interleaver. Moreover, FPA provides lower computation complexity in optimizing system parameters than the traditional EXIT chart method. Numerical results show that the design and optimization of the proposed code is more accurate, and support distinguishing between the users. The proposed code not only outperforms the traditional IDMA scheme in the bit error rate performance, but also provides much lower generation time of interleaver, especially for multiuser scenarios.

Real-time and contactless monitoring of vital signs, such as respiration and heartbeat, alongside reliable communication, is essential for modern healthcare systems, especially in remote and privacy-sensitive environments. Traditional wireless communication and sensing networks fall short in meeting all the stringent demands of eHealth, including accurate sensing, high data efficiency, and privacy preservation. To overcome the challenges, we propose a novel integrated sensing, computing, and semantic communication (ISCSC) framework. In the proposed system, a service robot utilises radar to detect patient positions and monitor their vital signs, while sending updates to the medical devices. Instead of transmitting raw physiological information, the robot computes and communicates semantically extracted health features to medical devices. This semantic processing improves data throughput and preserves the clinical relevance of the messages, while enhancing data privacy by avoiding the transmission of sensitive data. Leveraging the estimated patient locations, the robot employs an interacting multiple model (IMM) filter to actively track patient motion, thereby enabling robust beam steering for continuous and reliable monitoring. We then propose a joint optimisation of the beamforming matrices and the semantic extraction ratio, subject to computing capability and power budget constraints, with the objective of maximising both the semantic secrecy rate and sensing accuracy. Simulation results validate that the ISCSC framework achieves superior sensing accuracy, improved semantic transmission efficiency, and enhanced privacy preservation compared to conventional joint sensing and communication methods.

How to reduce the pilot overhead required for channel estimation? How to deal with the channel dynamic changes and error propagation in channel prediction? To jointly address these two critical issues in next-generation transceiver design, in this paper, we propose a novel framework named channel deduction for high-dimensional channel acquisition in multiple-input multiple-output (MIMO)-orthogonal frequency division multiplexing (OFDM) systems. Specifically, it makes use of the outdated channel information of past time slots, performs coarse estimation for the current channel with a relatively small number of pilots, and then fuses these two information to obtain a complete representation of the present channel. The rationale is to align the current channel representation to both the latent channel features within the past samples and the coarse estimate of current channel at the pilots, which, in a sense, behaves as a complementary combination of estimation and prediction and thus reduces the overall overhead. To fully exploit the highly nonlinear correlations in time, space, and frequency domains, we resort to learning-based implementation approaches. By using the highly efficient complex-domain multilayer perceptron (MLP)-mixer for across-space-frequency-domain representation and the recurrence-based or attention-based mechanisms for the past-present interaction, we respectively design two different channel deduction neural networks (CDNets). We provide a general procedure of data collection, training, and deployment to standardize the application of CDNets. Comprehensive experimental evaluations in accuracy, robustness, and efficiency demonstrate the superiority of the proposed approach, which reduces the pilot overhead by up to 88.9% compared to state-of-the-art estimation approaches and enables continuous operating even under unknown user movement and error propagation.

Presents corrections to the paper, Coverage Rate Analysis for Integrated Sensing and Communication Networks.

Benefited from the advances of deep learning (DL) techniques, deep joint source-channel coding (JSCC) has shown its great potential to improve the performance of wireless transmission. However, most of the existing works focus on the DL-based transceiver design of the JSCC model, while ignoring the resource allocation problem in wireless systems. In this paper, we consider a downlink resource allocation problem, where a base station (BS) jointly optimizes the compression ratio (CR) and power allocation as well as resource block (RB) assignment of each user according to the latency and performance constraints to maximize the number of users that successfully receive their requested content with desired quality. To solve this problem, we first decompose it into two subproblems without loss of optimality. The first subproblem is to minimize the required transmission power for each user under given RB allocation. We derive the closed-form expression of the optimal transmit power by searching the maximum feasible compression ratio. The second one aims at maximizing the number of supported users through optimal user-RB pairing, which is solved by utilizing bisection search as well as interior-point algorithm. To reduce the computational complexity, we propose a heuristic greedy algorithm to obtain a simplified problem. Then the Bregman alternating direction method of multipliers (BADMM) based algorithm is adopted to decompose the simplified problem into several subproblems that can be computed in parallel. Simulation results validate the effectiveness of the proposed resource allocation methods in terms of the number of satisfied users with given resources. It is also shown that the BADMM-based algorithm can significantly reduce the computational complexity and retain high performance.

While the average delay is traditionally an importance metric for system performance, the emerging technologies have given birth to a variety of critical applications, for which the deterministic delay guarantee is highly desired. When optimizing the average delay objective is embraced with satisfying the deterministic delay constraint, it leads to complicated coupling and brings new challenges to resource allocation. In this paper, we propose a renewal framework for multi-user power control and subband allocation to improve the comprehensive delay performance. The average delay objective is optimized under the Markov decision process (MDP) problem, while the deterministic delay constraint is satisfied through Lyapunov optimization with virtual queues. Due to the conflict of the inter-slot influence in MDP and the i.i.d. state requirement in Lyapunov approach, we exploit the recurrent property of queue states and construct a renewal system. By solving the equivalent infinite-horizon MDP in the renewal framework, we propose a resource allocation algorithm, which is proved to be asymptotically optimal. Finally, the simulation results demonstrate that the proposed scheme meets the deterministic delay constraint and achieves better average delay performance than existing baselines.

Deterministic demand of mission-critical applications is essential in edge computing systems for realizing Industry 4.0. However, the conventional average-based network slicing schemes incur unexpected long-tail delay, resulting in the failure to meet strict deterministic delay guarantee. To resolve this issue, in this paper, we construct a two-scale TNS-Net architecture for the URLLC slice under the network slicing paradigm, aiming to meet deterministic end-to-end (E2E) delay requirements of multiple users with minimal resource usage. We consider multi-access edge computing (MEC) and model it as a many-to-one cascade queue, which includes the offloading queues at the user equipments (UEs) and a computation queue at the server. To analyze the delay performance, we decompose the offloading process into transmission and vacation periods, and employ the weighted approximation to address the multi-UE coupling in the computation process to derive the closed-form approximate E2E delay distribution. Based on the derived delay distribution, we propose an iterative two-scale network slicing (TNS) algorithm to guarantee deterministic delay, and construct a TNS-based deep-unfolding neural network, called TNS-Net, to improve the solution in presence of inaccurate channel statistics. Moreover, for the training of TNS-Net with deterministic delay as the network input, we apply extreme value theory (EVT) to analyze the distribution characteristic of delay bound violation. Finally, simulation results demonstrate that our theoretical analysis provides a relatively accurate estimate and the proposed TNS-Net ensures better delay guarantee with lower resource consumption.

Semantic communication is envisioned as a key technology for future wireless networks due to its high communication efficiency. However, research combining semantic communication and advanced multiple access techniques, such as rate splitting multiple access (RSMA), is still lacking. In this paper, the problem of joint communication and computation resource allocation for probabilistic semantic communication (PSCom) with RSMA is investigated. In the considered model, the base station (BS) needs to transmit a large amount of data to multiple users with 1-layer RSMA. Due to limited communication resources, the BS is required to utilize semantic communication techniques to compress the original data. In this paper, we utilize knowledge graphs to represent semantic information and employ probabilistic graphs, which are shared between the BS and users, to further compress the knowledge graphs. The BS can use the probabilistic graph to compress the data to be transmitted, while the user can recover the compressed semantic information using the same shared probabilistic graph. The additional computation power required for semantic information compression inevitably results in a reduction in transmission power due to the limited total power budget. Considering the effect of semantic compression ratio, the semantic rate expression for RSMA is first obtained. Then, based on the obtained rate expression, an optimization problem is formulated with the aim of maximizing the sum of semantic rates of all users under total power, semantic compression ratio, and rate allocation constraints. To tackle this problem, an iterative algorithm is proposed, where the semantic compression ratio subproblem is addressed using a greedy algorithm, and the rate allocation and transmit beamforming design subproblem is solved using a successive convex approximation method. Numerical results validate the effectiveness of the proposed scheme.

With the explosive growth of maritime activities, it is expected to provide seamless communications with quality of service (QoS) guarantee over broad sea area. In the context, this paper proposes a space-air-ground-sea integrated maritime communication architecture combining satellite, unmanned aerial vehicle (UAV), terrestrial base station (TBS) and unmanned surface vessel (USV). Firstly, according to the distance away from the shore, the whole marine space is divided to coastal area, offshore area, middle-sea area and open-sea area, the maritime users in which are served by TBS, USV, UAV and satellite, respectively. Then, by exploiting the potential of integrated maritime communication system, a joint beamforming and trajectory optimization algorithm is designed to maximize the minimum transmission rate of maritime users. Finally, theoretical analysis and simulation results validate the effectiveness of the proposed algorithm.

Recently, studies have shown the potential of integrating field-type iterative methods with deep learning (DL) techniques in solving inverse scattering problems (ISPs). In this article, we propose a novel variational Born iterative network (VBIM-Net), to solve the full-wave ISPs with significantly improved structural rationality and inversion quality. The proposed VBIM-Net emulates the alternating updates of the total electric field and the contrast in the variational Born iterative method (VBIM) by multiple layers of subnetworks. We embed the analytical calculation of the contrast variation into each subnetwork, converting the scattered field residual into an approximate contrast variation and then enhancing it by a U-Net, thus avoiding the requirement of matched measurement dimension and grid resolution as in existing approaches. The total field and contrast of each layer’s output are supervised in the loss function of VBIM-Net, imposing soft physical constraints on the variables in the subnetworks, which benefits the model’s performance. In addition, we design a training scheme with extra noise to enhance the model’s stability. Extensive numerical results on synthetic and experimental data both verify the inversion quality, generalization ability, and robustness of the proposed VBIM-Net. This work may provide some new inspiration for the design of efficient field-type DL schemes.

Initial access (IA) is a procedure of establishing an initial connection between the base station (BS) and the users. In the fifth generation (5G) mobile communication system, the IA procedure includes beam management, which determines the beam pairs for random access (RA) and data transmission by beam sweeping. The existing beam sweeping method in the 3-rd generation partnership project (3GPP) standard mainly uses a predefined uniform beamforming codebook and sweeps the beams progressively, which is time-consuming and highly inflexible. In this paper, inspired by the fact that the highly non-uniform environment and user distribution mean part of the beam sweeping might be less beneficial, we propose a novel learning-based IA framework for the BS to optimize the beam sweeping patterns. Specifically, we resort to the deep reinforcement learning (DRL) approach to implicitly obtain the unknown environment and user distribution properties by continuously interacting with the environment, and then make decisions based on the rewards achieved by past actions. The simulation results show that our proposed scheme can save much time compared with the new radio (NR) and optimization methods under different datasets and conditions, which greatly improves the beam sweeping efficiency.

Initial random access is a crucial process in wireless communication networks, which sets up reliable connections between the base station (BS) and multiple active users. In this procedure, useful connection information can be naturally obtained to achieve user positioning, and the channel state information (CSI) of multiple users can be further exploited to realize environment sensing. On the other hand, environment sensing is highly related to user positioning as it requires user-specific CSI and benefits from multi-view observations from different user positions. Therefore, in this paper, we propose a joint initial random access, environment sensing, and user positioning framework. Specifically, oversampled cyclic prefixes (CPs) in orthogonal frequency division multiplexing (OFDM) systems, which contain rich environmental information, can be exploited to achieve enhanced channel estimation. Environment sensing and user positioning are further implemented based on the channel estimation results, and the scatter points are then clustered to reconstruct the environment objects. The simulation results show that the proposed framework can achieve a decimeter-level accuracy and a reconstruction ratio of about 89% for user positioning and environment sensing.

Integrated sensing and communication (ISAC) has emerged as a promising technology to facilitate high-rate communications and super-resolution sensing, particularly operating in the millimeter wave (mmWave) band. However, the vulnerability of mmWave signals to blockages severely impairs ISAC capabilities and coverage. To tackle this, an efficient and low-cost solution is to deploy distributed reconfigurable intelligent surfaces (RISs) to construct virtual links between the base stations (BSs) and users in a controllable fashion. In this paper, we model the generalized RIS-assisted mmWave ISAC networks considering the blockage effect, and examine the beneficial impact of RISs on the coverage rate utilizing stochastic geometry. Based on the proposed beam patterns and user association policies, we derive the conditional coverage probability and ergodic rate of communication and sensing dual functions for two association cases, as well as the marginal coverage rate using the distance-dependent thinning method. Taking into account the coupling effect of ISAC dual functions within the same network topology, we further calculate the joint coverage probability of ISAC performance. Simulation results verify the accuracy of derived theoretical formulations, and illustrate the impact of the RIS aperture, blockage, BS and RIS densities on ISAC coverage rates, which provide valuable guidelines for the practical network deployment. Specifically, our results indicate the superiority of the RIS deployment with the density of 40 km${}^{-2}$BSs, and that the joint coverage rate of ISAC performance exhibits potential growth from 62% to 97% with the deployment of RISs.

With the rapid popularity of vision-driven communication applications, object detection has become one of the fundamental techniques for performing practical vision tasks. In traditional communication systems, images are compressed for transmission, reconstructed at the receiver, and then processed by existing object detection algorithms. However, transmitting large amounts of images consumes significant storage and communication resources. To address this challenge, a semantic communication-based image reconstruction scheme has been proposed for object detection, which transmits only the semantic information relevant to image reconstruction. However, this method is prone to losing key information, such as object position and texture details, leading to degraded object detection performance. Additionally, it is sensitive to environmental factors such as weather and lighting, resulting in poor adaptability across multiple scenarios. To address these issues, we propose a multi-scale semantic communication framework for object detection that transmits only multi-scale semantic features relevant to the task and employs decoupling at the receiver to separate positional and classification information of target objects without requiring image reconstruction. To improve adaptability across multiple scenarios, we introduce a cross-domain object detection technique that ensures reliable object detection in new scenarios by optimizing the framework’s multi-scale semantic encoder through domain adversarial learning. Numerical results demonstrate that the proposed framework achieves mean average precision improvements of$15.4\% \sim 38.5\%$over the traditional communication framework within low to medium signal-to-noise ratio regions in additive white Gaussian noise and Rayleigh fading channels.

Advancements in emerging technologies, e.g., reconfigurable intelligent surfaces and holographic MIMO (HMIMO), facilitate unprecedented manipulation of electromagnetic (EM) waves, significantly enhancing the performance of wireless communication systems. To accurately characterize the achievable performance limits of these systems, it is crucial to develop a universal EM-compliant channel model. This paper addresses this necessity by proposing a comprehensive EM channel model tailored for realistic multi-path environments, accounting for the combined effects of antenna array configurations and propagation conditions in HMIMO communications. Both polarization phenomena and spatial correlation are incorporated into this probabilistic channel model. Additionally, physical constraints of antenna configurations, such as mutual coupling effects and energy consumption, are integrated into the channel modeling framework. Simulation results validate the effectiveness of the proposed probabilistic channel model, indicating that traditional Rician and Rayleigh fading models cannot accurately depict the channel characteristics and underestimate the channel capacity. More importantly, the proposed channel model outperforms free-space Green’s functions in accurately depicting both near-field gain and multi-path effects in radiative near-field regions. These gains are much more evident in tri-polarized systems, highlighting the necessity of polarization interference elimination techniques. Moreover, the theoretical analysis accurately verifies that capacity decreases with expanding communication regions of two-user communications.

Conventional wireless communication receivers guided by Bayesian inference methods need to know the exact statistical relationship among variables, which is hard to obtain accurately in wireless contexts, thus limiting the system performance. The recently emerging artificial intelligence (AI)-empowered algorithms have shown striking performances in exploring the implicit relationship among variables with specially designed Neural Networks (NNs). Therefore, it is preferable to integrate NNs with BPs in receiver design. Such approaches also leverage NNs’ lack of reasoning ability in large state spaces and traditional BPs’ lack of reasoning depth. However, conventional receiver modules are usually designed based on explicit mathematical derivations, which cannot be easily substituted with data-driven NNs as they may break the overall inner relationship of the algorithm. In this paper, we investigate how to beneficially incorporate NNs into the existing Belief Propagation (BP)-based framework, taking the traditional semi-blind estimation problem in an Orthogonal Frequency-Division Multiplexing (OFDM) receiver as an example. Unlike existing deep-unfolding approaches, we simply utilize NNs as embedded functional units rather than duplicate denoising modules. Through qualitative discussions and numerical results, we illustrate the characteristics, principles, and differences of our proposed architecture compared to the traditional BP framework and show the dramatic performance improvements brought by incorporating NNs with BP in this well-investigated problem. Recalling that the state evolution of NNs is different from that of traditional BP methods, we give some new insights and design principles which are somehow counterfactual. We also raise some open issues on the incorporated framework.

Space-air-ground integrated networks (SAGINs) are emerging as a pivotal element in the evolution of future wireless networks. Despite their potential, the joint design of communication and computation within SAGINs remains a formidable challenge. In this paper, the problem of energy efficiency in SAGIN-enabled probabilistic semantic communication (PSCom) system is investigated. In the considered model, a satellite needs to transmit data to multiple ground terminals (GTs) via an unmanned aerial vehicle (UAV) acting as a relay. During transmission, the satellite and the UAV can use PSCom technique to compress the transmitting data, while the GTs can automatically recover the missing information. The PSCom is underpinned by shared probabilistic graphs that serve as a common knowledge base among the transceivers, allowing for resource-saving communication at the expense of increased computation resource. Through analysis, the computation overhead function in PSCom is a piecewise function with respect to the semantic compression ratio. Therefore, it is important to make a balance between communication and computation to achieve optimal energy efficiency. The joint communication and computation problem is formulated as an optimization problem aiming to minimize the total communication and computation energy consumption of the network under latency, power, computation capacity, bandwidth, semantic compression ratio, and UAV location constraints. To solve this non-convex non-smooth problem, we propose an iterative algorithm where the closed-form solutions for computation capacity allocation and UAV altitude are obtained at each iteration. Numerical results show the effectiveness of the proposed algorithm.

Orthogonal delay-Doppler division multiplexing (ODDM) modulation has recently been considered a promising technology for enhancing communication system performance in high-mobility scenarios. Accurate and low-complexity channel estimation is one of the most significant challenges for massive multiple-input multiple-output (MIMO) ODDM systems, mainly due to the massive antenna arrays and high-mobility environments. In this paper, we focus on the downlink massive MIMO-ODDM communication systems, and propose a two-stage low-complexity channel estimation algorithm. Specifically, we first derive the effective channel model of the massive MIMO-ODDM systems, where the elements of the channel matrix do not follow a Bernoulli-Gaussian distribution, but their magnitudes do. Utilizing this characteristic, we employ the memory approximate message passing method to estimate the gains, delay, and Doppler of the multi-path channel, while the angles of the channel are estimated using the discrete Fourier transform method, achieving low-complexity Bayes-optimal results. Finally, numerical results demonstrate that the proposed algorithm can achieve improved estimation results, surpassing existing algorithms by approximately 2 dB.

In this paper, the energy efficiency maximization problem in space-air-ground integrated network (SAGIN)-enabled probabilistic semantic communication (PSC) is investigated. In the considered model, a satellite needs to transmit data to multiple ground terminals (GTs) via an unmanned aerial vehicle (UAV) acting as a relay. During transmission, the satellite and the UAV can use PSC technique to compress the transmitted data, while the GTs can automatically recover the original data. In the considered PSC system, shared probability graphs serve as a common knowledge base among the transceivers, allowing for resource-saving communication at the expense of increased computation resource. Therefore, it is important to study the trade-off between communication and computation to achieve optimal energy efficiency. The joint communication and computation problem is formulated as an optimization problem aiming to minimize the total communication and computation energy consumption of the network under latency, semantic compression ratio, and UAV location constraints. To solve this non-convex problem, we propose an alternating algorithm. Numerical results show the effectiveness of the proposed algorithm.

Millimeter-wave (mmWave) technology is increasingly recognized as a pivotal technology of the sixth-generation communication networks due to the large amounts of available spectrum at high frequencies. However, the huge overhead associated with beam training imposes a significant challenge in mmWave communications, particularly in urban environments with high background noise. To reduce this high overhead, we propose a novel solution for robust continuous-time beam tracking with liquid neural network, which dynamically adjust the narrow mmWave beams to ensure real-time beam alignment with mobile users. Through extensive simulations, we validate the effectiveness of our proposed method and demonstrate its superiority over existing state-of-the-art deep-learning-based approaches. Specifically, our scheme achieves at most 46.9% higher normalized spectral efficiency than the baselines when the user is moving at 5 m/s, demonstrating the potential of liquid neural networks to enhance mmWave mobile communication performance.

In this paper, the problem of low-latency communication and computation resource allocation for digital twin (DT) over wireless networks is investigated. In the considered model, multiple physical devices in the physical network (PN) needs to frequently offload the computation task related data to the digital network twin (DNT), which is generated and controlled by the central server. Due to limited energy budget of the physical devices, both computation accuracy and wireless transmission power must be considered during the DT procedure. This joint communication and computation problem is formulated as an optimization problem whose goal is to minimize the overall transmission delay of the system under total PN energy and DNT model accuracy constraints. To solve this problem, an alternating algorithm with iteratively solving device scheduling, power control, and data offloading subproblems. For the device scheduling subproblem, the optimal solution is obtained in closed form through the dual method. Numerical results verify that the proposed algorithm can reduce the transmission delay of the system by up to 51.2% compared to the conventional schemes.

Reconfigurable intelligent surfaces (RISs) have become a promising technology to meet the requirements of energy efficiency and scalability in future six-generation (6G) communications. However, a significant challenge in RISs-aided communications is the joint optimization of active and passive beamforming at base stations (BSs) and RISs respectively. Specif-ically, the main difficulty is attributed to the highly non-convex optimization space of beamforming matrices at both BSs and RISs, as well as the diversity and mobility of communication scenarios. To address this, we present a greenly gradient based meta learning beamforming (GMLB) approach. Unlike traditional deep learning based methods which take channel information directly as input, GMLB feeds the gradient of sum rate into neural networks. Coherently, we design a differential regulator to address the phase shift optimization of RISs. Moreover, we use the meta learning to iteratively optimize the beamforming matrices of BSs and RISs. These techniques make the proposed method to work well without requiring energy-consuming pretraining. Simulations show that GMLB could achieve higher sum rate than that of typical alternating optimization algorithms with the energy consumption by two orders of magnitude less.

Wireless communication systems operating in fading channels often demand pilots for channel estimation and data recovery, leading to substantial transmission overhead. In this paper, we propose a novel pilot-free semantic communication system designed for transmitting images over multi-user MIMO (MU-MIMO) fading channels. Specifically, our method involves extracting multi-scale semantic features from the source image at the transmitter, effectively embedding pilot-like information. At the receiver, we extract channel features from these semantic features at each scale, enabling the reconstruction of the source image without requiring explicit channel estimation and signal detection. To enhance the image reconstruction process, we introduce a novel module, called Resnet Transformer, which combines multi-head self-attention (MHSA) with Resnet block. Our experimental results demonstrate that this pilot-free system outperforms existing pilot-aided semantic communication methods in terms of perceptual quality and transmission efficiency.

In this paper, we propose an uplink multi-modal probabilistic semantic communication (PSCom) system that considers both communication and computation. In the considered PSCom model, the users and base station share the common knowledge, which is characterized by probability graph. Based on the shared probability graph, the original large-size semantic data is compressed into the small-size semantic information, which will introduce additional computation costs for semantic compression. Besides, semantic information recovered by the base station will also bring additional computation costs. Although this method incurs additional computation costs, it effectively reduces communication energy consumption. Based on the considered model, an optimization problem is formulated to minimize the total communication and computation energy consumption, considering latency, power budget, bandwidth, and semantic compression ratio constraints. To solve this mixed integer optimization problem, we first adopt a greedy algorithm for the semantic compression level selection of each model, thereby transforming the original problem into a continuous variable optimization problem. Then, derive the optimal solution of the communication power. Finally, the Lagrange multiplier method is used to determine the optimal bandwidth, which can result in a closed-form optimal solution. Simulation results validate the effectiveness of the proposed algorithm.

Learning-based semantic communication (SemCom) has emerged as a promising solution for the upcoming 6G networks. In this paper, we explore an evolving SemCom system for image transmission, which can continuously adapt and enhance its transmission efficiency by exploiting knowledge accumulated during previous transmissions. Specifically, we propose a novel channel-aware semantic encoder that utilizes a pretrained generative model to extract channel-correlated latent variables consisting of several semantic vectors from the input images, which can be directly transmitted over a noisy channel without further channel coding. Moreover, we introduce a dynamic code construction mechanism that dynamically updates the codebook with transmitted semantic vectors to eliminate the need to transmit similar codes in subsequent transmissions, thus further reducing the communication overhead. Simulation results highlight the evolving performance of the proposed system in terms of transmission efficiency, achieving superior perceptual quality with an average bandwidth compression ratio (BCR) of $1 / 192$ for a sequence of 100 test images compared to DeepJSCC and InverseJSCC. Code used in this paper is available at https://github.com/recusant7/GAN_SeCom.

Integrated sensing and communication (ISAC) is increasingly recognized as a pivotal technology for next-generation cellular networks, offering mutual benefits in both sensing and communication capabilities. This advancement necessitates a re-examination of the fundamental limits within networks where these two functionalities coexist via shared spectrum and infrastructures. However, traditional stochastic geometry-based performance analyses are confined to either communication or sensing networks separately. This paper bridges this gap by introducing a generalized stochastic geometry framework in ISAC networks. Based on this framework, we define and calculate the coverage rate of sensing and communication performance under resource constraints. Further, we present theoretical results for the coverage rate of unified ISAC performance, taking into account the coupling effects of dual functions in coexistence networks. Extensive numerical results validate the accuracy of all theoretical derivations, and also indicate that denser networks significantly enhance ISAC coverage. Specifically, increasing the base station density from 1 km-2to 10 km-2can boost the ISAC coverage rate from 1.4% to 39.8%.

It is well known that there is inherent radiation pattern distortion for the commercial base station antenna array, which usually needs three antenna sectors to cover all space. To eliminate pattern distortion and further enhance beamforming performance, we propose an electromagnetic hybrid beamforming (EHB) algorithm based on 3D superdirective holographic antenna arrays. Specifically, EHB consists of antenna excitation current vectors (analog beamforming) and digital precoding matrices, where the implementation of analog beamforming involves real-time adjustments to the radiation pattern to adapt to the wireless environment. Meanwhile, the digital beamforming is optimized based on the channel characteristics of analog beam-forming to further improve the achievable rate of communication systems. An electromagnetic channel model incorporating array radiation pattern and coupling effect is also developed to evaluate the benefits of our proposed scheme. Simulation results show that the proposed scheme achieves a sum rate gain of over 150 % compared to traditional beamforming algorithms.

Accurate localization of mobile terminals is crucial for integrated sensing and communication systems. Existing fingerprint localization methods, which deduce coordinates from channel information in pre-defined rectangular areas, struggle with the heterogeneous fingerprint distribution inherent in non-line-of-sight (NLOS) scenarios. To address the problem, we introduce a novel multi-source information fusion learning framework referred to as the Autosync Multi-Domain NLOS Localization (AMDNLoc). Specifically, AMDNLoc employs a two-stage matched filter fused with a target tracking algorithm and iterative centroid-based clustering to automatically and irregularly segment NLOS regions, ensuring uniform fingerprint distribution within channel state information across frequency, power, and time-delay domains. Additionally, the framework utilizes a segment-specific linear classifier array, coupled with deep residual network-based feature extraction and fusion, to establish the correlation function between fingerprint features and coordinates within these regions. Simulation results demonstrate that AMDNLoc significantly enhances localization accuracy by over 40% compared with traditional convolutional neural networks on the wireless artificial intelligence research dataset.

Millimeter wave (mmWave) has attracted considerable attention due to its wide bandwidth and high frequency. However, it is highly susceptible to blockages, resulting in significant degradation of the coverage and the sum rate. A promising approach is deploying distributed reconfigurable intelligent surfaces (RISs), which can establish extra communication links. In this paper, we investigate the impact of distributed RISs on the coverage probability and the sum rate in mmWave wireless communication systems. Specifically, we first introduce the system model, which includes the blockage, the RIS and the user distribution models, leveraging the Poisson point process. Then, we define the association criterion and derive the conditional coverage probabilities for the two cases of direct association and reflective association through RISs. Finally, we combine the two cases using Campbell's theorem and the total probability theorem to obtain the closed-form expressions for the ergodic coverage probability and the sum rate. Simulation results validate the effectiveness of the proposed analytical approach, demonstrating that the deployment of distributed RISs significantly improves the ergodic coverage probability by 45.4% and the sum rate by over 1.5 times.

This paper investigates semantic energy efficiency in a non-orthogonal multiple access (NOMA) enabled integrated sensing and semantic communication (ISSC) system. The model involves the base station (BS) transmitting information to multiple users while performing target sensing using dedicated beamforming. In the considered model, the BS needs to transmit substantial text data to each user using text semantic communication techniques while sensing the targets with certain constraints. Our goal is to maximize semantic energy efficiency and meet semantic communication and sensing accuracy requirements. We formulate an optimization problem for the beamforming matrix and semantic parameter, employing the Dinkelbach's algorithm for simplification and proposing an iterative solution. Numerical results validate the efficacy of the NOMA-ISSC scheme.

In this paper, the problem of joint transmission and computation resource allocation for probabilistic semantic communication (PSC) network with rate splitting multiple access (RSMA) is investigated. In the considered model, the base station (BS) needs to transmit a large amount of data, which is represented by substantial knowledge graphs, to multiple users. Due to limited communication resource, the BS needs to utilize semantic communication techniques to compress the large-sized data. In this paper, the semantic communication is enabled by shared probability graphs between the BS and users. The process of semantic compression requires computation power at the BS, which has an impact on limited power budget. Therefore, it is necessary to balance the power between transmission and computation. Based on the probability graph, the semantic rate related to semantic compression ratio is first theoretically formulated. Then, the problem is formulated as an optimization problem with the aim of maximizing the sum semantic rate of all users under total power, semantic compression ratio, and rate allocation constraints. To tackle this problem, an iterative algorithm is accordingly proposed to obtain a suboptimal solution. Numerical results validate the effectiveness of the proposed scheme.

High-resolution mmWave radar imaging plays an important role in applications such as autonomous driving. Beam-based imaging methods often require scanning the scene of interest with a sufficiently small angular stepsize to achieve high-resolution, thus leading to a large computational and storage burden. Compressive sensing (CS) is a promising strategy to reconstruct high-dimensional yet sparse signals from low-dimensional measurements with random sampling. Therefore in this paper, we conduct random space sampling by adding random phase shifts on the transmit antennas of a frequency modulated continuous wave (FMCW)-based radar system. We prove that the sensing model can be formulated as a CS problem and solved by Expectation-Maximization Gaussian-Mixture Approximate Message Passing (EMGMAMP)-based approaches. Simulation results show that the model has excellent imaging performance even with very few sensing measurements. To further improve the imaging quality, we consider a multi-view sensing scenario in which sensing results from different positions are fused by proper occlusion processing and coordinate transformation. Finally, appropriate evaluation metrics are proposed for target sensing results to validate the effectiveness of the proposed sensing model and algorithm.

Initial random access is a crucial process in wireless communication networks, which sets up reliable connections between multiple active users and the base station (BS). In this procedure, useful information, including beam pair, timing advance (TA), and channel state information (CSI), can be naturally obtained to achieve environment sensing and user positioning. Environment sensing is highly related to user positioning as it requires user-specific CSI, and more importantly, the multi-view observations from different user locations potentially benefit the fusion of the overall environment information. This makes joint environment sensing and user positioning of great significance. Moreover, initial random access provides observations from different users, avoiding the limited and insufficient observation of a single pair of transceivers, which helps to realize environment sensing in large scenarios. Therefore, in this paper, we propose a joint initial random access, environment sensing, and user positioning framework, exploiting direction, time-delay, reflection, and scattering information brought by beam pair, TA, and CSI. Furthermore, we illustrate the remarkable sensing and positioning performance of the proposed scheme in both light-of-sight (LoS) and non-light-of-sight (NLoS) scenarios.

For ultra-reliable low-latency communication (URLLC), retransmission data should be scheduled with different priorities due to the urgency, which brings new challenges in delay-oriented optimization, especially with deterministic delay constraint. In this paper, we propose an adaptive modulation and coding (AMC) scheme for both initial transmissions and retransmissions in separate data queues. Different from most of the existing works focusing on average delay, we jointly consider delay performance and deterministic delay requirement by combining the Markov decision process (MDP) with Lyapunov optimization technique. To overcome the coupling in the objective and the deterministic constraint, we transform this problem into an infinite horizon MDP by constructing a renewal system with sampling. Based on this, we propose a delay-optimal modulation and coding scheme (MCS) selection policy using reinforcement learning. Simulation results show that the proposed scheme achieves better delay performance than the conventional AMC schemes.

In massive Multiple-Input Multiple-Output (MIMO) -Orthogonal Frequency Division Multiplexing (OFDM) systems, channel estimation incurs high pilot overhead due to the high channel dimension, prompting the exploration of various algorithms to mitigate this cost. Semi-blind estimation, which usually employs a traditional iterative algorithm based on Bayesian infer-ence, proves effective in enhancing estimation performance with a limited number of pilots. Meanwhile, Neural Network (NN)-based channel mapping and prediction methods have demonstrated potential in predicting the full channel matrix with the estimation of a proportion, reducing the pilot overhead. However, how to merge these two methods for less pilot overhead is yet to be investigated. This paper introduces a hybrid model and data driven method that combines semi-blind estimation with NN-based frequency domain channel mapping, leveraging data priors as the inference algorithm while harnessing the nonlinear mapping capability offered by NNs. The proposed architecture holds promise for extension to other applications where the synergistic combination of inference algorithms and NN s proves advantageous. Numerical results validate the effectiveness of the proposed algorithm.

Recently , over-the-air computation (OAC) has shown potential in realizing computation tasks over wireless transmission. Through proper transmit and receive beamforming design, multiple-input multiple-output (MIMO)-based OAC systems can even realize partial functions of neural networks (NNs). In this paper, we propose an OAC-NN with reconfigurable intelligent surface (RIS)-aided MIMO, in which the NN computation task can be realized through updating the RIS reflection matrix. In the proposed structure, the communication system can complete the overall simple NN-based tasks only through multiple rounds of transmissions without introducing any additional computing resources. Numerical results reflect the effectiveness of the proposed scheme and the tradeoff between communication costs and computing performance.

This paper investigates the secure resource allocation for a downlink integrated sensing and communication system with multiple legal users and potential eavesdroppers. In the considered model, the base station (BS) simultaneously transmits sensing and communication signals through beamforming design, where the sensing signals can be viewed as artificial noise to enhance the security of communication signals. To further enhance the security in the semantic layer, the semantic information is extracted from the original information before transmission. The user side can only successfully recover the received information with the help of the knowledge base shared with the BS, which is stored in advance. Our aim is to maximize the sum semantic secrecy rate of all users while maintaining the minimum quality of service for each user and guaranteeing overall sensing performance. To solve this sum semantic secrecy rate maximization problem, an iterative algorithm is proposed using the alternating optimization method. The simulation results demonstrate the superiority of the proposed algorithm in terms of secure semantic communication and reliable detection.

Millimeter-wave (mmWave) radar imaging is envisioned as a promising technology for wireless communication system. Through modeling radar imaging as a compressed sensing (CS) problem, iterative algorithms represented by sparsity adaptive matching pursuit (SAMP) can be effectively adopted for high-quality imaging. In this paper, we introduce adaptive stepsize mechanism to replace the fixed stepsize setting in the existing SAMP method, forming a novel stepsize-adaptive SAMP (SA-SAMP) algorithm for mmWave radar imaging. The adaptive stepsize selection enhances the precision of sparsity estimation, thereby resulting in an augmented imaging speed while realizing high-quality results. Experiments on real radar dataset demon-strate the performance gains of the proposed algorithm in terms of peak signal-to-noise ratio, target-to-clutter ratio, and structure similarity index.

Mobile edge computing (MEC) in the next generation networks will provide computation services at the network edge to enrich the capabilities of mobile devices and lengthen their battery lives. However, the performance of MEC cannot be guaranteed, when large size local tasks are uploaded to the server simultaneously causing network congestion. As a new paradigm that focuses on transmitting the meaning of messages, semantic communications reveals the significant potential to reduce the network traffic. In this paper, we propose a semantic-aware joint communication and computation resource allocation framework for MEC systems. In the considered system, random tasks arrive at each terminal device (TD), which needs to be computed locally or offloaded to the MEC server. To further release the transmission burden, each TD sends the small-size extracted semantic information of tasks to the server instead of the original large-size raw data. An optimization problem of joint semantic-aware division factor, communication and computation resource management is formulated. The problem aims to minimize the energy consumption of the whole system, while satisfying long-term delay and processing rate constraints. To solve this problem, an online low-complexity algorithm is proposed. In particular, Lyapunov optimization is utilized to decompose the original coupled long-term problem into a series of decoupled deterministic problems without requiring the realizations of future task arrivals and channel gains. Then, the block coordinate descent method and successive convex approximation algorithm are adopted to solve the current time slot deterministic problem by observing the current system states. Moreover, the closed-form optimal solution of each optimization variable is provided. Simulation results show that the proposed algorithm yields up to 41.8% energy reduction compared to its counterpart without semantic-aware allocation.

In mission-critical Industrial Internet of Things (IIoT), multiple sensors make independent observations at different locations and then transmit them to the base station (BS) to obtain a global system state vector. Uploading observation information to the BS by active sensors is a multiple access process. As the task is to complete state estimation instead of maximizing the physical-layer capacity, conventional multiple access schemes cannot be applied directly to mission-critical IIoT applications. Therefore, next generation multiple access (NGMA) techniques are urgently needed to realize the key performance indicators for the design of IIoT networks. Note that, in mission-critical IIoT systems, each sensor can only obtain the observation of a subset of state variables, and the BS only cares about the state information embedded in that observation not the identities of the sensors. This indicates that the whole process of data transmission and state estimation can be totally unsourced, thus resulting in a highly efficient IIoT system implementation. Based on this crucial finding, in this article, we propose an unsourced multiple access (UMA)-based mission-critical IIoT system. Moreover, a decoupled UMA (D-UMA) scheme is proposed to improve transmission efficiency and state estimation performance. We analyse the fundamental aspects of how our design affects and guarantees the controllability, observability, and stability of an IIoT control system. Simulation results verify the remarkable performance of the proposed scheme compared with the conventional orthogonal multiple access (OMA) and nonorthogonal multiple access (NOMA) schemes.

Caching sheds a light on reducing long-distance data transmissions over networks, while raising significant privacy concerns. Moving one step ahead, collaborative edge caching is proposed to facilitate preserving user privacy via reducing the external data exposure. However, it still fails to avert the risk of privacy leakage from nearby edge devices. To tackle this issue, we develop an analytical framework for privacy preserving joint communication and content allocation algorithm for distributed collaborative edge caching systems, in which edge devices collaboratively cache and share the content items based on the dummy-based privacy preservation mechanism. Specifically, we define the system request uncertainty criterion from the perspective of information entropy to measure the privacy preservation performance. Consequently, the closed-form relationship between the system request uncertainty and the resource allocation decisions on both communication resources and content items can be derived. Then, we decompose the NP-hard resource management problem into two parts, and propose 1) an optimal dummy request allocation strategy through investigating special properties of the maximal allocation reward gain and 2) an asymptotically optimal content item allocation strategy with low complexity based on the extract penalty method (EPM), which are iterated to obtain a viable solution, followed by the proof of convergence and asymptotic monotone property. Finally, the performance improvements are verified by simulations.

The unmanned aerial vehicle (UAV) swarms have shown great potential to serve next-generation communication networks with their extraordinary flexibility, affordability, and the ability to collaboratively and autonomously provide Line-of-Sight (LoS) services. However, autonomous collaboration under wireless dynamics is challenging. Distributed learning (DL) provides a chance for the UAV swarms to operate intelligently under sophisticated dynamics, such that they can be applied to wireless communication service scenarios, as well as applications including multidirectional remote surveillance, and target tracking. In this survey, we first introduce several popular DL frameworks that are capable of managing a UAV swarm, these include federated learning (FL), multiagent reinforcement learning (MARL), distributed inference (DI), and split learning (SL). We also present a comprehensive overview of how these DL frameworks manage UAV swarms in regard to trajectory design, power control, wireless resource allocation, user assignment, perception, and satellite–drone integration. Then, we present several state-of-the-art applications of UAV swarms in wireless communication systems, such as reconfigurable intelligent surfaces (RISs), virtual reality (VR), and semantic communications (SemComs), and discuss the problems and challenges that DL-enabled UAV swarms can solve in these applications. Finally, we describe open problems of using DL in UAV swarms and future research directions of DL-enabled UAV swarms. In summary, this survey provides a concise survey of various DL applications for UAV swarms in extensive scenarios.

The expansion of the Internet of Things (IoT) has led to a significant surge in data flow over edge networks, posing substantial challenges to data mining and management. While federated edge learning (FEEL) effectively accomplishes global integration and local training based on the decentralized data sets, its deployment across expansive IoT networks introduces additional challenges. The primary issues stem from managing the interaction between the communication load and learning effectiveness. The communication loads driven by recurrent data exchanges between the user equipment (UE) and central servers exacerbate network congestion and latency issues. Moreover, the learning efficacy is undermined due to the typically nonindependent and identically distributed (non-IID) characteristics of real-world IoT data. In this article, a novel communication-efficient hierarchical FEEL framework is proposed to tackle these challenges. Specifically, UEs are adaptively clustered according to their link conditions, geographic locations, and data distributions. Small base stations (SBSs) collect local model updates from the UEs in their clusters and communicate with a macro base station (MBS) for the global model aggregation. To jointly maximize the communication gain (in terms of reducing latency) and the learning gain (in terms of improving accuracy), a clustering and resource allocation optimization problem is formulated, and a cross entropy-based method with low computational complexity is proposed. Numerical experiments validate that the proposed hierarchical FEEL system achieves fast convergence and significantly improves the system efficiency for various learning tasks and the system settings.

The advent of Industry 4.0 has positioned the industrial Internet of Things (IIoT) as a cornerstone of future industry. In this article, the problem of spectral-efficient communication and computation resource allocation for distributed reconfigurable intelligent surfaces (RISs) assisted probabilistic semantic communication (PSC) in IIoT is investigated. In the considered model, multiple RISs are deployed to serve multiple users, while PSC adopts compute-then-transmit protocol to reduce the size of the transmission data. To support the high-rate transmission, the semantic compression ratio, transmit power allocation, and distributed RISs deployment must be jointly considered. This joint communication and computation problem is formulated as an optimization problem whose goal is to maximize the sum semantic-aware transmission rate of the system under the total transmit power, phase shift, RIS-user association, and semantic compression ratio constraints. To solve this problem, a many-to-many matching scheme is proposed to solve the RIS-user association subproblem, the semantic compression ratio subproblem is addressed following the greedy policy, while the phase shift of RIS can be optimized using the tensor-based beamforming. Numerical results verify the superiority of the proposed algorithm.

Integrated sensing and communication (ISAC) is increasingly recognized as a pivotal technology for next-generation cellular networks, offering mutual benefits in both sensing and communication capabilities. This advancement necessitates a re-examination of the fundamental limits within networks where these two functions coexist via shared spectrum and infrastructures. However, traditional stochastic geometry-based performance analyses are confined to either communication or sensing networks separately. This paper bridges this gap by introducing a generalized stochastic geometry framework in ISAC networks. Based on this framework, we define and calculate the coverage and ergodic rate of sensing and communication performance under resource constraints. Then, we shed light on the fundamental limits of ISAC networks by presenting theoretical results for the coverage rate of the unified performance, taking into account the coupling effects of dual functions in coexistence networks. Further, we obtain the analytical formulations for evaluating the ergodic sensing rate constrained by the maximum communication rate, and the ergodic communication rate constrained by the maximum sensing rate. Extensive numerical results validate the accuracy of all theoretical derivations, and also indicate that denser networks significantly enhance ISAC coverage. Specifically, increasing the base station density from$1~\text {km}^{-2}$to$10~\text {km}^{-2}$can boost the ISAC coverage rate from 1.4% to 39.8%. Further, results also reveal that with the increase of the constrained sensing rate, the ergodic communication rate improves significantly, but the reverse is not obvious.

In integrated ground-air-space (IGAS) wireless networks, numerous services require sensing knowledge including location, angle, distance information, etc., which usually can be acquired during the beam training stage. On the other hand, IGAS networks employ large-scale antenna arrays to mitigate obstacle occlusion and path loss. However, large-scale arrays generate pencil-shaped beams, which necessitate a higher number of training beams to cover the desired space. These factors motivate our investigation into the IGAS beam training problem to achieve effective sensing services. To address the high complexity and low identification accuracy of existing beam training techniques, we propose an efficient hashing multi-arm beam (HMB) training scheme. Specifically, we first construct an IGAS single-beam training codebook for the uniform planar arrays. Then, the hash functions are chosen independently to construct the multi-arm beam training codebooks for each AP. All APs traverse the predefined multi-arm beam training codeword simultaneously and the multi-AP superimposed signals at the user are recorded. Finally, the soft decision and voting methods are applied to obtain the correctly aligned beams only based on the signal powers. In addition, we logically prove that the traversal complexity is at the logarithmic level. Simulation results show that our proposed IGAS HMB training method can achieve 96.4% identification accuracy of the exhaustive beam training method and greatly reduce the training overhead.

In this paper, we explore an efficient uncoupled unsourced random access (UURA) scheme for 6G massive communication. UURA is a typical framework of unsourced random access that addresses the problems of codeword detection and message stitching, without the use of check bits. Firstly, we establish a framework for UURA, allowing for immediate decoding of sub-messages upon arrival. Thus, the processing delay is effectively reduced due to the decreasing waiting time. Next, we propose an integrated decoding algorithm for sub-messages by leveraging matrix information geometry (MIG) theory. Specifically, MIG is applied to measure the feature similarities of codewords belonging to the same user equipment, and thus sub-message can be stitched once it is received. This enables the timely recovery of a portion of the original message by simultaneously detecting and stitching codewords within the current sub-slot. Furthermore, we analyze the performance of the proposed integrated decoding-based UURA scheme in terms of computational complexity and convergence rate. Finally, we present extensive simulation results to validate the effectiveness of the proposed scheme in 6G wireless networks.

To support complex communication scenarios in next-generation wireless communications, this paper focuses on a generalized MIMO (GMIMO) with practical assumptions, such as massive antennas, practical channel coding, arbitrary input distributions, and general right-unitarily-invariant channel matrices (covering Rayleigh fading, certain ill-conditioned and correlated channel matrices). The orthogonal/vector approximate message passing (OAMP/VAMP) receiver has been proved to be information-theoretically optimal in GMIMO, but it is limited to high-complexity linear minimum mean-square error (LMMSE). To solve this problem, a low-complexity memory approximate message passing (MAMP) receiver has recently been shown to be Bayes optimal but limited to uncoded systems. Therefore, how to design a low-complexity and information-theoretically optimal receiver for GMIMO is still an open issue. To address this issue, this paper proposes an information-theoretically optimal MAMP receiver and investigates its achievable rate analysis and optimal coding principle. Specifically, due to the long-memory linear detection, state evolution (SE) for MAMP is intricately multi-dimensional and cannot be used directly to analyze its achievable rate. To avoid this difficulty, a simplified single-input single-output (SISO) variational SE (VSE) for MAMP is developed by leveraging the SE fixed-point consistent property of MAMP and OAMP/VAMP. The achievable rate of MAMP is calculated using the VSE, and the optimal coding principle is established to maximize the achievable rate. On this basis, the information-theoretic optimality of MAMP is proved rigorously. Furthermore, the simplified SE analysis by fixed-point consistency is generalized to any two iterative detection algorithms with the identical SE fixed point. Numerical results show that the finite-length performances of MAMP with practical optimized low-density parity-check (LDPC) codes are 0.5 ~ 2.7 dB away from the associated constrained capacities. It is worth noting that MAMP can achieve the same performances as OAMP/VAMP with 4‰ of the time consumption for large-scale systems.

The next-generation wireless network is envisioned to support real-time broadband communication (RTBC) to provision services for immersive applications. Such applications (e.g., virtual reality, VR) usually need to simultaneously transmit multi-modal (e.g., visual, audio and haptic) data streams that have different traffic characteristics and transmission requirements, within multiple transmission time intervals (TTIs). In this paper, we formulate an optimization problem of multi-user multiple-input multiple-output (MIMO) precoding within multiple TTIs for multi-modal data transmission. As it is hard to find an optimal solution, we first resort to a novel soft actor-critic (SAC)-based learning approach. Specifically, a lightweight reinforcement learning architecture is employed to learn the adaptive priority weight of each user within multiple TTIs by taking into account its remaining multi-modal data amount and dynamic interaction state. The learned priority weights are then input to an iterative weighted minimum mean-square error (WMMSE) algorithm to adjust the precoder matrix and user transmission rates. With a scalable state design, the proposed algorithm can be tailored to different numbers of potential or active users. We also provide another practical solution to the formulated multi-TTI precoding problem, which transforms the problem into a single-TTI optimization problem by adding the quality-of-service (QoS) constraints into the traditional WMMSE problem and then solves it using the alternating direction method of multipliers (ADMM). Simulation results demonstrate the robustness and efficiency of the proposed algorithms, which show that the SAC-based precoding algorithm can achieve a 50.0% increment in system capacity compared to traditional WMMSE and a significant reduction in time complexity compared to the QoS-constrained WMMSE algorithm.

It is well known that there is inherent radiation pattern distortion for the commercial base station antenna array, which usually needs three antenna sectors to cover the whole space. To eliminate pattern distortion and further enhance beamforming performance, we propose an electromagnetic hybrid beamforming (EHB) scheme based on a three-dimensional (3D) superdirective holographic antenna array. Specifically, EHB consists of antenna excitation current vectors (analog beamforming) and digital precoding matrices, where the implementation of analog beamforming involves the real-time adjustment of the radiation pattern to adapt it to the dynamic wireless environment. Meanwhile, the digital beamforming is optimized based on the channel characteristics of analog beamforming to further improve the achievable rate of communication systems. An electromagnetic channel model incorporating array radiation patterns and the mutual coupling effect is also developed to evaluate the benefits of our proposed scheme. Simulation results demonstrate that our proposed EHB scheme with a 3D holographic array achieves a relatively flat superdirective beamforming gain and allows for programmable focusing directions throughout the entire spatial domain. Furthermore, they also verify that the proposed scheme achieves a sum rate gain of over 150% compared to traditional beamforming algorithms.

Ultra-reliable low-latency communication (URLLC) is an indispensable element towards supporting various latency-sensitive and reliability-critical applications. To optimize the average delay while satisfying the deterministic delay constraint, initial transmission and retransmission should be handled with different priorities due to the differentiated urgency, which creates complex interdependency and brings new technical challenges to delay-oriented optimization. In this paper, we propose a retransmission-aware adaptive modulation and coding (RAMC) scheme to improve the delay performance in URLLC scenarios. Specifically, we first establish a cascaded queue system, including an initial transmission queue and a retransmission queue. The deterministic delay constraint is satisfied through Lyapunov optimization, where we transform the Lyapunov drift-plus-penalty problem into an infinite horizon Markov decision process (MDP) by constructing a renewal system with sampling to overcome the challenge brought by queue coupling. Next, we propose the delay-optimal RAMC scheme by solving the associated Bellman equation by improved reinforcement learning, which is proved to be asymptotically optimal. Finally, the superiority of the proposed RAMC scheme is verified through simulations.

In high-dynamic low earth orbit (LEO) satellite communication (SATCOM) systems, frequent channel state information (CSI) acquisition consumes a large number of pilots, which is intolerable in resource-limited SATCOM systems. To tackle this problem, we propose to track the state-dependent parameters including Doppler shift and channel angles, by exploiting the physical and approximate on-orbit mobility characteristics for LEO satellite and ground users (GUs), respectively. As a prerequisite for tracking, we formulate the state evolution models for kinematic (state) parameters of both satellite and GUs, along with the measurement models that describe the relationship between the state-dependent parameters and states. Then the rough estimation of state-dependent parameters is initially conducted, which is used as the measurement results in the subsequent state tracking. Concurrently, the measurement error covariance is predicted based on the formulated Cramér-Rao lower bound (CRLB). Finally, with the extended Kalman filter (EKF)-based state tracking as the bridge, the Doppler shift and channel angles can be further updated and the CSI can also be acquired. Simulation results show that compared to the rough estimation methods, the proposed joint parameter and channel tracking (JPCT) algorithm performs much better in the estimation of state-dependent parameters. Moreover, as to the CSI acquisition, the proposed algorithm can utilize a shorter pilot sequence than benchmark methods under a given estimation accuracy.

A vital challenge in the deployment of federated learning (FL) over wireless networks is the high energy consumption incurred for the local computation and model update upload on energy-constrained devices such as IoT sensors. Equipping with energy harvesting (EH) modules is a promising solution that allows the devices to work in a self-sustainable manner. Moreover, quantizing the model updates can further improve the energy efficiency during the upload. In this paper, we propose an EH-enabled FL system with model quantization in which EH devices act as clients and client scheduling, model quantization, and transmit energy are jointly optimized to minimize the training loss while satisfying energy causality constraints and guaranteeing fairness in client selection. We formulate a non-convex mixed-integer nonlinear programming (MINLP) problem for the optimization. Then, by recasting the product of a continuous variable and a 0-1 variable in an equivalent linear form, we transform this non-convex MINLP problem into a convex problem and solve it. We present numerical evaluations on various datasets to show that our proposed system is stable and achieves high performance regardless of whether the loss function is convex or non-convex and whether the data distributions are independent and identically distributed (i.i.d.) or non-i.i.d.

In this paper, we investigate the issue of uplink integrated sensing and communication (ISAC) in 6G wireless networks where the sensing echo signal and the communication signal are received simultaneously at the base station (BS). To effectively mitigate the mutual interference between sensing and communication caused by the sharing of spectrum and hardware resources, we provide a joint sensing transmit waveform and communication receive beamforming design with the objective of maximizing the weighted sum of normalized sensing rate and normalized communication rate. It is formulated as a computationally complicated non-convex optimization problem, which is quite difficult to be solved by conventional optimization methods. To this end, we first make a series of equivalent transformation on the optimization problem to reduce the design complexity, and then develop a deep learning (DL)-based scheme to enhance the overall performance of ISAC. Both theoretical analysis and simulation results confirm the effectiveness and robustness of the proposed DL-based scheme for ISAC in 6G wireless networks.

Symbiotic radio (SR) offers potential benefits for large-scale Internet of Things networks through its spectrum and resource-sharing capabilities between primary and secondary systems. However, in high-mobility scenarios, rapidly changing channels present significant challenges for reliable and low-latency SR communications. In response to this challenge, this paper proposes employing orthogonal time-frequency space (OTFS) modulation for SR systems in hostile environments. By processing signals in the delay-Doppler (DD) domain, the fast-varying channels can be efficiently characterized using a few quasi-static DD domain parameters. To enable joint primary and secondary transmissions in SR, we propose modulating primary information using phase-shift keying modulation, while the secondary information is modulated into the frequencies of the periodic rectangular wave. However, the time-varying secondary signals are intertwined with unknown DD-domain channels, making it difficult to differentiate between secondary information and the original channel state information (CSI). To overcome this challenge, we introduce coherent detection methods for secondary information using both amplitude-based and sparsity-based techniques, leveraging the spectral characteristics of signal combinations with different frequencies. Moreover, recognizing that the periodic rectangular wave of the secondary transmission would reshape the Doppler profile of the equivalent channel in a highly structured manner, we propose an off-grid structured-sparse Bayesian learning-based CSI estimator. With the obtained equivalent CSI, we propose a low-complexity symbol-wise detection algorithm for detecting primary information, leveraging the pilot guards and interference cancellation technique. Finally, numerical results validate the effectiveness and superiority of the proposed estimator and detector.

Data collection and processing timely is crucial for mobile crowd integrated sensing, communication, and computation (ISCC) systems with various applications such as smart home and connected cars, which requires numerous integrated sensing and communication (ISAC) devices to sense the targets and offload the data to the base station (BS) for further processing. However, as the number of ISAC devices grows, there exists intensive interactions among ISAC devices in the processes of data collection and processing since they share the common network resources. In this paper, we consider the environment sensing problem in the large-scale mobile crowd ISCC systems and propose an efficient waveform precoding design algorithm based on the mean field game (MFG). Specifically, to handle the complex interactions among large-scale ISAC devices, we first utilize the MFG method to transform the influence from other ISAC devices into the mean field term and derive the Fokker-Planck-Kolmogorov equation, which models the evolution of the system state. Then, we derive the cost function based on the mean field term and reformulate the waveform precoding design problem. Next, we utilize the G-prox primal-dual hybrid gradient algorithm to solve the reformulated problem and analyze the computational complexity of the proposed algorithm. Finally, simulation results demonstrate that the proposed algorithm can solve the interactions among large-scale ISAC devices effectively in the ISCC process. In addition, compared with other baselines, the proposed waveform precoding design algorithm has advantages in improving communication performance and reducing cost function.

In large-scale multi-access edge computing (MEC) networks, each device should make the computation offloading decision distributively. In this paper, we target on a delay-optimal computation offloading problem in large-scale MEC systems, where each task has two properties: data size and computation amount. Because the detailed state information of massive devices are huge in large-scale systems, we propose a distributed computation offloading algorithm using the mean field game (MFG). To design the distributed computation offloading algorithm, we first formulate the delay-optimal computation offloading problem as a Markov decision process (MDP) and derive the Hamilton-Jaccobi-Bellman (HJB) equation with the unknown task allocation proportion, where the combined influence from other devices and MEC servers should be estimated. Based on MFG, we obtain the Fokker-Planck-Kolmogorov (FPK) equation to describe the evolution of the system’s collective behavior, with the influence from other devices and MEC servers formulated as the mean field. To solve the large-scale problem with the unknown allocation proportion, we propose a optimal computation offloading algorithm based on the generative adversarial networks (GAN) structure. For the generator, we generate the unknown task allocation proportion due to its non-calculability and insufficient dataset. For the discriminator, we train the value function, and propose a water-filling algorithm to prioritize the task offloading. Finally, the simulation results evaluate the performance of the proposed algorithm and show the performance gain compared to conventional algorithms.

Channel aging poses a huge challenge for the MU-MIMO (Multi-user Multiple-Input- Multiple Output) communications. To alleviate the problem, channel prediction is widely regarded as a promising mean. Existing channel prediction methods usually rely heavily on the assumption of parametric models. Severe performance degradation might occur when modeling error exists, which is unfortunately common in practice due to the complicated nature of wireless channels. To address the problem, we propose to use nonparametric regression methods for prediction. Nonparametric methods enjoy the advantage of not relying on the model assumption at all, enabling it very suit for making inference from complex channel data. In this paper, we present two nonparametric regression methods for MU-MIMO’s channel prediction, i.e., k-nearest neighbors (kNN) regression and its improved version, local polynomial regression (LPR). In addition, we propose to employ the Bayesian Information Criteria (BIC) to select the parameters in LPR, whereby we get the conclusion that local linear regression is recommended in practical applications. Furthermore, for the case of predicting the right singular matrix of the channel in specific, we provide a useful preprocessing procedure. Simulation results illustrate that our proposed nonparametric regression methods can outperform significantly the conventional parametric methods for channel prediction.

In this paper, we propose an innovative learning-based channel prediction scheme so as to achieve higher prediction accuracy and reduce the requirements of huge amounts and strict sequential format of channel data. Inspired by the idea of the neural ordinary differential equation (Neural ODE), we first prove that the channel prediction problem can be modeled as an ODE problem with a known initial value by analyzing the physical process of electromagnetic wave propagation within a mobile environment. Then, we design a novel physics-inspired spatial channel gradient network (SCGnet), which represents the derivative process of channel varying as a special neural network and can obtain the gradients at any relative displacement needed for the ODE solving. With the SCGnet, the static channel at any location served by the base station is accurately inferred through consecutive propagation and integration. Finally, we design an efficient recurrent positioning algorithm based on some prior knowledge of user mobility to obtain the velocity vector and propose an approximate Doppler compensation method to make up the instantaneous angular-delay domain channel. Only discrete historical channel data is needed for the training, whereas only a few fresh channel measurements are needed for the prediction, which ensures the scheme’s practicability. Comprehensive evaluations show that the proposed scheme is most efficient in representing, learning, and predicting mobile wireless channels.

The millimeter wave (mmWave) has received considerable interest due to its expansive bandwidth and high frequency. However, a noteworthy challenge arises from its vulnerability to blockages, leading to reduced coverage and achievable rate. To address these limitations, a potential solution is to deploy distributed reconfigurable intelligent surfaces (RISs), which comprise many low-cost and passively reflected elements, and can facilitate the establishment of extra communication links. In this paper, we leverage stochastic geometry to investigate the ergodic coverage probability and the achievable rate in both distributed RISs-assisted single-cell and multi-cell mmWave wireless communication systems. Specifically, we first establish the system model considering the stochastically distributed blockages, RISs and users by the Poisson point process. Then we give the association criterion and derive the association probabilities, the distance distributions, and the conditional coverage probabilities, for two cases of associations between base stations and users without or with RISs. Finally, we use Campbell’s theorem and the total probability theorem to obtain the closed-form expressions of the ergodic coverage probability and the achievable rate. Simulation results verify the effectiveness of our analysis method, and demonstrate that by deploying distributed RISs, the ergodic coverage probability is significantly improved by approximately 50%, and the achievable rate is increased by more than 1.5 times.

Reconfigurable intelligent surface (RIS) has become a promising technology to realize the programmable wireless environment via steering the incident signal in fully customizable ways. However, a major challenge in RIS-aided communication systems is the simultaneous design of the precoding matrix at the base station (BS) and the phase shifting matrix of the RIS elements. This is mainly attributed to the highly non-convex optimization space of variables at both the BS and the RIS, and the diversity of communication environments. Generally, traditional optimization methods for this problem suffer from the high complexity, while existing deep learning based methods are lacking in robustness in various scenarios. To address these issues, we introduce a gradient-based manifold meta learning method (GMML), which works without pre-training and has strong robustness for RIS-aided communications. Specifically, the proposed method fuses meta learning and manifold learning to improve the overall spectral efficiency, and reduce the overhead of the high-dimensional signal process. Unlike traditional deep learning based methods which directly take channel state information as input, GMML feeds the gradients of the precoding matrix and phase shifting matrix into neural networks. Coherently, we design a differential regulator to constrain the phase shifting matrix of the RIS. Numerical results show that the proposed GMML can improve the spectral efficiency by up to 7.31%, and speed up the convergence by 23 times faster compared to traditional approaches. Moreover, they also demonstrate remarkable robustness and adaptability in dynamic settings.

Initial access (IA) is a procedure of establishing an initial connection between the base station (BS) and the user. In the fifth generation (5G) millimeter wave (mmWave) communication system, the IA procedure includes beam management, which determines the beam pair for random access and data transmission by beam sweeping. The existing beam sweeping method in the 3-rd generation partnership project (3GPP) standard mainly uses a predefined uniform beamforming codebook and sweeps the beams progressively, which is time-consuming and highly inflexible. Note that the non-uniform and quasi-stationary environment and user cluster distribution information can be exploited for the BS to optimize the beam sweeping patterns. Therefore, this paper proposes a novel reinforcement learning (RL) framework for IA to efficiently acquire beam sweeping patterns. Specifically, a dimension-reduced beamforming codebook is designed to solve the problem of large search space and a comprehensive RL environment is constructed for the BS to capture the properties of environment layout and user distributions. Simulation results verify the remarkable performance of our proposed schemes in terms of beam sweeping efficiency.

Adaptive rate control for deep joint source and channel coding (JSCC) is considered as an effective approach to transmit sufficient information in scenarios with limited communication resources. We propose a deep JSCC scheme for wireless image transmission with entropy-aware adaptive rate control, using a single deep neural network to support multiple rates and automatically adjust the rate based on the feature maps of the input image and their entropy, as well as the channel conditions. In particular, we maximize the entropy of the feature maps to increase the average information carried by each transmitted symbol during the training. We further decide which feature maps should be activated based on their entropy, which improves the efficiency of the transmitted symbols. We also propose a pruning module to remove less important pixels in the activated feature maps in order to further improve transmission efficiency. The experimental results demonstrate that our proposed scheme learns an effective rate control strategy that reduces the required channel bandwidth while preserving the quality of the reconstructed images.

This paper considers a generalized multiple-input multiple-output (GMIMO) with practical assumptions, such as massive antennas, practical channel coding, arbitrary input dis-tributions, and general right-unitarily-invariant channel matrices (covering Rayleigh fading, certain ill-conditioned and corre-lated channel matrices). Orthogonal/vector approximate message passing (OAMP/VAMP) has been proved to be information-theoretically optimal in GMIMO, but it is limited to high complexity. Meanwhile, low-complexity memory approximate message passing (MAMP) was shown to be Bayes optimal in GMIMO, but channel coding was ignored. Therefore, how to design a low-complexity and information-theoretic optimal receiver for GMIMO is still an open issue. In this paper, we propose an information-theoretic optimal MAMP receiver for coded GMIMO, whose achievable rate analysis and optimal coding principle are provided to demonstrate its information-theoretic optimality. Specifically, state evolution (SE) for MAMP is intricately multi-dimensional because of the nature of local memory detection. To this end, a fixed-point consistency lemma is proposed to derive the simplified variational SE (VSE) for MAMP, based on which the achievable rate of MAMP is calcu-lated, and the optimal coding principle is derived to maximize the achievable rate. Subsequently, we prove the information-theoretic optimality of MAMP. Numerical results show that the finite-length performances of MAMP with optimized LDPC codes are about 1.0 ~ 2.7 dB away from the associated constrained capacities. It is worth noting that MAMP can achieve the same performance as OAMP/VAMP with 4%o of the time consumption for large-scale systems.

Precoding design for the downlink of multiuser multiple-input multiple-output (MU-MIMO) systems is a fundamental problem. In this paper, we aim to maximize the weighted sum rate (WSR) while considering both quality-of-service (QoS) constraints of each user and per-antenna power constraints (PAPCs) in the downlink MU-MIMO system. To solve the problem, we reformulate the original problem to an equivalent problem by using the well-known weighted minimal mean square error (WMMSE) framework, which can be tackled by iteratively solving three subproblems. Since the precoding matrices are coupled among the QoS constraints and PAPCs, we adopt alternating direction method of multipliers (ADMM) to obtain a distributed solution. Simulation results validate the effectiveness of the proposed algorithm.

The 5.5th Generation is envisioned to support the Real-time Broadband Communication (RTBC) scenarios, which needs to satisfy the enhanced Mobile Broadband(eMBB) and Ultra-Reliable Low-Latency Communication (uRLLC) services requirements simultaneously. As an essential technique to improve the capacity of systems, pre coding in RTBC faces the challenge of finding the optimal solution over long-term transmission with multimodal streams for the system. To meet these requirements, we propose a soft actor-critic (SAC) based multiple transmission time interval (TTl) intelligent precoding algorithm that optimizes the multi-user precoding scheme by learning the priority weight of each user in the iterative weighted minimum mean-square error (MMSE) algorithm. Considering the remaining multimodal data and dynamic activation state of real-time interaction users, we design a novel and lightweight reinforcement learning architecture scalable to different numbers of potential or active users. Simulation results demonstrate the robustness and superiority of our precoding algorithm, which achieves 50% performance improvement in system capacity compared to the weighted MMSE algorithm.

Future communication networks will integrate the sensing functions, requiring the system to utilize limited radio resources to simultaneously achieve high-throughput communication and high-precision sensing. Compressive sensing technology is of great potential for such applications. In this paper, we design an efficient multiple-input multiple-output (MIMO) - orthogonal frequency division multiplexing (OFDM) integrated sensing and communication (ISAC) system based on compressive sensing. Specifically, we obtain the delay-Doppler and angle of departure (AoD) / angle of arrival (AoA) measurements by exploiting the sparse and random time-frequency resource allocation patterns and array structures. Our approach leverages the sparsity of environmental information and employs the Kronecker method to construct a compressive measurement matrix with Vandermonde structure. Our method provides high-resolution performance comparable to Nyquist sampling, while significantly reducing the usage of time-frequency resource units and the number of antennas, without introducing excessive additional hardware links for sensing and computation. Numerical simulations demonstrate the feasibility of the method, and our results indicate that compressive sensing recovery algorithms outperform traditional method with a lower probability of recovery errors and better robustness.

The integration of sensing and communication (ISAC) is an essential function of future wireless systems. Due to its large available bandwidth, millimeter-wave (mmWave) ISAC systems are able to achieve high sensing accuracy. In this paper, we consider the multiple base-station (BS) collaborative sensing problem in a multi-input multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) mmWave communication system. Our aim is to sense a remote target shape with the collected signals which consist of both the reflection and scattering signals. We first characterize the mmWave's scattering and reflection effects based on the Lambertian scattering model. Then we apply the periodogram technique to obtain rough scattering point detection, and further incorporate the subspace method to achieve more precise scattering and reflection point detection. Based on these, a reconstruction algorithm based on Hough Transform and principal component analysis (PCA) is designed for a single convex polygon target scenario. To improve the accuracy and completeness of the reconstruction results, we propose a method to further fuse the scattering and reflection points. Extensive simulation results validate the effectiveness of the proposed algorithms.

Deep learning (DL) based semantic communication methods have been explored to transmit images efficiently in recent years. In this paper, we propose a generative model based semantic communication to further improve the efficiency of image transmission and protect private information. In particular, the transmitter extracts the interpretable latent representation from the original image by a generative model exploiting the GAN inversion method. We also employ a privacy filter and a knowledge base to erase private information and replace it with natural features in the knowledge base. The simulation results indicate that our proposed method achieves comparable quality of received images while significantly reducing communication costs compared to the existing methods.

Millimeter-wave (mmWave) imaging over wireless networks is one of the potential technologies in the design of integrated sensing and communication (ISAC) systems. To achieve complete and accurate sensing of the large-scale complex environment, multiple views from different user equipments (UEs) and base stations (BSs) in a wireless network should be fully and cooperatively exploited. In this paper, based on the uplink channels of the wireless cellular network, we propose a multi-view mmWave imaging architecture. In the proposed architecture, a single BS centrally or multiple BSs jointly process the transmitted data of UEs. Taking into account the complex physical propagation characteristics of mmWave in the environment, especially the occlusion effect, we exploit the multi-view sensing of the environment from various UEs and BSs. To solve the multi-view sensing problem for the considered model, we propose a generalized-approximate-message-passing-based multi-view sparse vector reconstruction (GAMP-MVSVR) algorithm to obtain the imaging results. In the proposed algorithm, a multi-layer factor graph is proposed to describe the data receiving and sending relationship, as well as the occlusion effect of mmWave propagation. The sum-product algorithm (SPA) is used to iteratively solve the imaging result. Specifically in each iteration, the occlusion relationship between the target object in the environment is recalculated according to the proposed occlusion detection rule, and in turn, used to estimate the scattering coefficients of the target objects. Simulation results verify the effectiveness of the proposed algorithm.

Benefited from the advances of deep learning (DL) techniques, deep joint source-channel coding (JSCC) has shown its great potential to improve the performance of wireless transmission. However, most of the existing works focus on the DL-based transceiver design of the JSCC model, while ignoring the resource allocation problem in wireless systems. In this paper, we consider a downlink resource allocation problem, where a base station (BS) jointly optimizes the compression ratio (CR) and power allocation as well as resource block (RB) assignment of each user according to the latency and performance constraints to maximize the number of users that successfully receive their requested content with desired quality. To solve this problem, we first decompose it into two subproblems without loss of optimality. The first subproblem is to minimize the required transmission power for each user under given RB allocation. We derive the closed-form expression of the optimal transmit power by searching the maximum feasible compression ratio. The second one aims at maximizing the number of supported users through optimal user-RB pairing, which we solve by utilizing bisection search as well as Karmarkar's algorithm. Simulation results validate the effectiveness of the proposed resource allocation method in terms of the number of satisfied users with given resources.

Coded distributed computing (CDC) can efficiently improve the performance of Map Reduce by mitigating the effects of straggling servers and decreasing the communication load simultaneously. In this paper, for the latency-constrained computation tasks, we use hybrid coded MapReduce and optimize the hybrid coded scheme to minimize the total latency, including the computation latency in Map phase and the communication latency in Shuffle phase. For the practical scenarios where the number of straggling servers is relatively small compared with the number of all servers, we reformulate the original optimization problem into a non-convex optimization problem. Then we use the successive convex approximation (SCA) algorithm to approximate the non-convex optimization problem into the iterations of a convex optimization problem. We prove the effectiveness and convergence of the proposed iterative SCA algorithm. Besides, the proposed SCA-based algorithm shows the effective convergence and outperforms the enumeration algorithm of the original problem with the approximated total latency and the short execution time via numerical experiments. Furthermore, the hybrid coded scheme achieves significant performance improvement in terms of decreasing the total latency compared with baseline schemes.

The advanced Deep Learning (DL) techniques have enabled the development of semantic communication systems by their remarkable information processing and end - to-end optimization capabilities. However, the lack of performance guarantee of these DL-based methods also brings concerns on the reliability of semantic communication systems. To address this issue, we propose a semantic communication scheme with reliability-driven retransmission requests in order to improve the transmission efficiency and guarantee the reliability of the inference result at the same time. In particular, the transmitter first sends a basic amount of the information, and the receiver infers with this information, and assesses the reliability of the results. If the derived reliability is below a given threshold, a retransmission request is sent to the sender for more information to be transmitted. We exploit the entropy of the output logits to quantify the reliability of the classification results. More specifically, lower entropy corresponds to higher confidence, indicating higher reliability. Experimental results validate the effectiveness of the proposed scheme in terms of improving transmission efficiency and computational efficiency while maintaining the reliability of the inference results.

With the rapid development of deep learning, the parameters of modern neural network models, especially in the field of Natural Language Processing (NLP) are extremely huge. When the parameters of the model are larger even than the storage memory of a single device, it is necessary to split the original big learning model into different parts with each part assigned to one device, thus realizing joint model training over different devices (i.e., distributed training). In this paper, we aim to introduce the advanced coding scheme into the distributed parallel framework, which leads to the perfect combination of coding and the underlying calculation of neural networks. The proposed scheme is not only able to avoid the impact of poor computing power or low bandwidth and even dropped devices (stragglers) on system performance but also reduce the communication load between different devices, thereby greatly improving the performance of distributed parallel systems.

The generalized linear system (GLS) has been widely used in wireless communications to evaluate the effect of nonlinear preprocessing on receiver performance. Generalized approximation message passing (AMP) is a state-of-the-art algorithm for the signal recovery of GLS, but it was limited to measurement matrices with independent and identically distributed (IID) elements. To relax this restriction, generalized orthogonal/vector AMP (GOAMP/GVAMP) for unitarily-invariant measurement matrices was established, which has been proven to be replica Bayes optimal in uncoded GLS. However, the information-theoretic limit of GOAMP/GVAMP is still an open challenge for arbitrary input distributions due to its complex state evolution (SE). To address this issue, in this paper, we provide the achievable rate analysis of GOAMP/GVAMP in GLS, establishing its information-theoretic limit (i.e., maximum achievable rate). Specifically, we transform the fully-unfolded state evolution (SE) of GOAMP/GVAMP into an equivalent single-input single-output variational SE (VSE). Using the VSE and the mutual information and minimum mean-square error (I-MMSE) lemma, the achievable rate of GOAMP/GVAMP is derived. Moreover, the optimal coding principle for maximizing the achievable rate is proposed, based on which a kind of low-density parity-check (LDPC) code is designed. Numerical results verify the achievable rate advantages of GOAMP/GVAMP over the conventional maximum ratio combining (MRC) receiver based on the linearized model and the BER performance gains of the optimized LDPC codes (0.8 ~ 2.8 dB) compared to the existing methods.

We consider a federated data analytics problem in which a server coordinates the collaborative data analysis of multiple users with privacy concerns and limited communication capability. The commonly adopted compression schemes introduce information loss into local data while improving communication efficiency, and it remains an open problem whether such discrete-valued mechanisms provide any privacy protection. In this paper, we study the local differential privacy guarantees of discrete-valued mechanisms with finite output space through the lens of $f$-differential privacy (DP). More specifically, we advance the existing literature by deriving tight $f$-DP guarantees for a variety of discrete-valued mechanisms, including the binomial noise and the binomial mechanisms that are proposed for privacy preservation, and the sign-based methods that are proposed for data compression, in closed-form expressions. We further investigate the amplification in privacy by sparsification and propose a ternary stochastic compressor. By leveraging compression for privacy amplification, we improve the existing methods by removing the dependency of accuracy (in terms of mean square error) on communication cost in the popular use case of distributed mean estimation, therefore breaking the three-way tradeoff between privacy, communication, and accuracy.

Viewing channel state information (CSI) as a fingerprint to infer user position is a promising technology. Recently, with the help of deep learning (DL) methods, the performance of CSI-based positioning has been further improved. However, the performance of DL methods is highly dependent on the number of training samples. Thus, achieving high performance with limited training samples has become an important topic. Analyzing the prior properties of the task and introducing the prior into the neural network is an effective means. In this paper, by analyzing the physical correlation between CSI and position, we point out that the CSI of each subcarrier is a valid fingerprint of user position in a massive multiple-input multiple-output (MIMO) system. With this property, we propose a positioning scheme based on multi-carrier cumulative learning neural network (MCCNet). Instead of directly learning the mapping from the entire MIMO-orthogonal frequency division multiplexing (OFDM) CSI to position, MCCNet first learns to map from the CSI of each subcarrier to position and then accumulates the extracted features from each subcarrier for the final position inference. This a priori design makes the feature extraction from the entire CSI downsize to the CSI of each subcarrier, reducing the learning burden. Simulation experiments on line-of-sight and non-line-of-sight scenarios both show that compared to the existing methods, MCCNet can reduce the averaged position error by at least 53% with the same training samples or achieve the same performance with only a quarter of training samples.

In this paper, we consider the problem of integrated sensing and communication (ISAC) system design over wireless networks. Specifically, the mobile station (MS) in the ISAC system sends uplink communication signal beams to the base station (BS), and the BS accomplishes the environment sensing by processing the propagation gain from received beams. Since the uniform discretization of the environment scenario has inaccurate descriptions of the BS/MS position and object occlusion relationship, we propose a non-uniform pixel discretization method. According to the location of the transceiver and the direction of beams, we discretize the environment into layered non-uniform pixels, which reflect the occlusion relationship between objects in the environment. Based on the sparse features of environmental scatterers, we propose an environment sensing algorithm based on compressed sensing and approximate message passing. The proposed algorithm achieves accurate environment sensing by iteratively removing occlusion interference. At the same time, with the continuous sweeping of the transmitting and receiving beams, the environment sensing results gradually improve. Finally, simulation results demonstrate the effectiveness of the proposed ISAC system design and algorithm.

Integrated sensing and communication (ISAC) systems aim to obtain the environment information using the wireless communication signals. However, most existing methods for ISAC systems require additional communication or hardware overheads, which pose a significant challenge for the resource-constrained wireless communication system. To address this issue, we propose a novel sidelobe-enhanced beam sweeping scheme, which leverages the extra information provided by the sidelobe compared to the traditional beam-based sensing algorithm. The proposed scheme takes into account the complete beam pattern including sidelobes, exploiting the different characteristics in each direction to simultaneously utilize multiple spatial angles. By effectively exploiting the sidelobes rather than treating them as interference, the proposed scheme can achieve comprehensive environmental information acquisition with high efficiency. Simulation results demonstrate that the proposed algorithm yields remarkable performance improvement.

Frequency modulated continuous wave (FMCW) radar can precisely detect moving objects utilizing the Doppler information. However, only exploiting the Doppler information in one frame can usually lead to object false detection when static background has large radar cross section or the moving objective occludes some static background. In this paper, we investigate the moving objective detection problem with FMCW radar through utilizing the Doppler information in multiple frames to increase objective detection accuracy. To solve this problem, an online tensor robust principal component analysis (RPCA) algorithm is proposed with low hardware and computation complexity. The proposed algorithm can maintain the intrinsic tensor data structure. Experimental results show that the proposed algorithm can accurately detect the static background and moving object even for the case of occlusion or static object with large RCS.

A large number of new applications are emerging in the future sixth-generation (6G) communication systems. Federated learning (FL) enables massive user equipments (UEs), such as mobile phones and Internet of Things (IoT) devices, to cooperatively learn a shared model for prediction in various applications, while keeping the training data local. However, in practical scenarios, there are still some problems in deploying FL systems, including serving a large number of active UEs, longtime delay, and the risk of UEs’ privacy leakage. To tackle these issues, we introduce unsourced random access (URA) into the FL systems. URA can support massive connectivity and its unsourced property can protect the UEs’ identity privacy. Moreover, considering the trade-off between communication and computation performance and the various importance of different UEs’ local models in training epochs, two importance metrics are designed. The UEs can decide their own active probability according to the metrics among the communication rounds, which avoids the additional cost of being scheduled by the base station (BS) and maximums the use of the limited communication resources to ensure UEs with higher priority can upload trained models, thus improving the training efficiency. Simulation results verify the remarkable communication and computation performance of the proposed schemes.

The widely applied orthogonal frequency division multiplexing (OFDM) system naturally contains oversampled cyclic prefixes (CP) in the generation process, which provide a wealth of environmental information and higher distance resolution for integrated sensing and communication (ISAC) but is underutilized. Therefore, we develop a compressed sensing (CS) model with oversampled CP, reaching the higher distance resolution limit corresponding to the sample rate of the analog-to-digital converter (ADC) than the fixed signal bandwidth. Since the measurement matrix formed by shifting adjacent oversampled CP pairs is ill-conditioned, we proposed random modulation and random extraction from multiple oversampled CP to increase the validity of the observations. To exploit the channel fading characteristics and sparsity of scattering points, we propose an element-by-element demodulator based on the orthogonal approximate message passing (OAMP) algorithm, called the element-wise OAMP (E-OAMP) algorithm. The simulation results validate the outstanding performance of the proposed algorithm over traditional CS algorithms.

In this paper, we present a probability graph-based semantic information compression system for scenarios where the base station (BS) and the user share common background knowledge. We employ probability graphs to represent the shared knowledge between the communicating parties. During the transmission of specific text data, the BS first extracts semantic information from the text, which is represented by a knowledge graph. Subsequently, the BS omits certain relational information based on the shared probability graph to reduce the data size. Upon receiving the compressed semantic data, the user can automatically restore missing information using the shared probability graph and predefined rules. This approach brings additional computational resource consumption while effectively reducing communication resource consumption. Considering the limitations of wireless resources, we address the problem of joint communication and computation resource allocation design, aiming at minimizing the total communication and computation energy consumption of the network while adhering to latency, transmit power, and semantic constraints. Simulation results demonstrate the effectiveness of the proposed system.

Edge learning is a promising enabler to leverage the distributed local data for powering the artificial intelligence at the edge network. Moreover, incorporating the external domain knowledge into purely data-driven learning models can further enhance the performance. In this paper, by taking both the benefits of edge learning and knowledge fusion, we propose a novel distributed knowledge-aware edge learning framework, in which the edge devices individually train the learning models with the assistance of the local knowledge bases at the edge devices and the global knowledge base at the edge server. Due to the limited cache capability, the edge device can only cache a small-scale local knowledge base, which restricts the performance gain by local knowledge fusion. Meanwhile, uploading local data from multiple edge devices for global knowledge fusion may lead to the air-interface congestion. To overcome these issues, we first formulate the global loss decay maximization problem with transmission scheduling decisions. Specifically, we derive the closed-form relationship between transmission scheduling and the learning performance. Then, we depict the implicit relationship between the knowledge fusion and the global loss decay via establishing a specific multi-armed bandit (MAB) framework, and derive an asymptotically-optimal solution accordingly. Extensive simulations demonstrate that the proposed policies outperform the state-of-art policies.

Polar code is envisioned as a promising candidate for ultra-reliable low-latency communications (URLLC) in fifth-generation (5G) communication and beyond. To decode polar code, a successive cancellation list (SCL) decoder with a large list size can provide near maximum likelihood (ML) decoding performance. However, a large list size will lead to unacceptable spatial complexity, making it impractical. When the list size is small, although the complexity is low, its performance still needs to be improved. The main reason is that the sequence features implied in log-likelihood ratio (LLR) sequences are lost during calculating path metrics used for path selection. Because of the excellent sequence feature extraction ability of the long short-term memory (LSTM) network, we propose an LSTM-based path selection mechanism to replace the path metric-based path selection mechanism in SCL. In our proposed scheme, the LSTM network selects the surviving path according to the LLR sequences corresponding to the current paths. Simulation results show the effectiveness of the proposed LSTM-based path selection mechanism.

This article investigates the resource allocation of a reconfigurable intelligent surface (RIS)-aided joint communication and sensing (JCAS) system in a coal mine scenario. In the JCAS system, an RIS is implemented at the corner of the zigzag tunnels to improve the complicated wireless environment, where ground obstacles frequently block direct links. In addition, a wireless backhaul base station with a limited energy budget is deployed in the depth of the mine to sense the target area and provide Internet of Things (IoT) services and communication services for users. Furthermore, a data center is placed on the ground to analyze the obtained data and route the communication data. Under this deployment, a joint optimization problem of RIS phase-shift matrix, RIS element switches, and area sensing time is proposed. We aim to maximize the successful sensed bits under total completion time, and maximum transmit power constraints. In order to solve this problem, an iterative algorithm is proposed. The successive convex approximation (SCA)-based algorithm is used for the RIS phase-shift matrix optimization subproblem. For the sensing time optimization subproblem, the quadratic approximation method is proposed to optimize the number of area perceptions. The coordinate descent method is utilized to optimize the RIS element switches. Simulation results show that the energy efficiency is improved by up to 38%, and 7% increases the specific data size compared with the benchmark solutions.

In Cooperative Intelligent Transportation Systems (C-ITS) and Connected Automated Vehicles (CAV), accessing multiple users and providing high-precision positioning are both vital. This paper aims to design an efficient deep learning approach to extend current Channel State Information (CSI)-based positioning to Frequency Division Multiple Access (FDMA) mode. In FDMA mode, different users are allocated with different subcarriers, making the user CSI have diverse frequency domain characteristics. The diverse frequency domain characteristics bring huge interference to the neural network for stable position inference, and efficient designs are required to handle this challenge. This paper proposes a novel approach named multi-frequency fusion learning for CSI-based positioning. By first using a shareable method to extract position-related features from CSI on each subcarrier independently and then fusing the obtained features, the designed neural network obtains excellent frequency domain flexibility to cope with the diverse frequency address challenge in FDMA mode. Meanwhile, we provide the feasibility analysis of this learning approach in massive Multiple-Input Multiple-Output (MIMO) systems to ensure its stable application. Based on the architecture of multi-frequency fusion learning, we propose two specific positioning schemes with differentiated designs. One is a Multi-Frequency Ensemble Network (MFENet), which extracts and fuses frequency-independent features to ensure the network is utterly unharmed by the complicated frequency domain characteristics. The other is a Multi-Frequency Cumulative Network (MFCNet), which uses sufficient feature accumulation to achieve high precision positioning. The key performance indices and applications on vehicles are comprehensively compared with popular deep-learning methods. Experiment results show the effectiveness and superiority of the proposed schemes.

Deep learning (DL) based semantic communication methods have been explored for the efficient transmission of images, text, and speech in recent years. In contrast to traditional wireless communication methods that focus on the transmission of abstract symbols, semantic communication approaches attempt to achieve better transmission efficiency by only sending the semantic-related information of the source data. In this paper, we consider semantic-oriented speech transmission which transmits only the semantic-relevant information over the channel for the speech recognition task, and a compact additional set of semantic-irrelevant information for the speech reconstruction task. We propose a novel end-to-end DL-based transceiver which extracts and encodes the semantic information from the input speech spectrums at the transmitter and outputs the corresponding transcriptions from the decoded semantic information at the receiver. In particular, we employ a soft alignment module and a redundancy removal module to extract only the text-related semantic features while dropping semantically redundant content, greatly reducing the amount of semantic redundancy compared to existing methods. We also propose a semantic correction module to further correct the predicted transcription with semantic knowledge by leveraging a pretrained language model. For the speech to speech transmission, we further include a CTC alignment module that extracts a small number of additional semantic-irrelevant but speech-related information, such as duration, pitch, power and speaker identification of the speech for the better reconstruction of the original speech signals at the receiver. We also introduce a two-stage training scheme which speeds up the training of the proposed DL model. The simulation results confirm that our proposed method outperforms current methods in terms of the accuracy of the predicted text for the speech to text transmission and the quality of the recovered speech signals for the speech to speech transmission, and significantly improves transmission efficiency. More specifically, the proposed method only sends 16% of the amount of the transmitted symbols required by the existing methods while achieving about a 10% reduction in WER for the speech to text transmission. For the speech to speech transmission, it results in an even more remarkable improvement in terms of transmission efficiency with only 0.2% of the amount of the transmitted symbols required by the existing method while preserving the comparable quality of the reconstructed speech signals.

Reconfigurable intelligent surface (RIS) can significantly enhance the service coverage of Tera-Hertz massive multiple-input multiple-output (MIMO) communication systems. However, obtaining accurate high-dimensional channel state information (CSI) with limited pilot and feedback signaling overhead is challenging, severely degrading the performance of conventional spatial division multiple access. To improve the robustness against CSI imperfection, this paper proposes a deep learning (DL)-based rate-splitting multiple access (RSMA) scheme for RIS-aided Tera-Hertz multi-user MIMO systems. Specifically, we first propose a hybrid data-model driven DL-based RSMA precoding scheme, including the passive precoding at the RIS as well as the analog active precoding and the RSMA digital active precoding at the base station (BS). To realize the passive precoding at the RIS, we propose a Transformer-based data-driven RIS reflecting network (RRN). As for the analog active precoding at the BS, we propose a match-filter based analog precoding scheme considering that the BS and RIS adopt the LoS-MIMO antenna array architecture. As for the RSMA digital active precoding at the BS, we propose a low-complexity approximate weighted minimum mean square error (AWMMSE) digital precoding scheme, and further design a model-driven deep unfolding active precoding network (DFAPN) by combining the proposed AWMMSE scheme with DL. Then, to acquire accurate CSI at the BS for the investigated RSMA precoding scheme to achieve higher spectral efficiency, we propose a CSI acquisition network (CAN) with low pilot and feedback signaling overhead. The proposed DL-based RSMA scheme for RIS-aided Tera-Hertz multi-user MIMO systems can exploit the advantages of RSMA and DL to improve the robustness against CSI imperfection, thus achieving higher spectral efficiency with lower signaling overhead.

In this paper, the problem of wireless resource allocation and semantic information extraction for energy efficient semantic communications over wireless networks with rate splitting is investigated. In the considered model, a base station (BS) first extracts semantic information from its large-scale data, and then transmits the small-sized semantic information to each user which recovers the original data based on its local common knowledge. At the BS side, the probability graph is used to extract multi-level semantic information. In the downlink transmission, a rate splitting scheme is adopted, while the private small-sized semantic information is transmitted through private message and the common knowledge is transmitted through common message. Due to limited wireless resource, both computation energy and transmission energy are considered. This joint computation and communication problem is formulated as an optimization problem aiming to minimize the total communication and computation energy consumption of the network under computation, latency, and transmit power constraints. To solve this problem, an alternating algorithm is proposed where the closed-form solutions for semantic information extraction ratio and computation frequency are obtained at each step. Numerical results verify the effectiveness of the proposed algorithm.

In split machine learning (ML), different partitions of a neural network (NN) are executed by different computing nodes, requiring a large amount of communication cost. As over-the-air computation (OAC) can efficiently implement all or part of the computation at the same time of communication, thus by substituting the wireless transmission in the traditional split ML framework with OAC, the communication load can be eased. In this paper, we propose to deploy split ML in a wireless multiple-input multiple-output (MIMO) communication network utilizing the intricate interplay between MIMO-based OAC and NN. The basic procedure of the OAC split ML system is first provided, and we show that the inter-layer connection in a NN of any size can be mathematically decomposed into a set of linear precoding and combining transformations over a MIMO channel carrying out multi-stream analog communication. The precoding and combining matrices which are regarded as trainable parameters, and the MIMO channel matrix, which are regarded as unknown (implicit) parameters, jointly serve as a fully connected layer of the NN. Most interestingly, the channel estimation procedure can be eliminated by exploiting the MIMO channel reciprocity of the forward and backward propagation, thus greatly saving the system costs and/or further improving its overall efficiency. The generalization of the proposed scheme to the conventional NNs is also introduced, i.e., the widely used convolutional NNs. We demonstrate its effectiveness under both the static and quasi-static memory channel conditions with comprehensive simulations.

To alleviate the local computation demands from the ever-increasing computation-intensive mobile applications, Mobile Edge Computing (MEC) has proved promising. Especially, by opportunistically offloading these computation tasks to the MEC server, the delay of computing could be significantly improved through communication. In this paper, we develop an analytical framework for joint communication and computation resources allocation for multi-user MEC systems. Specifically, to retrieve the combined effect of communication and computation capabilities, we establish a dual queue system, including a data queue sub-system and a computation queue sub-system. To address the associated stochastic resource optimization problem, we propose a low-complexity resource allocation algorithm by Lyapunov optimization to stabilize all the sub-queue systems. As the practical buffers are finite, the conventional delay analysis of Lyapunov optimization becomes inaccurate. Alternatively, we model the stochastic queue lengthes as discrete time controlled random walk processes, which are transformed to continuous time Stochastic Differential Equations (SDEs) with reflections by strong approximation. According to the steady state analysis on the SDEs, we derive closed-form steady state distributions of the queue lengths, and then obtain the average delay performance with finite buffers. Finally, the accuracy of the proposed delay analysis is verified through simulation.

In China, some e-commerce platform (EP) companies such as Alibaba and JD are now allowed to partner with network operators (NOs) to act as virtual network operators (VNOs) to provide mobile data services for mobile users (MUs). However, it is a question worth researching on how to generate more profits for all network players, with EP companies being VNOs, through appropriate integration of the VNO business and the companies' own e-commerce business. To address this issue, in this work we propose a novel incentive mechanism for advertising via mobile data reward, and model it as a three-stage static Stackelberg game. We obtain the closed-form optimal solution of the Nash equilibrium by backward induction. Besides, for the scenario lack of knowledge on the interaction between the NO and VNO in a dynamic game, we propose a deep Q-network (DQN) based algorithm to derive the optimal strategies of the NO and VNO. Simulation results show impact of system parameters on the utilities of game players and social welfare. We also study the impact of system parameters on different algorithms and discover that the proposed DQN-based algorithm can learn a good strategy as compared with the Stackelberg equilibrium solution.

In this paper, we aim to design an effective learning-based channel state information (CSI) feedback scheme for the multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems from a physics-inspired perspective. We first argue that the CSI matrix of a MIMO-OFDM system is physically closer to a two-dimensional (2-D) sequence rather than an image due to its apparent unsmoothness, non-scalability, and translational variance within both the spatial and frequency domains. On this basis, we introduce a 2-D long short-term memory (LSTM) neural network to represent the CSI and propose a 2-D sequence-to-sequence (Seq2Seq) model for CSI compression and reconstruction. Specifically, one two-layer 2-D LSTM is used for CSI feature extraction, and the other is used for CSI representation and reconstruction. The proposed scheme can not only fully utilize the unique 2-D characteristics of CSI but also preserve the index information and unsmooth features of the CSI matrix compared with current convolutional neural network (CNN) based schemes. We show that the computational complexity of the proposed scheme is linear in the number of transmit antennas and subcarriers. Its key performances, like reconstruction accuracy, convergence speed, generalization ability after short-term training, and robustness to lossy feedback, are comprehensively compared with existing popular convolutional networks. Experimental results show that our scheme can bring up to nearly 7 dB gain in reconstruction accuracy under the same overhead and reduce feedback overhead by up to 75% under the same accuracy compared with the conventional CNN-based approaches.

This paper investigates the utilization of triple polarization (TP) for multi-user (MU) wireless communication systems with holographic multiple-input multi-output surfaces (HMIMOSs), targeting capacity boosting and diversity exploitation without enlarging the antenna array sizes of the transceivers. We specifically consider that both the transmitter and receiver are equipped with an HMIMOS consisting of compact sub-wavelength TP patch antennas and operating in the near-field (NF) regime. To characterize TP MU-HMIMOS systems, a TP NF channel model is constructed using the dyadic Green’s function, whose characteristics are leveraged to design two precoding schemes for mitigating the cross-polarization and inter-user interference contributions. Specifically, a user-cluster-based precoding scheme that assigns different users to one of three polarizations, at the expense of system’s diversity, is presented together with a two-layer precoding technique that removes interference using a Gaussian elimination method. A theoretical correlation analysis for HMIMOS-based systems operating in the NF region is also derived, revealing that both the spacing of transmit patch antennas and user distance impact transmit correlation factors. Our numerical results showcase that the users located far from the transmit HMIMOS experience higher correlation than those closer in the NF region, resulting in a lower channel capacity. In terms of channel capacity, it is demonstrated that the proposed TP HMIMOS-based systems almost achieve 1.25 and 3 times larger gain compared to their dual-polarized version and conventional HMIMOS systems, respectively. It is also shown that the the proposed two-layer precoding scheme combined with two-layer power allocation realizes the highest spectral efficiency, among compared schemes, without sacrificing diversity.

Federated learning (FL) in a mobile edge network faces challenges from both communication and learning per-spectives. The typically non-i.i.d. data can lead to slow convergence and low accuracy. To ease these challenges, frequent communications between user equipments (UEs) and the cen-tral macro base station (MBS) are necessary, aggravating the communication burden. In this paper, a novel hierarchical FL framework is proposed to alleviate the biased convergence of the global model, achieving better communication and computation efficiency. Specifically, the UEs are adaptively clustered and allocated to specific small base stations (SBSs) according to channel conditions, geographic locations, and data distributions. The SBSs are further aggregated to the MBS, forming a hier-archical FL framework. The joint user clustering and wireless resource allocation optimization problem is formulated. To solve this problem, a cross entropy (CE) based method with low computational complexity is proposed. Simulation results validate that the proposed hierarchical FL system can save more than 87 percent training time under the EMNIST Letters dataset, achieving fast convergence and significantly improving the system efficiency.

This paper investigates an interesting wireless sensing problem which aims to focus on a specific target from the complicated background by exploiting the signals of a wireless communication system. In the considered context, multiple users send pilot signals to the base station (BS), which consistently collects and processes the received signals and gradually figures out the target within the focused area. This is by no means easy since all the background scatterers within the environment may produce severe interference to the received signals, which incurs possible divergence and large sensing error, especially when only limited wireless resource is available for sensing. To solve this issue, an iterative focusing algorithm is proposed, in which a rough sensing of the overall environment is performed to obtain an initial blurred imaging result. Then based on the initial environment sensing result, the proposed algorithm iteratively and gradually removes the background scatterers to obtain more and more accurate sensing results of the target object with limited system resource overhead. Simulation results verify the convergence and effectiveness of the proposed algorithm.

In this paper, we investigate the blind channel estimation problem for MIMO systems under Rayleigh fading channel. Conventional MIMO communication techniques require transmitting a considerable amount of training symbols as pilots in each data block to obtain the channel state information (CSI) such that the transmitted signals can be successfully recovered. However, the pilot overhead and contamination become a bottleneck for the practical application of MIMO systems with the increase of the number of antennas. To overcome this obstacle, we propose a blind channel estimation framework, where we introduce an auxiliary posterior distribution of CSI and the transmitted signals given the received signals to derive a lower bound to the intractable likelihood function of the received signal. Meanwhile, we generate this auxiliary distribution by a neural network based variational inference framework, which is trained by maximizing the lower bound. The optimal auxiliary distribution which approaches real prior distribution is then leveraged to obtain the maximum a posterior (MAP) estimation of channel matrix and transmitted data. The simulation results demonstrate that the performance of the proposed blind channel estimation method closely approaches that of the conventional pilot-aided methods in terms of the channel estimation error and symbol error rate (SER) of the detected signals even without the help of pilots.

In this paper, we aim at an efficient multi-beam combining design with only requiring receive power measurements for a millimeter-wave (mmWave) multi-input multi-output (MIMO) communication system. A spectrum efficiency maximization problem is formulated with both beam selection and power constraints. To solve this problem, a reinforcement learning (RL)-based multi-beam combining algorithm is proposed. In particular, a self-attention deep deterministic policy gradient (DDPG) scheme is used to adaptively learn the serving beam sets and the corresponding combining weights without any channel state information (CSI). Moreover, the transformer is integrated into the DDPG to precisely capture the signal directions and relevant strengths. Experimental results show the effectiveness of the proposed learning structure in terms of system achievable rate, convergence, and network robustness.

Obtaining accurate channel state information (CSI) is crucial and challenging for multiple-input multiple-output (MIMO) wireless communication systems. The conventional channel estimation method cannot guarantee the accuracy of mobile CSI while requiring high signaling overhead. Through exploring the intrinsic correlation among a set of historical CSI instances randomly obtained in a certain communication environment, channel prediction can significantly increase CSI accuracy and save signaling overhead. In this paper, we propose a novel channel prediction method based on ordinary differential equation (ODE)-recurrent neural network (RNN) for accurate and flexible mobile MIMO channel prediction. Different from existing works using sequential network structures for exploring the numerical correlation between observed data, our proposed method tries to represent the implicit physics process of path responses changing by a specially designed continuous learning network with ODE structure. Due to the targeted design of the learning network, our proposed method fits the mathematics feature of CSI data better and enjoy higher network interpretability. Experimental results show that the proposed learning approach outperforms existing methods, especially for long time interval of the CSI sequence and large channel measurement error.

In this paper, the problem of wireless resource allocation and semantic information extraction for energy efficient semantic communications over wireless networks is investigated. In the considered model, each user first extracts the semantic information from its large-scale data, and then transmits the small-sized semantic information to the base station (BS) which recovers the original data. Due to the limited energy budget of wireless users, both local computational energy and transmission energy must be considered. This joint computation and communication problem is formulated as an optimization problem whose goal is to minimize the total energy consumption of the network under a latency constraint. To solve this problem, an iterative algorithm is proposed where the optimal solution for joint bandwidth allocation, power control, and computation frequency optimization problem can be obtained. Numerical results show the effectiveness of the proposed algorithm.

The distributed alternating direction method of multipliers (ADMM) is an efficient distributed optimization algorithm, which however shows poor convergence in time-varying network topologies. To solve the challenge, we propose TV-ADMM, a novel distributed ADMM algorithm for time-varying communication networks. More specifically, importance weight parameters are introduced in message fusion, with the purpose of mitigating the potential error brought by the network topology dynamics. Based on that, the updating rules are designed with the first-order approximation and a Bregman divergence term, which can reduce the variance caused by the randomness and enhance the robustness. Moreover, we consider two different practical scenarios with time-varying communication network. In Scenario One, the communication between two nodes succeeds with certain probabilities, based on which the importance weight parameters are designed. Scenario Two considers mobile agents, where the communication link is determined by the distance between two agents. We derive the connectivity probability in this scenario and get the corresponding importance weight. Numerical simulations validate the effectiveness of the proposed algorithm in both scenarios, in comparison with the subgradient-based method.

Massive Machine-Type Communication (mMTC) is expected to support massive connectivity for a large number of machine-type devices (MTDs). In many practical applications, the base station (BS) only needs to recover the list of received messages instead of the identities of active users, which is called unsourced random access (URA). In this paper, we propose a rateless URA scheme, which builds a bridge between rateless code and URA. Specifically, every active user divides the message into several sub-blocks, selects certain sub-blocks and linearly combines them in each time slot according to the transmission pattern, which is randomly chosen from a common pattern matrix by the user before transmission. Then the index of the transmission pattern and the coded sub-block are stitched together, mapped into a codeword in a common codebook and transmitted. At the receiver, the decoder creates decoding trees to group the coded sub-blocks by transmission patterns and recover the original messages. Simulation results verify the remarkable performance of the proposed URA scheme.

In a massive multiple-input multiple-output (MIMO) system, channel state information (CSI) is essential for the base station (BS) to achieve high performance gain. The user equipment (UE) needs to estimate CSI and then feeds it back to the BS in the frequency division duplexing (FDD) mode. Effective compression of CSI will significantly reduce the cost of channel feedback, and many deep learning (DL) based channel compression schemes have been proposed to achieve this goal. In this paper, rather than viewing CSI as images as in most existing works, we propose a new perspective of viewing CSI as an information sequence and analogize CSI feedback to a machine translation task. Further, we propose a novel sequence to sequence (Seq2Seq) model for CSI feedback composed of only recurrent neural networks and a small-scale fully connected layer, avoiding the convolution and pooling structure commonly used in current DL-based works. The advantage of this scheme is that it fully integrates the physical characteristics of the MIMO channel in the spatial domain into the structure of the neural network and avoids the loss of some prominent unsmooth or discontinuous features caused by the inappropriate convolution and pooling operation. Simulation results show that the proposed Seq2Seq model outperforms other DL-based CSI compression techniques under various communication scenarios.

This paper investigates the problem of secrecy energy efficiency for an unmanned aerial vehicle (UAV) assisted joint sensing and communication system. In the considered system, there exists one UAV, one legal user, and one eavesdropper. The UAV needs to complete multiple tasks in multiple time cycles. In each time cycle, the UAV first flies to sense one task and then transmits the sensing results to the legal user. To maximize the secrecy energy efficiency of the system, a joint sensing and transmission time and UAV location optimization problem is formulated. To solve this non-convex fractional programming problem, the original problem is first divided into two subproblems. Each sub-problem can be easily transformed to a convex one by using the successive convex approximation (SCA) method and the Dinkelbach’s approach. Then, an iterative algorithm based on the alternating method is proposed. Simulation results reveal that our proposed algorithm is superior to the conventional algorithms in terms of secrecy energy efficiency.

Federated learning (FL), which enables multiple distributed devices (clients) to collaboratively train a global model without transmitting their private data, has attracted much attention in the Internet of Things (IoT) domain. Compared with centralized learning, FL has obvious privacy advantages because it can protect the clients’ raw data from direct access by adversaries. Furthermore, to prevent the adversaries from inferring private information from the transmitted parameters, several FL algorithms based on differential privacy (DP) have been proposed, where the clients add artificial noise to their local parameters for privacy protection. Nevertheless, the added noise would disrupt the learning process and degrade the performance of the trained model. Considering this, in this article, we develop a performance-enhanced DP-based FL (PEDPFL) algorithm, where a classifier-perturbation regularization method is proposed to improve the robustness of the trained model against DP-injected noise. We derive the theoretical privacy and convergence analysis of the proposed algorithm, and also demonstrate the influence of some hyperparameters on the convergence performance. Simulation results on real-world data sets show that the proposed algorithm has better classification performance than the existing DP-based FL algorithms at the same level of privacy protection, and thus, it is more applicable to IoT applications.

The core requirement of massive Machine-Type Communication (mMTC) is to support reliable and fast access for an enormous number of machine-type devices (MTDs). In many practical applications, the base station (BS) only concerns the list of received messages instead of the source information, introducing the emerging concept of unsourced random access (URA). Although some massive multiple-input multiple-output (MIMO) URA schemes have been proposed recently, the unique propagation properties of millimeter-wave (mmWave) massive MIMO systems are not fully exploited in conventional URA schemes. In grant-free random access, the BS cannot perform receive beamforming independently as the identities of active users are unknown to the BS. Therefore, only the intrinsic beam division property can be exploited to improve the decoding performance. In this paper, a URA scheme based on beam-space tree decoding is proposed for mmWave massive MIMO system. Specifically, two beam-space tree decoders are designed based on hard decision and soft decision, respectively, to utilize the beam division property. They both leverage the beam division property to assist in discriminating the sub-blocks transmitted from different users. Besides, the first decoder can reduce the searching space, enjoying a low complexity. The second decoder exploits the advantage of list decoding to recover the miss-detected packets. Simulation results verify the superiority of the proposed URA schemes compared to the conventional URA schemes in terms of error probability.

In this paper, we aim to efficiently and accurately predict the static channel impulse response (CIR) with only the user’s position information and a set of channel instances obtained within a certain wireless communication environment. Such a problem is by no means trivial since it needs to reconstruct the high-dimensional information (here the CIR everywhere) from the extremely low-dimensional data (here the location coordinates), which often results in overfitting and large prediction error. To this end, we resort to a novel physics-inspired generative approach. Specifically, we first use a forward deep neural network to infer the positions of all possible images of the source reflected by the surrounding scatterers within that environment, and then use the well-known Gaussian Radial Basis Function network (GRBF) to approximate the amplitudes of all possible propagation paths. We further incorporate the most recently developed sinusoidal representation network (SIREN) into the proposed network to implicitly represent the highly dynamic phases of all possible paths, which usually cannot be well predicted by the conventional neural networks with non-periodic activators. The resultant framework of Cosine-Gaussian Radial Basis Function network (C-GRBFnet) is also extended to the MIMO channel case. Key performance measures including prediction accuracy, convergence speed, network scale and robustness to channel estimation error are comprehensively evaluated and compared with existing popular networks, which show that our proposed network is much more efficient in representing, learning and predicting wireless channels in a given communication environment.

With the acceleration of a new round of global scientific, technological, and industrial revolution, the next generation of information and communication technology, i.e., 6G, will inject new momentum into industry transformation and upgrading, as well as into economic innovation and development.This will subsequently promote a global industrial integration.Wireless communication will be ubiquitous in all areas of future society, supporting novel applications with various performance requirements, such as immersive-or interactive-experience applications requiring a large bandwidth, autonomous driving and vehicle-to-everything applications requiring ultrahigh reliability and ultra-low latency, and applications for industrial Internet requiring massive machine-type connectivity.Facing the challenges of the post-Moore and post-pandemic era, wireless communication needs breakthroughs in network architecture to improve the intelligence, security, robustness, bandwidth, and heterogeneity.With this background, several important tendencies have emerged in the development of 6G wireless communications

Reconfigurable intelligent surfaces (RISs) have been recently considered as a promising candidate for energy-efficient solutions in future wireless networks. Their dynamic and low-power configuration enables coverage extension, massive connectivity, and low-latency communications. Due to a large number of unknown variables referring to the RIS unit elements and the transmitted signals, channel estimation and signal recovery in RIS-based systems are the ones of the most critical technical challenges. To address this problem, we focus on the RIS-assisted wireless communication system and present two joint channel estimation and signal recovery schemes based on message passing algorithms in this paper. Specifically, the proposed bidirectional scheme applies the Taylor series expansion and Gaussian approximation to simplify the sum-product procedure in the formulated problem. In addition, the inner iteration that adopts two variants of approximate message passing algorithms is incorporated to ensure robustness and convergence. Two ambiguities removal methods are also discussed in this paper. Our simulation results show that the proposed schemes show the superiority over the state-of-art benchmark method. We also provide insights on the impact of different RIS parameter settings on the proposed schemes.

Reconfigurable intelligent surface (RIS) can be employed in a cell-free system to create favorable propagation conditions from base stations (BSs) to users via configurable elements. However, prior works on RIS-aided cell-free system designs mainly rely on the instantaneous channel state information (CSI), which may incur substantial overhead due to extremely high dimensions of estimated channels. To mitigate this issue, a low-complexity algorithm via the two-timescale transmission protocol is proposed in this paper, where the joint beamforming at BSs and RISs is facilitated via alternating optimization framework to maximize the average weighted sum-rate. Specifically, the passive beamformers at RISs are optimized through the statistical CSI, and the transmit beamformers at BSs are based on the instantaneous CSI of effective channels. In this manner, a closed-form expression for the achievable weighted sum-rate is derived, which enables the evaluation of the impact of key parameters on system performance. To gain more insights, a special case without line-of-sight (LoS) components is further investigated, where a power gain on the order of$\mathcal {O}(M)$is achieved, with$M$being the BS antennas number. Numerical results validate the tightness of our derived analytical expression and show the fast convergence of the proposed algorithm. Findings illustrate that the performance of the proposed algorithm with two-timescale CSI is comparable to that with instantaneous CSI in low or moderate SNR regime. The impact of key system parameters such as the number of RIS elements, CSI settings and Rician factor is also evaluated. Moreover, the remarkable advantages from the adoption of the cell-free paradigm and the deployment of RISs are demonstrated intuitively.

The roll-out of various emerging wireless services has triggered the need for the sixth-generation (6G) wireless networks to provide functions of target sensing, intelligent computing and information communication over the same radio spectrum. In this paper, we provide a unified framework integrating sensing, computing, and communication to optimize limited system resource for 6G wireless networks. In particular, two typical joint beamforming design algorithms are derived based on multi-objective optimization problems (MOOP) with the goals of the weighted overall performance maximization and the total transmit power minimization, respectively. Extensive simulation results validate the effectiveness of the proposed algorithms. Moreover, the impacts of key system parameters are revealed to provide useful insights for the design of integrated sensing, computing, and communication (ISCC).

The challenge in massive Machine Type Communication (mMTC) is to support reliable and instant access for an enormous number of machine-type devices (MTDs). In some particular applications of mMTC, the access point (AP) only has to know the messages received, but not where they source from, thus giving rise to the concept of unsourced random access (URA). In this paper, we propose a novel URA scheme exploiting the elegant properties of Reed-Muller (RM) sequences. Specifically, after dividing the message of an active user into several information chunks, RM sequences are used to carry those chunks, for exploiting the vast sequence space to improve the spectral efficiency, and their nested structure to enable reliable and efficient sequence detection. Next, we further explore a novel structural property of RM sequences for designing sparse patterns which carry part of the information and serve as the hints of coupling the information chunks of a single user. The factors affecting the performance of our slot-based RM detection are characterized. Besides, the complexity of the proposed message stitching method is analyzed and compared to the commonly used tree coding approach. Our simulation results verify the enhanced performance of the proposed URA scheme in error probability and computational complexity compared to the existing counterpart.

Recently, 360-degree mobile virtual reality video (MVRV) has become increasingly popular because it can provide users with an immersive experience. However, MVRV is usually recorded in a high resolution and is sensitive to latency, which indicates that broadband, ultra-reliable, and low-latency communication is necessary to guarantee the users’ quality of experience. In this paper, we propose a mobile edge computing (MEC)-based 360-degree MVRV streaming scheme with field-of-view (FoV) prediction, which jointly considers video coding, proactive caching, computation offloading, and data transmission. To meet the requirement of stringent end-to-end (E2E) latency, the user’s viewpoint prediction is utilized to cache video data proactively, and computing tasks are partially offloaded to the MEC server. In addition, we propose an analytical model based on diffusion process to study the packet transmission process of 360-degree MVRV in multihop wired/wireless networks and analyze the performance of the MEC-enabled scheme. The simulation results verify the accuracy of the analysis and the effectiveness of the proposed MVRV streaming scheme in reducing the E2E delay. Furthermore, the analytical framework sheds some light on the impacts of system parameters, e.g., FoV prediction accuracy and transmission rate, on the balance between computation delay and communication delay.

Channel estimation is one of the key issues in practical massive multiple-input multiple-output (MIMO) systems. Compared with conventional estimation algorithms, deep learning (DL) based ones have exhibited great potential in terms of performance and complexity. In this paper, an attention mechanism, exploiting the channel distribution characteristics, is proposed to improve the estimation accuracy of highly separable channels with narrow angular spread by realizing the “divide-and-conquer” policy. Specifically, we introduce a novel attention-aided DL channel estimation framework for conventional massive MIMO systems and devise an embedding method to effectively integrate the attention mechanism into the fully connected neural network for the hybrid analog-digital (HAD) architecture. Simulation results show that in both scenarios, the channel estimation performance is significantly improved with the aid of attention at the cost of small complexity overhead. Furthermore, strong robustness under different system and channel parameters can be achieved by the proposed approach, which further strengthens its practical value. We also investigate the distributions of learned attention maps to reveal the role of attention, which endows the proposed approach with a certain degree of interpretability.

Accurate and efficient estimation of the high dimensional channels is one of the critical challenges for practical applications of massive multiple-input multiple-output (MIMO). In the context of hybrid analog-digital (HAD) transceivers, channel estimation becomes even more complicated due to information loss caused by limited radio-frequency chains. The conventional compressive sensing (CS) algorithms usually suffer from unsatisfactory performance and high computational complexity. In this paper, we propose a novel deep learning (DL) based framework for uplink channel estimation in HAD massive MIMO systems. To better exploit the sparsity structure of channels in the angular domain, a novel angular space segmentation method is proposed, where the entire angular space is segmented into many small regions and a dedicated neural network is trained offline for each region. During online testing, the most suitable network is selected based on the information from the global positioning system. Inside each neural network, the region-specific measurement matrix and channel estimator are jointly optimized, which not only improves the signal measurement efficiency, but also enhances the channel estimation capability. Simulation results show that the proposed approach significantly outperforms the state-of-the-art CS algorithms in terms of estimation performance and computational complexity.

Millimeter-wave (mmW)/Terahertz (THz) wideband communication employing a large-scale antenna array is a promising technique of the sixth-generation (6G) wireless network for realizing massive machine-type communications (mMTC). To reduce the access latency and the signaling overhead, we design a grant-free random access scheme based on joint active device detection and channel estimation (JADCE) for mmW/THz wideband massive access. In particular, by exploiting the simultaneously sparse and low-rank structure of mmW/THz channels with spreads in the delay-angular domain, we propose two multi-rank aware JADCE algorithms via applying the quotient geometry of product of complex rank-$L$matrices with the number of clusters$L$. It is proved that the proposed algorithms require a smaller number of measurements than the currently known bounds on measurements of conventional simultaneously sparse and low-rank recovery algorithms. Statistical analysis also shows that the proposed algorithms can linearly converge to the ground truth with low computational complexity. Finally, extensive simulation results confirm the superiority of the proposed algorithms in terms of the accuracy of both activity detection and channel estimation.

Intelligent reflecting surface (IRS) is a newly emerged technology that can increase the energy and spectral efficiency of wireless communication systems. This paper considers an IRS-aided multi-user multiple-input single-output (MISO) communication system, and presents a detailed analysis and optimization framework for the weighted sum-rate (WSR) of the downlink transmission over Rician fading channels. Unlike most of the prior works where the active beamformer at the base station (BS) and passive beamformer at the IRS are jointly designed based on the instantaneous channel state information (CSI), this paper proposes a low-complexity transmission protocol where the IRS passive beamforming and BS power allocation coefficient vector are optimized in the large timescale based on the statistical CSI, and the BS transmit beamforming is designed in the small timescale based on only the instantaneous CSI of the effective BS-user channels. Therefore, the channel training overhead in each channel coherence interval under our proposed protocol is independent of the number of IRS reflecting elements, which is in sharp contrast to most of the prior works. By considering maximum-ratio transmit beamforming at the BS, we derive a lower bound of the ergodic WSR in closed-form. Then, we propose an efficient algorithm to jointly optimize the IRS passive beamforming and BS power allocation coefficient vector for maximizing the ergodic WSR lower bound. Numerical results validate the tightness of our derived WSR bound and show that the proposed scheme outperforms various existing schemes in terms of complexity or capacity performance.

The conventional federated learning (FL) framework usually assumes synchronous reception and fusion of all the local models at the central aggregator and synchronous updating and training of the global model at all the agents as well. However, in a wireless network, due to limited radio resource, inevitable transmission failures and heterogeneous computing capacity, it is very hard to realize strict synchronization among all the involved user equipments (UEs). In this paper, we propose a novel asynchronous FL framework, which well adapts to the heterogeneity of users, communication environments and learning tasks, by considering both the possible delays in training and uploading the local models and the resultant staleness among the received models that has heavy impact on the global model fusion. A novel centralized fusion algorithm is designed to determine the fusion weight during the global update, which aims to make full use of the fresh information contained in the uploaded local models while avoiding the biased convergence by enforcing the impact of each UE’s local dataset to be proportional to its sample share. Numerical experiments validate that the proposed asynchronous FL framework can achieve fast and smooth convergence and enhance the training efficiency significantly.

Channel estimation is one of the key issues in practical massive multiple-input multiple-output (MIMO) systems. Compared with conventional estimation algorithms, deep learning-based designs have exhibited great potential in terms of both performance and complexity. In this paper, an attention-aided deep neural network is proposed for channel estimation in hybrid analog-digital massive MIMO systems. Specifically, the integrated attention mechanism automatically realizes the “divide-and-conquer” policy to exploit the distribution characteristics of highly separable channels with narrow angular spread. Simulation results show that the channel estimation performance is significantly improved with the aid of attention at the cost of small complexity overhead, and the strong robustness further strengthens the practical value of the proposed approach. Moreover, the distributions of learned attention maps are also investigated to reveal the role of attention and endow the proposed approach with a certain degree of interpretability.

To meet the ever-increasing demand for mobile video services, one of the effective solutions is caching some popular videos in edge nodes. In this paper, we propose an online stochastic learning algorithm with two time scales for joint caching and transmission optimization in the video frame level. To overcome the drift distortion caused by the dependency among video frames, the transmission process is formulated as an infinite horizon Markov decision process (MDP). We derive the equivalent Bellman equation and design the online value iteration algorithm via stochastic approximation for transmission. Due to the lack of the expression between the system performance and the caching policy, we design a gradient-free stochastic optimization algorithm to update the caching policy. Finally, simulation results show that our proposed algorithm achieves better performance than conventional caching algorithms.

In this paper, the problem of optimal passive beamforming design is studied for a reconfigurable intelligent surface (RIS) assisted full-duplex (FD) communication system. In the studied model, two devices communicate with each other using one RIS under the FD mode. Each of the device will receive not only the message from the other device but also the self-interference. The main problem of this work is to minimize the sum transmit power by jointly optimizing the reflection coefficients matrix and the transmit power of devices. To solve this problem, a dual method is proposed, where the dual problem is formulated as a semidefinite programming problem. After solving the dual problem, the phase beamforming of the RIS is obtained in the closed form. Simulation results show that the proposed scheme can reduce up to 66% sum transmit power compared to a conventional RIS assisted half-duplex mode.

Federated learning (FL) has gained considerable attention of wireless communications community owing to its nature of decentralized training and privacy-preserving. However, with limited radio resources and increasing number of user equipments (UEs), it is very hard to realize strictly synchronous model updating among all the involved UEs as required in the traditional FL algorithms. In this paper, we propose a novel asynchronous FL framework, which considers the potential failures in uploading the local models and the resultant varying degrees of staleness among the models for global update. Specifically, we first design two working modes for adapting to systems with different communication environments and tasks of different difficulty. Next, a central model fusion algorithm is designed for carefully determining the fusion weight during the global update. On one hand, it aims to make the most of the fresh information contained in the uploaded local models. On the other hand, it avoids the biased convergence by making the impact of each UE be proportional to its sample share. Numerical experiments validate that the proposed asynchronous FL framework can achieve the fast and smooth convergence and enhance the training efficiency significantly.

In China, some e-commerce platform (EP) companies such as Alibaba and JD have been now allowed to partner with network operators (NOs) to act as virtual network operators (VNOs) to provide mobile data services for mobile users (MUs). However, it is a question worth researching on how to generate more profits for all network players after EP companies being VNOs through appropriate integration of the VNO business and the companies’ own e-commerce business. To address this issue, in this work we propose a novel incentive mechanism for advertising via mobile data reward, and model it as a three-stage Stackelberg game. In Stage I, the NO decides the price of mobile data for the VNO; in Stage II, the VNO decides its data plan fee for MUs and the ad price for e-commerce merchants (EMs); in Stage III, the MUs make their own decisions on the data plan subscription and the number of ads to be watched, while the EMs decide the number of ad slots they buy from the EP. We obtain the closed-form optimal solution of the Nash equilibrium by backward induction. Simulation results show the impact of the system parameters on the utilities of game players and social welfare, and reveal that the solution can indeed lead to a quadri-win outcome in some cases. At the same time, we summarize some insights that have economic guidance.

Mobile channel estimation is very challenging as usually it requires more pilots and channel observations to obtain the channel state information (CSI) and the resultant estimation accuracy may decrease with the number of antennas and sub-carriers. Through exploring the long-and-short-term intrinsic spatial and temporal correlation among a set of historic channel instances randomly obtained within a certain communication environment, channel prediction can help increase the CSI accuracy w.r.t. to that obtained from only the pilots, and thus save signaling overhead and computational cost. In this paper, we propose a novel generative Periodic-Activator-enabled Auto Encoder-LSTM network (GPAE-LSTMnet) for accurate channel prediction of mobile MIMO channels, which first compresses the high dimensional channel matrix with high-frequency features to a low dimensional space with relatively low-frequency feature space that has high data smoothness and is suitable for time-series sequence prediction. After that, a LSTM network is used to predict the channel in the low dimensional space, which ensures high accuracy and low computational cost. Experimental results show that our proposed learning structure outperforms existing methods especially when the dimension of CSI to be predicted is relatively high, the time interval of the CSI sequence is relatively long and the number of network parameters is highly limited.

Reconfigurable intelligent surfaces (RISs) have been recently considered as a promising candidate for energy-efficient solutions in future wireless networks. Their dynamic and low-power configuration enables coverage extension, massive connectivity, and low-latency communications. Due to a large number of unknown variables referring to the RIS unit elements and the transmitted signals, channel estimation and signal recovery in RIS-based systems are the ones of the most critical technical challenges. To address this problem, we focus on the RIS-assisted multi-user wireless communication system and present a joint channel estimation and signal recovery algorithm in this paper. Specifically, we propose a bidirectional approximate message passing algorithm that applies the Taylor series expansion and Gaussian approximation to simplify the sum-product algorithm in the formulated problem. Our simulation results show that the proposed algorithm shows the superiority over a state-of-art benchmark method. We also provide insights on the impact of different RIS parameter settings on the proposed algorithms.

Existing hybrid automatic repeat request (HARQ) schemes are mainly designed at bit-level, in which the bits with lower reliability are re-encoded and then re-transmitted. However, this may lead to complicated and nonflexible bit-wise joint design among all the re-transmitted packets and introduce extra decoding latency. In this paper, we propose a novel HARQ scheme with multi-packet retransmission and packet-wise polarization, MPPP-HARQ, which simply retransmits the polar coded version of the failed packets as well as the new information packets instead of any specific part of them, so as to avoid all complicated and trivial inner-bit manipulation and greatly reduce the system complexity. Just like the bit-wise polarization, the packet-wise polarization is realized by proper modulo two addition among packets. By proper combination of multiple packets at each retransmission, we can reliably recover all the received packets at the decoder using a joint iterative decoding algorithm. Simulation results show that compared to the traditional HARQ protocol, MPPP-HARQ achieves superior performance and obtains a significant performance gain in terms of throughput and error rate. Index Terms-Polar code, HARQ, joint iterative decoding algorithm, packet-wise polarization

Millimeter-wave (mmWave) imaging has attracted much attention due to its potential applications in next generation wireless networks. However, how to design robust and efficient signaling and reconstruction algorithm still remains very challenging. In this paper, with the aid of the newly emerged intelligent reflecting surfaces (IRS), we propose a novel millimeter-wave computational imaging method exploiting the enormous illuminating patterns provided by Reed-Muller (RM) sequences. In particular, we construct a deterministic sensing matrix using the RM sequences with which to stimulate the objects via an intelligent reflecting surface, so as to modulate the amplitude and phase of the incident wave indirectly and increase the electromagnetic degrees of freedom. Compared with Zadoff-Chu sequence, Hadamard sequence and random sequence modulated signal illumination method, our proposed approach converges faster and achieves nearly the same accuracy as the illuminations increase, and has less hardware overhead.

In this paper, we try to solve the problem of wireless channel prediction in a fixed area based only on position information of user's equipment. It is the first time that such a problem is proposed and discussed. Different from recent channel prediction methods which need a sequence of measured channel state information (CSI) as known factor, we view this task as a generative problem. A large amount of CSI data measured in the historical communication process can be made use of directly. For solving this problem in a data-driven way, a novel physics-inspired learning structure (C-GRBF) is proposed which fits the physics process of channel impulse response formulating perfectly. Scattering environment information is learned as parameters of the network and the principle of electromagnetic wave propagation is implicit represented by the structure of the network. In the meantime, the reason why conventional universal learning structures fail in solving this problem is analyzed. Experimental results show great performance in prediction accuracy, convergence speed and network robustness of the proposed learning structure.

Recently, millimeter wave (mmWave) imaging has received widespread attention. However, due to its nonlinearity and ill-posedness, it is challenging to reconstruct the precise electromagnetic properties of unknown targets from the measured scattered fields. In this paper, a new concentrative intelligent reflecting surface (IRS) aided computational imaging scheme is proposed. In the scheme, by dividing the region of imaging (ROI) into pixels, the imaging process is transformed into a compressed sensing problem. This paper proposes a fast block sparse Bayesian learning (BSBL) algorithm, which exploits the block sparsity of the reflection vector of ROI, and reduces the computational complexity through the generalized approximate message passing (GAMP) algorithm. Finally, the simulation results validate the performance advantages of the proposed algorithm and the efficiency of IRS in the imaging process.

Reconfigurable Intelligent Surfaces (RISs) have been recently considered as an energy-efficient solution for future wireless networks. Their dynamic and low-power configuration enables coverage extension, massive connectivity, and low-latency communications. Channel estimation and signal recovery in RIS-based systems are among the most critical technical challenges, due to the large number of unknown variables referring to the RIS unit elements and the transmitted signals. In this paper, we focus on the downlink of a RIS-assisted multi-user Multiple Input Single Output (MISO) communication system and present a joint channel estimation and signal recovery scheme based on the PARAllel FACtor (PARAFAC) decomposition. This decomposition unfolds the cascaded channel model and facilitates signal recovery using the Bilinear Generalized Approximate Message Passing (BiG-AMP) algorithm. The proposed method includes an alternating least squares algorithm to iteratively estimate the equivalent matrix, which consists of the transmitted signals and the channels between the base station and RIS, as well as the channels between the RIS and the multiple users. Our selective simulation results show that the proposed scheme outperforms a benchmark scheme that uses genie-aided information knowledge. We also provide insights on the impact of different RIS parameter settings on the proposed scheme.

In this article, we investigate the issue of massive access in a beyond fifth-generation (B5G) multibeam low-Earth orbit (LEO) satellite Internet-of-Things (IoT) network in the presence of channel phase uncertainty due to channel-state information (CSI) conveyance from the devices to the satellite via the gateway. Rather than time-division multiple access (TDMA) or frequency-division multiple access (FDMA) with multicolor pattern, a new nonorthogonal multiple access (NOMA) scheme is adopted to support massive IoT distributed over a very wide range. Considering the limited energy on the LEO satellite, two robust beamforming algorithms against channel phase uncertainty are proposed for minimizing the total power consumption in the scenarios of noncritical IoT applications and critical IoT applications, respectively. Both theoretical analysis and simulation results validate the effectiveness and robustness of the proposed algorithms for supporting massive access in satellite IoT.

The cache-enabled multicast content pushing, which multicasts the content items to multiple users and caches them until requested, is a promising technique to alleviate the heavy network load by enhancing the traffic offloading. This, in turn, has called for the optimization of content pushing strategy while considering both the transmission and caching resources, which jointly result in the complicated coupling among pushing decisions and lead to high computational complexity. Unlike most existing approaches which simplify the pushing problem via bypassing the complicated coupling, in this paper, we propose a multicast content pushing strategy to maximize the offloaded traffic with the cost on content caching based on structured deep learning. Specifically, we design the convolution stage to extract the spatio-temporal correlations of one content item between different pushing decisions, and construct the fully-connected stage to capture the spatial coupling among the decisions of pushing different content items to different user devices. Moreover, to address the absence of the ground truth on multicast content pushing, we relax the transmission constraint to derive a performance upper bound for guiding the training direction. This relaxed problem is solved based on dynamic programming in a bottom-up manner. Compared to the state-of-the-art baselines including both the traditional model-based and the general neural network-based strategies, the proposed pushing strategy achieves significant performance gain in both the random-generated dataset and the real LastFM dataset. In addition, it is also shown that the proposed strategy is robust to the uncertainty of user request information.

Wireless communication in the TeraHertz band (0.1--10 THz) is envisioned as one of the key enabling technologies for the future sixth generation (6G) wireless communication systems scaled up beyond massive multiple input multiple output (Massive-MIMO) technology. However, very high propagation attenuations and molecular absorptions of THz frequencies often limit the signal transmission distance and coverage range. Benefited from the recent breakthrough on the reconfigurable intelligent surfaces (RIS) for realizing smart radio propagation environment, we propose a novel hybrid beamforming scheme for the multi-hop RIS-assisted communication networks to improve the coverage range at THz-band frequencies. Particularly, multiple passive and controllable RISs are deployed to assist the transmissions between the base station (BS) and multiple single-antenna users. We investigate the joint design of digital beamforming matrix at the BS and analog beamforming matrices at the RISs, by leveraging the recent advances in deep reinforcement learning (DRL) to combat the propagation loss. To improve the convergence of the proposed DRL-based algorithm, two algorithms are then designed to initialize the digital beamforming and the analog beamforming matrices utilizing the alternating optimization technique. Simulation results show that our proposed scheme is able to improve 50\% more coverage range of THz communications compared with the benchmarks. Furthermore, it is also shown that our proposed DRL-based method is a state-of-the-art method to solve the NP-hard beamforming problem, especially when the signals at RIS-assisted THz communication networks experience multiple hops.

With the increasing development of Internet of Things (IoT), the upcoming sixth-generation (6G) wireless network is required to support grant-free random access of a massive number of sporadic traffic devices. In particular, at the beginning of each time slot, the base station (BS) performs joint activity detection and channel estimation (JADCE) based on the received pilot sequences sent from active devices. Due to the deployment of a large-scale antenna array and the existence of a massive number of IoT devices, conventional JADCE approaches usually have high computational complexity and need long pilot sequences. To solve these challenges, this paper proposes a novel deep learning framework for JADCE in 6G wireless networks, which contains a dimension reduction module, a deep learning network module, an active device detection module, and a channel estimation module. Then, prior-feature learning followed by an adaptive-tuning strategy is proposed, where an inner network composed of the Expectation-maximization (EM) and back-propagation is introduced to jointly tune the precision and learn the distribution parameters of the device state matrix. Finally, by designing the inner layer-by-layer and outer layer-by-layer training method, a feature-aided adaptive-tuning deep learning network is built. Both theoretical analysis and simulation results confirm that the proposed deep learning framework has low computational complexity and needs short pilot sequences in practical scenarios.

Federated learning (FL) is a privacy-preserving machine learning setting that enables many devices to jointly train a shared global model without the need to reveal their data to a central server. However, FL involves a frequent exchange of the parameters between all the clients and the server that coordinates the training. This introduces extensive communication overhead, which can be a major bottleneck in FL with limited communication links. In this paper, we consider training the binary neural networks (BNNs) in the FL setting instead of the typical real-valued neural networks to fulfill the stringent delay and efficiency requirement in wireless edge networks. We introduce a novel FL framework of training BNNs, where the clients only upload the binary parameters to the server. We also propose a novel parameter updating scheme based on the Maximum Likelihood (ML) estimation that preserves the performance of the BNN even without the availability of aggregated real-valued auxiliary parameters that are usually needed during the training of the BNN. Moreover, for the first time in the literature, we theoretically derive the conditions under which the training of BNN is converging. Numerical results show that the proposed FL framework significantly reduces the communication cost compared to the conventional neural networks with typical real-valued parameters, and the performance loss incurred by the binarization can be further compensated by a hybrid method.

While the Nyquist rate serves as a lower bound to sample a general bandlimited signal with no information loss, the sub-Nyquist rate may also be sufficient for sampling and recovering signals under certain circumstances. Previous works on sub-Nyquist sampling achieved dimensionality reduction mainly by transforming the signal in certain ways. However, the underlying structure of the sub-Nyquist sampled signal has not yet been fully exploited. In this paper, we study the fundamental limit and the method for recovering data from the sub-Nyquist sample sequence of a linearly modulated baseband signal. In this context, the signal is not eligible for dimension reduction, which makes the information loss in sub-Nyquist sampling inevitable and turns the recovery into an under-determined linear problem. The performance limits and data recovery algorithms of two different sub-Nyquist sampling schemes are studied. First, the minimum normalized Euclidean distances for the two sampling schemes are calculated which indicate the performance upper bounds of each sampling scheme. Then, with the constraint of a finite alphabet set of the transmitted symbols, a modified time-variant Viterbi algorithm is presented for efficient data recovery from the sub-Nyquist samples. The simulated bit error rates (BERs) with different sub-Nyquist sampling schemes are compared with both their theoretical limits and their Nyquist sampling counterparts, which validates the excellent performance of the proposed data recovery algorithm.

Reconfigurable intelligent surface (RIS) is a promising solution to build a programmable wireless environment with reconfigurable passive elements, which can achieve high spectral and energy efficiency. In this paper, we investigate the ergodic capacity of RIS-assisted multi-user multiple-input single-output (MISO) wireless systems in both uplink and downlink scenarios. Unlike most of prior works, where instantaneous channel state information (CSI) is assumed, we consider the realistic scenario with only statistical CSI. For both scenarios, we first present an analytical expressions for the ergodic sum capacity of the system. Based on which, the joint power control (or transmit beamforming) and phase shift design problem maximizing the ergodic sum capacity is formulated. Capitalizing on the alternating direction method of multipliers (ADMM), fractional programming (FP) and alternating optimization (AO) methods, efficient suboptimal solutions are obtained for the non-convex design problems. Simulation results are presented to validate the accuracy of the analytical ergodic sum capacity expressions and evaluate the impact of key system parameters such as CSI, Rician$K$factor, number of RIS elements, and RIS location on the ergodic capacity performance. The findings suggest that the proposed statistical CSI design achieves decent performance compared with the instantaneous CSI based design. Moreover, a signal hot spot can be created when placing the RIS close to the users.

This paper considers an angle-domain intelligent reflecting surface (IRS) system. We derive maximum likelihood (ML) estimators for the effective angles from the base station (BS) to the user and the effective angles of propagation from the IRS to the user. It is demonstrated that the accuracy of the estimated angles improves with the number of BS antennas. Also, deploying the IRS closer to the BS increases the accuracy of the estimated angle from the IRS to the user. Then, based on the estimated angles, we propose a joint optimization of BS beamforming and IRS beamforming, which achieves similar performance to two benchmark algorithms based on full CSI and the multiple signal classification (MUSIC) method respectively. Simulation results show that the optimized BS beam becomes more focused towards the IRS direction as the number of reflecting elements increases. Furthermore, we derive a closed-form approximation, upper bound and lower bound for the achievable rate. The analytical findings indicate that the achievable rate can be improved by increasing the number of BS antennas or reflecting elements. Specifically, the BS-user link and the BS-IRS-user link can obtain power gains of order N and NM2, respectively, where N is the antenna number and M is the number of reflecting elements.

In this article, we propose a novel distributed alternating direction method of multipliers (ADMM) algorithm with synergetic communication and computation, called SCCD-ADMM, to reduce the total communication and computation cost of the system. Explicitly, in the proposed algorithm, each node interacts with only part of its neighboring nodes, the number of which is progressively determined according to a heuristic searching procedure, which takes into account both the predicted convergence rate and the communication and computation costs at each iteration, resulting in a trade-off between communication and computation. Then the node chooses its neighboring nodes according to an importance sampling distribution derived theoretically to minimize the variance with the latest information it locally stores. Finally, the node updates its local information with a new update rule which adapts to the number of communication nodes. We prove the convergence of the proposed algorithm and provide an upper bound of the convergence variance brought by randomness. Extensive simulations validate the excellent performances of the proposed algorithm in terms of convergence rate and variance, the overall communication and computation cost, the impact of network topology as well as the time for evaluation, in comparison with the traditional counterparts.

Reconfigurable Intelligent Surfaces (RISs) have been recently considered as an energy-efficient solution for future wireless networks due to their fast and low-power configuration, which has increased potential in enabling massive connectivity and low-latency communications. Accurate and low-overhead channel estimation in RIS-based systems is one of the most critical challenges due to the usually large number of RIS unit elements and their distinctive hardware constraints. In this paper, we focus on the uplink of a RIS-empowered multi-user Multiple Input Single Output (MISO) uplink communication systems and propose a channel estimation framework based on the parallel factor decomposition to unfold the resulting cascaded channel model. We present two iterative estimation algorithms for the channels between the base station and RIS, as well as the channels between RIS and users. One is based on alternating least squares (ALS), while the other uses vector approximate message passing to iteratively reconstruct two unknown channels from the estimated vectors. To theoretically assess the performance of the ALS-based algorithm, we derived its estimation Cramér-Rao Bound (CRB). We also discuss the downlink achievable sum rate computation with estimated channels and different precoding schemes for the base station. Our extensive simulation results show that our algorithms outperform benchmark schemes and that the ALS technique achieves the CRB. It is also demonstrated that the sum rate using the estimated channels always reach that of perfect channels under various settings, thus, verifying the effectiveness and robustness of the proposed estimation algorithms.

Content popularity prediction plays an important role on proactive content caching. Different to most of the existing works which focus on improving the popularity prediction accuracy, in this article, we consider the content caching oriented popularity prediction through a weighted clustering approach in order to improve the caching performance. We formulate the loss of the cache hit ratio as the system regret to indicate the caching performance, and construct a clustering-based popularity prediction framework for overcoming the user request sparsity with considering the similarity of popularity evolution trends. For depicting the explicit relationship between the caching performance and the popularity prediction accuracy, we derive the popularity prediction error distribution of each content, and design the caching threshold. By extracting the insights in the relationship between the popularity prediction accuracy and the user clustering strategy, we develop a weighted clustering-based popularity prediction algorithm, which takes the caching regret probability of files as the weights. Based on two real-world datasets, the simulation results demonstrate that the proposed popularity prediction scheme achieves better caching performance than the state-of-the-art schemes.

In this article, we investigate the issue of integrated sensing, computation and communication (SCC) in beyond fifth-generation (B5G) cellular internet of things (IoT) networks. According to the characteristics of B5G cellular IoT, a comprehensive design framework integrating SCC is put forward for massive IoT. For sensing, highly accurate sensed information at IoT devices are sent to the base station (BS) by using non-orthogonal communication over wireless multiple access channels. Meanwhile, for computation, a novel technique, namely over-the-air computation (AirComp), is adopted to substantially reduce the latency of massive data aggregation via exploiting the superposition property of wireless multiple access channels. To coordinate the co-channel interference for enhancing the overall performance of B5G cellular IoT integrating SCC, two joint beamforming design algorithms are proposed from the perspectives of the computation error minimization and the weighted sum-rate maximization, respectively. Finally, extensive simulation results validate the effectiveness of the proposed algorithms for B5G cellular IoT over the baseline ones.

In distributed random access (RA), each device should consider its own information as well as the influence from others, which is difficult to obtain with diverse capabilities of devices. In this paper, we study the RA problem for massive devices with heterogeneous device capabilities in large-scale energy harvesting IoT networks. To deal with the overload issue for massive devices with different capabilities, we propose an optimal RA policy by improving the conventional mean field games (MFG) via exchanging the mean field terms (MFT) among devices. Specifically, we formulate the delay optimal problem as a two-dimensional Markov Decision Process (MDP) problem involving both energy and data states. For distributed deployment of massive RA, we divide the MDP into multiple per-device subproblems, and propose the distributed RA scheme by solving the Hamilton-Jacobi-Bellman (HJB) equation using stochastic learning. Considering the deviation of MFT estimation induced by heterogeneous device capabilities, we design an MFT consensus scheme based on stochastic approximation by information exchange among neighbor devices. For reducing the state space and exchanging the MFT efficiently, we adopt the number of simultaneous access devices instead of the conventional MFT. Furthermore, we prove the convergence of the proposed scheme with coupling MFT and Q-factor. Finally, simulation results demonstrate that the proposed RA scheme outperforms baseline schemes on delay performance, especially in the heavy traffic load regime.

Massive machine-type communication (mMTC) is expected to provide reliable and low-latency connectivity for an enormous number of devices, which turn active sporadically or frequently. In this highly dynamic situation, it is crucial to design efficient random access (RA) procedures to cope both with the flood of simultaneous access requests and with the potential access failures. In this article, by exploiting the large sequence space, the excellent correlation property and especially the elegant nested structure of Reed-Muller (RM) sequences, we propose a new RA scheme, which facilitates both instantaneous access for newly active users and incremental access for the existing users who suffer from detection failures. In particular, when a failure occurs, the user continues accessing the channel employing an expanded RM sequence, which is combined with the previously received ones at the access point (AP) to form a longer sequence so as to attain potentially better detection probability. Furthermore, a recursive detection algorithm is designed for jointly detecting the resultant RM sequences and the channel coefficients of both the newly active users and the existing ones. The performance of the proposed algorithm is analyzed in detail. Our simulation results validate the analysis and show the scheme's superior access probability, access latency and computational complexity.

This paper proposes an angle domain framework for the design of an intelligent reflecting surface (IRS) system. The maximum likelihood (ML) principle is applied to derive the estimators for the effective angles among the base station (BS), IRS and user. It is demonstrated that the accuracy of the estimated angles improves with the number of BS antennas. Also, deploying the IRS closer to the BS increases the accuracy of the estimated angle from the IRS to the user. Then, exploiting the estimated angles, we propose a joint design of BS beamforming and IRS beamforming. Simulation results show that our proposed algorithm, which only needs few angle information, achieves nearly the same performance as the algorithm requiring full channel state information (CSI). Moreover, the optimized BS beam becomes more focused towards the IRS direction as the number of reflecting elements increases.

This paper proposes a novel location information aided design framework for multiple intelligent reflecting surface (IRS) systems. Assuming practical and imperfect user location information, the effective angles from the IRS to the users are estimated, which is then used to design the transmit beam and IRS beam. Furthermore, closed-form expressions for the achievable rate are derived. The analytical findings indicate that the achievable rate can be improved by increasing the number of base station (BS) antennas or reflecting elements. Specifically, a power gain of order NM2is achieved, where N is the number of BS antennas and M is the number of reflecting elements. Moreover, with a large number of reflecting elements, the individual signal to interference plus noise ratio (SINR) is proportional to M. Also, it has been shown that high location uncertainty would significantly degrade the achievable rate. Besides, IRSs should be deployed at distinct directions (relative to the BS) and be far away from each other to reduce the interference from multiple IRSs.

This paper designs a cooperative activity detection framework for massive grant-free random access in the sixth-generation (6G) cell-free wireless networks based on the covariance of the received signals at the access points (APs). In particular, multiple APs cooperatively detect the device activity by only exchanging the low-dimensional intermediate local information with their neighbors. The cooperative activity detection problem is non-smooth and the unknown variables are coupled with each other for which conventional approaches are inapplicable. Therefore, this paper proposes a covariance-based algorithm by exploiting the sparsity-promoting and similarity-promoting terms of the device state vectors among neighboring APs. An approximate splitting approach is proposed based on the proximal gradient method for solving the formulated problem. Simulation results show that the proposed algorithm is efficient for large-scale activity detection problems while requires shorter pilot sequences compared with the state-of-art algorithms in achieving the same system performance.

Millimeter-wave/Terahertz (mmW/THz) communications have shown great potential for wideband massive access in next-generation cellular internet of things (IoT) networks. To decrease the length of pilot sequences and the computational complexity in wideband massive access, this paper proposes a novel joint activity detection and channel estimation (JADCE) algorithm. Specifically, after formulating JADCE as a problem of recovering a simultaneously sparse-group and low rank matrix according to the characteristics of mmW/THz channel, we prove that jointly imposing l0norm and low rank on such a matrix can achieve a robust recovery under sufficient conditions, and verify that the number of measurements derived for the mmW/THz wideband massive access system is significantly smaller than currently known measurements bound derived for the conventional simultaneously sparse and low-rank recovery. Furthermore, we propose a multi-rank aware method by exploiting the quotient geometry of product of complex rank-Lmaxmatrices with the maximum number of scattering clusters Lmax. Theoretical analysis and simulation results confirm the superiority of the proposed algorithm in terms of computational complexity, detection error rate, and channel estimation accuracy.

The random access (RA) decision of a device should depend on both its own state and the influence of others for avoiding the overload. However, the heterogeneous characteristics of massive devices lead to the difficult for estimating the influence. In this paper, we consider the RA problem for the large-scale energy harvesting IoT networks. To deal with the overload issue for massive heterogeneous devices, we propose a delay-optimal RA strategy by improving the conventional mean field games (MFG) via exchanging the mean field terms (MFT) among devices. Specifically, we formulate the delay-optimal problem as a two-dimensional Markov decision process (MDP) problem. For distributed deployment of massive random access, we divide the MDP into multiple per-device subproblems. With the given influence of other devices, i.e., MFT, we solve the per-device MDP and propose the optimal RA scheme via Hamilton-Jacobi-Bellman (HJB) equation. To obtain optimal access strategy, we adopt an online learning scheme to estimate the influence from others, where we transform MFT into the number of simultaneous access devices in order to reduce the state space significantly. Considering that the heterogeneous devices will cause the deviation of MFT estimation, we design a consensus scheme for the MFT based on stochastic approximation by information exchange among neighbor devices. Finally, simulation results show that the proposed RA scheme achieves a good delay performance comparing with other baselines.

In the mMTC scenario, enormous devices turn active sporadically or frequently to seek for opportunities to transmit short packets. In this highly dynamic situation, it is critical to design efficient random access (RA) procedures to cope both with the flood of simultaneous access requests and with the potential access failures. In this paper, we propose an incremental RA scheme exploiting the nested Reed-Muller (RM) sequences. Specifically, the users who suffer from access failures expand their RM sequences following the given expansion rule, which utilizes both the nested structure and the cross-correlation property of RM sequences. At the receiver, a recursive detection algorithm is proposed, which exploits the discrepancy in the sequence length to detect the retransmission users progressively. On the other hand, new active users continuously spring up in the system, thus causing the incremental number of users seeking for access. In this case, the proposed scheme can detect newly active users together with retransmission ones with great detection capability and low access latency. Our simulation results verify the superior performance of the proposed RA scheme.

Currently, it is still challenging for Video Streaming Service Providers (VSSPs) to develop advanced video delivery in wireless communications due to the limitation of network resources. In this paper, we propose a pricing strategy to effectively allocate heterogeneous resources, including transmission bandwidth and caching space, to improve users' Quality of Experience (QoE). Due to the properties of the two-hop video streaming model we propose, a video can be partially cached in the local storages to provide low waiting time and avoid playback interrupt. To design the pricing strategy for two heterogeneous resources, we first quantitatively analyzing the QoE with partial video caching by adopting diffusion approximation with stochastic data arrival. Based on the above QoE model, we develop an ascending auction framework for pricing heterogeneous resources. Furthermore, we prove that our proposed pricing strategy achieves asymptotically optimal social surplus and ε-incentive compatibility. Finally, simulation results show that our proposed pricing strategy can achieve better performance on the social surplus compared to the conventional pricing strategies.

In this paper, we design a multi-beam low earth orbit (LEO) satellite internet of things (IoT) for beyond fifth-generation (B5G) wireless networks. Rather than time division multiple access (TDMA), a new non-orthogonal multiple access (NOMA) scheme is adopted to support massive IoT over a very wide range. In order to reduce the power consumption of multi-beam satellite, a spot beam design algorithm is proposed with the goal of minimizing the total power consumption subject to quality-of-service (QoS) requirements. Furthermore, considering high computational complexity of spot beam design in the context of massive IoT, a simple multi-beam design algorithm is provided. Finally, simulation results confirm the effectiveness of the proposed algorithms over conventional ones.

The beyond fifth-generation (B5G) cellular internet of things (IoT) network is required to support low-power and wide-coverage wireless access for a massive number of devices. However, the severe co-channel interference caused by massive access decreases the reliability and coverage. To solve this problem, we design a large intelligent reflecting surface (IRS) aided massive access framework, including channel estimation and information transmission. Furthermore, we analyze the performance of the proposed framework, and reveal the role of the large IRS. It is found that the large IRS is beneficial to improve the performance of edge-devices, and thus enhance the coverage. Moreover, we find that the refection coefficient should be carefully chosen according to channel conditions and system parameters to improve the performance.

This paper considers a multi-antenna multicast system with programmable metasurface (PMS) based transmitter. Taking into account of the finite-resolution phase shifts of PMSs, a novel beam training approach is proposed, which achieves comparable performance as the exhaustive beam searching method but with much lower time overhead. Then, a closed-form expression for the achievable individual rate is presented, which is valid for arbitrary system configurations. Besides, assuming a large number of reflecting elements, a simple approximated expression for the multicast rate is derived. A closed-form solution is obtained for the optimal power allocation scheme, and it is shown that equal power allocation is optimal when the number of reflecting elements is sufficiently large. The analytical findings indicate that, increasing the number of radio frequency (RF) chains or reflecting elements can significantly improve the multicast rate, and as the phase shift number becomes larger, the multicast rate improves first and gradually converges to a limit. Moreover, increasing the number of users would significantly degrade the multicast rate, but this rate loss can be compensated by implementing a large number of reflecting elements.

In this paper, we investigate the issue of integrated computation and communication in beyond fifth-generation (B5G) cellular internet of things (IoT) networks with massive connectivity. By exploiting the open nature of wireless channels, a comprehensive deign framework integrating computation and communication over the same spectrum is first put forward for massive IoT. To achieve efficient integration of computation and communication under practical but adverse conditions, a robust algorithm is proposed by jointly optimizing transmit power and receive beamforming, with the goal of minimizing the computation error of computation signals while guaranteeing the requirement of communication signals. Finally, extensive simulations validate the robustness and effectiveness of the proposed algorithm for B5G cellular IoT.

In this work, we study the cooperative and non-cooperative transmission schemes design for live VR video broadcast scenarios by utilizing non-orthogonal multiple access (NOMA), considering that users’ viewports partly overlap due to behavioral coherence. To characterize the performance of the proposed cooperative and non-cooperative transmission schemes, the exact and asymptotic expressions of outage probability, as well as the average outage capacity under imperfect successive interference cancellation (SIC), are derived, respectively. Based on the asymptotic outage probability results, we optimize the power allocation to maximize the average outage capacity of the proposed schemes. Finally, simulation results demonstrate that both of the proposed schemes can achieve a considerable performance gain over the traditional orthogonal multiple access (OMA) scheme in average outage capacity, and each of the proposed schemes has its advantages and applicable scenarios.

Video surveillance systems or Internet of Multimedia Things are playing a more and more important role in our daily life. To obtain useful surveillance information timely and accurately, not only image recognition algorithms but also computing and communication resources can be bottlenecks of the whole system. In this article, taking face recognition application as an example, we study how to build video surveillance systems by utilizing mobile edge computing (MEC), one of the 5G's key technologies. Specifically, to achieve high recognition accuracy and low recognition time, we design image recognition algorithms for both the camera sensor and MEC server, and utilize the action-value methods to train actions of the system by jointly optimizing offloading decision and image compression parameters. The experimental results show the advantages of the proposed system for enabling communication environment-adaptive, efficient, and intelligent video surveillance.

This article addresses the issue of symbol detection in ambient backscatter communication systems with the multiantenna reader. Focusing on the ON-OFF keying modulation, the optimal detector minimizing the bit error rate (BER) is devised based on the maximum a posteriori principle. We then analyze the exact closed-form BER expression for the optimal detector. Moreover, simple approximate BER expressions are derived in certain asymptotic regimes. Furthermore, a simple energy detector is analyzed as a benchmark scheme, and the asymptotic BER is devised in a closed form. The findings of this article suggest that implementing multiple antennas at the reader is an effective means to enhance the BER performance and extend the tag-reader communication range. Also, the optimal detector always outperforms the energy detector. In particular, the optimal detector can avoid the error floor phenomenon, which is inevitable for the energy detector in the high SNR regime.

In this article, we apply the large intelligent reflecting surface (IRS) technique in beyond fifth-generation (B5G) cellular Internet of Things (IoT) to satisfy the requirements of massive connectivity, low power, and wide coverage. First, we design a framework for the large IRS-aided B5G cellular IoT, including channel estimation, uplink data transmission, and downlink data transmission. Then, we analyze the performance of the proposed framework, and reveal the impacts of key parameters of the large IRS on the spectral efficiency. Next, we propose a low-complexity time-length allocation algorithm to minimize the total energy consumption of B5G cellular IoT. Finally, extensive simulation results validate the accuracy of the derived theoretical expressions and the effectiveness of the proposed algorithm.

This paper considers a multi-antenna multicast system with programmable metasurface (PMS) based transmitter. Taking into account of the finite-resolution phase shifts of PMSs, a novel beam training approach is proposed, which achieves comparable performance as the exhaustive beam searching method but with much lower time overhead. Then, a closed-form expression for the achievable multicast rate is presented, which is valid for arbitrary system configurations. In addition, for certain asymptotic scenario, simple approximated expressions for the multicase rate are derived. Closed-form solutions are obtained for the optimal power allocation scheme, and it is shown that equal power allocation is optimal when the pilot power or the number of reflecting elements is sufficiently large. However, it is desirable to allocate more power to weaker users when there are a large number of RF chains. The analytical findings indicate that, with large pilot power, the multicast rate is determined by the weakest user. Also, increasing the number of radio frequency (RF) chains or reflecting elements can significantly improve the multicast rate, and as the phase shift number becomes larger, the multicast rate improves first and gradually converges to a limit. Moreover, increasing the number of users would significantly degrade the multicast rate, but this rate loss can be compensated by implementing a large number of reflecting elements.

This article proposes a novel location information aided multiple intelligent reflecting surface (IRS) systems. Assuming imperfect user location information, the effective angles from the IRS to the users are estimated, which is then used to design the transmit beam and IRS beam. Furthermore, closed-form expressions for the achievable rate are derived. The analytical findings indicate that the achievable rate can be improved by increasing the number of base station (BS) antennas or reflecting elements. Specifically, a power gain of order N M2is achieved, where N is the antenna number and M is the number of reflecting elements. Moreover, with a large number of reflecting elements, the individual signal to interference plus noise ratio (SINR) is proportional to M, while becomes proportional to M2as non-line-of-sight (NLOS) paths vanish. Also, it has been shown that high location uncertainty would significantly degrade the achievable rate. Besides, IRSs should be deployed at distinct directions (relative to the BS) and be far away from each other to reduce the interference from multiple IRSs. Finally, an optimal power allocation scheme has been proposed to improve the system performance.

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

In beyond fifth-generation (B5G) cellular internet of things (IoT) networks, energy supply and data aggregation of a massive number of devices are two vitally challenging issues. To address these challenges, we propose a wireless powered MIMO over-the-air computation (AirComp) design framework. Firstly, wireless power transfer (WPT) is utilized to charge massive IoT devices simultaneously by exploiting the open nature of wireless broadcast channel. Then, AirComp is adopted to reduce latency of massive data aggregation via exploring the superposition property of wireless multiple-access channel. To realize efficient convergence of energy supply and data aggregation in practical IoT networks, a robust design algorithm is provided by jointly optimizing beamforming of both WPT and AirComp. Finally, extensive simulation results validate the robustness and effectiveness of the proposed algorithm over the baseline ones.

Content caching should be updated according to the time-varying content popularity. However, with the consideration of the time-consuming cache replacement process, it is necessary to predict the content popularity and adjust the cached contents beforehand. In this paper, we propose a popularity prediction scheme for content caching. To overcome the request sparsity and exploit the diversity of popularity evolution trends, the users are grouped into non-overlapped clusters for predicting content popularity for each cluster respectively. Different to most of the existing works which focus on the accuracy of prediction, we consider the effect of the prediction error to content caching and adopt the loss of the cache hit ratio as the system regret. In the proposed clustered popularity prediction scheme, the system regret is estimated by analyzing its own prediction error distribution and obtaining the influence from those of other contents through an online learning framework. To achieve the optimal system performance, we design a K-mean clustering algorithm according to the expected regret and the popularity evolution trends. The simulation results show that the proposed clustered popularity prediction scheme achieves better caching performance than the state-of-the-art caching schemes.

With mobile edge computing (MEC), the data compression at the edge devices can effectively improve the communication efficiency by transmitting the compressed data. In this paper, we construct a joint data compression and transmission scheduling framework to optimize the system throughput with the limited transmission resources. Different to most of the existing works, we consider the interaction between the data compression and data transmission to achieve the optimal throughput. Specifically, to explore the effect of data compression, we construct a queue system through constructing the mapping between the original data queues and the compressed data queues under different compression schemes (including the uncompressed queues). We design the transmission scheduling algorithm based on Lyapunov optimization according to the original data queues. Due to the nature that the data compression does not change the original data queue length directly, we choose the optimal data compression scheme considering the achieved utilities when the compressed data are transmitted, which can be estimated via Q-learning. In addition, we theoretically prove the queue stability under our proposed joint data compression and transmission scheduling algorithm. The simulation results show that the proposed algorithm has better delay performance than the conventional schemes.

This paper investigates the performance of angle-domain millimeter-wave (mmWave) multi-input multi-output (MIMO) non-orthogonal multiple access (NOMA) systems in the presence of angular estimation error. A closed-form expression for the achievable rate of the system is derived. Based on which, a simple asymptotic approximation is obtained. The findings of paper suggest that, with a large number of BS antennas, the user rate is mainly constrained by the antenna number to beam number ratio (ANTBNR) and the spatial direction distance. In particular, increasing the ANTBNR would cause a severe rate loss, and the achievable rate is an increasing function with respect to the spatial direction distance. Capitalizing on this key observation, a novel cluster grouping scheme is designed to reduce the inter-cluster interference, which shows significant performance gain over a random cluster grouping scheme. Finally, simulation results are provided to corroborate the analytical results.

In order to support massive connections over limited radio spectrum for the cellular Internet-of-Things (IoT) in the fifth-generation (5G) wireless network, we propose a new non-orthogonal beamspace multiple access framework. First, we analyze the performance of the proposed non-orthogonal beamspace multiple access scheme, and derive an upper bound on the weighted sum rate in terms of channel conditions and system parameters. Then, we provide a transmit beam construction algorithm for further improving the overall performance. Finally, extensive simulation results show that substantial performance gain can be obtained by the proposed non-orthogonal beamspace multiple access scheme over the baseline ones.

With the increasing development of cellular internet of things (IoT), the upcoming fifth generation (5G) wireless network is required to support massive IoT with sporadic traffic. In order to realize massive access over limited radio spectrum, a three-phase transmission protocol which consists of device detection and channel estimation, uplink data transmission and downlink data transmission is designed for the cellular IoT. In particular, we analyze the performance of the proposed transmission protocol, and reveal the impact of system parameters on the sum rate. Finally, simulation results validate the effectiveness of the theoretical claims.

Ambient backscatter communication is a newly emerged ultra-low-power technology for the internet of things network. In this paper, we study the symbol detection of multi-antenna ambient backscatter communication system. In particular, the maximum-eigenvalue detector is derived from general likelihood ratio test, and the approximative BER expressions are characterized. The analytical results show that the BER of the proposed detector decreases with sampling rate N, signal-to-noise ratio γ and number of receiving antenna M, however, settles in high γ or M regime. In addition, we find the proposed detector eliminates the knowledge of noise power, which is of uncertainty and difficult to estimate. Then the simulation results validate the correctness of the theoretical analysis, and show that the proposed detector outperforms the existing energy detector in terms of BER performance.

Considering the limitations on communication capability in the big data era, measurement selection plays an important role in obtaining the desired information by collecting only a part of data from the sensors. In this paper, we study the large-scale measurement selection problem, and propose a distributed algorithm exploiting the sparsity property extracted from the on-line data processing. Different to the existing works, we propose a mission-oriented framework to analyze the performance improvements for the specific mission of collecting new data. Specifically, a Bayesian hierarchical prior is adopted in order to quantify the importance of uncollected data by the on-line inference from the collected data. Based on the sparsity property obtained by on-line data processing, the sensors with important uncollected data will have high priority to access. Due to the massive number of sensors, the measurement selection algorithm is executed distributively at each device according to the common information broadcast by the fusion center. Simulation results demonstrate the performance gain of our proposed measurement selection method compared to the conventional schemes.

In this paper, we develop a dynamic communication-efficient computation load scheduling framework to complete the computation tasks with coded MapReduce considering arbitrary arrival and strict delay constraints over time-varying computing resource. Our goal is to minimize the communication load under the time-varying excess computing resources. We first reduce this problem to a task scheduling problem with exploiting the property of the computing repetition in the coded MapReduce framework. For the case that the full information about the available computing resource is known in advance, we obtain the optimal offline computation load scheduling algorithm. Guided by the optimal algorithm, we propose a dynamic online algorithm based on the predicted computing resource. Finally, our proposed algorithm is evaluated by simulation to demonstrate the superiority of the proposed algorithms over the conventional algorithms.

In this paper, we propose a novel approach, namely, Transform Domain Equalization (TrDE), for high mobility wireless communications in which the channel often experiences serious doubly selective fading. In particular, we design a special framing and modulation scheme with parameters tuned to those of the doubly selective fading channel so as to facilitate the equalization. Data symbols in one block are arranged in a compact matrix form and modulated into the time- frequency domain (TFD). When transmitted over a doubly selective channel, the matrix is expanded along both dimensions as a result of the delay spread and the Doppler spread. By inserting proper Zero Paddings (ZP) into this matrix, the expanded array can be equalized in the transform domain (TrD) with a one-tap equalizer, where the TrD is orthogonal to the original TFD through a two-dimensional fast Fourier Transform (2D FFT). The proposed TrDE effectively mitigates the doubly selectivity with relatively low complexity because only a one-tap equalizer is needed. Further, by averaging over time and frequency, it achieves the maximum joint multipath-Doppler diversity. Both theoretical analysis and simulation results are presented to validate its excellent performances.

In this paper, we propose an uplink joint time and angle domain sparse code multiple access (TASCMA) scheme for mmWave systems to improve the system capacity and user con⌉nectivity. This proposed scheme mainly includes three processes: sparse spreading, antenna allocation and beamforming, which are coordinated by the base station (BS). The spreading process is conducted over time domain by associating a specific sparse spreading vector with each user according to its location, while the antenna allocation and beamforming processes are conducted over angle domain to superimpose the user signals in a beam to further achieve the multiplexing gain. At each user, it spreads its signal with its own spreading sequence and then sends the spread signal to the mmWave channel. At the BS receiver, first the receive beamforming is performed and then a corresponding factor graph is constructed, based on which, message passing algorithm (MPA) is performed to recover all users' transmitted messages. Simulation results show that the proposed TASCMA scheme has better Bit Error Rate(BER) performance than the existing schemes. Keywords-TASCMA, Sparse spread spectrum, Antenna allo⌉cation, Beamforming, MPA.

With the increasing development of the cellular Internet of Things (IoT), the upcoming fifth-generation wireless network is required to support massive access of sporadic traffic devices. In this context, we design a three-phase transmission protocol which consists of device detection and channel estimation, uplink data transmission, and downlink data transmission for the cellular IoT, so as to realize massive access over limited radio spectrum. We analyze the performance of the proposed transmission protocol and derive closed-form expressions for the uplink and downlink achievable rates in terms of channel conditions and system parameters. Moreover, to improve the overall performance, we propose a length allocation algorithm by coordinating the three-phase transmission protocol in the unified sense. Extensive simulation results show that substantial performance gain can be obtained by the proposed algorithm.

Wireless sensor networks (WSNs) act as the backbone of Internet of Things (IoT) technology. In WSN, field sensing and fusion are the most commonly seen problems, which involve collecting and processing of a huge volume of spatial samples in an unknown field to reconstruct the field or extract its features. One of the major concerns is how to reduce the communication overhead and data redundancy with prescribed fusion accuracy. In this paper, an integrated communication and computation framework based on meta-learning is proposed to enable adaptive field sensing and reconstruction. It consists of a stochastic-gradient-descent (SGD)-based base-learner used for the field model prediction aiming to minimize the average prediction error, and a reinforcement meta-learner aiming to optimize the sensing decision by simultaneously rewarding the error reduction with samples obtained so far and penalizing the corresponding communication cost. An adaptive sensing algorithm based on the above two-layer meta-learning framework is presented. It actively determines the next most informative sensing location, and thus considerably reduces the spatial samples and yields superior performance and robustness compared with conventional schemes. The convergence behavior of the proposed algorithm is also comprehensively analyzed and simulated. The results reveal that the proposed field sensing algorithm significantly improves the convergence rate.

This paper investigates the achievable performance of cell-free massive multiple-input multiple-output (MIMO) systems with low resolution analog-to-digital converters (ADCs) at both the access points (APs) and users. A closed-form expression for the achievable rate is derived, which enables the study of the effects of AP number, antenna number per AP, user number, and ADC resolution on the achievable rate. In addition, a simple asymptotic approximation for the individual user rate is presented, which shows that the user rate is mainly constrained by the ADC resolution at the user. Moreover, we propose an ADC resolution bits allocation scheme aiming at maximizing the sum rate subject to the total ADC resolution bits constraint, which substantially outperforms the equal ADC resolution bits allocation scheme. Furthermore, a max-min power control scheme is proposed, which not only ensures user fairness, but also improves the achievable rate. Finally, simulation results are provided to corroborate the analytical results.

The problem of Byzantine attack in collaborative spectrum sensing (CSS) is considered in this paper. To defend against Byzantine attack, a robust defense framework is proposed to efficiently identify the Byzantine attackers. Specifically, we first propose a robust defense framework, where a reference is built based on the extended sensing, and the transmit results and sensors are continuously evaluated via the reference and identified at intervals. In the framework, except of data falsification, multiple practical factors are considered, including the variation characteristic of sensors' attributes, reporting channel imperfection, and inference errors based on the transmit results. Further, we derive the closed-form expressions of the reference and the identification performance and make optimization of the identification threshold in two cases: with and without the prior knowledge of attack behaviors, where the probability of correctly detecting Byzantine attackers is maximized under the constraint of the probability of falsely identifying honest sensors as attackers. In particular, when the prior knowledge is unavailable, maximized likelihood estimation is made based on the reference to achieve the optimization. Furthermore, we present in-depth simulations to demonstrate the high robustness of the proposed defence framework to multiple practical factors under a homogeneous scenario and a heterogeneous scenario.

In existing communication systems, the channel state information of each user equipment (UE) should be repeatedly estimated when it moves to a new position or when another UE takes its place. The underlying ambient information, including the specific layout of potential reflectors, which provides more detailed information about all UEs' channel structures, has not been fully explored and exploited. In this paper, we rethink the mm-wave channel estimation problem in a new and indirect way, i.e., instead of estimating the resultant composite channel response at each time, and for any specific location, we first conduct the ambient perception exploiting the fascinating radar capability of a mm-wave antenna array and then accomplish the location-based sparse channel reconstruction. In this way, the sparse channel for a quasi-static UE arriving at a specific location can be rapidly synthesized based on the perceived ambient information, thus greatly reducing the signaling overhead and online computational complexity. Based on the reconstructed mm-wave channel, single-beam mm-wave communication is designed and evaluated which shows an excellent performance. Such an approach, in fact, integrates the radar with communication, which may possibly open a new paradigm for future communication system design.

Mobile edge computing (MEC) has recently emerged as a promising technology to release the tension between computation-intensive applications and resource-limited mobile terminals (MTs). In this paper, we study the delay-optimal computation offloading in computation-constrained MEC systems. We consider the computation task queue at the MEC server due to its constrained computation capability. In this case, the task queue at the MT and that at the MEC server are strongly coupled in a cascade manner, which creates complex interdependences and brings new technical challenges. We model the computation offloading problem as an infinite horizon average cost Markov decision process (MDP) and approximate it to a virtual continuous time system (VCTS) with reflections. Different from most of the existing works, we develop the dynamic instantaneous rate estimation for deriving the closed-form approximate priority functions in different scenarios. Based on the approximate priority functions, we propose a closed-form multi-level water-filling computation offloading solution to characterize the influence of not only the local queue state information (LQSI) but also the remote queue state information (RQSI). Furthermore, we discuss the extension of our proposed scheme to multi-MT multi-server scenarios. Finally, the simulation results show that the proposed scheme outperforms the conventional schemes.

In this paper, we investigate the issue of sustainable communications for beyond fifth-generation (B5G) cellular internet of things (IoT) networks under adverse but practical conditions. A massive number of simple IoT devices without batteries harvest requisite energy from a part of the received signal. A design framework including channel state information (CSI) acquisition, signal construction, information decoding and energy harvesting, is first provided for sustainable communications of massive IoT. Then, based on the proposed design framework, we reveal the impacts of practically adverse factors, e.g., channel uncertainty, successive interference cancellation (SIC) and non-linear energy harvesting, on the performance of B5G cellular IoT. Furthermore, in order to effectively alleviate the impacts of these adverse factors, an outage-constrained robust algorithm is designed to maximize the overall performance of sustainable B5G cellular IoT. Finally, extensive simulation results validate the robustness and effectiveness of the proposed algorithm over the baseline ones.

The ambient backscatter communication is a newly rising paradigm for the Internet-of-Things networks, which enables the connection of low-cost devices. This paper proposes a novel MFSK modulation for the Tag of ambient backscatter communications systems, and the corresponding detectors are designed depending on the capability of the Reader. In the case the Reader is not capable of removing the direct interference from the ambient source, a maximum likelihood detector is proposed. In another case, leveraging on the frequency shift feature of the MFSK modulation, the Reader can remove the direct interference. Then, a simple energy detector is proposed, and the closed-form expressions for the symbol error rate (SER) and outage probability of the system are derived. The findings of this paper suggest that the proposed MFSK modulation outperforms the popular ON-OFF keying modulation, and the impact of modulation order on the SER performance depends heavily on the operating bit signal to noise ratio. Moreover, it is shown that it is desirable to place the Tag close to the Reader in terms of minimizing the outage probability.

This paper deals with symbol detection in ambient backscatter communication systems with multi-antenna reader. Unlike most of the existing works which assume deterministic ambient radio frequency (RF) signals, we consider another important scenario with complex Gaussian RF signals. Focusing on the on-off keying modulation, the optimal detector minimizing the bit error rate (BER) is devised based on the maximum a posteriori principle, and an exact closed-form expression for the BER is derived. In addition, a simple energy detector is proposed to serve as a performance benchmark. Simulation results show that, implementing multiple antennas at the reader is an effective means to enhance the BER performance. Also, the proposed optimal detector always outperforms the energy detector. Furthermore, unlike the energy detector, whose BER settles in the high signal to noise ratio regime, no error floor exists for the proposed optimal detector.

We study the effects of low resolution analog-to-digital converters (ADCs) on cell-free massive multiple-input multiple-output (MIMO) systems. We first derive a closed-form expression for the achievable rate, which enables efficient evaluation of the impact of key parameters on system performance. Then, we give a simple approximate closed-form expression for the individual user rate, which indicates that the ADC resolution at the user is the main constraining factor for the user rate. Furthermore, we study the allocation of ADC resolution bits among different access points (APs) with fixed total resolution bits. It has been shown that our proposed ADC resolution bits allocation scheme is substantially superior to the equal ADC resolution bits allocation scheme. Finally, we provide simulation results to verify our analytical results.

Mobile edge computing (MEC) has recently emerged as a promising technology to release the tension between computation-intensive applications and resource-limited mobile terminals (MTs). In this paper, we construct a Markov decision process (MDP) framework to optimize the delay performance in computation-constrained mobile edge networks. Different to most of the existing works, we consider the computation task queue at the MEC server due to its constrained computation capability. In this case, the task queue at the MT and that at the MEC server are mutually strongly coupled in a cascade manner, which creates complex interdependence and brings new technical challenges. To address the challenge, the infinite horizon average cost MDP is reformulated to a virtual continuous time system (VCTS) with reflections. We derive the closed-form approximate priority functions for the MDP in different system scenarios with dynamic rate estimation. Based on the approximate priority function, we propose a multi-level water filling solution to characterize the influence of not only the local queue state information (LQSI) but also the remote queue state information (RQSI) on the computation offloading policy in a closed form. Finally, the simulation results show that the proposed computation offloading scheme outperforms the conventional schemes.

Cooperative content caching has been demonstrated to achieve significant performance gain over the conventional content caching paradigm by exploiting content diversity through the participation of multiple cooperative nodes. Although cooperative content caching has the potential to increase the efficiency, an improper coalition formation may result in severe performance degradation. Therefore, the cooperative nodes should be carefully selected according to their interests in different content objects. In this paper, we develop an analytical framework for cooperative content caching from a coalitional game perspective. The cooperation issue for content caching among nodes is studied by the coalitional game theory, and the associated problems are analyzed in different cases that the utility transfer among nodes is allowed or not. If the utility transfer is allowed, by exploiting the properties of the coalitional costs, we derive the non-empty property of the core of a transferable utility coalitional game, and prove that the grand coalition is stable in spite of the presence of coalition costs. If the utility transfer is not allowed, we adopt a non-transferable utility coalitional game model. The grand coalition is not always stable in the presence of coalition costs. A merge and split algorithm is proposed to form the coalitional structure for iteratively improving the caching performance. Finally, the simulation results demonstrate the cooperation gains on both the sum and individual utilities in different scenarios.

As a promising candidate for future 5G and beyond, millimeter-wave (mmWave) has received considerable attention. However, mmWave channel estimation remains to be a challenging problem. To tackle this issue, this paper proposes a mmWave joint radar and communication system for indoor scenarios. By cooperating with mmWave radar, the propagation environment is perceived. Then, high-accuracy channel estimates are obtained, which helps in designing more accurate precoders and combiners. Compared with the conventional channel estimation methods, the proposed scheme significantly reduces the spectrum overhead and computational complexity, especially when massive antennas and multiple users are involved. Our results may shed some lights on the system design for future mmWave communications.

With the demand of high data rate and low latency in fifth generation (5G), deep neural network decoder (NND) has become a promising candidate due to its capability of one-shot decoding and parallel computing. In this paper, three types of NND, i.e., multi-layer perceptron (MLP), convolution neural network (CNN) and recurrent neural network (RNN), are proposed with the same parameter magnitude. The performance of these deep neural networks are evaluated through extensive simulation. Numerical results show that RNN has the best decoding performance, yet at the price of the highest computational overhead. Moreover, we find there exists a saturation length for each type of neural network, which is caused by their restricted learning abilities.

Reed-Muller (RM) sequences have been widely used in compressed sensing (CS) to construct deterministic measurement matrices and extract attributes of sparse signals. However, if the signal is not sparse enough, the existing reconstruction algorithms encounter serious performance degradation. In this paper, invoking the elegant nested structure of second-order RM sequences, a soft-decision reconstruction algorithm is proposed. With the soft- information passing through the nested structure, the proposed reconstruction algorithm outperforms the existing ones. Notably, the performance can be further improved based on shuffling operations. Numerical results verify the good performance of the proposed algorithm and show that it preserves quite low computational complexity.

Energy harvesting technology enables the devices to collect the energy from the surrounding environment. In energy harvesting networks, besides the coupling among different devices, the data transmission depends on the available energy of the devices as well, which leads to a complicated coupling and brings new technical challenges for delay optimization. In this paper, we study delay-optimal random access for large-scale energy harvesting networks. To overcome the challenges, we model a two-dimensional Markov decision process (MDP) with reflections to address the coupling between data and energy, and adopt the mean field game (MFG) theory to address the mutual coupling between devices by utilizing the large-scale property. Specifically, we decompose the optimization problem into two parts. First, to obtain the optimal access policy of each device, we derive the Hamilton-Jacobi-Bellman (HJB) equation which needs the statistical information of other devices. Second, to model the evolution of the state distribution in the system, we derive the Fokker-Planck-Kolmogorov (FPK) equation which needs the access policy of the devices. By solving these two coupled equations iteratively, we obtain the delay-optimal access solution by adopting the Lax-Friedrichs scheme and Lagrange relaxation method. Finally, the numerical results show that the proposed algorithm achieves significant performance gain compared to conventional algorithms.

How to construct polar codes for arbitrary channels has been one of the most appealing and challenging problems in polar coding theory. In this paper, recalling the fact that any continuous distribution function can be approximated by a linear combination of several Gaussian functions with given accuracy, we firstly decompose the underlying channel into a set of independent parameterized Gaussian channels based on its arbitrary channel coefficients. Secondly, we prove that, the constituent channels can still be polarized with probabilistic channel switching using the general polarization matrix, and the average capacity can still be achieved. In this sense, constructing polar codes for an arbitrary channel is equivalent to handling its constituent channels. Thirdly, the extrinsic information transfer (EXIT) analysis is exploited to achieve the mutual information of the constituent channels. Finally, numerical results validate the superior performance of the proposed scheme compared to the existing methods.

In this paper, we consider a half-duplex decode-and- forward (DF) relaying system with rateless codes and buffered relay. Invoking rateless codes, both the source and the relay can automatically adapt their transmission rates according to the instantaneous channel state information (CSI). Furthermore, the buffered relay provides flexibility for realizing opportunistic relaying based on the CSI of the source-relay and the relay- destination links. For a given relaying decision function, we successfully derive the optimal opportunistic rateless coded relaying protocol and analytically obtain the corresponding throughput. Numerical results verify our analyses and show a significant improvement compared to the conventional non-buffered relaying protocol.

Hybrid automatic repeat request transmission scheme with Chase combining (HARQ-CC) has been widely used to improve the throughput in wireless communication systems. However, for punctured polar codes, HARQ-CC cannot make up for the vacancies of the Log Likelihood Ratios (LLRs) of the punctured bits which are never transmitted at all, thus degrading the system efficiency. To address this issue, this paper proposes a new HARQ scheme based on the equivalent puncturing patterns (EPPs) of punctured polar codes. It successfully complements the LLRs of punctured bits, and therefore improves the system throughput. Moreover, we show that the proposed scheme can be implemented efficiently with the same complexity as HARQ-CC. Numerical results indicate that, for punctured polar codes with 21.875% puncturing proportion, the proposed HARQ achieves 7.5% improvement on the normalized system throughput compared with HARQ-CC.

This paper considers the security issue of the fifth-generation wireless networks with massive connections, where multiple eavesdroppers aim to intercept the confidential messages through active eavesdropping. To realize secure massive access, non-orthogonal channel estimation and non-orthogonal multiple access techniques are combined to enhance the signal quality at legitimate users, while the inter-user interference is harnessed to deliberately confuse the eavesdroppers even without exploiting artificial noise. We first analyze the secrecy performance of the considered secure massive access system and derive a closed-form expression for the ergodic secrecy rate. In particular, we reveal the impact of some key system parameters on the ergodic secrecy rate via asymptotic analysis with respect to a large number of antennas and a high transmit power at the base station. Then, to fully exploit the inter-user interference for security enhancement, we propose to optimize the transmit powers in the stages of channel estimation and multiple access. Finally, extensive simulation results validate the effectiveness of the proposed secure massive access scheme.

We consider a multipair half-duplex relay communication network, where the relay is deployed with one-analog-to-digital converters and one-bit digital-to-analog converters. To suppress the interpair interference and quantization artifacts, we propose nonlinear precoding schemes to forward the quantized signals at the relay. We first present a technique based on gradient projection, and then show how to refine the solution using ordered quantization and perturbation methods. For the single-user case with BPSK symbols, we obtain a closed-form solution for the optimal transmit vector. Numerical results verify that the proposed precoding design significantly outperforms quantized linear precoding strategies.

Big longitudinal data provide more reliable information for decision making and are common in all kinds of fields. Trajectory pattern recognition is in an urgent need to discover important structures for such data. Developing better and more computationally-efficient visualization tool is crucial to guide this technique. This paper proposes an enhanced projection pursuit (EPP) method to better project and visualize the structures (e.g. clusters) of big high-dimensional (HD) longitudinal data on a lower-dimensional plane. Unlike classic PP methods potentially useful for longitudinal data, EPP is built upon nonlinear mapping algorithms to compute its stress (error) function by balancing the paired weights for between and within structure stress while preserving original structure membership in the high-dimensional space. Specifically, EPP solves an NP hard optimization problem by integrating gradual optimization and non-linear mapping algorithms, and automates the searching of an optimal number of iterations to display a stable structure for varying sample sizes and dimensions. Using publicized UCI and real longitudinal clinical trial datasets as well as simulation, EPP demonstrates its better performance in visualizing big HD longitudinal data.

To achieve spectral-efficient massive access in future wireless networks, this paper proposes a comprehensive fully non-orthogonal communication framework. First, we design a fully non-orthogonal communication scheme which consists of non-orthogonal channel estimation and non-orthogonal multiple access. Then, we analyze the performance of the proposed fully non-orthogonal communication, and derive a tight lower bound on the spectral efficiency in terms of key system parameters and channel conditions. Meanwhile, several novel insights are provided on spectral efficiency via asymptotic analysis in three important cases, i.e., a large number of base station (BS) antennas, a high BS transmit power, and perfect channel state information (CSI) at the BS. Finally, we optimize the performance of the proposed fully non-orthogonal communication and present two simple but efficient optimization algorithms for maximizing the weighted sum of spectral efficiency. Extensive simulation results validate the effectiveness of the proposed schemes.

Cooperative multicast has been demonstrated to achieve significant performance gain over the classic source-destination transmission paradigm by exploiting spatial diversity through the participation of multiple relay nodes. As a major technical challenge, the selection of relays for a multicast session has significant impact on the multicast performance. The challenge is even more pronounced when the number of channels is limited as the relay selection is in this context coupled with channel allocation. The goal of this paper is to design a fair multicast relay selection scheme with limited channel resources. Specifically, we establish an analytical framework for this joint relay selection and channel allocation problem and develop a lexicographic max-min multicast relay selection scheme. Our design consists of two technical steps. First, we consider the maximization of the minimal data rate. By decoupling relay selection and channel allocation, the problem is transformed to a max-min-max problem, which is difficult to solve. To make this problem tractable, we reformulate it as a convex optimization problem via relaxation and smoothing, and prove the asymptotic equivalence from a geometrical perspective. Second, we propose an adjustment algorithm based on the initial max-min solution, and prove that the proposed scheme achieves lexicographic optimality. Finally, our proposed algorithm is evaluated by simulation to show its superiority over the conventional schemes.

In this paper, we consider a point-to-point multi-input multi-output wireless-powered communication system, where the source S is powered by a dedicated power beacon (PB) with multiple antennas. Employing the time splitting protocol, the energy constrained source S first harvests energy through the radio-frequency signals sent by the PB and then uses this energy to transmit information to the destination D. Unlike several prior works, we assume that the energy transfer link is subjected to Rician fading, which is a real fading environment, due to relatively short range power transfer distance and the existence of a strong line of sight path. We present a comprehensive analysis of the achievable ergodic capacity in two scenarios, depending on the availability of channel state information (CSI) at PB, namely, the absence of CSI and partial CSI. For the former case, equal power allocation is used, while for the later one, energy beamforming is used to enhance the energy transfer efficiency. For both the cases, closed-form expressions for the upper and lower bounds of the ergodic capacity are derived. Furthermore, the optimal time split is discussed, and the capacity in the low and high signal-to-noise ratio regimes is studied through simple closed-form expressions. Numerical results and simulations are provided to validate the theoretical analysis. The results show that the Rician factor K has a significant impact on the ergodic capacity performance, and this impact strongly depends on the availability of the CSI at the PB.

Distributed data processing over networks has received a lot of attention due to its wide applicability. In this paper, we consider the multitask problem of in-network distributed estimation. For the multitask problem, the unknown parameter vectors (tasks) for different nodes can be different. Moreover, considering some real application scenarios, it is also assumed that there are some similarities among the tasks. Thus, the intertask cooperation is helpful to enhance the estimation performance. In this paper, we exploit an additional special characteristic of the vectors of interest, namely, joint sparsity, aiming to further enhance the estimation performance. A distributed jointly sparse multitask algorithm for the collaborative sparse estimation problem is derived. In addition, an adaptive intertask cooperation strategy is adopted to improve the robustness against the degree of difference among the tasks. The performance of the proposed algorithm is analyzed theoretically, and its effectiveness is verified by some simulations.

This paper considers a two-way half-duplex decode-and-forward relaying system, where multiple pairs of single-antenna users exchange information via a multiple-antenna relay. Assuming that the channel knowledge is nonideal and the relay employs maximum ratio processing, we derive a largescale approximation of the sum spectral efficiency (SE) that is tight when the number of relay antennas M becomes very large. Furthermore, we study how the transmit power scales with M to maintain a desired SE. In particular, three special powerscaling cases are discussed and the corresponding asymptotic SE is deduced with clear insights. Our elegant power-scaling laws reveal a tradeoff between the transmit powers of the user/relay and pilot symbol. Finally, we formulate a power allocation problem in terms of maximizing the sum SE and obtain a local optimum by solving a sequence of geometric programming problems.

Ambient backscatter communication is a newly emerged paradigm, which utilizes the ambient radio frequency signal as the carrier to reduce the system battery requirement, and is regarded as a promising solution for enabling large-scale deployment of future Internet of Things networks. The key issue of ambient backscatter communication systems is how to perform reliable detection. In this paper, we propose novel encoding methods at the information tag and devise the corresponding symbol detection methods at the reader. In particular, Manchester coding and differential Manchester coding are adopted at the information tag, and the corresponding semi-coherent Manchester (SeCoMC) and non-coherent Manchester (NoCoMC) detectors are developed. In addition, analytical bit-error-rate (BER) expressions are characterized for both detectors assuming either complex Gaussian or unknown deterministic ambient signal. Simulation results show that the BER performance of unknown deterministic ambient signal is better, and the SeCoMC detector outperforms the NoCoMC detector. Finally, compared with the prior detectors for ambient backscatter communications, the proposed detectors have the advantages of achieving superior BER performance with lower communication delay.

Spectrum aggregation (SA) across heterogeneous channels, including both dedicated and shared channels, provides the potential for improving spectrum utilization and fulfilling the requirement of broadband services. Heterogeneous SA brings new technical challenges on multisystem coexistence on shared channels and the resource allocation over heterogeneous channels. In this paper, we develop an analytical framework for heterogeneous SA from a queue stability perspective. To make all systems on the shared channels stable, we design a resource allocation algorithm for the coexistence of multiple systems. Specifically, we derive the closed-form modified water-filling power control for the single-pair case by Lyapunov optimization and prove that it achieves the queue stability for all systems. Based on the results, we propose a low-complexity suboptimal resource allocation algorithm for multipair SA, which is a NP-hard problem. We partition user pairs into groups by using graph coloring and allocate the shared channels to pair groups according to the maximal weight bipartite matching model. The simulation results verify the queue stability and show that the proposed schemes outperform the conventional schemes.

In this paper, we consider a power beacon (PB) assisted two-way relaying network, where two single antenna energy constrained users first harvest energy from a multi-antenna PB and then communicate with each other with the assistance of a relay. The key aim of the paper is to design the energy beamforming vector and time split parameter for optimizing the system performance. In particular, two different design objectives are investigated, namely, max-min rate design and sum rate maximization design. Due to the non-convex nature of the optimization problems, the global optimal solutions are difficult to obtain. Instead, we propose an alternating optimization design framework where near optimal performance can be achieved through iterative optimization. In addition, to further reduce the computation complexity, closed-form suboptimal designs are provided. Simulation results are presented to validate the effectiveness of the proposed alternating optimization design and suboptimal design. The outcomes of the paper suggest that adopting the proposed energy beamforming vector at the PB can substantially boost the system performance. Also, the topology of the network has a significant impact on the achievable performance.

In successive cancellation (SC) polar decoding, an incorrect estimate of any prior unfrozen bit may bring about severe error propagation in the following decoding, thus it is desirable to find out and correct an error as early as possible. In this paper, we first construct a critical set S of unfrozen bits, which with high probability (typically >99%) includes the bit where the first error happens. Then we develop a progressive multi- level bit-flipping decoding algorithm to correct multiple errors over the multiple-layer critical sets each of which is constructed using the remaining undecoded subtree associated with the previous layer. The level in fact indicates the number of independent errors that could be corrected. We show that as the level increases, the block error rate (BLER) performance of the proposed progressive bit flipping decoder competes with the corresponding cyclic redundancy check (CRC) aided successive cancellation list (CA-SCL) decoder, e.g., a level 4 progressive bit-flipping decoder is comparable to the CA-SCL decoder with a list size of L=32. Furthermore, the average complexity of the proposed algorithm is much lower than that of a SCL decoder (and is similar to that of SC decoding) at medium to high signal to noise ratio (SNR).

Cooperative caching is a promising technology for enhancing user experience and reducing redundant transmissions through the participation of multiple caching nodes. In this paper, we design a clustering algorithm for the sectionalized caching, in which each user divides its caching space into two parts and the contents cached in these two parts are determined according to the individual interest and the joint interest of all users in the same cluster respectively. Different to most of the existing works forming the clusters based on the interest similarity of all files, we adopt the piecewise interest similarity as the criterion of clustering, which takes advantage of the content diversity and contributes to the reduction of the transmission delay. We measure the gain of the cooperation between two users and obtain the piecewise interest similarity for two users accordingly. Since the gain of clustered caching highly depends on the formed cluster structure, we estimate the gain of the clustered caching based on the piecewise interest similarities by online learning and propose an affinity propagation (AP) based clustering algorithm. Finally, our proposed clustering algorithm is evaluated by simulation to show its superiority over the conventional clustering algorithms.

We study the impact of using one-bit analog-to-digital and digital-to-analog converters in a multipair amplify-and-forward MIMO relaying system. The relay estimates the channel state information using training data, and then uses the channel estimate to perform maximum ratio combining and maximum ratio transmission. An exact achievable rate is derived for the system under general assumptions on the quantization noise, and then a closed-form asymptotic approximation is derived, which enables efficient evaluation of the impact of key parameters on system performance. Contrary to the conventional unquantized systems, the performance is seen to depend on the specific pilot sequences that are employed. In addition, the sum rate gap between the double quantized relay system and an ideal unquantized system is shown to be a factor of 4/π2in the low source power regime.

This paper investigates a power beacon (PB) assisted two-way relaying network, in which two single antenna energy constraint users first harvest energy from a multi-antenna PB and then communicate with each other with the assistance of a relay. Considering user fairness, we propose the max-min design that maximizes the minimum rate of the two users and investigate the corresponding optimal energy beamformer and time split. Due to the non-convex nature of the optimization problems, an alternating optimization design is proposed in which the optimal beamformer and time split can be solved via a low-complexity algorithm and convex optimization, respectively. Our results show that the proposed alternating optimization design yields near optimal performance, and implementing multiple antennas at the PB can significantly improve the system performance.

We propose to use second order Reed-Muller (RM) sequence for user identification in 5G grant-free access. The benefits of RM sequences mainly lie in two folds, (i) support of much larger user space, hence lower collision probability and (ii) lower detection complexity. These two features are essential to meet the massive connectivity (107links/km2), ultra-reliable and low-latency requirements in 5G, e.g., one-shot transmission (≤ Ims) with ≤ 10-4packet error rate. However, the nonorthogonality introduced during sequence space expansion leads to worse detection performance. In this paper, we propose a noiseresilient detection algorithm along with a layered sequence construction to meet the harsh requirements. Link-level simulations in both narrow-band and OFDM-based scenarios show that RM sequences are suitable for 5G.

Centralized joint processing has been demonstrated to achieve significant performance gain over the conventional per-cell processing by avoiding the inter-cell interference in cloud radio access networks (C-RANs), but full scale cooperation over the entire network is infeasible due to its huge computational complexity. The goal of this paper is to design remote radio head (RRH) clustering and the associated power allocation algorithm under computational resource constraint for C-RANs, which is challenging due to the combinatorial clustering problem with non-convex constraints. To overcome the challenge, our design consists of two technical steps. 1) Considering the maximization of the sum data rate, we reformulate both the objective function and the computational resource constraint via relaxation and successive convex approximation (SCA) technique. 2) By exploiting the structure of this problem, we propose a low-complexity algorithm to decouple RRH clustering and power allocation and solve them iteratively. Furthermore, we prove the convergence property of the proposed iterative algorithm. Finally, our proposed algorithm is evaluated by simulation to show its superiority on throughput performance over conventional algorithms.

This paper considers a multipair amplify-and-forward massive MIMO relaying system with low-resolution ADCs at both the relay and destinations. The channel state information (CSI) at the relay is obtained via pilot training, which is then utilized to perform simple maximum-ratio combining/maximum-ratio transmission processing by the relay. Also, it is assumed that the destinations use statistical CSI to decode the transmitted signals. A closed-form approximation of the achievable sum rate is presented, which enables the efficient evaluation of the impact of key system parameters on the system performance. It is found that increasing the number of relay antennas is an effective method to compensate for the rate loss caused by coarse quantization. Moreover, it is desirable to deploy the low-resolution ADCs at the relay and high-resolution ADCs at the destination.

Massive Multiple-Input Multiple-Output (MIMO), small cell, and full-duplex are promising techniques for future 5G communication systems, where interference has become the most challenging issue to be coped with. In this paper, we provide an interference coordination framework for a two-tier heterogeneous network (HetNet) that consists of a massive-MIMO enabled macro-cell base station (MBS) and a number of full-duplex small-cell base stations (SBSs). To suppress the interferences and maximize the throughput, the full-duplex mode of each SBS, i.e., in-band or out-of-band, which has different impact on the interference pattern, should be carefully selected. To address this problem, a distributed graph color algorithm (DGCA) based on price is proposed. Numerical results demonstrate that DGCA significantly improves the system throughput.

In this paper, we study the delay-aware access control of massive random access for machine-type communications (MTC). We model this stochastic optimization problem as an infinite horizon average cost Markov decision process. To deal with the distributive requirement and the exponential computational complexity, we first exploit the property of successful access probability to transform the coupling to the constraint on the number of MTC devices attempting to access. As a result, we decompose the Bellman equation into multiple fixed point equations for each MTC device by primal-dual decomposition. Based on the equivalent per-MTC fixed point equations, we propose the online hierarchical stochastic learning algorithm to estimate the local Q-factors and determine the access decision at the MTC devices separately with the assistance of the base station which broadcasts common control information only. Finally, the simulation result shows that the proposed hierarchical stochastic learning algorithm has significant performance gain over the baseline algorithm.

Massive number of machine-type communications (MTC) devices may lead to access overloading. In heterogeneous networks, the load balancing between macrocells and small cells and the backhaul capacity limitation bring new technical challenges due to their mutual coupling. In this paper, we adopt access class barring (ACB) for access control and jointly optimize access control, load balancing and preamble allocation in backhaul-constrained heterogeneous networks. Since this joint optimization problem is intractable due to its combinatorial nature, we decompose the problem and solve it in two steps. First, by introducing an auxiliary variable and exploiting the monotonicity property of objective function, we derive a closed- form solution for the optimal ACB parameter with the given cell load and number of preambles. Second, we propose an iterative algorithm to further optimize load balancing and preamble allocation with optimal ACB. Furthermore, we prove its convergence property by monotone convergence theorem. Simulation results show that the proposed massive access algorithm achieves significant performance gain compared to existing algorithms.

Cooperative multicast has been demonstrated to achieve significant performance gain over the classic source-destination transmission paradigm by exploiting spatial diversity through the participation of multiple relay nodes. As a major technical challenge, the selection of relays for a multicast session has significant impact on the multicast performance. The challenge is even more pronounced when the number of channels are limited as the relay selection is in this context coupled with channel allocation. We establish an analytical framework for joint relay selection and channel allocation problem and develop a lexicographic max-min multicast relay selection scheme. Our design consists of two technical steps. 1) We consider the maximization of the minimal data rate. By decoupling relay selection and channel allocation, the problem is transformed to a max-min-max problem, which is difficult to solve. To make this problem tractable, we reformulate it as a convex optimization problem via relaxation and smoothing, and prove the asymptotic equivalence from a geometrical perspective. 2) We propose an adjustment algorithm based on the initial max-min solution, and prove that the proposed scheme achieves lexicographic optimality.

As one of the 5G physical-layer technique candidates, universal filtered multi-carrier (UFMC) supports sliced spectrum communication by filtering a series of subcarriers. The filter design greatly affects the inter-sub-band interference in UFMC and hence is crucial to the overall system performance. Here we first propose a novel UFMC scheme, namely UFMC-AIC, which incorporates active interference cancellation (AIC) into UFMC to considerably reduce the inter-sub-band interference. Then we propose a modified version of UFMC-AIC which optimizes the weighting factors of the interference cancellation subcarriers (ICSs) under a total power constraint. Simulation results show that the modified UFMC-AIC scheme can achieve higher transmission rate and better BER performance than the conventional UFMC either for AWGN channel or for Rayleigh channel. Moreover, we validate that our proposal can significantly reduce the filter design requirements.

This paper aims to provide a comprehensive solution for the design, analysis, and optimization of a multiple-antenna non-orthogonal multiple access (NOMA) system for multiuser downlink communication with both time duplex division and frequency duplex division modes. First, we design a new framework for multiple-antenna NOMA, including user clustering, channel state information (CSI) acquisition, superposition coding, transmit beamforming, and successive interference cancellation. Then, we analyze the performance of the considered system, and derive exact closed-form expressions for average transmission rates in terms of transmit power, CSI accuracy, transmission mode, and channel conditions. For further enhancing the system performance, we optimize three key parameters, i.e., transmit power, feedback bits, and transmission mode. Especially, we propose a low-complexity joint optimization scheme, so as to fully exploit the potential of multiple-antenna techniques in NOMA. Moreover, through asymptotic analysis, we reveal the impact of system parameters on average transmission rates, and hence present some guidelines on the design of multiple-antenna NOMA. Finally, simulation results validate our theoretical analysis, and show that a substantial performance gain can be obtained over traditional orthogonal multiple access technology under practical conditions.

Massive multiple-input multiple-output (MIMO), small cell, and full-duplex are promising techniques for future 5G communication systems, where interference has become the most challenging issue to be addressed. In this paper, we provide an interference coordination framework for a two-tier heterogeneous network (HetNet) that consists of a massive-MIMO enabled macro-cell base station (MBS) and a number of full-duplex small-cell base stations (SBSs). To suppress the interferences and maximize the throughput, the full-duplex mode of each SBS at the wireless backhaul link (i.e., in-band or out-of-band), which has a different impact on the interference pattern, should be carefully selected. To address this problem, we propose two centralized algorithms, a genetic algorithm (GEA) and a greedy algorithm (GRA). To sufficiently reduce the computational overhead of the MBS, a distributed graph coloring algorithm (DGCA) based on price is further proposed. Numerical results demonstrate that the proposed algorithms significantly improve the system throughput.

This letter investigates the performance of a legitimate surveillance system, where a legitimate monitor aims to eavesdrop on a dubious decode-and-forward relaying communication link. In order to maximize the effective eavesdropping rate, two strategies are proposed, where the legitimate monitor adaptively acts as an eavesdropper, a jammer, or a helper. In addition, the corresponding optimal jamming beamformer and jamming power are presented. Numerical results demonstrate that the proposed strategies attain better performance compared with intuitive benchmark schemes. Moreover, it is revealed that the position of the legitimate monitor plays an important role on the eavesdropping performance for the two strategies.

With the fast development of vehicular technologies, the number of mobile terminals as well as their moving speeds has increased conspicuously, making it meaningful to re-examine the impact of mobility on communication performance of the existing cellular systems. In this paper, the uplink sum rate of a multiple-input multiple-output orthogonal frequency-division multiple access (MIMO-OFDMA) cellular system with nodes moving randomly at different speeds is studied. In such a multi-user environment, mobility not only causes inter sub-carrier interferences (ICI) among different users, but also brings the so-called channel aging which together with the above interferences incur outdated and inaccurate channel estimation and thus entail additional pilot overhead. Here, we first derive the relationship between the users' mobility and the ICI power, characterize the channel aging, and derive the overall multi-user interference caused by the outdated and inaccurate channel state information as a function of mobility. Then, by capitalizing on the above results, the sum rate of the MIMO-OFDMA uplink is theoretically analyzed. Moreover, an adaptive transmission scheme in which the users adjust the pilot percentage according to mobility statistics is proposed to maximize the sum rate. Finally, numerical results are provided to validate our analyses.

Universal filtered multi-carrier (UFMC) is one of the enabling techniques for 5G, which supports sliced spectrum by filtering successive sub-bands. The filter design is an essential and challenging issue, which is critical to the performance of UFMC. In this paper, we propose a novel UFMC scheme, namely, UFMC-AIC, which incorporates active interference cancellation into UFMC so as to mitigate the inter-sub-band interference. A non-convex optimization problem is formulated, which aims to maximize the overall signal-to-interference-plus-noise ratio performance by adaptively optimizing the weighting factors of the interference cancellation subcarriers under the power constraints. The minorization-maximization method is applied to solve the problem in the ideal case when the sub-band states and the channel state information are perfectly known at the base station. A practical distributed solution is further obtained in a closed form by the Lagrangian dual method, which largely reduces the computational complexity with only slight performance loss compared with the ideal case. Simulation results show that our proposal with lower cost of filter can still achieve better bit error rate performance than the conventional UFMC under different carrier frequency offsets for the Rayleigh block-fading channel.

This paper considers a multipair amplify-and-forward massive MIMO relaying system with low-resolution analog-to-digital converters (ADCs) at both the relay and destinations. The channel state information (CSI) at the relay is obtained via pilot training, which is then utilized to perform simple maximum-ratio combining/maximum-ratio transmission processing by the relay. Also, it is assumed that the destinations use statistical CSI to decode the transmitted signals. Exact and approximated closed-form expressions for the achievable sum rate are presented, which enable the efficient evaluation of the impact of key system parameters on the system performance. In addition, optimal relay power allocation scheme is studied, and power scaling law is characterized. It is found that, with only low-resolution ADCs at the relay, increasing the number of relay antennas is an effective method to compensate for the rate loss caused by coarse quantization. However, it becomes ineffective to handle the detrimental effect of low-resolution ADCs at the destination. Moreover, it is shown that deploying massive relay antenna arrays can still bring significant power savings, i.e., the transmit power of each source can be cut down proportional to 1/M to maintain a constant rate, where M is the number of relay antennas.

This paper investigates the impact of the channel state information (CSI) and antenna correlation at the multi-antenna relay on the performance of wireless powered dual-hop amplify-and-forward relaying systems. Depending on the available CSI at the relay, two different scenarios are considered, namely, instantaneous CSI and statistical CSI where the relay has access only to the antenna correlation matrix. Adopting the power-splitting architecture, we present a detailed performance study for both cases. Closed-form analytical expressions are derived for the outage probability and ergodic capacity. In addition, simple high signal-to-noise ratio (SNR) outage approximations are obtained. Our results show that, antenna correlation itself does not affect the achievable diversity order, the availability of CSI at the relay determines the achievable diversity order. Full diversity order can be achieved with instantaneous CSI, while only a diversity order of one can be achieved with statistical CSI. In addition, the transmit antenna correlation and receive antenna correlation exhibit different impacts on the ergodic capacity. Moreover, the impact of antenna correlation on the ergodic capacity also depends heavily on the available CSI and operating SNR.

In this paper, we consider a three-node cooperative wireless powered communication system consisting of a multi-antenna hybrid access point (H-AP) and a single-antenna relay and a single-antenna user. The energy constrained relay and user first harvest energy in the downlink and then the relay assists the user using the harvested power for information transmission in the uplink. The optimal energy beamforming vector and the time split between harvest and cooperation are investigated. To reduce the computational complexity, suboptimal designs are also studied, where closed-form expressions are derived for the energy beamforming vector and the time split. For comparison purposes, we also present a detailed performance analysis in terms of the achievable outage probability and the average throughput of an intuitive energy beamforming scheme, where the H-AP directs all the energy towards the user. The findings of the paper suggest that implementing multiple antennas at the H-AP can significantly improve the system performance, and the closed-form suboptimal energy beamforming vector and time split yields near optimal performance. Also, for the intuitive beamforming scheme, a diversity order of N+1/2 can be achieved, where N is the number of antennas at the H-AP.

Spreading a large file consisting of many packets over a mobile network is challenging due to the short meeting duration for each transmission. Moreover, two typical causes of inefficient file spreading are duplicate packet reception at the destination nodes and excessive overhead exchanges. We propose to employ fountain codes at the source node to jointly addresses the three issues: 1) each coded packet can be small enough to fit into the meeting duration; 2) duplicate packet reception is significantly reduced since each coded packet is innovative; and 3) overhead is greatly saved by using file-level ACK instead of packet-level ACK. We conduct performance analysis in terms of the source-to-destination file delay and source-to-destination file spreading time in both non-relaying and relaying scenarios. While packet duplication can be eliminated in the former scenario, there is still a non-trivial duplication probability if relaying is allowed. Therefore, we propose a fountain-coded two-hop relaying (FTTR) protocol to further reduce the packet duplication ratio so that the spreading performance does not degrade with network size. The file spreading time and packet duplication ratio of FTTR are derived in closed form and verified through simulations.

To improve national security, government agencies have long been committed to enforcing powerful surveillance measures on suspicious individuals or communications. In this paper, we consider a wireless legitimate surveillance system, where a full-duplex multi-antenna legitimate monitor aims to eavesdrop on a dubious communication link between a suspicious pair via proactive jamming. Assuming that the legitimate monitor can successfully overhear the suspicious information only when its achievable data rate is no smaller than that of the suspicious receiver, the key objective is to maximize the eavesdropping non-outage probability by joint design of the jamming power, receive and transmit beamformers at the legitimate monitor. Depending on the number of receive/transmit antennas implemented, i.e., single-input single-output, single-input multiple-output, multiple-input single-output, and multiple-input multiple-output (MIMO), four different scenarios are investigated. For each scenario, the optimal jamming power is derived in a closed form and efficient algorithms are obtained for the optimal transmit/receive beamforming vectors. Moreover, low-complexity suboptimal beamforming schemes are proposed for the MIMO case. Our analytical findings demonstrate that by exploiting multiple antennas at the legitimate monitor, the eavesdropping non-outage probability can be significantly improved compared with the single-antenna case. In addition, the proposed suboptimal transmit zero-forcing scheme yields similar performance as the optimal scheme.

This paper investigates the impact of antenna correlation on wireless powered dual-hop multi-antenna relaying systems with instantaneous channel state information (CSI) or statistical CSI at the relay. Considering the power-splitting architecture, we study the outage probability as well as the achievable diversity order of the system for amplify-and-forward protocol. Our results show that, antenna correlation itself does not affect the achievable diversity order, the availability of CSI at the relay determines the achievable diversity order. Full diversity order can be achieved with instantaneous CSI, while only unit diversity order can be achieved with statistical CSI. In addition, with instantaneous CSI, antenna correlation is detrimental in the moderate and high SNR regime while it results in a better performance when the operating SNR is low. On the contrary, antenna correlation is always beneficial with only statistical CSI.

In this paper, we construct an analytical design framework for energy efficient scheduling for delay-constrained spectrum aggregation (ESSA), where the practical hardware limitations on SA capability bring various technical challenges. Specifically, the conventional water-filling power control cannot be adopted over all the channels, and the delay-aware scheduling solution should interact with the channel allocation. To overcome these challenges, we design the ESSA scheduling scheme in two steps. First, with given rate vector and channel allocation, we minimize the total power consumption for SA, including both the transmit power as well as the circuit power. Due to the properties of delay-constrained SA, we divide the scheduled users into conforming and nonconforming user sets, and design their water-filling power allocation strategies differentially. Second, based on the differentiated water-filling power control, we optimize the channel allocation and rate control iteratively via Lyapunov optimization to minimize the power consumption with the average delay constraint. The proposed ESSA scheme is finally evaluated by simulation results.

We consider a multi-pair two-way amplify-and-forward relaying system with a massive antenna array at the relay and estimated channel state information, assuming maximum-ratio combining/transmission processing. Closed-form approximations of the sum spectral efficiency are developed and simple analytical power scaling laws are presented, which reveal a fundamental trade-off between the transmit powers of each user/the relay and of each pilot symbol. Finally, the optimal power allocation problem is studied.

In future Internet of Things (IoT) networks, the explosive growth of mobile devices compel us to reconsider the effectiveness of the current frequency-division multiple access (FDMA) schemes. Devices' differentiated mobility features and diversified scattering environments make it more complicated to characterize the multi-user interference. In this paper, we thoroughly analyze the impacts of devices' mobility on the inter-sub-carrier interference (ICI) in an IoT system based on the 3GPP narrow-band orthogonal frequency-division multiple access (NB-OFDMA) protocol, and obtain the relationship between the system sum-rate and devices' mobility. Our results may shed some lights on the system design under the mobile scenarios.

In this paper, we investigate energy cooperation among sustainable cooperative relay nodes, which adopt decode- and-forward (DF) relaying. The relay nodes work on either information decoding (ID) mode or energy harvesting (EH) mode when receiving the signal from the source. Due to different harvested energy at the relay nodes, effective energy cooperation can benefit the energy efficiency, but suffer from practical energy loss when achieving energy cooperation. By solving the problem using Lagrangian duality, we obtain the optimal energy cooperation policy for the relay nodes and further discuss its two-level waterfilling structure. Then we derive the optimal and low-complexity EH/ID mode selecting algorithm with the two-level waterfilling energy cooperation policy. Finally, we compare the throughput between the systems with and without energy cooperation to show the benefit of energy cooperation.

In this study, a rate‐varying space‐time pre‐coding scheme for a multiple‐input multiple‐output system in a fast‐fading channel is proposed. In particular, unlike the traditional fixed rate space‐time coding scheme, the transmitter which has no channel state information keeps choosing a pre‐coding matrix randomly at each space‐time symbol period to deliver the information until it is successfully decoded by the receiver. Since the number of received symbol vectors needed to successfully decode the information is determined by the channel realisation, the coding rate of the proposed scheme can adapt to the fast‐fading channel. The exact expression of the equivalent signal‐to‐noise ratio gain after the post‐combining and zero‐forcing equalisation at the receiver side is derived, and a design criteria to achieve optimal performance in terms of average error probability and ergodic capacity under the mean power constraint is provided. Based on this, a minimal variance pre‐coding (MVP) scheme is designed, which is compared with an intuitive Gaussian pre‐coding scheme. It is shown that the MVP scheme has better performance and could adapt to different channel conditions.

This paper focuses on low complexity successive cancellation list (SCL) decoding of polar codes. In particular, using the fact that splitting may be unnecessary when the reliability of decoding the unfrozen bit is sufficiently high, a novel splitting rule is proposed. Based on this rule, it is conjectured that, if the correct path survives at some stage, it tends to survive till termination without splitting with high probability. On the other hand, the incorrect paths are more likely to split at the following stages. Motivated by these observations, a simple counter that counts the successive number of stages without splitting is introduced for each decoding path to facilitate the identification of correct and incorrect paths. Specifically, any path with counter value larger than a predefined threshold ω is deemed to be the correct path, which will survive at the decoding stage, while other paths with counter value smaller than the threshold will be pruned, thereby reducing the decoding complexity. Furthermore, it is proved that there exists a unique unfrozen bit uN-K1+1, after which the successive cancellation decoder achieves the same error performance as the maximum likelihood decoder if all the prior unfrozen bits are correctly decoded, which enables further complexity reduction. Simulation results demonstrate that the proposed low complexity SCL decoder attains performance similar to that of the conventional SCL decoder, while achieving substantial complexity reduction.

A novel low-complexity multiuser adaptive modulation (MAM) scheme is proposed for uplink massive multiple input and multiple output systems with zero-forcing detection, which requires only slow-varying large-scale shadowing information for the users. Closed-form expressions are derived for the average spectral efficiency (ASE) and bit error outage (BEO). In addition, for MAM with joint power control, optimal power adaptation strategy and switching thresholds are obtained. Compared with the conventional fast adaptive modulation, which requires fast-varying small-scale fading information, the proposed MAM scheme achieves similar ASE performance with slight increase in BEO.

This paper considers a wirelessly powered wiretap channel, where an energy constrained multi-antenna information source, powered by a dedicated power beacon, communicates with a legitimate user in the presence of a passive eavesdropper. Based on a simple time-switching protocol, where power transfer and information transmission are separated in time, we investigate two popular multi-antenna transmission schemes at the information source, namely, maximum ratio transmission and transmit antenna selection. Closed-form expressions are derived for the achievable secrecy outage probability and average secrecy rate for both schemes. In addition, simple approximations are obtained at the high signal-to-noise ratio (SNR) regime. Our results demonstrate that by exploiting the full knowledge of channel state information (CSI), we can achieve a better secrecy performance, e.g., with full CSI of the main channel, the system can achieve substantial secrecy diversity gain. On the other hand, without the CSI of the main channel, no diversity gain can be attained. Moreover, we show that the additional level of randomness induced by wireless power transfer does not affect the secrecy performance in the high SNR regime. Finally, our theoretical claims are validated by the numerical results.

This paper proposes a novel power beacon-assisted wiretap channel, where an energy constrained source, powered by a dedicated power beacon, aims to establish secure communications with a legitimate user in the presence of an eavesdropper. The power beacon and the source are assumed to have multiple antennas, while the legitimate user and the eavesdropper are equipped with a single antenna each. Considering a time-switching protocol, the power beacon first charges the source through wireless power transfer, and then acts as a friendly jammer to assist the source for secure communication. Depending on the available channel state information (CSI) at the power beacon, we propose several different jamming schemes for secrecy performance enhancement. Closed-form expressions are derived for the achievable secrecy outage probability of the proposed schemes. In addition, simple and accurate approximations are obtained at the high signal-to-noise ratio regime. The outcomes suggest that jamming is an effective approach to improve the secrecy performance: with full CSI available at the power beacon, employing simple zero-forcing scheme attains significant secrecy diversity gain and without the CSI of the eavesdropper link, jamming provides only some array gain. Moreover, we show that the proposed limited feedback-based random beamforming is an effective approach, which may outperform the antenna selection scheme. Furthermore, numerical results reveal that there exists a unique time switching ratio that maximizes the effective secrecy throughput.

An important application for modern large-scale networks is to spread the information efficiently to the largest audience. To better understand the theoretical underpinnings, a novel graph metric named mobile conductance was proposed in our previous work to evaluate the information spreading time of a connected mobile network. By capturing the details of both network structure and mobility pattern, this metric essentially determines the network bottleneck for conducting information flow under general network mobility. Despite major relaxation on node mobility, only slight relaxation on network connectivity was made in our previous work. In this paper, we make another major relaxation on the network connectivity by extending the mobile-conductance based analytical model to the sparse setting, hence offering a unified view. Interestingly, a penalty factor is identified for information spreading in sparse networks as compared to the connected scenario, which is then intuitively interpreted and verified by simulations. By jointly considering mobility and connectivity, we derive the mobile conductance for various mobility models with general connectivity. Using these analytical results, the mobility-connectivity tradeoff is quantitatively analyzed to determine how much mobility may be exploited to compensate for network connectivity deficiency.

In vehicular communications, traffic-related information should be spread over the network as quickly as possible to maintain a safer transportation system. This motivates us to develop more efficient information propagation schemes. In this paper, we propose a novel virtual-MIMO-enabled information dissemination scheme in which the vehicles opportunistically form virtual antenna arrays to boost the transmission range, and therefore, accelerate information propagation along the highway. We model the information propagation process as a renewal reward process and investigate in detail the information propagation speed (IPS) of the proposed scheme. The corresponding closed-form IPS is derived, which shows that the IPS increases cubically with the vehicle density but will ultimately converge to a constant upper bound. Moreover, increased mobility also facilitates the information spreading by offering more communication opportunities. However, the limited network density essentially determines the bottleneck in information spreading. Extensive simulations are carried out to verify our analysis. We also show that the proposed scheme exhibits a significant IPS gain over its conventional counterpart.

In the future Internet of Things (IoT), a multitude of objects are to be provided with always-on-line connections and dynamic access to the network services, which brings new challenges to multiple access protocol design. In this paper, a novel multiple access protocol, termed as "multi-carrier rateless multiple access (MC-RMA)'', is proposed to overcome such challenges. Instead of assigning the available resource elements (REs) to the users in a fixed and centralized manner, the base station (BS) simply assigns a specific distribution profile to each user, with which each user randomly chooses a certain number of coded symbols, linearly combines them, and then transmits the resultant signal to BS until receiving an acknowledgement (ACK) from BS. The transmission process of each user, as well as those of all the users as a whole, resembles a special form of linear superposition rateless coding and the user information can be decoded using the low-complexity belief propagation (BP) algorithm. Due to the inherent rate adaptation property of rateless codes, the proposed RMA protocol is quite suitable for dynamic massive access in future IoT.

In vehicular communications, traffic-related information should be spread over the network as quickly as possible to maintain a safer transportation system. This motivates us to develop more efficient information propagation schemes. In this paper, we propose a novel virtual-MIMO-enabled information dissemination scheme, in which the vehicles opportunistically form virtual antenna arrays to boost the transmission range and therefore accelerate information propagation along the highway. We model the information propagation process as a renewal reward process and investigate in detail the Information Propagation Speed (IPS) of the proposed scheme. The corresponding closed-form IPS is derived, which shows that the IPS increases cubically with the vehicle density but will ultimately converge to a constant upper bound. Moreover, increased mobility also facilitates the information spreading by offering more communication opportunities. However, the limited network density essentially determines the bottleneck in information spreading. Extensive simulations are carried out to verify our analysis. We also show that the proposed scheme exhibits a significant IPS gain over its conventional counterpart.

In high mobility wireless communication scenarios, the most challenging issue is to cope with the extremely fast fading channel. Compared with its static counterpart, channel estimation in high mobility scenarios consumes excessive energy and spectrum to achieve similar performance. To address this issue, we exploit delay correlation with sequential antenna-hopping (AH) to convert the rapid fading channels to a virtual slow-fading channel. As a result, robust communication can be achieved with much less channel estimation overhead even under high mobility. Finally, two important performance metrics, namely, channel estimation mean square error (MSE) and transmission symbol error rate (SER), are re-examined for this virtual slow-fading channel. Numerical results verify the good performance of the proposed scheme and elucidate its effectiveness in high mobility communication scenarios.

We investigate the achievable sum rate and energy efficiency of zero-forcing precoded downlink massive multiple-input multiple-output systems in Ricean fading channels. A simple and accurate approximation of the average sum rate is presented, which is valid for a system with arbitrary rank channel means. Based on this expression, the optimal power allocation strategy maximizing the average sum rate is derived. Moreover, considering a general power consumption model, the energy efficiency of the system with rank-1 channel means is characterized. Specifically, the impact of key system parameters, such as the number of users N, the number of BS antennas M, Ricean factor K and the signal-to-noise ratio (SNR) ρ are studied, and closed-form expressions for the optimal ρ and M maximizing the energy efficiency are derived. Our findings show that the optimal power allocation scheme follows the water filling principle, and it can substantially enhance the average sum rate in the presence of strong line-of-sight effect in the low SNR regime. In addition, we demonstrate that the Ricean factor K has significant impact on the optimal values of M, N and ρ.

Inspired by the green communication trend of next-generation wireless networks, we propose an opportunistic wireless information and energy transfer relaying scheme for sustainable cooperative relaying, in which the relay nodes are powered only by their independent harvested energy to forward the desired information to the destination. Thus there exists an inherent tradeoff between the information decoding (ID) and the energy harvesting (EH) for forwarding. We first analyze the tradeoff and formulate an optimization problem on joint EH/ID receive mode selection and transmit power control for the relay nodes. By Lagrangian optimality, we obtain the optimal solution in opportunistic relay and power control. Due to the NP-hard property of the EH/ID mode selection, we propose a low-complexity algorithm by relaxation for EH/ID mode selection. The simulation results show that the performance of the low-complexity algorithm is close to the optimal solution, and outperforms the baselines.

A framework on decentralized interference coordination based on the pricing mechanism is developed for device-to-device (D2D) communication underlying cellular systems to guarantee quality of service (QoS) of both cellular users (CUs) and D2D links. We aim at coordinating two types of interference: inter-layer interference from D2D pairs to CUs and intra-layer interference among D2D pairs. The former is mitigated by the base station through setting a price on the channel being reused by D2D pairs while the latter is solved by a game-theoretic approach, in which the D2D pairs compete for the spectrum until a Nash Equilibrium (NE) is achieved. Finally, numerical results verify that the proposed distributed scheme is effective for the interference coordination and its performance is close to the centralized scheme.

We consider a dual-hop full-duplex (FD) relaying system, where the energy constrained relay node is powered by radio frequency signals from the source using the time-switching architecture. Both the amplify-and-forward and decode-and-forward relaying protocols are studied. Specifically, we provide an analytical characterization of the achievable throughput of three different communication modes, namely, instantaneous transmission, delay-constrained transmission, and delay tolerant transmission. In addition, the optimal time split is studied for different transmission modes. Our results reveal that, when the time split is optimized, FD relaying could substantially boost the system throughput compared to the conventional half-duplex relaying architecture for all three transmission modes. In addition, it is shown that the instantaneous transmission mode has the highest throughput. However, compared to the delay tolerant transmission mode, the throughput gap is negligible. Unlike the instantaneous time split optimization which requires instantaneous channel state information, the optimal time split in the delay tolerant transmission mode depends only on the statistics of the channel, hence, is attractive for practical implementation.

This paper investigates the impact of the multiple antenna and co-channel interference (CCI) on the outage performance of a dual-hop amplify-and-forward energy harvesting relaying network. The energy constrained relay is powered by radio frequency signals and employs the power splitting receiver architecture. To exploit the benefit of multiple antennas, two different linear processing schemes are investigated, namely, Maximum ratio combining/maximal ratio transmission (MRC/ MRT) and Minimum mean-square error/MRT (MMSE/MRT). For both schemes, a new closed-form outage lower bound and a simple high signal-to-noise ratio outage approximation are derived, respectively. Also, the achievable diversity order is quantified. In addition, we study the optimal power splitting ratio which minimizes the outage probability. Our results show that, by increasing the energy harvesting capability, the implementation of multiple antennas significantly improves the systems performance. Moreover, CCI could be potentially exploited to boost the performance, while how much performance gain can be obtained depends on the choice of the linear processing scheme.

This paper investigates the achievable sum-rate of uplink massive multiple-input multiple-output (MIMO) systems considering a practical channel impairment, namely, aged channel state information (CSI). Taking into account both maximum ratio combining (MRC) and zero-forcing (ZF) receivers at the base station, we present tight closed-form lower bounds on the sum-rate for both receivers, which provide efficient means to evaluate the sum-rate of the system. More importantly, we characterize the impact of channel aging on the power scaling law. Specifically, we show that the transmit power of each user can be scaled down by 1/√(M), which indicates that aged CSI does not affect the power scaling law; instead, it causes only a reduction on the sum rate by reducing the effective signal-to-interference-and-noise ratio (SINR).

This paper studies the capacity scaling law of the multi-pair relay network with K source-destination pairs and M relays, where each node is equipped with a single antenna and works in half duplex mode. With the conventional two-slot relaying, the capacity was found to scale as K/2 log (M)+O(1) for fixed K and M → ∞. This paper shows that the capacity scaling law can be further improved to K log (M)+O(1) with successive relaying, as if the relays were full duplex. This scaling law can be achieved by a distributed coherent amplify-and-forward scheme, which only requires local channel state information (CSI) at each relay and statistical CSI at the sources and destinations.

A novel rateless coded Vector Orthogonal Frequency Division Multiplexing (V-OFDM) system for adaptive transmission over wireless channels is studied in this paper. In particular, an iterative receiver with joint V-OFDM equalization and ratelss decoding is designed, which exchanges the logarithmic likelihood information between the belief propagation decoder and the maximum likelihood equalizer or the hard/soft decision based equalizer recursively. Both analytical and numerical results show that the proposed algorithm significantly improves the system performance with a modest complexity.

A joint modulation and coding scheme is proposed based on Rateless Codes and Vector OFDM systems to combat fading over doubly selective channels. A rateless encoder can generate potentially unlimited coded symbol. Vector OFDM is a general transmission scheme, where OFDM and Single-Carrier systems can be seen as two extreme cases. The main focus of this paper is to design and analyze a Rateless Coded Vector OFDM scheme to obtain extra gain and improve the system performance. Firstly, we derive the lower bound of the decoding complexity of the block universal Raptor Codes and the upper bound of the probability to decode successfully in Binary Erasure Channels. Secondly, we analyze the joint scheme of Rateless Coded Vector OFDM with a novel proposition proposed, which reveals the impacts of the degree distribution on the system performance. According to the proposition, two practical Rateless Coded Vector OFDM methods are presented, which can achieve considerable joint Multipath-and-Doppler diversity gain and coded modulation gain to improve the reliability of the system. Finally, simulation results are demonstrated to validate the proposed scheme.

The distribution of information bits and frozen bits on the decoder graph can be exploited to simplify the successive‐cancellation (SC) decoding of polar codes. In this study, the authors establish a general simplified SC decoding framework for polar codes with arbitrary block‐length, which is independent of any specific or explicit generator matrices and considerably reduces decoding complexity while retaining the same error performance. Then, based on the fact that the complexity reduction depends on the distribution of information bits, a so‐called m ‐radius reselection scheme is proposed to construct multiple feasible information sets which are different from the original one defined by Arıkan. In this way, flexible tradeoffs between performance and complexity could be achieved.

In this letter, we revisit the pre-coder design problem for the amplify-and-forward two-way relay channel with multiple antennas equipped at each node. Based on generalized singular value decomposition (GSVD), we propose a novel pre-coding technique named Hermitian Relay Pre-coding (HRP) with a relatively low design complexity. Compared with the existing GSVD pre-coding method, HRP can achieve a higher rate by avoiding zero-forcing operation but keeping the same design complexity.

This letter maximizes the achievable throughput of a relay-assisted wirelessly powered communications system, where an energy constrained source, assisted by an energy constrained relay and both powered by a dedicated power beacon (PB), communicates with a destination. Considering the time splitting approach, the source and relay first harvest energy from the PB, which is equipped with multiple antennas, and then transmits the information to destination. Simple closed-form expressions are derived for the optimal PB energy beamforming vector and time split for energy harvesting and information transmission. Numerical results and simulations demonstrate the superior performance compared with some intuitive benchmark beamforming scheme. Also, it is found that placing the relay at the middle of the source-destination path is no longer optimal.

This paper investigates the achievable sum-rate of massive multiple-input multiple-output (MIMO) systems in the presence of channel aging. For the uplink, by assuming that the base station (BS) deploys maximum ratio combining (MRC) or zero-forcing (ZF) receivers, we present tight closed-form lower bounds on the achievable sum-rate for both receivers with aged channel state information (CSI). In addition, the benefit of implementing channel prediction methods on the sum-rate is examined, and closed-form sum-rate lower bounds are derived. Moreover, the impact of channel aging and channel prediction on the power scaling law is characterized. Extension to the downlink scenario and multicell scenario is also considered. It is found that, for a system with/without channel prediction, the transmit power of each user can be scaled down at most by 1/√M (where M is the number of BS antennas), which indicates that aged CSI does not degrade the power scaling law, and channel prediction does not enhance the power scaling law; instead, these phenomena affect the achievable sum-rate by degrading or enhancing the effective signal to interference and noise ratio, respectively.

Provisioning reliable wireless services for railway passengers is becoming an increasingly critical problem to be addressed with the fast development of high speed trains (HST). In this paper, exploiting the linear mobility inherent to the HST communication scenario, we discover a new type of spatial-temporal correlation between the base station and moving antenna array on the roof top of the train. Capitalizing on the new spatial-temporal correlation structure and properties, an improved differential space-time modulation (DSTM) scheme is proposed. Analytical expressions are obtained for the pairwise error probability of the system. It is demonstrated that the proposed approach achieves superior error performance compared with the conventional DSTM scheme. In addition, an adaptive method, which dynamically adjusts the transmission block length is proposed to further enhance the system performance. Numerical results are provided to verify the performance of the proposed schemes.

This paper investigates the average throughput of a wireless-powered communications system, where an energy constrained source, powered by a dedicated power beacon (PB), communicates with a destination. It is assumed that the PB is capable of performing channel estimation, digital beamforming, and spectrum sensing as a communication device. Considering a time-splitting approach, the source first harvests energy from the PB equipped with multiple antennas, and then transmits information to the destination. Assuming Nakagami-m fading channels, analytical expressions for the average throughput are derived for two different transmission modes, namely, delay tolerant and delay intolerant. In addition, closed-form solutions for the optimal time split, which maximize the average throughput are obtained in some special cases, i.e., high-transmit power regime and large number of antennas. Finally, the impact of cochannel interference is studied. Numerical and simulation results have shown that increasing the number of transmit antennas at the PB is an effective tool to improve the average throughput and the interference can be potentially exploited to enhance the average throughput, since it can be utilized as an extra source of energy. Also, the impact of fading severity level of the energy transfer link on the average throughput is not significant, especially if the number of PB antennas is large. Finally, it is observed that the source position has a great impact on the average throughput.

In this paper, an energy harvesting dual-hop relaying system without/with the presence of co-channel interference (CCI) is investigated. Specifically, the energy constrained multi-antenna relay node is powered by either the information signal of the source or via the signal receiving from both the source and interferer. In particular, we first study the outage probability and ergodic capacity of an interference free system, and then extend the analysis to an interfering environment. To exploit the benefit of multiple antennas, three different linear processing schemes are investigated, namely, 1) Maximum ratio combining/maximum ratio transmission (MRC/MRT), 2) Zero-forcing/MRT (ZF/MRT) and 3) Minimum mean-square error/MRT (MMSE/MRT). For all schemes, both the systems outage probability and ergodic capacity are studied, and the achievable diversity order is also presented. In addition, the optimal power splitting ratio minimizing the outage probability is characterized. Our results show that the implementation of multiple antennas increases the energy harvesting capability, hence, significantly improves the systems performance.

Increasing population density, closer social contact and interactions make epidemic control difficult. Traditional offline epidemic control methods (e.g., using medical survey or medical records) or model-based approach are not effective due to its inability to gather health data and social contact information simultaneously or impractical statistical assumption about the dynamics of social contact networks, respectively. In addition, it is challenging to find optimal sets of people to be quarantined to contain the spread of epidemics for large populations due to high computational complexity. Unlike these approaches, in this paper, a novel cluster-based epidemic control scheme is proposed based on Smartphone-based body area networks. The proposed scheme divides the populations into multiple clusters based on their physical location and social contact information. The proposed control schemes are applied within the cluster or between clusters. Further, we develop a computational efficient approach called UGP to enable an effective cluster-based quarantine strategy using graph theory for large scale networks (i.e., populations). The effectiveness of the proposed methods is demonstrated through both simulations and experiments on real social contact networks.

A pricing-based joint spectrum and power allocation framework is proposed for decentralized interference coordination among device-to-device (D2D) communications and cellular users (CUs), with the quality-of-service guarantee. The interlayer interference from D2D pairs to CUs is controlled by the base station through setting a price for each D2D channel usage. The intralayer interference among D2D pairs is mitigated distributively using a game-theoretic approach, where the D2D pairs compete for the spectrum until a Nash equilibrium is achieved. The effectiveness of the proposed strategy, including a practical scheme with limited signaling overhead, is demonstrated through comparing with a centralized scheme.

In this paper, we study the problem of physical layer security in large-scale multiple input multiple output (LS-MIMO) systems. The large number of antenna elements in LS-MIMO system is exploited to enhance transmission security and improve system performance, especially when the eavesdropper is closer to the information source and has more antennas than the legitimate user. However, in practical systems, the problem becomes challenging because the eavesdropper channel state information (CSI) is usually unavailable without cooperation and the legitimate CSI may be imperfect due to channel estimation error. In this paper, we first analyze the performance of physical layer security without eavesdropper CSI and with imperfect legitimate CSI, and then propose an energy-efficient power allocation scheme to meet the demand for wireless security and quality of service (QoS) simultaneously. Finally, numerical results validate the effectiveness of the proposed scheme.

In this paper, we investigate the random access for an energy harvesting secondary user (SU) in a cognitive radio system, in which the SU harvests energy from radio frequency (RF) radiation of the primary user (PU). With multipacket reception channel model, the SU can increase its throughput through not only utilizing the idle periods of the PU, but also opportunistically sharing the PU spectrum with some probability when the PU is active. By choosing the appropriate random access probability, we maximize the throughput of the SU under the constraint of the primary queue stability. We also define the energy-limited region and spectrum-limited region to specify the tradeoff between the SU performance and the PU activity. Furthermore, we investigate the effect of the primary queueing delay constraint on the throughput performance of the SU.

Wireless information and energy transfer (WIET) is a prominent technology to prolong the lifetime of battery-charging wireless networks. In this paper, we exploit the benefit of massive MIMO for WIET under external interference, and propose the antenna partition for information decoding and energy harvesting. Considering the effects of the external interference, i.e., interfering the information reception and benefiting the energy harvesting, we analyze the tradeoff between the data rate and the harvested energy, and obtain the achievable rate-energy (R-E) region. Then, we propose a low-complexity receive antenna partition algoritinterference mitigationhm for WIET in massive MIMO systems with the consideration of interference mitigation. The algorithm maximizes the data rate while guaranteeing a minimum harvested energy. It is found that the SNR of the low-complexity algorithm is at least an approximable half of the optimal SNR. Simulation results verify our theoretical claims and show the effectiveness of the proposed low-complexity antenna partition algorithm.

In vehicular communications, traffic-related information should be spread over the network as quickly as possible to maintain a safer transportation system. This motivates us to develop more efficient information propagation schemes. In this paper, we propose a novel cluster-based cooperative information forwarding scheme, in which the vehicles opportunistically form virtual antenna arrays to boost one-hop transmission range and therefore accelerate information propagation along the highway. Both closed-form results of the transmission range gain and the improved Information Propagation Speed (IPS) are derived and verified by simulations. It is observed that the proposed scheme demonstrates the most significant IPS gain in moderate traffic scenarios, whereas too dense or too sparse vehicle density results in less gain. Moreover, it is also shown that increased mobility offers more contact opportunities and thus facilitates information propagation.

A class of recovering algorithms for 1-bit compressive sensing (CS) named Soft Consistency Reconstructions (SCRs) are proposed. Recognizing that CS recovery is essentially an optimization problem, we endeavor to improve the characteristics of the objective function under noisy environments. With a family of re-designed consistency criteria, SCRs achieve remarkable counter-noise performance gain over the existing counterparts, thus acquiring the desired robustness in many real-world applications. The benefits of soft decisions are exemplified through structural analysis of the objective function, with intuition described for better understanding. As expected, through comparisons with existing methods in simulations, SCRs demonstrate preferable robustness against noise in low signal-to-noise ratio (SNR) regime, while maintaining comparable performance in high SNR regime.

Distributive service data storage at the caching-enabled base stations (BSs) can reduce the traffic load in future cellular networks. Taking the limited caching space into account, it is necessary for the BSs to adjust their caching data based on service popularity in order to achieve better caching efficiency. In this paper, we investigate the cache replacement strategy for BSs to minimize the transmission cost between BSs in cellular networks. The cache replacement problem is modelled as a Markov Decision Process (MDP). Without extra information exchange about caching data between the BSs, we propose a distributed cache replacement strategy based on Q-learning. Especially, we calculate the transmission cost for possible cache replacement actions according to the previous data request and transmission between BSs. The convergence of the proposed distributed cache replacement strategy is proved by sequential stage game model. Simulation results verify the convergence of the proposed cache replacement strategy and show its performance gain compared to conventional strategies.

Opportunistic forwarding assisted by mobile relays is an effective way of improving network capacity and packet delivery ratio in delay tolerant networks (DTNs). However, such performance gain comes at the price of increased energy consumption due to the duplicated transmissions at relays. In this paper, we investigate how energy harvesting, a promising technique of enabling sustainable communications, can be exploited to improve the performance of opportunistic forwarding in mobile DTNs. Specifically, we formulate the problem using a Markov Decision Process (MDP) framework in which each source should strike a balance between exploitation, by forwarding the packet to the relay currently in contact, and exploration, by waiting for possible better relays in the future, given the harvested energy constraint. The formulated MDP having exponential complexity, we devise a heuristic relay-assisted opportunistic forwarding scheme, termed as adaptive M-step lookahead scheme, to alleviate the computation complexity, where M can be adjusted adaptively according to both the current energy and the energy that might be harvested in the future. Simulation results show that our proposed algorithm can use the harvested energy more efficiently, especially for the circumstance where the energy harvesting rate is low.

Mobile relay-assisted forwarding can improve the network capacity, but meanwhile increase the energy consumption. In this paper, we propose two distance-based energy-efficient opportunistic forwarding (DEEOF) schemes in mobile delay tolerant networks (DTNs). The proposed schemes strike a balance between energy consumption and network performance by maximizing the energy efficiency while maintaining a high packet delivery ratio from two different angles. Specifically, in the developed algorithms, we introduce the forwarding equivalent energy-efficiency distance (FEED) to quantify the transmission distances achieving the same energy efficiency at different time instances. The expected energy efficiency can thus be estimated based on the FEED. Furthermore, the distribution of the greatest forwarding energy efficiency in the predicted period is investigated to provide more accurate prediction for the energy efficiency. The forwarding decision in the algorithms is made by comparing the current energy efficiency and the estimated future expectation. The performance improvement of the proposed algorithms is also demonstrated by simulation, especially for systems where the source has very limited battery reserves.

The increase of antennas can greatly improve the performance of MIMO systems, in the meanwhile, the precise channel state information is difficult to obtain since the channel matrix of massive MIMO is much more complex than the traditional one. In this paper, we propose a limited feedback strategy named hybrid limited feedback with selective eigenvalue information (HLFSEI), which jointly considers the conventional quantized feedback and codebook based feedback. In HLFSEI, because of the large amount of the elements of channel matrix, the quantized information of only selective eigenvalue elements is fed back from the receiver to the transmitter with feedback-link capacity constraint. We study the optimal feedback bit allocation of both feedback methods to maximize the throughput by theoretic deduction. Finally, we evaluate the performance of the proposed limited feedback scheme by simulation and show its performance gain compared to conventional feedback strategies.

Some multilevel codes which are constructed based on polar codes have been introduced recently, and show good performance with elaborately designed rates at each level and special multistage decoder. However, the multistage decoding is usually implemented in a sequential manner, which might yield poor performance at lower levels since the corresponding component codes are decoded without any information from higher levels, and the decoding errors occurred at previous stages could hardly be corrected. Thus, an iterative multistage decoding algorithm for polar code based multilevel codes is introduced in this paper, which could further improve the performance by feeding the reliable information obtained at higher levels back to the first level, and correct the decoding errors occurred at lower levels. Our proposed algorithm has only moderate complexity, and it is found that, the first iteration contributes the most performance improvement.

This paper investigates the impact of different linear processing techniques on the outage performance of dual-hop amplify-and-forward (AF) relaying systems with co-channel interference (CCI) at the multiple antenna relay. Specifically, three heuristic linear precoding schemes are proposed to combat the detrimental effect of CCI, namely, 1) Maximum ratio combining/maximal ratio transmission (MRC/MRT), 2) Zero-forcing/MRT (ZF/MRT), 3) Minimum mean-square error/MRT (MMSE/MRT). New exact outage expressions as well as simple high signal-to-noise ratio (SNR) outage approximations are derived for all three schemes. Our results demonstrate that while the MRC/MRT and the MMSE/MRT schemes achieve a full diversity order of N, the ZF/MRT scheme can only achieve a diversity order of N-M, where N is the number of relay antennas and M is the number of interferers. In addition, it is observed that the MMSE/MRT scheme always achieves the best outage performance. The ZF/MRT scheme outperforms the MRC/MRT scheme in the low SNR regime, and exhibits an inferior performance compared to the MRC/MRT scheme in the high SNR regime.

Puncturing is widely used to generate rate-compatible codes. However, for punctured polar codes, some puncturing patterns may greatly affect the split bit channels and cause considerable performance loss. In this paper, we aim to investigate how the split bit channels are affected by various puncturing patterns, and then evaluate the performances of these patterns. We propose a search algorithm to design good punctured polar codes, and prove that designing the optimal puncturing pattern for output bits is equivalent to finding the optimal puncturing pattern for frozen bits. We also propose a heuristic approach based on the idea of polarization to select the position of each punctured output bit, and simulations show that the puncturing pattern designed this way almost achieves the same performance as the optimal one selected by exhaustive search.

In this paper, our recently proposed mobile-conductance based analytical framework is extended to the sparse settings, thus offering a unified tool for analyzing information spreading in mobile networks. A penalty factor is identified for information spreading in sparse networks as compared to the connected scenario, which is then intuitively interpreted and verified by simulations. With the analytical results obtained, the mobility-connectivity tradeoff is quantitatively analyzed to determine how much mobility may be exploited to make up for network connectivity deficiency.

Polar codes can achieve symmetric capacity of a channel with a simple encoder and a successive cancellation (SC) decoder, both with complexity of the order of O(N log N). Polar codes of length N = lnmay have a faster polarization rate compared to polar codes of length N = 2n. In this paper, we introduce a simple and explicit method to obtain the recursive formulas of SC decoder for any polar code of length ln, which helps us better understand the essence of SC decoding. Based on this, we give a complete proof that the simplified SC decoding introduced in previous works can also be applicable for polar codes of all kinds of kernels.

We consider a dual-hop full-duplex relaying system, where the energy constrained relay node is powered by radio frequency signals from the source using the time-switching architecture, both the amplify-and-forward and decode-and-forward relaying protocols are studied. Specifically, we provide an analytical characterization of the achievable throughput of three different communication modes, namely, instantaneous transmission, delay-constrained transmission, and delay tolerant transmission. In addition, the optimal time split is studied for different transmission modes. Our results reveal that, when the time split is optimized, the full-duplex relaying could substantially boost the system throughput compared to the conventional half-duplex relaying architecture for all three transmission modes. In addition, it is shown that the instantaneous transmission mode attains the highest throughput. However, compared to the delay-constrained transmission mode, the throughput gap is rather small. Unlike the instantaneous time split optimization which requires instantaneous channel state information, the optimal time split in the delay-constrained transmission mode depends only on the statistics of the channel, hence, is suitable for practical implementations.

In this paper, we analyze the ergodic capacity of a dual-hop amplify-and-forward relaying system, where the relay is equipped with multiple antennas and subject to co-channel interference and the additive white Gaussian noise. Specifically, we consider three heuristic precoding schemes, where the relay first applies the: 1) maximal-ratio combining (MRC); 2) zero-forcing (ZF); and 3) minimum mean-squared error (MMSE) principle to combine the signal from the source, and then steers the transformed signal toward the destination with the maximum ratio transmission (MRT) technique. For the MRC/MRT and MMSE/MRT schemes, we present new tight analytical upper and lower bounds for the ergodic capacity, while for the ZF/MRT scheme, we derive a new exact analytical ergodic capacity expression. Moreover, we make a comparison among all three schemes, and our results reveal that, in terms of the ergodic capacity performance, the MMSE/MRT scheme always has the best performance and the ZF/MRT scheme is slightly inferior, while the MRC/MRT scheme is always the worst one. Finally, the asymptotic behavior of ergodic capacity for the three proposed schemes are characterized in large N scenario, where N is the number of relay antennas. Our results reveal that, in the large N regime, both the ZF/MRT and MMSE/MRT schemes have perfect interference cancellation capability, which is not possible with the MRC/MRT scheme.

Linear encoders with good joint spectra are suitable candidates for optimal lossless joint source-channel coding (JSCC), where the joint spectrum is a variant of the input-output complete weight distribution and is considered good if it is close to the average joint spectrum of all linear encoders (of the same coding rate). In spite of their existence, little is known on how to construct such encoders in practice. This paper is devoted to their construction. In particular, two families of linear encoders are presented and proved to have good joint spectra. The first family is derived from Gabidulin codes, a class of maximum-rank-distance codes. The second family is constructed using a serial concatenation of an encoder of a low-density parity-check code (as outer encoder) with a low-density generator matrix encoder (as inner encoder). In addition, criteria for good linear encoders are defined for three coding applications: 1) lossless source coding; 2) channel coding; and 3) lossless JSCC. In the framework of the code-spectrum approach, these three scenarios correspond to the problems of constructing linear encoders with good kernel spectra, good image spectra, and good joint spectra, respectively. Good joint spectra imply both good kernel spectra and good image spectra, and for every linear encoder having a good kernel (respectively, image) spectrum, it is proved that there exists a linear encoder not only with the same kernel (respectively, image) but also with a good joint spectrum. Thus, a good joint spectrum is the most important feature of a linear encoder.

This paper addresses the optimal transmission scheduling problem in hybrid energy supply systems with the save-then-transmit protocol, where the energy supply of the transmitter comes from both the primary battery and the energy harvester. We first consider minimizing the outage probability for a given amount of battery energy by optimizing the saving factor. It is demonstrated that harvesting external energy is unnecessary for a large spectral efficiency requirement. Then, we consider joint packet scheduling and saving factor optimization to the battery energy consumption minimization (BECM) problem in both single packet arrival and burst packet arrival scenarios. Both optimal and suboptimal offline policies with full information on the traffic profile, the harvesting power, and the channel state are developed. We also propose an optimal online policy in the case that only causal information is available. Numerical results are presented that validate the effectiveness of the proposed algorithms.

This paper presents an analytical investigation on the performance of Rayleigh-product MIMO channels with linear minimum mean-square-error (MMSE) or zero-forcing (ZF) receivers. For MMSE receivers, exact closed-form expressions for the ergodic sum-rate of the system are derived. In addition, simplified expressions are obtained for the key parameters dictating the sum-rate performance of the system in the high signal-to-noise ratio (SNR) regime (i.e., high SNR slope and power offset) and low SNR regime (i.e., minimum energy per information bit required to convey any positive rate and the wideband slope). While for ZF receivers, tight closed-form upper and lower bounds for the ergodic sum-rate of the system are derived. It is analytically proven that the ZF and MMSE receivers achieve the same sum rate performance in the high SNR regime. Moreover, for both MMSE and ZF receivers, the achievable diversity-multiplexing tradeoff (DMT) of Rayleigh-product MIMO channels is characterized. The findings suggest that a larger number of scatterers will improve the the performance of Rayleigh-product MIMO channels with linear receivers, and the ZF receivers achieve the same performance as the MMSE receivers in Rayleigh-product MIMO channels in the high SNR regime. Moreover, it is demonstrated that as long as the number of the scatterers is greater than the number of receive antennas, linear receivers achieve the optimal DMT.

This paper considers a dual-hop amplify-and-forward (AF) relaying system where the relay is equipped with multiple antennas, while the source and the destination are equipped with a single antenna. Assuming that the relay is subjected to co-channel interference (CCI) and additive white Gaussian noise (AWGN) while the destination is corrupted by AWGN only, we propose three heuristic relay precoding schemes to combat the CCI, namely, 1) Maximum ratio combining/maximal ratio transmission (MRC/MRT), 2) Zero-forcing/MRT (ZF/MRT), 3) Minimum mean-square error/MRT (MMSE/MRT). We derive new exact outage expressions as well as simple high signal-to-noise ratio (SNR) outage approximations for all three schemes. Our findings suggest that both the MRC/MRT and the MMSE/MRT schemes achieve a full diversity of N, while the ZF/MRT scheme achieves a diversity order of N-M, where N is the number of relay antennas and M is the number of interferers. In addition, we show that the MMSE/MRT scheme always achieves the best outage performance, and the ZF/MRT scheme outperforms the MRC/MRT scheme in the low SNR regime, while becomes inferior to the MRC/MRT scheme in the high SNR regime. Finally, in the large N regime, we show that both the ZF/MRT and MMSE/MRT schemes are capable of completely eliminating the CCI, while perfect interference cancelation is not possible with the MRC/MRT scheme.

Accurate and real-time tracing of epidemic sources is critical for epidemic origin analyses and control when outbreaks of epidemic diseases occur. Such tracing requires the simultaneous availability of information about social interactions among people as well as their body vital signs. Existing epidemic control methods are limited due to their inability to collect the above two types of information at the same time. In this paper, for the first time, we propose integrating wireless body area networks (WBANs) for body vital signs collection with mobile phones for social interaction sensing to achieve the desired epidemic source tracing. In particular, we design a mobile phone capability driven hierarchical social interaction detection framework integrated with WBANs. With this framework, we further propose a set of epidemic source tracing and control algorithms including genetic algorithm based search and dominating set identification algorithms to effectively identify epidemic sources and inhibit epidemic spread. We have also conducted extensive simulations, analyses, and case studies based on real data sets, which demonstrate the accuracy and effectiveness of our proposed solutions.

In this paper, we propose a general analytical framework for information spreading in mobile networks based on a new performance metric, mobile conductance, which allows us to separate the details of mobility models from the study of mobile spreading time. We derive a general result for the information spreading time in mobile networks in terms of this new metric, and instantiate it through several popular mobility models. Large scale network simulation is conducted to verify our analysis.

Cooperative communication has been studied extensively as a promising technique for improving the performance of terrestrial wireless networks. However, in underwater cooperative acoustic networks, long propagation delays and complex acoustic channels make the conventional relay selection schemes designed for terrestrial wireless networks inefficient. In this paper, we develop a new best relay selection criterion, called COoperative Best Relay Assessment (COBRA), for underwater cooperative acoustic networks to minimize the one-way packet transmission time. The new criterion takes into account both the spectral efficiency and the underwater long propagation delay to improve the overall throughput performance of the network with energy constraint. A best relay selection algorithm is also proposed based on COBRA criterion. This algorithm only requires the channel statistical information instead of the instantaneous channel state. Our simulation results show a significant decrease on one-way packet transmission time with COBRA. The throughput and delivery ratio performance improvement further verifies the advantages of our proposed criterion over the conventional channel state based algorithms.

A novel belief propagation algorithm with gradual edge removal (ERBP) is proposed for Raptor codes over additional white gaussian noise (AWGN) channel. Specifically, the variable nodes with sufficiently high confidence and the corresponding edges are removed gradually from the Tanner graph during the BP iterations so as to reduce the decoding complexity. Then considering the intrinsic nature of incremental redundancy of Raptor codes, we extend the ERBP algorithm into a progressive manner, i.e., the so-called PERBP algorithm, which not only conducts edge removal operation but also makes use of the decoding states from the previous decoding attempts. Simulation results show that compared with the conventional BP algorithm, both ERBP and PERBP algorithms provide drastically reduced decoding complexity without much loss of coding performance.

In this paper, we propose to generate rateless codes with unequal error protection (UEP) property based on an improved weighted-selection approach. By selecting input symbols of different importance through Bernoulli experiments when generating an output symbol, the error in degree splitting suffered by the conventional UEP method can be avoided. Then by analyzing our proposed method and the conventional method on BIAWGN channel based on the EXtrinsic Information Transfer (EXIT) chart with Gaussian approximation, we prove that the improved method has better UEP performance than the conventional method. Finally the simulation results verify the efficiency of our proposed method.

This paper aims to design a class of Raptor codes with optimized complexity for the binary input additive white Gaussian noise (BI-AWGN) channel under the joint decoding framework in which soft information is exchanged between the pre-code and the LT code iteratively. Utilizing the belief propagation (BP) decoder, the decoding complexity is measured by the average number of arithmetic operations needed to correctly recover each information bit. Based on the analytical asymptotic convergence analysis which is built upon extrinsic information transfer (EXIT) charts, we develop a numerical approximation for the number of iterations needed for measuring the decoding complexity, and then formulate an optimization problem for the design of efficient output degree distributions. We further discuss the fundamental problem of complexity-rate tradeoff in Raptor code design. Simulations show that the optimized distribution indeed achieves lower complexity without much performance loss compared to other existing rate-optimized Raptor Codes.

In this paper, we propose a new physical layer network coding (PNC) scheme based on rateless coding for two-way relay systems which consist of two end nodes and one relay. The two-way relay channel is studied from a new perspective of end-to-end channel equivalence, and a novel end-to-end demodulator is proposed based on the resultant end-to-end equivalent channel. Unlike the conventional demodulator for the point-to-point channel, our proposed demodulator takes into account the potential error brought by the PNC mapping at the relay, thus improves the system reliability and throughput. The theoretical end-to-end achievable rate is further derived, which serves as an upper bound for the proposed demodulation scheme. Simulation results validate the high efficiency of the proposed scheme in terms of BER and system throughput.

The finite-SNR Diversity-Multiplexing Tradeoff (DMT) problem is studied in the context of spectrum aggregation, where there exist multiple noncontiguous (discrete) spectrum segments each of which has its own power limit. Firstly, we give an optimal power allocation strategy to achieve the ergodic capacity under Rayleigh fading. Secondly, two different coding schemes, i.e., coding without across sub-channels and coding across, are presented to achieve the lower and upper bound of diversity gain of this system under the same multiplexing gain. Then the finite-SNR DMT under i.i.d Rayleigh fading is derived, and its accurate expression for the coding-without-across scheme and the upper bound for the coding-across scheme are achieved respectively. The asymptotic performances of both schemes are also obtained. Under the equal sub-channel bandwidth setting, for infinite SNR, the coding-across scheme achieves M (the number of sub-channels) times diversity gain against the without-coding-across scheme. The loss of diversity gain caused by non-identical bandwidth and power limit is also analyzed.

Two‐path successive relaying (TPSR) is an effective way to reduce the multiplex loss induced by the half‐duplex operation of the relay node in a conventional relay network. One crucial issue in TPSR network is that the listening relay always suffers inevitable inter‐relay interference (IRI), which degrades detection performance at the destination. In this study, a concatenated channel‐and‐network coding approach is proposed to solve the problem. In particular, a highly flexible channel code, namely, rateless code, is employed at the source to provide resilience to the residual IRI and reduce the retransmissions, which might break the system steady state of successive relaying. Then recognising the special interference structure, physical‐layer network coding is incorporated into the forwarding scheme of the relay nodes to exploit network diversity and improve system efficiency. By extrinsic information transfer analysis, the minimum number of required code symbols for successful data recovery are calculated, and degree distribution of the rateless code is optimised.

In this study, the authors propose a new class of rateless codes applicable to noisy channels, that is, the so‐called accumulate rateless (AR) codes, which concatenate the low density generator matrix (LDGM) rateless code with a simple post‐code, that is, a post‐position accumulator. The new coding structure is not only effective in reducing the ‘error floor’ as observed in traditional LDGM rateless codes such as Luby transform (LT) codes, but also quite simple for realisation as compared with the rateless codes using a pre‐coding structure such as Raptor codes. The extrinsic information transfer charts and the corresponding projection of intersectant curves for both the systematic and non‐systematic AR codes are analysed. Based on these, their convergence performance and optimal degree distributions are investigated. Simulation results show that, the performance of AR codes over additive white Gaussian noise channel is comparable to that of Raptor codes.

Opportunistically sharing the white spaces, or the temporarily unoccupied spectrum licensed to the primary user (PU), is a practical way to improve the spectrum utilization. In this paper, we consider the fundamental problem of rate regions achievable for multiple secondary users (SUs) which send their information to a common receiver over such a white space channel. In particular, the PU activities are treated as on/off side information, which can be obtained causally or non-causally by the SUs. The system is then modeled as a multi-switch channel and its achievable rate regions are characterized in some scenarios. Explicit forms of outer and inner bounds of the rate regions are derived by assuming additional side information, and they are shown to be tight in some special cases. An optimal rate and power allocation scheme that maximizes the sum rate is also proposed. The numerical results reveal the impacts of side information, channel correlation and PU activity on the achievable rates, and also verify the effectiveness of our rate and power allocation scheme. Our work may shed some light on the fundamental limit and design tradeoffs in practical cognitive radio systems.

This paper presents an analytical investigation on the outage performance of dual-hop multiple antenna amplify-and-forward relaying systems in the presence of interference. For both the fixed-gain and variable-gain relaying schemes, exact analytical expressions for the outage probability of the systems are derived. Moreover, simple outage probability approximations at the high signal-to-noise-ratio regime are provided, and the diversity order achieved by the systems are characterized. Our results suggest that variable-gain relaying systems always outperform the corresponding fixed-gain relaying systems. In addition, the fixed-gain relaying schemes only achieve diversity order of one, while the achievable diversity order of the variable-gain relaying scheme depends on the location of the multiple antennas.

In this paper, we analyze the effect of mobility on information spreading in geometric networks through natural random walks. Specifically, our focus is on epidemic propagation via mobile gossip, a variation from its static counterpart. Our contributions are twofold. Firstly, we propose a new performance metric, mobile conductance, which allows us to separate the details of mobility models from the study of mobile spreading time. Secondly, we utilize geometrical properties to explore this metric for several popular mobility models, and offer insights on the corresponding results. Large scale network simulation is conducted to verify our analysis.

In this paper, we propose a three-stage rateless coded protocol for a half-duplex time-division two-way relay system, where two terminals send messages to each other through a relay between them. In the protocol, each terminal takes one of the first two stages respectively to encode its message using rateless code and broadcast the result until the relay acknowledges successful decoding. During the third stage, the relay combines and re-encodes both messages with a joint network-channel coding scheme based on rateless coding which provides incremental redundancy. Together with the packets received directly in previous stages, each terminal then retrieves the desired message using an iterative decoder. The degree profiles of the specific rateless codes, i.e., Raptor codes, implemented at both terminals and the relay, are jointly optimized for both the AWGN channel and the Rayleigh block fading channel through solving a set of linear programming problems. Simulation results show that, the system throughput as well as the error rate achieved by the optimized degree profiles always outperforms those achieved by the conventional degree profile optimized for Binary Erasure Channel (BEC) and the previous network coding scheme with rateless codes.

In this paper, we consider an asymmetric time-division multiple access relay system consisting of two sources, one relay and one destination, where the channel conditions and message lengths of the two sources are allowed to be different. To enhance the link robustness and the system throughput, joint network-channel coding (JNCC) is employed with a specially designed rateless code which conducts both the channel coding and network coding simultaneously. In particular, at the sources, messages are rateless coded and then broadcasted to the relay and destination. While at the relay, a novel two-dimensional (2-D) LT code is proposed, which jointly encodes the precoded message bits of the two sources using a 2-D degree profile and at the meantime completes the network coding inherently. Interestingly, the proposed scheme can be degraded to several conventional JNCC schemes based on rateless coding. To further approach the theoretical limit, the corresponding degree profiles implemented at both sources and the relay are jointly designed based on the extrinsic information transfer (EXIT) function analysis. Simulations show that our proposed JNCC scheme with the optimized degree profiles outperforms other JNCC schemes with the conventional profiles both on the BER and throughput performances.

The practical hardware limitations bring technical challenges to cognitive radio, e.g. limited capability of spectrum sensing and certain frequency range of spectrum access. In this paper, we propose a rollout-based joint spectrum sensing and access policy incorporating the hardware limitations of both sensing capability and spectrum aggregation, in which the optimal policy is shown to be PSPACE-hard. Two heuristic policies are proposed to serve as base policies, based on which the developed rollout-based policy approximates the value function and determines the appropriate spectrum sensing and access actions. We establish mathematically that the rollout-based policy achieves better performance than the base policy. We also demonstrate that the low-complexity rollout-based policy leads to only slight performance loss compared with the optimal policy.

In this paper, we design a joint channel-network coding scheme based on rateless code for the three-stage two-way relay system, where two terminals send messages to each other through a relay between them. Each terminal takes one of the first two stages to encode its message using a Raptor Code and then broadcasts the result into the air, respectively. In the third stage, upon successfully decoding the corresponding messages, the relay node re-encodes them with the new Raptor Codes, and then XORs the outputs and broadcasts the result to both terminals. Together with the packets received directly in previous stages, each terminal then retrieves the desired message using an iterative decoder. Here, the degree profiles of the Raptor Codes used at each node are jointly optimized through solving a set of linear programming problems. Simulations show that, the system throughput achieved by the optimized degree profiles always outperforms the one with conventional degree profile optimized for binary erasure channel (BEC) and the conventional network coding scheme with rateless coding.

This paper proposes a new user-differentiation-based resource allocation (UDRA) scheme for OFDMA downlink systems. The UDRA scheme can strike a good balance between quality-of-service (QoS) requirement guarantee and system throughput enhancement, whereas conventional schemes cannot explicitly and accurately control this tradeoff. The UDRA scheme first performs a utility-based resource allocation algorithm for urgent users to fulfill QoS requirement, then it performs a maximum-sum-rate resource allocation algorithm for regular users to enhance system throughput. To achieve the balance between QoS guarantee and throughput enhancement, the UDRA scheme further designs a fuzzy logic user identifier (FLUI) to intelligently differentiate users from urgent and regular. Simulation results show that the proposed UDRA scheme enhances the system throughput by 23.1%, 14.1%, and 9.7%, compared to the PSRA [1], ARRA [2], and U-TMCR [3] schemes, respectively, under a QoS requirement guarantee.

In this paper, we propose a joint spectrum sensing and spectrum access tradeoff framework for a multi-user multiple-input single-output (MISO) cognitive radio network with limited feedback in order to improve spectrum efficiency. Firstly, we reduce the feedback information of requests for data retransmission by employing the recently developed incremental redundancy channel code, namely rateless code, for the data transmission of secondary users. Then by jointly considering the effects of multi-user cooperative sensing and multi-user MISO beamforming to the system performances, we propose a feedback bit allocation method for both the spectrum sensing results and the quantized channel state information under a sum feedback amount constraint, so as to maximize the throughput of cognitive radio network while protecting primary user from interference. Finally, simulation results are shown to validate the effectiveness of our proposed method.

Relay assisted transmission could efficiently enhance the performance of Device-to-Device (D2D) communication when D2D user equipments (UEs) are too far away from each other or the quality of D2D channel is not good enough for direct communication. The relay selection problem for D2D communication underlaying cellular network is studied in this paper. We proposed a distributed relay selection method for relay assisted D2D communication system. The method firstly coordinates the interference caused by the coexistence of D2D system and cellular network and eliminates improper relays correspondingly. Next, the best relay is chosen among the optional relays using a simple distributed method. Numerical results show that performance of the proposed method is close to the optimal (centralized) method.

Geometry-based optimal power control was proposed in [14] to transform the power-control problem to a new geometrical problem on the position relationship between a line and some points. This scheme provides a novel visual perspective and lowers the complexity of optimization. We generalize this scheme to a larger class of power-control optimization problems so as to maximize the network utility with multiple average and peak power constraints in wireless networks. To facilitate the handling of the geometrical model, we define a subset of geometrical models with specified characteristics, called a regular geometrical model, and derive the type of power-control problems eligible for the regular geometrical model. For such a type of problems, two strategies are proposed for the construction of the regular geometrical model. Utilizing geometrical properties, we propose a novel geometry-based optimization scheme for the general power-control problem. Its computational complexity is significantly lower than the conventional algorithms. We also provide a further discussion on irregular geometrical model cases. Finally, we provide two examples of deploying the proposed geometry-based power-control scheme.

Rateless coding is a new type of feed-forward incremental redundancy channel coding technique which can be incorporated with MIMO technology to exploit both the diversity and coding gain with possibly reduced channel feedback. In this paper, the benefits of limited feedback beamforming and rateless coding are investigated jointly in a multiple-input single-output (MISO) downlink system. Based on weight enumerator analysis and with the goal of improving the effective channel gain, we propose an adaptive bit allocation scheme by taking advantage of the inherent relationship between the feedback codebook size and the number of transmitted coded bits. Given the feedback codebook size, we derive the required minimum number of coded bits for a reliable data recovery. In addition, for the service with time delay constraint, the required feedback codebook size is also determined. Finally, numerical results are presented to validate our theoretical analysis.

Transmit power and feedback bandwidth are two limited and interrelated resources which are crucial to system performance in wireless channel with feedback, so it is necessary to maximize their utilization efficiencies in the joint sense. In this paper, we investigate the inherent relationship between transmit power and codebook size in multiuser limited feedback MISO system by making use of Grassmann line packing theory, as an effort to provide an insight on how to jointly distribute the two resources to fulfill the diverse requirements. Then, the impact of feedback delay on the tradeoff relation is characterized in detail, and we find that even with relatively small delay, there is considerable performance loss with respect to the ideal case. Thereby, much more transmit power or feedback bandwidth should be consumed to achieve the same performance target. Finally, the theoretical claims are validated by numerical results.

In MIMO broadcast channels, the Block Diagonalization (BD) transmit precoding performs better than the Zero-Forcing (ZF) transmit precoding when the user equipment has multiple receive antennas and can support multiple data streams. The number of users simultaneously supported by BD is much less than the total number of users in the system, so selecting proper users and/or receive antennas is important to achieve high overall sum capacity. So far, the capacity-based and norm-based user and receive antenna selection algorithms are proposed, which are greedy based and have much lower complexity than the brute-force based algorithms. In this paper, the orthogonality-based user and receive antenna selection algorithms are employed. The idea of the algorithm is simple, however, we show by detailed complexity analysis and extensive simulations that the orthogonality-based user and receive antenna selection achieves much better complexity and performance tradeoff than the existing capacity-based and norm-based algorithms. So, the orthogonality-based algorithms are more attractive for practical MIMO broadcast systems.

In cognitive radio networks, multiple discontinuous spectrum opportunities are detected by spectrum sensing and utilized together to satisfy the service requirement by spectrum aggregation. In this paper, we develop an analytical framework for joint spectrum sensing and access scheme based on Partially Observable Markov Decision Process (POMDP). Considering the hardware limitations, the spectrum aggregation range is restricted and only a part of channels can be sensed. For obtaining the reward function of POMDP, the channel switch probability is estimated by theoretic deduction. Under this POMDP framework, an optimal spectrum sensing and access scheme is proposed to minimize the channel switch times. Simulation results show that our proposed optimal scheme reduces the times of channel switches significantly.

The problem of diversity-multiplexing-rate trade-off (DMRT) in limited feedback beamforming systems is addressed here. A major difference from previous works is that the authors consider the trade-off in finite-signal-to-noise ratio (SNR) regime. Under this condition, the feedback rate has a great impact on the conventional diversity-multiplexing relationship. Hence, the release of the trade-off among diversity, multiplexing and feedback rate has a practical benefit to the design and adoption of space–time signals in limited feedback beamforming systems. Asymptotical analysis shows that the obtained trade-off is consistent with the conventional one when feedback rate equals to zero and the SNR approaches infinity. Furthermore, considering the channel dynamics, the authors investigate the effect of imperfect channel state information (CSI) caused by channel estimation error and feedback delay on the DMRT. It is found that in the presence of imperfect CSI, besides the system parameters, such as the number of transmit and receive antennas, maximum diversity order and multiplexing gain are also limited by SNR and correlation coefficient between the obtained CSI and the real CSI. Finally, our theoretical claims are validated by the numerical results.

Wireless body area networks (BANs) have drawn much attention from research community and industry in recent years. Multimedia healthcare services provided by BANs can be available to anyone, anywhere, and anytime seamlessly. A critical issue in BANs is how to preserve the integrity and privacy of a person's medical data over wireless environments in a resource efficient manner. This paper presents a novel key agreement scheme that allows neighboring nodes in BANs to share a common key generated by electrocardiogram (ECG) signals. The improved Jules Sudan (IJS) algorithm is proposed to set up the key agreement for the message authentication. The proposed ECG-IJS key agreement can secure data communications over BANs in a plug-n-play manner without any key distribution overheads. Both the simulation and experimental results are presented, which demonstrate that the proposed ECG-IJS scheme can achieve better security performance in terms of serval performance metrics such as false acceptance rate (FAR) and false rejection rate (FRR) than other existing approaches. In addition, the power consumption analysis also shows that the proposed ECG-IJS scheme can achieve energy efficiency for BANs.

In this paper, the benefit of rateless codes combining with multi-antenna technique is exploited to provide quality-of-service (QoS) guarantee while maximizing the spectral efficiency in a limited feedback multiuser MIMO downlink. Critical to the design of such a system is the achievement of channel state information (CSI) at the base station (BS) to schedule the optimal users and pre-cancel the interuser interference. In order to reduce feedback amount and decrease scheduling complexity simultaneously, we propose to adopt multi-beam opportunistic beamforming (MOBF) and opportunistic space division multiple access (OSDMA) for the cases of noise and interference limited, respectively. Through theoretical analysis, it is found that, in order to satisfy QoS requirement, the admissible number of users has an upper bound, which is a function of data arrival rate and QoS requirement. With the purpose of balancing the achievable rate and feedback amount, we introduce a new concept, namely netput, as the difference of the above focused factors. By maximizing the netput, we derive the optimal feedback thresholds for the two transmission modes, respectively. Furthermore, according to the characteristics of the considered system, we obtain the optimal switch thresholds for the two modes in terms of the number of users and the SNR, respectively. Finally, our theoretical claims are validated by the numerical results.

This paper considers the power allocation problem in a relay-assisted Time-Division-Duplex-based multiple-cell system. In such a system, the achievable data rate of each user is not only coupled with those of others due to the inherent dynamic spatial frequency reuse therein, but also coupled between consecutive time slots due to the required two-hop transmission, which results in the so-called "spatial-coupling" and "time-coupling" nature, respectively. To address both of the two coupling effects in a relay assisted TDD cellular system, we formulate an optimal power allocation problem for the asynchronous scenario of the system where different cells work independently. We observe that the problem is non-convex and NP-hard, but can be converted to Geometric Programming (GP) problems and solved by interior-point methods. In case there is no central controller and/or massive information exchange among cells is prohibitive, game theory is employed to derive the distributed solutions. Finally, we validate the proposed algorithms by extensive simulations.

We investigate the ergodic sum rate of multiple-input multiple-output Nakagami-m fading channels with linear receivers. In particular, both mean square error and zero-forcing receivers are considered. For dual transmit antenna configurations, we present new, closed-form upper bounds on the ergodic sum rate of both receivers. Moreover, we derive exact expressions for the two key parameters dictating the sum rate behavior in the low signal to noise ratio regime, namely the minimum energy per information bit to reliably convey any positive rate and the wideband slope. By doing so, we are able to explicitly demonstrate the sub-optimality of linear receivers compared to optimal receivers, and draw useful insights into the impact of model parameters (e.g., number of antennas, fading parameters).

Communication overhead should be minimized when designing iterative scheduling algorithms for input-queued packet switches. In general, the overall communication overhead is a function of the number of iterations required per time slot (M) and the data bits exchanged in an input-output pair per iteration (B). In this paper, we aim at maximizing switch throughput while minimizing communication overhead. We first propose a single-iteration scheduling algorithm called Highest Rank First (HRF). In HRF, the highest priority is given to the preferred input-output pair calculated in each local port at a RR (Round Robin) order. Only when the preferred VOQ(i,j) is empty, input i sends a request with a rank number r to each output. The request from a longer VOQ carries a smaller r. Higher scheduling priority is given to the request with a smaller r. To further cut down its communication overhead to 1 bit per request, we design HRF with Request Compression (HRF/RC). The basic idea is that we transmit a single bit code in request phase. Then r can be decoded at output ports from the current and historical codes received. The overall communication overhead for HRF/RC becomes 2 bits only, i.e. 1 bit in request phase and 1 bit in grant phase. We show that HRF/RC renders a much lower hardware cost than multi-iteration algorithms and a single-iteration algorithm π-RGA [11]. Compared with other iterative algorithms with the same communication overhead (i.e. SRR [10] and 1-iteration iSLIP [6]), simulation results show that HRF/RC always produces the best delay-throughput performance.

A novel Distributed Spectrum-Aware Clustering (DSAC) scheme is proposed in the context of Cognitive Radio Sensor Networks (CRSN). DSAC aims at forming energy efficient clusters in a self-organized fashion while restricting interference to Primary User (PU) systems. The spectrum-aware clustered structure is presented where the communications consist of intra- cluster aggregation and inter-cluster relaying. In order to save communication power, the optimal number of clusters is derived and the idea of groupwise constrained clustering is introduced to minimize intra-cluster distance under spectrum-aware constraint. In terms of practical implementation, DSAC demonstrates preferable scalability and stability because of its low complexity and quick convergence under dynamic PU activity. Finally, simulation results are given to validate the proposed scheme.

A novel carrier sensing method with self-cancelation of the inter-carrier emission inevitably existent in OFDMA-based coexisting systems is proposed. Different from the conventional energy-based carrier detector, which directly compares the carrier energy with some predefined thresholds without considering such inter-carrier emissions, our proposed method can remove them before making decision, thus significantly reduces the false-alarm probability and provides more access opportunities. Firstly, the detection model considering the inter-carrier emission is presented, and then the distribution of inter-carrier energy emission is investigated. Secondly, the energy emission of the detected adjacent carriers got by conservative pre-decision are gradually removed from the carrier to be detected. Finally, the performance of the proposed method is evaluated in terms of detection probability and false-alarm probability.

A novel concept of Joint Source and Channel Sensing (JSCS) is introduced in the context of Cognitive Radio Sensor Networks(CRSN). Every sensor node has two basic tasks: application-oriented source sensing and ambient-oriented channel sensing. The former is to collect the application-specific source information and deliver it to the access point within some limit of distortion, while the latter is to find the vacant channels and provide spectrum access opportunities for the sensed source information. With in-depth exploration, we find that these two tasks are actually interrelated when taking into account the energy constraints. The main focus of this paper is to minimize the total power consumed by these two tasks while bounding the distortion of the application-specific source information. Firstly, we present a specific slotted sensing and transmission scheme, and establish the multi-task power consumption model. Secondly, we jointly analyze the interplay between these two sensing tasks, and then propose a proper sensing and power allocation scheme to minimize the total power consumption. Finally, simulation results are given to validate the proposed scheme.

In this paper, we propose a new user scheduling scheme which can reduce the system feedback significantly for the MIMO downlink system in multiuser cellular networks. The proposed scheme first performs user scheduling according to the valuebook-based scalar feedback of all users' channel gains and then applies block diagonalization (BD) precoding according to the codebook-based matrix feedback of the selected users' channel matrices. A novel method to generate the valuebook is designed. The proposed user scheduling scheme can reduce both the multiuser interference and the feedback bits with low computational complexity. It can be observed from the simulation results that, in the interference-limited SNR regime, the proposed scheme can achieve a sum rate very close to the case when the coordinated base stations know the channel state information perfectly.

This paper addresses the cross-layer design of multi-user opportunistic spectrum access (OSA) in cognitive radio networks under the framework of grouped cooperative sensing. We attempt to reveal the mutually conflict nature in the following two processes: a) multi-user collaboration during cooperative sensing in PHY layer; b) the contention emerged during competitive medium access in MAC layer. Specifically, higher reliability of cooperative sensing requires more engaged cognitive users (CUs), which, in turn, may cause more intense competition in access, thereby reducing the overall channel utilization efficiency. By exploiting the inherent interplay between these two processes, we tradeoff their performance with respect to the number of cooperating users in one group. The optimal number that maximizes the channel revenue is determined by a binary search method we propose. Finally, the analysis and performance are validated by extensive simulations.

Spectrum sensing is adopted to detect the presence of primary users in cognitive radio networks. Considering the sensing overhead, it is not always a good choice that the secondary user senses the channel all the time. In this paper, we propose an opportunistic spectrum sensing decision method according to the primary user's traffic. First, the traffic of primary user is observed and estimated. For estimating the parameters of the primary traffic, the Maximum Likelihood estimation is adopted and the confidence interval is calculated based on finite observed samples. Then, according to the estimated primary traffic information, a decision criterion is proposed for determining whether to sense the channel. The simulation results show that the performance of the proposed opportunistic sensing scheme is much better than that when sensing at every timeslots and very close to that in the ideal case in which the primary traffic information is obtained perfectly.

In this paper, we consider the adaptive power allocation in the MIMO cognitive network with limited feedback. It is well known that, with the view of guaranteeing the link quality of primary receiver (PRx), the transmit power of second transmitter (STx) is strictly constrained, resulting in that it is difficult to provides QoS guarantee to second receiver (SRx) in the fading channel. This paper focuses on searching a feasible scheme to satisfy the QoS of PRx and SRx currently. First, we prove that the capacity of SRx can be obviously improved without adding interference to PRx by exploiting the spatial degrees of freedom of MIMO. Then, in order to making use of the benefit of MIMO, joint maximum ratio transmission (MRT) and maximum ratio combination (MRC) with finite rate channel information feedback, a way to adapt the signal to the instantaneous channel condition, is adopted. The relationship of QoS provisioning and feedback amount is researched so as to meet the performance requirements with the least feedback bits. Next, based on the above MIMO setting, an adaptive power allocation strategy is proposed to maximize the average capacity of SRx while satisfy the dual QoS requirements. Finally, numerical results reconfirms the effectiveness of the proposed adaptive strategy.

In this paper, the problem of initial link establishment in multi-channel Cognitive Radio Networks (CRN) is investigated. Different from the traditional scenario, in which the transceiver pair can establish an initial link through exchanging the control messages on the Common Control Channel (CCC), a more realistic scenario in the absence of CCC is considered in our work. Without the help of CCC, an initial link establishment approach based on the technique of group hopping is proposed and analyzed. In the asymptotic scenario N → ∞ (N: the number of available channels), we firstly derive the analytical expression of the condition, under which the initial link can be successfully established with probability arbitrarily close to 1. Secondly, we present the upper bound of average trial times and derive another condition, under which the initial link can be established within the required trial times in an average sense. Finally, we validate the analysis through extensive simulations.

Rateless code usually generates a potentially infinite number of coded packets at the encoder and collects enough packets at the decoder to ensure reliable recovery of multiple information packets. The conventional rateless decoder usually works in a parallel manner which needs to initiate a new belief propagation (BP) decoding procedure upon each newly received collection of coded packets, thereby resulting in prohibitive decoding complexity in practice. In this paper, we present a novel serial decoding algorithm, i.e., the serial storage belief propagation (SS BP) algorithm, for rateless codes over noisy channels. Specifically, upon receiving a new group of coded packets, the decoder initiates a new attempt to decode all the packets received so far, using the results of the previous attempt as initial input. Moreover, in each iteration of the new attempt, the decoder serially propagates the messages group by group from the most recent one to the earliest one. In this way, the newly updated messages can be propagated faster, expediting the recovery of information packets. In addition, the proposed serial decoding algorithm has significantly lower complexity than the existing parallel decoding algorithms. Simulation results validate its effectiveness in AWGN, Rayleigh, and Rician fading channels.

The average weight distribution of a regular low-density parity-check (LDPC) code ensemble over a finite field is thoroughly analyzed. In particular, a precise asymptotic approximation of the average weight distribution is derived for the small-weight case, and a series of fundamental qualitative properties of the asymptotic growth rate of the average weight distribution are proved. Based on this analysis, a general result, including all previous results as special cases, is established for the minimum distance of individual codes in a regular LDPC code ensemble.

In this paper, we investigate a channel selection algorithm for distributed spectrum access in a multichannel multiuser cognitive radio (CR) network employing rateless codes. Each secondary user (SU) uses rateless codes to increase the tolerance of interference either from other SUs or from the primary user (PU). However, due to the unpredictable arrival of PU and the inaccuracy of spectrum sensing, the more the number of channels each SU selects, the more interference to PU will be caused. With the purpose of protecting PU from interference, we derive the optimal number of channels selected by SU to maximize the throughput of SU. Both theoretical analysis and simulation results demonstrate the efficiency of the proposed algorithm.

To improve the performance of a relay system with multiple channels, the following issues should be addressed. Namely, how to allocate the power at source and relay to subchannels, how to pair subchannels of the first and second hops, and which users should be scheduled to which subchannel pair. Considering these issues in the design of an optimal joint resource allocation scheme in orthogonal channels, in this paper we study a multi-user network with single regenerative relay node. A stochastic optimization problem to maximize system ergodic throughput with joint transmission power constraint and user average data rate is formulated. To satisfy user average data rate request, a scheme with a weighted factor associated to each user at each time slot is proposed. The stochastic approximation method is utilized to estimate this weighted factor and the proof of optimality is given. With the help of this weighted factor, the problem is converted into a deterministic optimization problem in each time slot and the Lagrange dual method can be employed to derive the optimal solution. Finally, the Stochastic Optimal Programming (SOP) is used to evaluate the performance by computer simulations.

In this paper, a framework of opportunistic beamforming for multiuser MIMO downlink employing rateless codes is considered. By exploiting the benefits of a combination of opportunistic beamforming and rateless codes, the system performance is obviously improved while guaranteeing QoS requirement, such as maximum average delay. It is proved that, in order to satisfying delay constraint, the maximum number of admissible users is bounded. With the purpose of achieving a balance between throughput and feedback, a novel concept of netput is introduced accordingly. Through maximizing the netput, the optimal feedback threshold in terms of the number of user, the maximum delay constraint, transmit power and the arrival rate of user data at the base station (BS) is derived. Moreover, the impact of feedback delay on the netput is investigated in detail and the corresponding feedback threshold is given. Finally, our theoretical claims are validated by the numerical results.

In the considered multiple access network, the primary users transmit in the allocated mutual orthogonal channels. Meanwhile, the secondary user conducts spectrum sensing and seeks transmission opportunities in the temporarily unoccupied primary channels. In order to improve the stable throughput of secondary user, we propose a cognitive relay strategy, which suggests the secondary user to intelligently relay packets for some selected primary users in addition to sending its own packets. The criterions of selecting primary users are given in system with and without sensing errors, respectively. Numerical results show the benefits of cognitive relay strategy in terms of secondary throughput.

Multiple access technology is a key issue to efficiently share the available scarce sources among a large number of users. We present a novel multiple access scheme denominated as rateless multiple access (RMA) scheme, in which users do not coordinate their transmissions but implement a random multiple access scheme as simply as time-slotted ALOHA system does. Each user utilizes Rateless codes with its own seed to protect its message, conquer the problem of collisions to packets and distinguish from each other. In this paper, we investigate the scenario where the transmission is over the noisy channel and present the schemes of both the transmitters and the receiver. Simulation results finally demonstrate the efficiency of the proposed RMA scheme.

In cognitive radio networks, multiple spectrum opportunities can be used together to satisfy the service requirement by spectrum aggregation. In this paper, an admission control algorithm and a spectrum assignment strategy are proposed in order for both increasing the spectrum aggregation aware access capacity and decreasing the channel switch times when the channel states change. Considering different bandwidth requirement of secondary users, the proposed greedy admission algorithm takes limited aggregation capability into account. The channel switch times of secondary users at sensing moments is minimized based on the prediction of primary activities and the corresponding channel state transitions. The concept of outage probability is introduced into the scheme to indicate the probability of channel switch. The numerical results show the performance improvement of the proposed algorithms.

In this paper, an optimal resource allocation scheme is proposed for multi-user multi-channel cognitive radio networks under imperfect spectrum sensing. The channel dynamic model and the sensing errors are considered together to derive the metric of mean delay for each user-channel combination based on the vacation queueing model. Finally, the optimal resource allocation is determined according to the average system delay by bipartite graph matching. The simulation results indicate that the proposed mean delay metric can represent the transmission performance successfully.

We present a novel multiple access scheme denominated as Rateless Multiple Access (RMA) scheme. As a kind of decentralized technology, RMA protocol implements a random multiple access scheme as simply as traditional ALOHA protocol does and can achieve nearly optimal performance of throughput. We investigate an ideal scenario where the length of delivered messages of each user is the same and analyze the maximum throughput of the system. Also, we present the scheduling scheme by design the optimal access probability of each user. The design is only according to the maximum number of active users in the system, so that each transmitter does not need to "listen before talk". Simulation results finally demonstrate the efficiency of the proposed scheme.

In this paper we study the problem of relay-enhanced cell (REC) coverage for which relay location is optimized to maximize the achievable REC radius. The problem is investigated for both Rayleigh and Rician relay fading channels, under a pre-determined user's outage probability constraint. We propose a statistical approach to formulate the problem and develop an optimization algorithm for it. The analytical derivation is justified by numerical simulations.

In this paper, we address the problem of subchannel, rate and power allocation in OFDM-based two-tier femtocell networks, which comprise a conventional macrocell and multiple femtocells. Our objective is maximizing the multiple femtocell users' weighted rate sum by jointly adjusting their subchannel, rate and power allocation, under the constraints of cross-tier interference (CTI) between macrocell and multiple femtocells. Then we propose an optimal distributed resource allocation algorithm based on Lagrangian dual method, and interpret it from economics angle. In order to illustrate the benefit of allowing femtocells to share the subchannels occupied by macrocell, we compare our system with another one (macrocell guard system), in which the femtocells can only use the subchannels unoccupied by macrocell. Simulation results validate the proposed algorithm, and show that our system can obtain better performance than the macrocell guard system.

In this paper, based on the effective capacity theory, we identify the maximal arrival rate of secondary user that an arbitrary ON/OFF primary channel can sustain, in the presence of sensing errors. We find that, the arrival rate of secondary user with statistical QoS requirement, is limited by both the effective capacity provided by primary channel, and the packet collision probability constraint of primary user. In general, the above two constraints result in unequal arrival rates, exhibiting different impacts on spectrum utilization. Based on this observation, two spectrum utilization approaches, i.e., η-probability random access and sensing parameter adjustment, respectively, are proposed to fully utilize the transmission opportunities, depending on whether the sensing parameters could be adjusted or not. In specific, the η-probability random access approach reserves more transmission opportunities for other secondary users, which increases network throughput, while sensing parameter adjustment approach yields improved arrival rate for specific secondary user. Performances of the proposed approaches are validated by numerical results.

In this paper, we will study the joint problem of power allocation, relay selection and subchannel pairing in OFDM based amplify-and-forward multiple relays system. The optimization problem of maximizing system capacity under joint power constraint at source and relays is firstly formulated. Then, based on Lagrangian dual method, an optimal algorithm to the problem is derived with high SNR assumption. Both computational complexity and dual gap are analyzed. Through simulations, we show that the performance of the proposed algorithm is perfectly matched with that of exhaustive search method and the computational complexity of the proposed algorithm is acceptable for practical implementation.

In this paper, the problem of the multi-user uplink scheduling in cognitive radio cellular network (CogCell) is investigated. The objective is to maximize the system throughput, while protecting the QoS of primary user (PU) from being affected by secondary user (SU). Here, PU's QoS is represented by its signal-to-interference-plus-noise (SINR) outage probability. It is equivalent to say that SU can increase its transmit power to enhance the system performance as long as PU's SINR outage probability does not exceed the predefined threshold. So the first scheduling algorithm is proposed to maximize the system throughput through utilizing the multi-user diversity. Different from the first algorithm which does not take the fairness among SUs into account, the second scheduling algorithm with considering proportional fairness among SUs is proposed. It is shown to provide a satisfactory tradeoff between maximizing the system throughput and achieving fairness among SUs. Finally, these proposed algorithms are validated through extensive simulations.

In cognitive radio systems, the design of spectrum sensing has to face the challenges of radio sensitivity and wide-band frequency agility. It is difficult for a single cognitive user to achieve timely and accurate wide-band spectrum sensing because of hardware limitations. However, cooperation among cognitive users may provide a way to do so. In this paper, we consider such a cooperative wide-band spectrum sensing problem with each of the cognitive users able to imperfectly sense only a small portion of spectrum at a time. The goal is to maximize the average throughput of the cognitive network, given the primary network’s collision probability thresholds in each spectrum sub-band. The solution answers the essential questions: to what extent should each cognitive user cooperate with others and which part of the spectrum should the user choose to sense? An exhaustive search is used to find the optimal solution and a heuristic cooperative sensing algorithm is proposed to simplify the computational complexity. Inspired by this optimization problem, two practical cooperative sensing strategies are then presented for the centralized and distributed cognitive network respectively. Simulation results are given to demonstrate the promising performance of our proposed algorithm and strategies.

In the presence of multiple non-regenerative relays, we derived optimal joint power allocation, relay selection, and subchannel pairing schemes in orthogonal frequency division multiplexing (OFDM) based wireless networks. The Lagrange dual method was employed to design the optimal algorithm. First, the optimization problem was formulated for the single-relay system and the optimal centralized algorithm was presented by resolving the dual problem. Next, the optimal algorithm for a multi-relay system was proposed in a similar way. Compared with the exhaustive search method, the computational complexity of the proposed optimal algorithms was reduced from non-polynomial to polynomial time. Finally, the centralized algorithm was extended to the distributed algorithm, which was more feasible for the practical system. Simulation results verify our analysis.

Relay selection and subchannel pairing are important issues in OFDM based cooperative wireless systems to improve the system performance and reliability. Most of the relay selection schemes in modern literatures are centralized under the assumption that the overall channel condition is known at the source node, each relay node and the destination node which is impractical. In this paper, we will propose a low computational complexity distributed relay selection and subchannel pairing algorithms under limited channel state information. Through the computer simulation, we found that under the limited channel state information constraint, the proposed algorithm can achieve a much better system performance than the traditional relay selection and subchannel matching schemes and earn most of the system performance of the optimal one.

In this paper, a distributed spectrum access algorithm for Cognitive Wireless Network (CogWN) is proposed. The objective is to maximize the number of admitted Secondary Users (SUs) under the constraint of Interference Temperature (IT) at Measurement Point (MP), while providing AQP (Active users' Quality of service Protection) at the same time. Here AQP means that the Signal to Interference plus Noise Ratios (SINRs) of all active SUs will not fluctuate below their predetermined thresholds during the process of new SUs' spectrum access. In addition, an alarm mechanism is introduced into CogWN, which ensures the IT at MP is always below the predefined threshold, so that the communication of primary system is protected from being affected by SUs. Finally, the proposed algorithm is evaluated through extensive simulations, and the results show that it has a better performance than the traditional algorithm.

Recently, distributed antenna system (DAS) has received considerable attentions due to its promising potential in various metrics compared with traditional co-located MIMO (C-MIMO) systems. Aiming to further improve the performance of the DAS, linear precoding along with distributed space time codes is proposed to exploit the diversity and coding gains simultaneously. The focus of this paper is on analyzing and designing a simple but powerful codebook suitable for the DAS to quantize the precoding information. Furthermore, a suboptimal power allocation strategy that requires no additional information at the base station (BS), is derived by minimizing the upper bound on the average pairwise error probability (PEP) of the preceded space time codewords. Numerical results show that obvious gains can be obtained over conventional transmission schemes, such as antenna selection.

In cognitive radio systems, as the sensing ability of single cognitive user is limited, the opportunistic spectrum access has to allow different cognitive users to cooperatively search for and exploit instantaneous spectrum availability. In this paper, we address the design of opportunistic spectrum access for cognitive radio system employing cooperative spectrum sensing. The opportunistic spectrum access strategy contains two parts: an access policy to maximize the transmission throughput in each channel, and a cooperative spectrum sensing strategy to maximize the overall transmission throughput of the cognitive network. We first propose the optimal access policy as an optimal tradeoff point between transmission collision and overlooked opportunity in each channel. Then the optimal cooperative spectrum sensing strategy is studied to solve the following questions: to what extent each cognitive user should cooperate with others and which part of spectrum it should choose to sense. Simulation results are given to demonstrate the promising performance.

Multi-channel spectrum sensing is prevailing but also very challenging in wideband cognitive radio systems. Conventional multi-channel spectrum detection such as channel-by-channel scan costs much time and energy. This paper aims to show a novel multi-user cooperative spectrum sensing method which can reduce the sensing ability requirement for secondary users while still guaranteeing the sensing accuracy and effectiveness in a multi-channel cognitive radio context. In our proposed method, each cognitive user chooses an Ideal-Soliton-Distributed number of channels to sense, and the partial detection results are then passed to a confusion center which uses a specially designed Belief Propagation (BP) algorithm to infer the spectrum activities of all the channels. A heuristic method to release the detected channels from the whole spectrum bands is also proposed to reduce the sensing complexity further. Simulation results show that the proposed sensing methods can obtain excellent performance.

In this paper, we consider delay-optimal MIMO precoder and power allocation design for a MIMO Link in wireless fading channels. There are L data streams spatially multiplexed onto the MIMO link with heterogeneous packet arrivals and delay requirements. The transmitter is assumed to have knowledge of outdated channel state information (CSIT) as well as the joint queue state information (QSI) of the L buffers. Using static sorting of the L eigenchannels, we decompose the L-dimensional MDP into L independent 1-dimensional MDP and derived low complexity precoding and power control policies (with linear complexity) to minimize average delays of the L application streams.

Abstract In this paper, a cross‐layer analytical framework is proposed to analyze the throughput and packet delay of a two‐hop wireless link in wireless mesh network (WMN). It considers the adaptive modulation and coding (AMC) process in physical layer and the traffic queuing process in upper layers, taking into account the traffic distribution changes at the output node of each link due to the AMC process therein. Firstly, we model the wireless fading channel and the corresponding AMC process as a finite state Markov chain (FSMC) serving system. Then, a method is proposed to calculate the steady‐state output traffic of each node. Based on this, we derive a modified queuing FSMC model for the relay to gateway link, which consists of a relayed non‐Poisson traffic and an originated Poisson traffic, thus to evaluate the throughput at the mesh gateway. This analytical framework is verified by numerical simulations, and is easy to extend to multi‐hop links. Furthermore, based on the above proposed cross‐layer framework, we consider the problem of optimal power and bandwidth allocation for QoS‐guaranteed services in a two‐hop wireless link, where the total power and bandwidth resources are both sum‐constrained. Secondly, the practical optimal power allocation algorithm and optimal bandwidth allocation algorithm are presented separately. Then, the problem of joint power and bandwidth allocation is analyzed and an iterative algorithm is proposed to solve the problem in a simple way. Finally, numerical simulations are given to evaluate their performances. Copyright © 2008 John Wiley & Sons, Ltd.

Abstract A novel random forwarding protocol for distributed wireless sensor networks (WSNs) is reported in this paper. The proposed protocol features the utilization of azimuth angle of the nodes involved and opportunistic selection of the relaying nodeviacontention among neighbors. First, the protocol with precise angle information is discussed and its multi‐hop performance is evaluated by means of both simulation and analysis in terms of average number of hops to the sink node. Simulation results show that it performs well especially in network connectivity. As a complement, node mobility is also introduced to evaluate multi‐hop performance in real environments. Then, a simple MAC scheme to ensure that the node with the highest angular advantage will be selected as a relay is proposed, and subsequently energy and latency performances based on it are evaluated. It is shown that our protocol can effectively deliver data with half number of active neighbors required in geographical random forwarding (GeRaF). Finally, a practical method is presented to estimate azimuth angle, combined with which, our forwarding protocol is shown to achieve the performance very close to the case with precise angle information. Copyright © 2008 John Wiley & Sons, Ltd.

Cognitive radio (CR) has the potential to substantially improve the system capacity and adaptability of wireless mesh network (WMN). In this paper we investigate the achievable performance gain of cognitive wireless mesh network (CWMN), in which all nodes are equipped with CRs, by jointly optimizing spectrum allocation, routing and time scheduling. The formulated optimization problem aims to minimize the system activation time to satisfy the given traffic demands, under the constraint of multiple access interference and the limited available spectrum bands at each node. Then we develop a column generation (CG) approach to solve this problem. Our analytical model is validated by the simulation results, which provide a better performance compared with fixed bandwidth allocation.

In this paper, the problem of wireless resource management in broadband cognitive OFDMA networks is addressed. Our objective is to maximize multiple cognitive users' weighted rate sum by jointly adjusting their rate, frequency, and power resource, under the constraints of multiple primary users' interference temperatures. First, we formulate the studied problem as a nonlinear and non-convex optimization problem. Secondly, we analyze this problem, and propose a centralized algorithm based on Lagrangian duality theory to solve it, which can be proved to be optimal and have polynomial time complexities. Finally, we show that this centralized algorithm can be distributively implemented by introducing the idea of virtual clock, and the distributed algorithm can be interpreted as an interesting distributed negotiated secondary market approach. We believe that our work will provide a good reference for the emerging cognitive network protocol design.

In this paper, we study the problem of bit and power allocation in broadband cognitive radio system. In the broadband communication system, the primary transmitter employs Orthogonal Frequency Division Multiplexing (OFDM) technique on the whole bandwidth. A cognitive user, which has the ability of detecting the transmission of the primary link on each subcarrier, attempts to use the same bandwidth. The goal of this paper is to study how to optimally allocate bit and power on each subcarrier at the cognitive transmitter so that the sum-rate of the cognitive user is maximized under the condition that the primary transmission is not affected. Based on the framework presented by A. Jovicic and P. Viswanath, we first formulate the problem into an optimization problem with integer variables and then propose a greedy bit and power allocation algorithm. We also prove that the proposed algorithm is the optimal solution to the optimization problem.

IEEE 802.16e and 802.20 are emerging as two promising technologies for broadband wireless access systems. In order to improve capacity and coverage performance, both of them addressed a fractional frequency reuse (FFR) scheme to combat co-channel interference in multi-cell deployment, which is referred to as FFR16and FFR20respectively in this paper. As for the scheme of FFR20, virtual area partition is proposed in this paper, and fractional frequency reuse factor (FFRF) is achieved by tuning the parameter of signal strength ratio. The optimal combination of resource allocation strategy and signal strength ratio is determined through simulation. In order to compare the performance of FFR16and FFR20, three evaluation metrics are introduced, including average throughput, outage probability and spectrum efficiency. Simulation results show that outage probability is much lower under FFR20scheme, which goes beyond the acceptable range when FFRF is smaller than 2.4. The resource utilization efficiencies of both are continuously increasing with regard to FFRF within range [2.4, 3]. Besides, FFR20outperforms FFR16under given conditions, in terms of average throughput and spectrum efficiency.

The problem of wireless resource management in broadband cognitive OFDMA networks is addressed. The objective is to maximise the multiple cognitive users' weighted rate sum by jointly adjusting their rate, frequency and power resource, under the constraints of multiple primary users' interference temperatures. First, based on two interpretations of the interference temperatures, the problem studied is formulated as two nonlinear and non-convex optimisation problems. Secondly, these two problems are analysed, and a centralised greedy algorithm is proposed to solve one problem, as well as a centralised algorithm based on Lagrangian duality theory for the other. The two centralised algorithms are shown to be optimal and both have polynomial time complexities. Finally, it is shown that the two centralized algorithms can be distributively implemented by introducing the idea of virtual clock. And the distributed algorithms can be interpreted as an interesting distributed negotiated secondary market approach. It is believed that the work will provide a good reference for the emerging cognitive network protocol design.