Massive access or communication, emerging as one of six usage scenarios in 6G, has attracted considerable recent attention due to its potential to empower next-generation industrial cyber-physical systems such as smart grids, factory automation, industrial internet-of-things (IIoT), etc. However, to guarantee its QoS, the associated power control becomes computationally intractable with a huge number of users. In this paper, we present a tractable algorithm for power control in massive access, based on mean-field approximations. In particular, our aim is to maximize the overall throughput in each scheduling period, at the beginning of which each user has a finite number of backlogged bits. To achieve this goal and overcome the curse of dimensionality, a mean-field game (MFG) is formulated. Unfortunately, the formulated MFG is still a non-convex optimization problem. Enlightened by MAPEL, an efficient solver for non-convex power control problem, we leverage multiplicative linear fractional programming (MLFP) to tackle the non-convexity in our formulated MFG. Furthermore, the mean-field approximation assisted power control strategy requires low signaling overhead consumed for estimation and feedback of channel state information (CSI). Simulation results demonstrate that the proposed tractable power control attains substantial performance gains in both the overall throughput and computational complexity.

Movable antenna (MA) systems have emerged as a promising technology for future wireless communication systems. The movement of antennas gives rise to mutual coupling (MC) effects, which have been previously ignored and can be exploited to enhance the capacity of multiple-input multiple-output (MIMO) systems. To this end, we first model an MA-enabled point-to-point MIMO communication system with MC effects using a circuit-theoretic framework. The capacity maximization problem is then formulated as a non-concave optimization problem and solved via a block coordinate ascent (BCA)-based algorithm. The subproblem of optimizing MA positions is challenging due to the presence of the analytically intractable MC matrices. To overcome this difficulty, we develop a trust region method (TRM)-based algorithm to optimize MA positions, wherein Sylvester equations are employed to compute the derivatives of the inverse square roots of the MC matrices. Simulation results show significant capacity gains from leveraging MC effects, primarily due to customizable MC matrices and superdirectivity.

Integrated sensing and communications (ISAC) is a key enabler for 6G applications such as drone surveillance, urban air mobility, and low-altitude logistics. However, the hybrid ISAC signal, which comprises deterministic pilots and random data payloads, poses challenges for target detection, since 1) these components jointly affect both the mean and variance of the received signal, and 2) the random data payloads are typically unknown to the sensing receiver in bistatic systems. To address these, we develop a generalized likelihood ratio test (GLRT)-based detector that exploits the known pilots and the statistical properties of the unknown payloads. Given the exact performance is analytically intractable, an asymptotic analysis of the false alarm probability is conducted. Simulation results validate the theoretical derivations and demonstrate the superiority of the proposed detector, which highlights the importance of tailored ISAC detection that fully leverages data payload resources.

With the unprecedented capability of configuring antenna positions, fluid antenna systems (FASs) have been recognized as a key enabler for secure communications. However, it is challenging to optimize the secrecy rate by reconfiguring positions of fluid antennas based on the fast-changing instantaneous channel state information (CSI). Considering the effectiveness of regularized zero-forcing (RZF) and zero-forcing (ZF) precoding in mitigating information leakage in physical layer security, we propose a two-timescale design to maximize ergodic secrecy sum rate (ESSR), where only the statistical CSI is utilized for the port selection of FASs. For that purpose, we first derive the analytical expression for the ESSR of FASs with RZF/ZF precoding by utilizing random matrix theory (RMT). Then, based on the evaluation results, we propose a two-timescale algorithm to maximize the ESSR by optimizing both port selection of FASs and regularization factor of RZF. Numerical simulations validate the accuracy of the proposed ESSR evaluation and show that the proposed two-timescale design could improve the ESSR performance significantly when compared with the uniform port selection.

Massive multiple-input multiple-output (MIMO) is a key enabler for the high data rates required by the sixth-generation networks, yet its performance hinges on effective beam management with low training overhead. This paper proposes an interpretable framework to tackle beam alignment in mixed line-of-sight (LoS) and non-line-of-sight (NLoS) propagation environments. Our approach utilizes multimodal data to construct virtual base stations (VBSs), which are geometrically defined as mirror images of the base station across reflecting surfaces reconstructed from 3D LiDAR points. These VBSs provide a sparse and spatial representation of the dominant features of the wireless environment. Based on the constructed VBSs, we develop a VBS-assisted beam alignment scheme comprising coarse channel reconstruction followed by partial beam training. Numerical results demonstrate that the proposed method achieves near-optimal performance in terms of spectral efficiency.

The reconfigurability of fluid antenna systems (FASs) and reconfigurable intelligent surfaces (RISs) provides significant flexibility in optimizing channel conditions by jointly adjusting the positions of fluid antennas and the phase shifts of RISs. However, it is challenging to acquire the instantaneous channel state information (CSI) for both fluid antennas and RISs, while frequent adjustment of antenna positions and phase shifts will significantly increase the system complexity. To tackle this issue, this paper investigates the two-timescale design for FAS-RIS multi-user systems with linear precoding, where only the linear precoder design requires instantaneous CSI of the end-to-end channel, while the FAS and RIS optimization relies on statistical CSI. The main challenge comes from the complex structure of channel and inverse operations in linear precoding, such as regularized zero-forcing (RZF) and zero-forcing (ZF). Leveraging on random matrix theory (RMT), we first investigate the fundamental limits of FAS-RIS systems with RZF/ZF precoding by deriving the ergodic sum rate (ESR). This result is utilized to determine the minimum number of selected antennas to achieve a given ESR. Based on the evaluation result, we propose an algorithm to jointly optimize the antenna selection, regularization factor of RZF, and phase shifts at the RIS. Numerical results validate the accuracy of performance evaluation and demonstrate that the performance gain brought by joint FAS and RIS design is more pronounced with a larger number of users.

With its capability to obtain a good tradeoff between complexity and performance, regularized zero-forcing (RZF) has been widely investigated to enhance the physical layer security. However, the associated reliability performance, i.e., secrecy outage probability (SOP), is not yet available in the literature. In this paper, we characterize the secrecy performance of RZF in the multi-user, downlink multiple-input single-output system. For this purpose, we first set up a central limit theorem for the joint distribution of users’ signal-to-interference-plus-noise ratio and eavesdropper’s signal-to-noise ratio by leveraging random matrix theory. The result is then utilized to obtain a closed-form approximation for the ergodic secrecy rate and SOP of three typical scenarios: the case with only external Eves, the case with only internal Eves, and that with both. The derived results are then used to evaluate the percentage of users in secrecy outage and the required number of transmit antennas to achieve a positive secrecy rate. It is shown that, with equally-capable Eves, the secrecy loss caused by external Eves is higher than that caused by internal Eves. Numerical simulations validate the accuracy of the theoretical results and demonstrate the advantage of RZF over other linear transmitters such as ZF.

Recent advancements in large-scale position-reconfigurable antennas have opened up new dimensions to effectively utilize the spatial degrees of freedom (DoFs) of wireless channels. However, the deployment of existing antenna placement schemes is primarily hindered by their limited scalability and frequently overlooked near-field effects in large-scale antenna systems. In this article, we propose a novel antenna placement approach tailored for near-field massive multiple-input multiple-output systems, which effectively exploits the spatial DoFs to enhance spectral efficiency. For that purpose, we first reformulate the antenna placement problem in the angular domain, resulting in a weighted Fekete problem. We then derive the optimality condition and reveal that the optimal antenna placement is in principle an electrostatic equilibrium problem. To further reduce the computational complexity of numerical optimization, we propose an ordinary differential equation (ODE)-based framework to efficiently solve the equilibrium problem. In particular, the optimal antenna positions are characterized by the roots of the polynomial solutions to specific ODEs in the normalized angular domain. By simply adopting a two-step eigenvalue decomposition (EVD) approach, the optimal antenna positions can be efficiently obtained. Furthermore, we perform an asymptotic analysis when the antenna size tends to infinity, which yields a closed-form solution. Simulation results demonstrate that the proposed scheme efficiently harnesses the spatial DoFs of near-field channels with prominent gains in spectral efficiency and maintains robustness against system parameter mismatches. In addition, the derived asymptotic closed-form solution closely approaches the theoretical optimum across a wide range of practical scenarios.

Pinching-antenna systems (PASS) have been recently proposed to improve the performance of wireless networks by reconfiguring both the large-scale and small-scale channel conditions. However, existing studies ignore the physical constraints of antenna placement and assume fixed antenna radiation power. To overcome this limitation, this paper investigates the design of PASS, taking into account the motion power consumption of pinching antennas (PAs) and the impact of adjustable antenna radiation power. To that end, we minimize the average power consumption for a given quality-of-service (QoS) requirement by jointly optimizing the antenna positions, antenna radiation power ratios, and transmit beamforming. To the best of the authors’ knowledge, this is the first work to consider radiation power optimization in PASS, which provides an additional degree of freedom (DoF) for system design. The cases with both continuous and discrete antenna placement are considered, where the main challenge lies in the fact that the antenna positions affect both the magnitude and phase of the channel coefficients of PASS, making system optimization very challenging. To tackle the resulting unique obstacles, an alternating direction method of multipliers (ADMM)-based framework is proposed to solve the problem for continuous antenna movement, while its discrete counterpart is formulated as a mixed integer nonlinear programming (MINLP) problem and solved by the block coordinate descent (BCD) method. Simulation results validate the performance enhancement achieved by incorporating PA movement power consumption and adjustable radiation power into the PASS design, while also demonstrating the efficiency of the proposed optimization framework. The benefits of PASS over conventional multiple-input multiple-output (MIMO) systems in mitigating the large-scale path loss and inter-user interference are also revealed.

Dyslexia is a common neurobiological learning disorder significantly impacting reading, writing, and spelling worldwide. Early identification and intervention are essential, but most pre-screening tools focus on Latin languages, leaving Chinese-speaking students underserved. To address this gap, we conduct semi-structured interviews with special education (special-ed) teachers to gather their needs for dyslexia pre-screening tailored to Chinese contexts. Using their insights, we have developed DysVis, a user-centered data visualization system that combines handwriting analysis, body movement keypoint conversion, and a comprehensive visualization interface. DysVis provides teachers with multi-level visualizations, such as performance overviews, task analyses, handwriting observations, and behavioural insights, enabling them to identify the root causes of learning difficulties. Our evaluations, including case studies, a user study, and expert interviews, demonstrate that DysVis is user-friendly and effective in quickly identifying at-risk students, ultimately enhancing learning outcomes for Chinese-speaking students with dyslexia.

Fluid antenna systems (FAS) offer enhanced spatial diversity for next-generation wireless systems. However, acquiring accurate channel state information (CSI) remains challenging due to the large number of reconfigurable ports and the limited availability of radio-frequency (RF) chains— particularly in high-dimensional FAS scenarios. To address this challenge, we propose an efficient posterior sampling-based channel estimator that leverages a diffusion model (DM) with a simplified U-Net architecture to capture the spatial correlation structure of two-dimensional FAS channels. The DM is initially trained offline in an unsupervised way and then applied online as a learned implicit prior to reconstruct CSI from partial observations via posterior sampling through a denoising diffusion restoration model (DDRM). To accelerate the online inference, we introduce a skipped sampling strategy that updates only a subset of latent variables during the sampling process, thereby reducing the computational cost with minimal accuracy degradation. Simulation results demonstrate that the proposed approach achieves significantly higher estimation accuracy and over 20× speedup compared to state-of-the-art compressed sensing-based methods, highlighting its potential for practical deployment in high-dimensional FAS.

The orthogonal time frequency space (OTFS) modulation is a promising technique to provide reliable communications in high-mobility scenarios. However, the performance analysis for coded OTFS systems is not available in the literature. This paper investigates the extrinsic information transfer (EXIT) behaviour of LDPC coded OTFS systems with message passing (MP) detection. Different from conventional EXIT analysis, which is normally obtained by Monte Carlo simulation, the exact distribution of the extrinsic information for MP detection is presented in this paper. Then, the EXIT function for the LDPC decoder is given with the prior information of the MP detector, which illustrates the convergence behaviour of the LDPC coded OTFS systems. Finally, an algorithm is proposed for calculating the decoding threshold for LDPC coded OTFS modulation, which can be used to design LDPC codes for OTFS systems. Numerical results verify the accuracy of the EXIT analysis, and the 5G NR LDPC code optimised by the proposed method demonstrates 1 dB to 1.5 dB performance gains over the original codes.

The reconfigurability of fluid antenna systems (FASs) and reconfigurable intelligent surfaces (RISs) can be jointly utilized to achieve unprecedented degrees of freedom for wireless communication systems. However, adjusting fluid antennas and RISs based on instantaneous channel state information (CSI) is highly challenging. To tackle this challenge, we propose a two-timescale approach for FAS-RIS-aided multi-user systems with regularized zero-forcing (RZF)/zero-forcing (ZF) precoding, where only statistical CSI is required for FAS and RIS optimization. To achieve this goal, we first obtain the closed-form evaluation for the ergodic sum rate (ESR) of FAS-RIS aided multi-user systems with RZF/ZF precoding by exploiting random matrix theory (RMT). Then, we propose an ESR maximization algorithm by jointly optimizing the port selection for FASs, phase shifts at the RIS, and regularization factor of RZF. Numerical results validate the approximation accuracy of the derived ESR evaluation and demonstrate that the performance enhancement benefiting from the joint design of FASs and RISs becomes more prominent when the number of users becomes larger.

Task-oriented communication focuses on extracting and transmitting only the information relevant to specific tasks, effectively minimizing communication overhead. Most existing methods prioritize reducing this overhead during inference, often assuming feasible local training or minimal training communication resources. However, in real-world wireless systems with dynamic connection topologies, training models locally for each new connection is impractical, and task-specific information is often unavailable before establishing connections. Therefore, minimizing training overhead and enabling label-free, task-agnostic pre-training before the connection establishment are essential for effective task-oriented communication. In this paper, we tackle these challenges by employing a mutual information maximization approach grounded in self-supervised learning and information-theoretic analysis. We propose an efficient strategy that pre-trains the transmitter in a task-agnostic and label-free manner, followed by joint fine-tuning of both the transmitter and receiver in a task-specific, label-aware manner. Simulation results show that our proposed method reduces training communication overhead to about half that of full-supervised methods using the SGD optimizer, demonstrating significant improvements in training efficiency.

Large language models (LLMs) have achieved remarkable success across various artificial intelligence tasks. However, their enormous sizes and computational demands pose significant challenges for the deployment on edge devices. To address this issue, we present a distributed on-device LLM inference framework based on tensor parallelism, which partitions neural network tensors (e.g., weight matrices) of LLMs among multiple edge devices for collaborative inference. Nevertheless, tensor parallelism involves frequent all-reduce operations to aggregate intermediate layer outputs across participating devices during inference, resulting in substantial communication overhead. To mitigate this bottleneck, we propose an over-the-air computation method that leverages the analog superposition property of wireless multipleaccess channels to facilitate fast all-reduce operations. To minimize the average transmission mean-squared error, we investigate joint model assignment and transceiver optimization, which can be formulated as a mixed-timescale stochastic non-convex optimization problem. Then, we develop a mixed-timescale algorithm leveraging semidefinite relaxation and stochastic successive convex approximation methods. Comprehensive simulation results will show that the proposed approach significantly reduces inference latency while improving accuracy. This makes distributed ondevice LLM inference practical for resource-constrained edge devices.

With a massive number of antennas densely deployed in a compact area, holographic multiple-input multiple-output (HMIMO) systems are envisioned to be a key enabling technology for improving the data rate and coverage of 6 G networks. Unfortunately, the reduced spacing between radiation elements, which enables HMIMO to better exploit the channel propagation characteristics, also causes increased mutual coupling (MC) and reduced radiation efficiency. It is thus critical to understand the effect of MC on the capacity of HMIMO systems, which is not yet available in the literature. In this paper, we investigate the ergodic mutual information (EMI) and associated capacity-achieving transmit covariance design for HMIMO systems with MC. To this end, we first derive the closed-form expression for the EMI of HMIMO systems with MC, by leveraging random matrix theory (RMT). Then, based on the derived results, we propose an MC-aware algorithm to maximize the EMI by optimizing the transmit covariance matrix. Numerical simulations validate the accuracy of the theoretical analysis and the effectiveness of the proposed MC-aware algorithm. It is observed that the halfwavelength antenna spacing is not optimal especially with low signal-to-noise ratio.

The spiked covariance model, characterized by a population covariance matrix perturbed by a low-rank matrix, plays a crucial role in data analysis. In this context, the low-rank deformation typically signifies the underlying signal composition, while the extreme eigenvalues and eigenvectors of the sample covariance matrix contain valuable information about the signal. While the spiked covariance model has been extensively studied, its behavior under dimension reduction techniques, such as random projection, remains largely unexplored. These dimension reduction methods are commonly employed to manage the computational complexity associated with high-dimensional data. In this work, we study the behavior of the extreme eigenvalues and eigenvectors of the spiked covariance model with random projection. Specifically, we identify the exact critical threshold for the empirical eigenvalues to be out of the main bulk of the spectrum. Additionally, we determine the asymptotic positions of the isolated eigenvalues, as well as the projections of the isolated eigenvectors. It is quantitatively shown that the signal strength decreases under projection, and the isolated eigenvectors carry the information of the projected signal. Based on the above results, we propose a linear detection method for strong signals and analyze its performance limits. Simulation results validate the accuracy of the theoretical analysis.

Integrated sensing and communication (ISAC) systems operating at terahertz (THz) bands are envisioned to enable both ultra-high data-rate communication and precise environmental awareness for next-generation wireless networks. However, the narrow width of THz beams makes them prone to misalignment and necessitates frequent beam prediction in dynamic environments. Multimodal sensing, which integrates complementary modalities such as camera images, positional data, and radar measurements, has recently emerged as a promising solution for proactive beam prediction. Nevertheless, existing multimodal approaches typically employ static fusion architectures that cannot adjust to varying modality reliability and contributions, thereby degrading predictive performance and robustness. To address this challenge, we propose a novel and efficient multimodal mixtureof-experts (MoE) deep learning framework for proactive beam prediction in THz ISAC systems. The proposed multimodal MoE framework employs multiple modality-specific expert networks to extract representative features from individual sensing modalities, and dynamically fuses them using adaptive weights generated by a gating network according to the instantaneous reliability of each modality. Simulation results in realistic vehicle-to-infrastructure (V2I) scenarios demonstrate that the proposed MoE framework outperforms traditional static fusion methods and unimodal baselines in terms of prediction accuracy and adaptability, highlighting its potential in practical THz ISAC systems with ultra-massive multiple-input multiple-output (MIMO).

Orthogonal time frequency space (OTFS) modulation is a promising technology for mitigating severe Doppler effects in high-mobility scenarios. However, existing OTFS channel estimation methods neglect the Doppler Squint Effect (DSE), which incurs serious performance loss. In this paper, we propose a channel estimation algorithm based on Newton's method to accurately estimate fractional delay and Doppler in OTFS systems with DSE. In particular, we obtain the maximum delay and Doppler grid spacing for codebook design to guarantee the convergence of the algorithm. Additionally, we derive the Cramér-Rao lower bound (CRLB) for testing channel parameter estimation performance of our proposed algorithm. Simulation results demonstrate that our proposed algorithm outperforms the orthogonal matching pursuit (OMP) algorithm in terms of normalized mean square error (NMSE), surpassing the Newtonized OMP algorithm with traditional dictionary matrix and approaching the CRLB performance.

The development of Policy Iteration (PI) has inspired many recent algorithms for Reinforcement Learning (RL), including several policy gradient methods that gained both theoretical soundness and empirical success on a variety of tasks. The theory of PI is rich in the context of centralized learning, but its study under the federated setting is still in the infant stage. This paper investigates the federated version of Approximate PI (API) and derives its error bound, taking into account the approximation error introduced by environment heterogeneity. We theoretically prove that a proper client selection scheme can reduce this error bound. Based on the theoretical result, we propose a client selection algorithm to alleviate the additional approximation error caused by environment heterogeneity. Experiment results show that the proposed algorithm outperforms other biased and unbiased client selection methods on the federated mountain car problem, the MuJoCo Hopper problem, and the SUMO-based autonomous vehicle training problem by effectively selecting clients with a lower level of heterogeneity from the population distribution.

Task-oriented communication aims to extract and transmit task-relevant information to significantly reduce the communication overhead and transmission latency. However, theunpredictabledistribution shifts between training and test data, includingdomain shiftandsemantic shift, can dramatically undermine the system performance. In order to tackle these challenges, it is crucial to ensure that the encoded features can generalize todomain-shifteddata and detectsemantic-shifteddata, while remaining compact for transmission. In this paper, we propose a novel approach based on the information bottleneck (IB) principle and invariant risk minimization (IRM) framework. The proposed method aims to extract compact and informative features that possess high capability for effectivedomain-shift generalizationand accuratesemantic-shift detectionwithout any knowledge of the test data during training. Specifically, we propose an invariant feature encoding approach based on the IB principle and IRM framework fordomain-shiftgeneralization, which aims to find the causal relationship between the input data and task result by minimizing the complexity and domain dependence of the encoded feature. Furthermore, we enhance the task-oriented communication with the label-dependent feature encoding approach forsemantic-shift detectionwhich achieves joint gains in IB optimization and detection performance. To avoid the intractable computation of the IB-based objective, we leverage variational approximation to derive a tractable upper bound for optimization. Extensive simulation results on image classification tasks demonstrate that the proposed scheme outperforms state-of-the-art approaches and achieves a better rate-distortion tradeoff.

Task-oriented communication presents a promising approach to improve the communication efficiency of edge inference systems by optimizing learning-based modules to extract and transmit relevant task information. However, real-time applications face practical challenges, such as incomplete coverage and potential malfunctions of edge servers. This situation necessitates cross-model communication between different inference systems, enabling edge devices from one service provider to collaborate effectively with edge servers from another. Independent optimization of diverse edge systems often leads to incoherent feature spaces, which hinders the cross-model inference for existing task-oriented communication. To facilitate and achieve effective cross-model task-oriented communication, this study introduces a novel framework that utilizes shared anchor data across diverse systems. This approach addresses the challenge of feature alignment in both server-based and on-device scenarios. In particular, by leveraging the linear invariance of visual features, we propose efficient server-based feature alignment techniques to estimate linear transformations using encoded anchor data features. For on-device alignment, we exploit the angle-preserving nature of visual features and propose to encode relative representations with anchor data to streamline cross-model communication without additional alignment procedures during the inference. The experimental results on computer vision benchmarks demonstrate the superior performance of the proposed feature alignment approaches in cross-model task-oriented communications. The runtime and computation overhead analysis further confirm the effectiveness of the proposed feature alignment approaches in real-time applications.

Sensing performance is typically evaluated by classical radar metrics, such as Cramér-Rao bound and signal-to-clutter-plus-noise ratio. The recent development of the integrated sensing and communication (ISAC) framework motivated the efforts to unify the performance metric for sensing and communication, where sensing mutual information (SMI) was proposed as a sensing performance metric withdeterministicsignals. However, the communication need in ISAC systems necessitates the transmission ofrandomsignals for sensing applications, whereas an explicit evaluation for the SMI with random signals is not yet available in the literature. This paper aims to fill the research gap and investigate the unification of sensing and communication performance metrics. For that purpose, we first derive the explicit expression for the SMI with random signals utilizing random matrix theory. On top of that, we further build up the connections between SMI and traditional sensing metrics, such as ergodic minimum mean square error (EMMSE), ergodic linear minimum mean square error (ELMMSE), and ergodic Bayesian Cram´er- Rao bound (EBCRB). Such connections open up the opportunity to unify sensing and communication performance metrics, which facilitates the analysis and design for ISAC systems. Finally, SMI is utilized to optimize the precoder for both sensing-only and ISAC applications. Simulation results validate the accuracy of the theoretical results and the effectiveness of the proposed precoding design.

Reconfigurable intelligent surfaces (RISs) have been shown effective in strengthening the physical layer security of wireless systems, and the two-timescale design was proposed to tackle the challenges in channel estimation and phase-shift control. However, existing maximum ratio transmission (MRT) based precoding design is not efficient in mitigating information leakage. To this end, this paper considers the performance analysis and two-timescale design for RIS-aided multiple-input single-output (MISO) secure communications with regularized zero-forcing (RZF) and zero-forcing (ZF) precoding, which is not available in the literature. The major challenges come from the two-hop channel and the inverse structure in the precoding matrix. By utilizing random matrix theory, we first evaluate the fundamental limits of the considered system by deriving a closed-form expression for the ergodic secrecy sum rate (ESSR). Then, we determine the optimal regularization factor of the RZF precoder and evaluate the ESSR over independent and identically distributed (i.i.d.) channels in the high SNR regime. The results indicate that when the number of reconfigurable elements at the RIS is overwhelmingly larger than that of transmit antennas and users, the ESSR of the two-hop channel approaches that of the single-hop channel. Based on the performance analysis, we propose a two-timescale algorithm to maximize the ESSR by optimizing the regularization factor of RZF and the phase shifts of the RIS alternatively. Simulation results validate the accuracy of the theoretical analysis and the effectiveness of the proposed algorithm.

The classical Rician Weichselberger channel and the emerging holographic multiple-input multiple-output (MIMO) channel share a common characteristic of non-separable correlation, which captures the interdependence between transmit and receive antennas. However, this correlation structure makes it very challenging to characterize the fundamental limits of non-centered (Rician), non-separable MIMO channels. In fact, there is a dearth of existing literature that addresses this specific aspect, underscoring the need for further research in this area. In this paper, we investigate the mutual information (MI) of non-centered non-separable MIMO channels, where both the line-of-sight and non-line-of-sight components are considered. By utilizing random matrix theory (RMT), we set up a central limit theorem for the MI and give the closed-form expressions for its mean and variance. The derived results are then utilized to determine the ergodic MI and outage probability of holographic MIMO channels. Numerical simulations validate the accuracy of the theoretical results.

Multi-antenna relays and intelligent reflecting surfaces (IRSs) have been utilized to construct favorable channels to improve the performance of wireless systems. A common feature between relay systems and IRS-aided systems is the two-hop multiple-input multiple-output (MIMO) channel. As a result, the mutual information (MI) of two-hop MIMO channels has been widely investigated with very engaging results. However, a rigorous investigation on the fundamental limits of two-hop MIMO channels, i.e., the first and second-order analysis, is not yet available in the literature, due to the difficulties caused by the two-hop (product) channel and the noise introduced by the relay (active IRS). In this paper, we employ large random matrix theory, specifically Gaussian tools, to derive the closed-form deterministic approximation for the mean and variance of the MI. Additionally, we determine the convergence rate for the mean, variance and the characteristic function of the MI, and prove the asymptotic Gaussianity. Furthermore, we also investigate the analytical properties of the fundamental equations that describe the closed-form approximation and prove the existence and uniqueness of the solution. An iterative algorithm is then proposed to obtain the solutions for the fundamental equations. Numerical results validate the accuracy of the theoretical analysis.

Cell-free massive MIMO is one of the core technologies for next-generation wireless networks. It is expected to bring enormous benefits, including ultra-high reliability, data throughput, energy efficiency, and uniform coverage. However, the radically distributed architecture of cell-free massive MIMO necessitates new paradigms for transceiver design, especially by exploiting efficient distributed processing algorithms. In this paper, we propose a distributed expectation propagation (EP) detector for cell-free massive MIMO, which consists of two modules: a nonlinear module at the central processing unit (CPU) and a linear module at each access point (AP). The turbo principle in iterative channel decoding is utilized to compute and pass the extrinsic information between the two modules. An analytical framework is provided to characterize the asymptotic performance of the proposed EP detector with a large number of antennas. Furthermore, a distributed iterative channel estimation and data detection (ICD) algorithm is developed to handle the practical scenario with imperfect channel state information (CSI). Simulation results will show that the proposed method outperforms existing detectors for cell-free massive MIMO systems in terms of the bit-error rate and the developed theoretical analysis can be utilized as an asymptotic lower bound. Finally, it is shown that with imperfect CSI, the proposed ICD algorithm can significantly improve the system performance and reduce the pilot overhead.

In frequency-division duplex (FDD) massive multiple-input multiple-output (MIMO) systems, the growing number of base station antennas leads to prohibitive feedback overhead for downlink channel state information (CSI). To address this challenge, state-of-the-art (SOTA) fully data-driven deep learning (DL)-based CSI feedback schemes have been proposed. However, the high computational complexity and memory requirements of these methods hinder their practical deployment on resource-constrained devices like mobile phones. To solve the problem, we propose a model-driven DL-based CSI feedback approach by integrating the wisdom of compressive sensing and learning to optimize (L2O). Specifically, only a linear learnable projection is adopted at the encoder side to compress the CSI matrix, thereby significantly cutting down the user-side complexity and memory expenditure. On the other hand, the decoder incorporates two specially designed components, i.e., a learnable sparse transformation and an element-wise L2O reconstruction module. The former is developed to learn a sparse basis for CSI within the angular domain, which explores channel sparsity effectively. The latter shares the same long short term memory (LSTM) network across all elements of the optimization variable, eliminating the retraining cost when problem scale changes. Simulation results show that the proposed method achieves a comparable performance with the SOTA CSI feedback scheme but with much-reduced complexity, and enables multiple-rate feedback.

Maneuvering target tracking is an important service of future wireless networks to assist innovative applications such as intelligent transportation. However, tracking maneuvering targets by cellular networks faces many challenges. In particular, the dense network and high-speed targets make the selection of the sensing nodes (SNs) and the associated power allocation very challenging. Existing methods demonstrated engaging performance, but with high computational complexity. In this paper, we propose a model-driven deep learning (DL)-based approach for SN selection. To this end, we first propose an iterative SN selection method by jointly exploiting the majorization-minimization (MM) framework and the alternating direction method of multipliers (ADMM). Then, we unfold the iterative algorithm as a deep neural network and prove its convergence. The proposed method achieves lower computational complexity, as the number of layers is less than the number of iterations required by the original algorithm, and each layer only involves simple matrix-vector additions/multiplications. Finally, we propose an efficient power allocation method based on fixed point (FP) water filling and solve the joint SN selection and power allocation problem under the alternative optimization framework. Simulation results show that the proposed method achieves better performance than conventional optimization-based algorithms with much lower computational complexity.

Simultaneous wireless information and power transfer (SWIPT) has been proposed to offer communication services and transfer power to the energy harvesting receiver (EHR) concurrently. However, existing works mainly focused on static EHRs, without considering the location uncertainty caused by the movement of EHRs and location estimation errors. To tackle this issue, this paper considers the sensing-assisted SWIPT design in a networked integrated sensing and communication (ISAC) system in the presence of location uncertainty. A two-phase robust design is proposed to reduce the location uncertainty and improve the power transfer efficiency. In particular, each time frame is divided into two phases, i.e., sensing and WPT phases, via time-splitting. The sensing phase performs collaborative sensing to localize the EHR, whose results are then utilized in the WPT phase for efficient WPT. To minimize the power consumption with given communication and power transfer requirements, a two-layer optimization framework is proposed to jointly optimize the time-splitting ratio, coordinated beamforming policy, and sensing node selection. Simulation results validate the effectiveness of the proposed design and demonstrate the existence of an optimal time-splitting ratio for given location uncertainty.

The additional degree of freedom (DoF) in the distance domain of near-field communication offers new opportunities for physical layer security (PLS) design. However, existing works mainly consider static eavesdroppers, and the related study with mobile eavesdroppers is still in its infancy due to the difficulty in obtaining the channel state information (CSI) of the eavesdropper. To this end, we propose to leverage the sensing capability of integrated sensing and communication (ISAC) systems to assist PLS design. To comprehensively study the dynamic behaviors of the system, we propose a Pareto optimization framework, where a multi-objective optimization problem (MOOP) is formulated to simultaneously optimize three key performance metrics: power consumption, number of securely served users, and tracking performance, while guaranteeing the achievable rate of the users with a given leakage rate constraint. A globally optimal design based on the generalized Bender’s decomposition (GBD) method is proposed to achieve the Pareto optimal solutions. To reduce the computational complexity, we further design a low-complexity algorithm based on zero-forcing (ZF) beamforming and successive convex approximation (SCA). Simulation results validate the effectiveness of the proposed algorithms and reveal the intrinsic trade-offs between the three performance metrics. It is observed that near-field communication offers a favorable beam diffraction effect for PLS, where the energy of the information signal is nulled around the eavesdropper and focused on the users.

This study addresses the core challenges in federated learning (FL), namely achieving optimal model utility, safeguarding local data privacy, and maintaining efficient communication. While previous research has focused on either the privacy-utility or communication-utility trade-offs, the investigation of simultaneously considering utility, privacy protection, and communication efficiency has been largely overlooked. In this paper, we propose a novel training framework for FL that combines communication efficiency and differential privacy. Specifically, we employ quantization and binomial noise on model updates to enhance privacy protection and communication efficiency concurrently. Through convergence and privacy analysis, we formulate an optimization problem that maximizes model utility while adhering to privacy and communication constraints. Additionally, we introduce an adaptive algorithm to determine key system parameters, including the level of quantization and privacy noise. Simulation results validate the effectiveness of our proposed FL framework and parameter optimization algorithm.

The multiple-input and multiple-output (MIMO) technique is considered as a promising approach for improving the throughput and reliability of optical fiber communications. However, the finite blocklength (FBL) analysis of optical fiber MIMO systems is not available in the literature. Considering the Jacobi MIMO channel, which was proposed to model the nearly lossless propagation and the crosstalks in optical fiber channels, this paper studies the optimal average error probability (OAEP) of optical fiber multicore/multimode systems in the FBL regime. In particular, we consider the case where the coding rate is in the ${\mathcal{O}}\left({\frac{1}{{\sqrt {LM} }}}\right)$ proximity of the capacity, with M and L denoting the number of transmit channels and blocklength, respectively. To this end, a central limit theorem (CLT) for the information density is first established in the asymptotic regime where the blocklength and the number of transmit, receive, and available channels approach infinity with fixed ratios. With the aid of the CLT, the closed-form upper and lower bounds for the OAEP with the concerned rate are then derived. It is shown that the derived bounds could degenerate to those for Rayleigh MIMO channels if the number of available channels goes to infinity. Numerical simulations indicate that the derived bounds are closer to the performance of low-density parity check (LDPC) coding schemes than outage probability, thus providing a better characterization with the concerned the rate.

Sensing performance is typically evaluated by classical metrics, such as Cramer-Rao bound and signal- to-clutter-plus-noise ratio. The recent development of the integrated sensing and communication (ISAC) framework motivated the efforts to unify the metric for sensing and communication, where researchers have proposed to utilize mutual information (MI) to measure the sensing performance with deterministic signals. However, the need to communicate in ISAC systems necessitates the use of random signals for sensing applications and the closed-form evaluation for the sensing mutual information (SMI) with random signals is not yet available in the literature. This paper investigates the SMI and precoder design for sensing applications with random signals. For that purpose, we first derive the closed-form expression for the SMI with random signals by utilizing random matrix theory. The result reveals some interesting physical insights regarding the relation between the SMI with deterministic and random signals. The derived SMI is then utilized to optimize the precoder by leveraging a manifold-based optimization approach. The accuracy of the theoretical analysis and the effectiveness of the proposed precoder design method are validated by simulation results.

Holographic MIMO (HMIMO) has recently been recognized as a promising enabler for future 6G systems through the use of an ultra-massive number of antennas in a compact space to exploit the propagation characteristics of the electromagnetic (EM) channel. Nevertheless, the promised gain of HMIMO could not be fully unleashed without an efficient means to estimate the high-dimensional channel. Bayes-optimal estimators typically necessitate either a large volume of supervised training samples or a priori knowledge of the true channel distribution, which could hardly be available in practice due to the enormous system scale and the complicated EM environments. It is thus important to design a Bayes-optimal estimator for the HMIMO channels in arbitrary and unknown EM environments, free of any supervision or priors. This work proposes a self-supervised minimum mean-square-error (MMSE) channel estimation algorithm based on powerful machine learning tools, i.e., score matching and principal component analysis. The training stage requires only the pilot signals, without knowing the spatial correlation, the ground-truth channels, or the received signal-to-noise-ratio. Simulation results will show that, even being totally self-supervised, the proposed algorithm can still approach the performance of the oracle MMSE method with an extremely low complexity, making it a competitive candidate in practice.

Students with autism spectrum disorders (ASD) often feel insecure in new environments due to social challenges, unfamiliarity, and a lack of support or understanding. Despite considerable efforts dedicated to assisting students in adapting to new environments and understanding appropriate behaviours in public settings, there remains a lack of interactive and personalized learning systems. In this work, we developed a robot-assisted scenario training (RAST) system to facilitate inclusive learning and arouse students' learning interests. With the RAST system, we seek to identify effective interactions that can improve students' engagement. To this end, we invited 13 students with ASD to participate in an evaluation study. In the study, self- determination theory (SDT) measures students' learning engagement. Learning engagement and effectiveness are evaluated using variance analysis (ANOVA). Students also participated in interviews to report their user experience regarding the system. The results reveal that learning with the RAST system can significantly arouse students' intrinsic motivation and improve their behavioural, emotional, and cognitive engagement. Additionally, students with ASD increased their learning performance by 8.33%. Furthermore, students exhibited a high level of engagement in scenario training with certain types of interactions, including personalized functions, visual cues and sound quality. Overall, the RAST system demonstrates promising capabilities in enhancing students' learning engagement and proficiency with ASD.

Recently, neural image compression has made significant progress in reducing rate-distortion and has received widespread attention. However, existing methods focus more on perfecting entropy models yet overlook the ability of their encoder networks to extract non-linear features of images, which can promote compression performance. In this paper, we design a learning-based asymmetric image compression network to enhance the feature representation capability for improved compression quality. Firstly, we propose a high-preserving information block (HPIB) consisting of a high-frequency filtering module (HFM) and a feature modulation module (FMM) to fully utilize the different frequency information in images. Secondly, we progressively use the HPIB layer to design a high-performance encoder network for high-fidelity feature extraction. Results from extensive experiments demonstrate that our network performs superior to the prior art in terms of both PSNR and MS-SSIM metrics and achieves 3.91% and 8.88 % BD-rate over VVC on the Kodak and CLIC datasets, respectively.

Storytelling is vital to children’s development by nurturing creative thinking, effective communication, and self-expression. Many tools have been created to support children’s creativity. Unfortunately, the existing tools do not adequately integrate visual elements with storytelling, limiting children’s imaginative potential. This study addresses the gap by introducing a proxy visual storytelling tool (PVST) that employs a character-based approach (i.e., proxy character assembling) to enhance children’s creativity and storytelling skills. Through a comparative study using Kurt Vonnegut’s “The Shape of Stories" theory, the PVST was evaluated. The results from a pilot test show that the PVST can increase children’s sense of agency and engagement in the storytelling learning process. Additionally, it can stimulate children’s creative imagination, improve their storytelling abilities, and enable them to construct more fluent and articulate narratives. The findings highlight the importance of incorporating visual storytelling elements in enhancing children’s creativity and storytelling skills, ultimately fostering a more engaging and enriching learning experience.

To meet the stringent requirements of future communication systems, ultra-massive multiple-input and multiple-output (UM-MIMO) technology has garnered significant attention as a key enabling technology for 6G. However, the deployment of UM-MIMO introduces new challenges, particularly the near-field effect. In this paper, by leveraging the unique characteristics of near-field channels, we propose a novel near-field channel estimation algorithm based on the Newton's method. We also design a near-field codebook that meets the requirements for convergence guarantee. Our algorithm overcomes the limitations of existing approaches by offering a low-complexity, tuning-free, and convergence-guaranteed solution. Simulation results show that our proposed algorithm outperforms state-of-the-art baselines in terms of estimation accuracy, establishing its effectiveness in near-field channel estimation for UM-MIMO systems.

Because of its privacy-preserving capability, federated learning (FL) has attracted significant attention from both academia and industry. However, when being implemented over wireless networks, it is not clear how much communication error can be tolerated by FL. This paper investigates the robustness of FL to the uplink and downlink communication error. Our theoretical analysis reveals that the robustness depends on two critical parameters, namely the number of clients and the numerical range of model parameters. It is also shown that the uplink communication in FL can tolerate a higher bit error rate (BER) than downlink communication, and this difference is quantified by a proposed formula. The findings and theoretical analyses are further validated by extensive experiments.

Maneuvering target tracking will be one of the es-sential applications for future perceptive mobile networks, where multiple sensing nodes (SNs) collaboratively track the same tar-get. However, the associated SN selection is a substantial challenge due to stringent latency requirements of sensing applications. In this paper, we propose a model-driven approach by unfolding conventional optimization-based methods to tackle this problem. To this end, we first propose an iterative selection method based on the majorization-minimization (MM) framework and the alternating direction method of multipliers (ADMM). Then, we unfold the MM-ADMM approach as a deep neural network (DNN) to reduce the computational complexity and improve the performance, by leveraging an enhanced surrogate function. Simulation results demonstrate that the unfolded DNN outper-forms conventional methods with much lower computational complexity.

Intelligent reflecting surfaces (IRSs) have emerged as a promising technology to improve the efficiency of wireless communication systems. However, passive IRSs suffer from the “multiplicative fading” effect, where the transmit signal will go through two fading hops. With the ability to amplify and reflect signals, active IRSs offer a potential way to tackle this issue, where the amplification energy only experiences the second hop. However, the fundamental limit and system design for active IRSs have not been fully understood, especially for multiple-input multiple-output (MIMO) systems. In this paper, we consider the analysis and design for the large-scale active IRS-aided MIMO system assuming only statistical channel state information (CSI) at the transmitter and the IRS. The characterization of the fundamental limit, i.e., ergodic rate, turns out to be a very difficult problem. To this end, we leverage random matrix theory (RMT) to derive the deterministic approximation (DA) for the ergodic rate, and then design an algorithm to jointly optimize the transmit covariance matrix at the transmitter and the reflection matrix at the active IRS. Numerical results demonstrate the accuracy of the derived DA and the effectiveness of the proposed optimization algorithm. Interesting physical insights regarding the advantage of active IRSs over their passive counterparts and the optimal power allocation between the transmitter and IRS are unveiled.

Federated learning (FL) is a promising paradigm for privacy-preserving deep learning using data distributed on Internet of Things devices. Traditional model sharing-based methods, e.g., federated averaging (FedAvg), suffer from high communication overhead and difficulty in accommodating heterogeneous model architectures. Federated distillation (FD) is a recently proposed alternative to enable communication-efficient and robust FL, as well as heterogeneous client models. However, there is a lack of theoretical understanding of FD-based methods, and their design guidelines remain elusive. This article presents a generic meta-algorithm for FD, generalizing most existing FD training algorithms. By studying a linear classification problem, we show that, with sufficient distillation samples, the training performance of the meta-algorithm is the same as the vanilla FedAvg. To guide the algorithm design and improve communication efficiency, we further investigate the binary classification problem with a Gaussian mixture model, which shows that more distillation data and sampling data with higher confidence improve the training performance. Furthermore, we propose an effective distillation data sampling technique to improve the performance of the FD-meta algorithm, which also reduces communication overhead. Simulations on the benchmark data sets validate the theoretical findings and demonstrate that our proposed algorithm effectively reduces the communication overhead while achieving a satisfactory performance.

The rapid development of radio access network (RAN) slicing and control- and user-plane separation (CUPS) has created a new paradigm for future networks, namely, CUPS-based RAN slicing. In this article, we formulate the utility optimization problems of the CUPS-based RAN slicing system and propose a Lyapunov-based deep reinforcement learning (L-DRL) framework to solve them. Specifically, we propose that the control plane (CP) and user plane (UP) slices should control their respective power and subcarrier resources. First, we provide coverage-driven slices in the CP for coverage control and data-driven slices in the UP for diverse user requests, where we consider the influence of coverage-driven slices on data-driven slices. Second, we define the system’s utilities as income minus cost, and we formulate the utility maximization problem of the UP as a mixed-integer nonlinear programming (MINLP) problem, which is NP-hard because it considers both continuous actions (densities deployment and power allocation) and discrete action (subcarrier allocation). Furthermore, we design an alternating optimization method for the CP and UP based on the densities of deployment. Finally, we develop a novel framework for mixed-action optimization problems and propose a specific Lyapunov-based asynchronous advantage actor–critic (L-A3C) algorithm. Simulation results demonstrate that our proposed Lyapunov-based A3C (L-A3C) algorithm outperforms the standard A3C algorithm in terms of the convergence while achieving higher performance than Lyapunov optimization. Moreover, our proposed CUPS-based RAN slicing scheme surpasses the benchmark RAN slicing schemes in terms of the achievable rate and delay.

As an attractive enabling technology for next-generation wireless communications, network slicing supports diverse customized services in the global space–air–ground-integrated network (SAGIN) with diverse resource constraints. In this article, we dynamically consider three typical classes of radio access network (RAN) slices, namely, high-throughput slices, low-delay slices and wide-coverage slices, under the same underlying physical SAGIN. The throughput, the service delay, and the coverage area of these three classes of RAN slices are jointly optimized in a nonscalar form by considering the distinct channel features and service advantages of the terrestrial, aerial, and satellite components of acrshortpl SAGIN. A joint central and distributed multiagent deep deterministic policy gradient (CDMADDPG) algorithm is proposed for solving the above problem to obtain the Pareto-optimal solutions. The algorithm first determines the optimal virtual unmanned aerial vehicle (vUAV) positions and the interslice subchannel and power sharing by relying on a centralized unit. Then, it optimizes the intraslice subchannel and power allocation, and the virtual base station (vBS)/vUAV/virtual low Earth orbit (vLEO) satellite deployment in support of three classes of slices by three separate distributed units. Simulation results verify that the proposed method approaches the Pareto-optimal exploitation of multiple RAN slices, and outperforms the benchmarkers.

Multiple access is the cornerstone technology for each generation of wireless cellular networks, which fundamentally determines the method of radio resource sharing and significantly influences both the system performance and transceiver complexity. Meanwhile, resource allocation (RA) design plays a crucial role in multiple access, as it can manage both encompassing radio resources and interference, and it is critical for providing high-speed and reliable communication services to multiple users. Given that the RA design is intrinsically scenario-specific and the optimization tools for RA design are typically varied, in this article, we present a comprehensive tutorial overview for junior researchers in this field, aiming to offer a foundational guide for RA design in the context of next-generation multiple access (NGMA). Our discussion spans a broad range of fundamental topics: from typical system models, through intriguing problem formulation in RA design, to the exploration of various potential optimization solution methodologies. Initially, we identify three types of channels in future wireless cellular networks over which NGMA will be implemented, namely, natural channels, reconfigurable channels, and functional channels. Natural channels are traditional uplink and downlink communication channels; reconfigurable channels are defined as channels that can be proactively reshaped via emerging platforms or techniques, such as intelligent reflecting surface (IRS), unmanned aerial vehicle (UAV), and movable/fluid antenna (M/FA); and functional channels support not only communication but also other functionalities simultaneously, with typical examples, including integrated sensing and communication (ISAC) and joint computing and communication (JCAC) channels. Then, we introduce NGMA models applicable to these three types of channels that cover most of the practical communication scenarios of future wireless communications. Subsequently, we articulate the key optimization technical challenges inherent in the RA design for NGMA, categorizing them into rate-, power-, and reliability-oriented RA designs. The corresponding optimization approaches for solving the formulated RA design problems are then presented. Finally, the simulation results are presented and discussed to elucidate the practical implications and insights derived from RA designs in NGMA.

The Rayleigh-product channel model is utilized to characterize the rank deficiency caused by keyhole effects. However, the finite blocklength analysis for Rayleigh-product channels is not available in the literature. In this paper, we will characterize the mutual information density (MID) and perform the FBL analysis to reveal the impact of rank-deficiency in Rayleigh-product channels. To this end, we first set up a central limit theorem for the MID over Rayleigh-product MIMO channels in the asymptotic regime where the number of scatterers, number of antennas, and blocklength go to infinity at the same pace. Then, we utilize the CLT to obtain the upper and lower bounds for the packet error probability, whose approximations in the high and low signal to noise ratio regimes are then derived to illustrate the impact of rank-deficiency. One interesting observation is that rank-deficiency degrades the performance of MIMO systems with FBL and the fundamental limits of Rayleigh-product channels degenerate to those of the Rayleigh case when the number of scatterers approaches infinity.

Intelligent reflecting surface (IRS) provides an energy-efficient way to construct channels and enables the joint transceiver and channel design. In this paper, we consider the analysis and design of IRS-aided multiple-input multiple-output (MIMO) secure communications. We first investigate the fundamental limits of IRS-aided wiretap MIMO communications by determining the ergodic secrecy rate (ESR) and secrecy outage probability (SOP), which are not yet available in the literature. For that purpose, we derive the central limit theorem (CLT) for the joint distribution of the mutual information (MI) statistics over IRS-aided MIMO wiretap channels by utilizing random matrix theory (RMT). The CLT is then used to obtain the closed-form expressions for ESR and SOP, which are also extended to the scenario with multiple multi-antenna eavesdroppers. Based on the theoretical results, algorithms for maximizing the artificial noise (AN)-aided ESR and minimizing SOP are proposed. Numerical simulations validate the accuracy of the theoretical results and effectiveness of the proposed optimization algorithms.

As one of the core technologies for 5G systems, massive multiple-input multiple-output (MIMO) introduces dramatic capacity improvements along with very high beamforming and spatial multiplexing gains. When developing efficient physical layer algorithms for massive MIMO systems, message passing is one promising candidate owing to its superior performance. However, as their computational complexity increases dramatically with the problem size, the state-of-the-art message passing algorithms cannot be directly applied to future 6G systems, where an exceedingly large number of antennas are expected to be deployed. To address this issue, we propose a model-driven deep learning (DL) framework, namely the AMP-GNN for massive MIMO transceiver design, by considering thelow complexityof the AMP algorithm andadaptabilityof GNNs. Specifically, the structure of the AMP-GNN network is customized by unfolding the approximate message passing (AMP) algorithm and introducing a graph neural network (GNN) module into it. The permutation equivariance property of AMP-GNN is proved, which enables the AMP-GNN to learn more efficiently and to adapt to different numbers of users. We also reveal the underlying reason why GNNs improve the AMP algorithm from the perspective of expectation propagation, which motivates us to amalgamate various GNNs with different message passing algorithms. In the simulation, we take the massive MIMO detection to exemplify that the proposed AMP-GNN significantly improves the performance of the AMP detector, achieves comparable performance as the state-of-the-art DL-based MIMO detectors, and presents strong robustness to various mismatches.

One of the most critical challenges for deploying distributed learning solutions, such as federated learning (FL), in wireless networks is the limited battery capacity of mobile clients. While it is a common belief that the major energy consumption of mobile clients comes from the uplink data transmission, this paper presents a novel finding, namely channel decoding also contributes significantly to the overall energy consumption of mobile clients in FL. Motivated by this new observation, we propose an energy-efficient adaptive channel decoding scheme that leverages the intrinsic robustness of FL to model errors. In particular, the robustness is exploited to reduce the energy consumption of channel decoders at mobile clients by adaptively adjusting the number of decoding iterations. We theoretically prove that wireless FL with communication errors can converge at the same rate as the case with error-free communication provided the bit error rate (BER) is properly constrained. An adaptive channel decoding scheme is then proposed to improve the energy efficiency of wireless FL systems. Experimental results demonstrate that the proposed method maintains the same learning accuracy while reducing the channel decoding energy consumption by$\sim ~20$% when compared to an existing approach.

Task-oriented communication is an emerging paradigm for next-generation communication networks, which extracts and transmits task-relevant information, instead of raw data, for downstream applications. Most existing deep learning (DL)-based task-oriented communication systems adopt a closed-world assumption, assuming either the same data distribution for training and testing, or the system could have access to a large out-of-distribution (OoD) dataset for retraining. However, in practical open-world scenarios, task-oriented communication systems will be exposed to unknown OoD data. The powerful approximation ability of learning methods may force the task-oriented communication systems to overfit the training data (i.e., in-distribution data). Therefore, these systems tend to provide overconfident judgments when encountering OoD data. Based on the information bottleneck (IB) framework, we propose a class conditional IB (CCIB) approach to address this problem, supported by information-theoretical insights. The idea is to extract distinguishable features from in-distribution data while keeping their compactness and informativeness. It is achieved by imposing the class conditional latent prior distribution and enforcing the latent of different classes to be far away from each other. Simulation results shall demonstrate that the proposed approach detects OoD data more efficiently than the baselines and state-of-the-art approaches, without compromising the rate-distortion tradeoff.

Federated learning (FL) is a privacy-preserving collaborative learning framework, and differential privacy can be applied to further enhance its privacy protection. Existing FL systems typically adopt Federated Average (FedAvg) as the training algorithm and implement differential privacy with a Gaussian mechanism. However, the inherent privacy-utility trade-off in these systems severely degrades the training performance if a tight privacy budget is enforced. Besides, the Gaussian mechanism requires model weights to be of high-precision. To improve communication efficiency and achieve a better privacy-utility trade-off, we propose a communication-efficient FL training algorithm with differential privacy guarantee. Specifically, we propose to adopt binary neural networks (BNNs) and introduce discrete noise in the FL setting. Binary model parameters are uploaded for higher communication efficiency and discrete noise is added to achieve the client-level differential privacy protection. The achieved performance guarantee is rigorously proved, and it is shown to depend on the level of discrete noise. Experimental results based on MNIST and Fashion-MNIST datasets will demonstrate that the proposed training algorithm achieves client-level privacy protection with performance gain while enjoying the benefits of low communication overhead from binary model updates.

Different from conventional radar, the cellular network structure integrated sensing and communication (ISAC) systems enables collaborative sensing by multiple sensing nodes, e.g., base stations (BSs). However, existing works normally assume designated BSs as the sensing nodes, and thus can't fully exploit the macro-diversity gain. In the paper, we propose a joint BS selection, user association, and beamforming design to tackle this problem. In particular, we minimize the total transmit power by the above-mentioned joint design, while guaranteeing the communication and sensing performance measured by the signal-to-interference-plus-noise ratio (SINR) for the communication users and the Cramer-Rae lower bound (CRLB) for location estimation, respectively. An alternating optimization (AO)-based algorithm is developed to solve the non-convex problem. Simulation results validate the effectiveness of the proposed algorithm and unveil the benefits brought by collaborative sensing and BS selection.

Reliability is of paramount importance for the physical layer of wireless systems due to its decisive impact on end-to-end performance. However, the uncertainty of prevailing deep learning (DL)-based physical layer algorithms is hard to quantify due to the black-box nature of neural networks. This limitation is a major obstacle that hinders their practical deployment. In this paper, we attempt to quantify the uncertainty of an important category of DL-based channel estimators. An efficient statistical method is proposed to make blind predictions for the mean squared error of the DL-estimated channel solely based on received pilots, without knowledge of the ground-truth channel, the prior distribution of the channel, or the noise statistics. The complexity of the blind performance prediction is low and scales only linearly with the number of antennas. Simulation results for ultra-massive multiple-input multiple-output (UM-MIMO) channel estimation with a mixture of far-field and near-field paths are provided to verify the accuracy and efficiency of the proposed method.

Efficient massive/ultra-massive multiple-input multiple-output (MIMO) detection algorithms with satisfactory performance and low complexity are critical to meet the high throughput and ultra-low latency requirements in 5G and beyond communications, given the extremely large number of antennas. In this paper, we propose a low complexity graph neural network (GNN) enhanced approximate message passing (AMP) algorithm, AMP-GNN, for massive/ultra-massive MIMO detection. The structure of the neural network is customized by unfolding the AMP algorithm and introducing the GNN module for multiuser interference cancellation. Numerical results will show that the proposed AMP-GNN significantly improves the performance of the AMP detector and achieves comparable performance as the state-of-the-art deep learning-based MIMO detectors but with reduced computational complexity. Furthermore, it presents strong robustness to the change of the number of users.

The integration of sensing and communication enables wireless communication systems to serve environment-aware applications. In this paper, we propose to leverage sensing to enhance physical layer security (PLS) in multiuser communication systems in the presence of a suspicious target. To this end, we develop a two-phase framework to first estimate the location of the potential eavesdropper by sensing and then utilize the estimated information to enhance PLS for communication. In particular, in the first phase, a dual-functional radar and communication (DFRC) base station (BS) exploits a sensing signal to mitigate the sensing information uncertainty of the potential eavesdropper. Then, in the second phase, to facilitate joint sensing and secure communication, the DFRC BS employs beamforming and artificial noise to enhance secure communication. The design objective is to maximize the system sum rate while alleviating the information leakage by jointly optimizing the time allocation and beamforming policy. Capitalizing on monotonic optimization theory, we develop a two-layer globally optimal algorithm to reveal the performance upper bound of the considered system. Simulation results show that the proposed scheme achieves a significant sum rate gain over two baseline schemes that adopt existing techniques. Moreover, our results unveil that ISAC is a promising paradigm for enhancing secure communication in wireless networks.

One of the fundamental issues for Federated Learning (FL) is data heterogeneity, which causes accuracy degradation, slow convergence, and the communication bottleneck issue. Although the impact of data heterogeneity on supervised FL has been widely studied, the related investigation for Federated Reinforcement Learning (FRL) is still in its infancy. In this paper, we first define the type and level of data heterogeneity for FRL systems. By inspecting the connection between the global and local objective functions, we prove that local training can benefit the global objective, if the local update is properly penalized by the total variation (TV) distance between the local and global policies. A necessary condition for the global policy to be learn-able from the local environments is also derived, which is directly related to the heterogeneity level. Based on the theoretical result, a Kullback-Leibler (KL) divergence based penalty is proposed to directly constrain the model outputs in the distribution space and the convergence proof of the proposed algorithm is also provided. By jointly penalizing the divergence of the local policy from the global policy with a global penalty and penalizing each iteration of the local training with a local penalty, the proposed method achieves a better trade-off between training speed (step size) and convergence. Experiment results on two popular Reinforcement Learning (RL) experiment platforms demonstrate the advantage of the proposed algorithm over existing methods in accelerating and stabilizing the training process with heterogeneous data.

Sensing will become an important service of future wireless networks to assist innovative applications such as autonomous driving and environment monitoring. Perceptive mobile networks (PMNs) were proposed to incorporate sensing capability into current cellular networks. However, the interference management between sensing and communication, as well as the collaborative sensing by multiple sensing nodes (SNs), faces significant challenges. In this paper, we first propose a two-stage protocol to tackle the interference between two sub-systems, where the echoes created by communication signals, i.e., interference for sensing, are estimated in the clutter estimation (CE) stage and then utilized for interference management in the target sensing (TS) stage. Then, a networked sensing detector is derived to exploit the perspectives provided by multiple SNs for sensing the same target. The macro-diversity from multiple SNs, the array gain, and the higher angular resolution from multiple receive antennas of each SN are then investigated to reveal the benefit of networked sensing. Furthermore, we derive the sufficient condition for one SN’s contribution to be positive, based on which a SN selection algorithm is proposed. To reduce the communication workload, we propose a distributed model-driven deep-learning algorithm that utilizes partially-sampled data for CE. Simulation results demonstrate the benefits of networked sensing and validate the higher efficiency of the proposed CE algorithm than existing methods.

The asymptotic mutual information (MI) analysis for multiple-input multiple-output (MIMO) systems over double-scattering channels has achieved engaging results, but the convergence rates of the mean, variance, and the distribution of the MI are not yet available in the literature. In this paper, by utilizing the large random matrix theory (RMT), we give a central limit theory (CLT) for the MI and derive the closed-form approximation for the mean and the variance by a new approach—Gaussian tools. The convergence rates of the mean, variance, and the characteristic function are proved to be${\mathcal {O}}\left({\frac {1}{N}}\right)$for the first time, where$N$is the number of receive antennas. Furthermore, the impact of the number of effective scatterers on the mean and variance was investigated in the moderate-to-high SNR regime with some interesting physical insights. The proposed evaluation framework can be utilized for the asymptotic performance analysis of other systems over double-scattering channels.

Federated learning (FL) is a powerful distributed machine learning framework where a server aggregates models trained by different clients without accessing their private data. Hierarchical FL, with a client-edge-cloud aggregation hierarchy, can effectively leverage both the cloud server’s access to many clients’ data and the edge servers’ closeness to the clients to achieve a high communication efficiency. Neural network quantization can further reduce the communication overhead during model uploading. To fully exploit the advantages of hierarchical FL, an accurate convergence analysis with respect to the key system parameters is needed. Unfortunately, existing analysis is loose and does not consider model quantization. In this paper, we derive a tighter convergence bound for hierarchical FL with quantization. The convergence result leads to practical guidelines for important design problems such as the client-edge aggregation and edge-client association strategies. Based on the obtained analytical results, we optimize the two aggregation intervals and show that the client-edge aggregation interval should slowly decay while the edge-cloud aggregation interval needs to adapt to the ratio of the client-edge and edge-cloud propagation delay. Simulation results shall verify the design guidelines and demonstrate the effectiveness of the proposed aggregation strategy.

Deep learning-based approaches have been developed to solve challenging problems in wireless communications, leading to promising results. Early attempts adopted neural network architectures inherited from applications such as computer vision. They often yield poor performance in large scale networks (i.e., poor scalability) and unseen network settings (i.e., poor generalization). To resolve these issues, graph neural networks (GNNs) have been recently adopted, as they can effectively exploit the domain knowledge, i.e., the graph topology in wireless communications problems. GNN-based methods can achieve near-optimal performance in large-scale networks and generalize well under different system settings, but the theoretical underpinnings and design guidelines remain elusive, which may hinder their practical implementations. This paper endeavors to fill both the theoretical and practical gaps. For theoretical guarantees, we prove that GNNs achieve near-optimal performance in wireless networks with much fewer training samples than traditional neural architectures. Specifically, to solve an optimization problem on an$n$-node graph (where the nodes may represent users, base stations, or antennas), GNNs’ generalization error and required number of training samples are$\mathcal {O}(n)$and$\mathcal {O}(n^{2})$times lower than the unstructured multi-layer perceptrons. For design guidelines, we propose a unified framework that is applicable to general design problems in wireless networks, which includes graph modeling, neural architecture design, and theory-guided performance enhancement. Extensive simulations, which cover a variety of important problems and network settings, verify our theory and the effectiveness of the proposed design framework.

Most students with specific learning disabilities (SLDs) have difficulties in reading and writing. The SLDs pre-screening is crucial because the golden period for therapy is before six years old. However, many students in Hong Kong receive SLDs assessments after the golden period. Also, the SLDs pre-screening is challenging, especially in a language with the logographic script but without prominent sound-script correspondence (e.g., Chinese, Japanese). To make pre-screening SLDs in Chinese more effective and efficient, we designed a new comprehensive pre-screening game for SLDs in Chinese (i.e., dyslexia, dysgraphia, and dyspraxia). Notably, we designed a Chinese morphological awareness puzzle that challenges students to recognize different words made up with the first character that is identical and the second character that is different, such as樹枝 (literally means tree branch),樹幹 (literally means tree truck),樹葉 (literally means tree leaves), and樹根 (literally means tree root). We experimented with students, which showed that our game can effectively pre-screen students with SLDs in Chinese. Our work contributes an approach to quick SLDs in Chinese pre-screening, potentially useful for other logographic languages (e.g., Japanese).

Students with specific learning disabilities (SLDs) often experience reading, writing, attention, and physical movement coordination difficulties. However, in Hong Kong, it takes years for special education needs coordinators (SENCOs) and special-ed teachers to pre-screen and diagnose students with SLDs. Therefore, many students with SLDs missed the golden time for special interventions (i.e., before six years old). In addition, although there are screening tools for students with SLDs in Chinese and Indo-European languages (e.g., English and Spanish), they did not provide a student data visualization dashboard that could help teachers speed up the pre-screening process. Therefore, we designed a new visualization dashboard for Hong Kong SENCOs and special-ed teachers to assist them in pre-screening students with SLDs. Our formative study showed that our current design met teachers’ need to quickly identify a student’s specific under-performing tasks and effectively collect evidence about how the student was affected by SLDs. Future work will further test the efficacy of our design in real life.

In limited feedback multi-user multiple-input multiple-output (MU-MIMO) cellular networks, users send quantized information about the channel conditions to the associated base station (BS) for downlink beamforming. However, channel quantization and beamforming have been treated as two separate tasks conventionally, which makes it difficult to achieve global system optimality. In this paper, we propose an augmented deep unfolding (ADU) approach that jointly optimizes the beamforming scheme at the BSs and the channel quantization scheme at the users. In particular, the classic WMMSE beamformer is unrolled and a deep neural network (DNN) is leveraged to preprocess its input to enhance the performance. The variational information bottleneck technique is adopted to further improve the performance when the feedback capacity is strictly restricted. Simulation results demonstrate that the proposed ADU method outperforms all the benchmark schemes in terms of the system average rate.

Federated learning (FL) is an emerging learning paradigm without violating users' privacy. However, large model size and frequent model aggregation cause serious communication bottleneck for FL. To reduce the communication volume, techniques such as model compression and quantization have been proposed. Besides the fixed-bit quantization, existing adaptive quantization schemes use ascending-trend quantization, where the quantization level increases with the training stages. In this paper, we first investigate the impact of quantization on model convergence, and show that the optimal quantization level is directly related to the range of the model updates. Given the model is supposed to converge with the progress of the training, the range of the model updates will gradually shrink, indicating that the quantization level should decrease with the training stages. Based on the theoretical analysis, a descending quantization scheme named FedDQ is proposed. Experimental results show that the proposed descending quantization scheme can save up to 65.2% of the communicated bit volume and up to 68% of the communication rounds, when compared with existing schemes.

Federated learning (FL) is a promising paradigm to enable privacy-preserving deep learning from distributed data. Most previous works are based on federated average (FedAvg), which, however, faces several critical issues, including a high communication overhead and the difficulty in dealing with heterogeneous model architectures. Federated Distillation (FD) is a recently proposed alternative to enable communication-efficient and robust FL, which achieves orders of magnitude reduction of the communication overhead compared with FedAvg and is flexible to handle heterogeneous models at the clients. However, so far there is no unified algorithmic framework or theoretical analysis for FD-based methods. In this paper, we first present a generic meta-algorithm for FD and investigate the influence of key parameters through empirical experiments. Then, we verify the empirical observations theoretically. Based on the empirical results and theory, we propose a communication-efficient FD algorithm with active data sampling to improve the model performance and reduce the communication overhead. Empirical simulations on benchmark datasets will demonstrate that our proposed algorithm effectively and significantly reduces the communication overhead while achieving a satisfactory performance.

Intelligent reflecting surfaces (IRSs) are promising enablers for next-generation wireless communications due to their reconfigurability and high energy efficiency in improving poor propagation condition of channels, e.g., limited scattering environment. However, most existing works assumed full-rank channels requiring rich scatters, which may not be available in practice. To analyze the impact of rank-deficient channels and mitigate the ensued performance loss, we consider a large-scale IRS-aided MIMO system with statistical channel state information (CSI), where the double-scattering channel is adopted to model rank deficiency. By leveraging random matrix theory (RMT), we first derive a deterministic approximation (DA) of the ergodic rate with low computational complexity and prove the existence and uniqueness of the DA parameters. Then, we propose an alternating optimization algorithm for maximizing the DA with respect to phase shifts and signal covariance matrices. Numerical results will show that the DA is tight and our proposed method can effectively mitigate the performance loss induced by channel rank deficiency.

The mutual information (MI) of Gaussian multi-input multi-output (MIMO) channels has been evaluated by utilizing random matrix theory (RMT) and shown to asymptotically follow Gaussian distribution, where the ergodic mutual information (EMI) converges to a deterministic quantity. However, with non-Gaussian channels, there is a bias between the EMI and its deterministic equivalent (DE), whose evaluation is not available in the literature. This bias of the EMI is related to the bias for the trace of the resolvent in large RMT. In this paper, we first derive the bias for the trace of the resolvent, which is further extended to compute the bias for the linear spectral statistics (LSS). Then, we apply the above results on non-Gaussian MIMO channels to determine the bias for the EMI. It is also proved that the bias for the EMI is −0.5 times of that for the variance of the MI. Finally, the derived bias is utilized to modify the central limit theory (CLT) and calculate the outage probability. Numerical results show that the modified CLT significantly outperforms previous methods in approximating the distribution of the MI and improves the accuracy for the outage probability evaluation.

The conventional design of wireless communication systems typically relies on established mathematical models that capture the characteristics of different communication modules. Unfortunately, such design cannot be easily and directly applied to future wireless networks, which will be characterized by large-scale ultra-dense networks whose design complexity scales exponentially with the network size. Furthermore, such networks will vary dynamically in a significant way, which makes it intractable to develop comprehensive analytical models. Recently, deep learning-based approaches have emerged as potential alternatives for designing complex and dynamic wireless systems. However, existing learning-based methods have limited capabilities to scale with the problem size and to generalize with varying network settings. In this paper, we propose a scalable and generalizable neural calibration framework for future wireless system design, where a neural network is adopted to calibrate the input of conventional model-based algorithms. Specifically, the backbone of a traditional time-efficient algorithm is integrated with deep neural networks to achieve a high computational efficiency, while enjoying enhanced performance. The permutation equivariance property, carried out by the topological structure of wireless systems, is furthermore utilized to develop a generalizable neural network architecture. The proposed neural calibration framework is applied to solve challenging resource management problems in massive multiple-input multiple-output (MIMO) systems. Simulation results will show that the proposed neural calibration approach enjoys significantly improved scalability and generalization compared with the existing learning-based methods.

Integrated sensing and communication (ISAC) creates a platform to exploit the synergy between two powerful functionalities that have been developing separately. However, the interference management and resource allocation between sensing and communication have not been fully studied. In this paper, we consider the design of perceptive mobile networks (PMNs) by adding sensing capability to current cellular networks. To avoid full-duplex operation, we propose the PMN with distributed target monitoring terminals (TMTs) where passive TMTs are deployed over wireless networks to locate the sensing target (ST). To manage the interference between sensing and communication, we jointly optimize the transmit and receive beamformers towards the communication user equipment (UEs) and the ST by alternating-optimization (AO) and prove its convergence. To reduce computation complexity and obtain physical insights, we further investigate the use of linear transceivers, including zero forcing and beam synthesis (B-syn). Our analysis revealed interesting physical insights: 1) instead of forming dedicated sensing signals, it is more efficient to redesign the communication signals for both communication and sensing purposes and “leak” communication energy for sensing; 2) the amount of energy leakage from one UE to the ST depends on their relative locations.

In cell-free massive MIMO networks, an efficient distributed detection algorithm is of significant importance. In this paper, we propose a distributed expectation propagation (EP) detector for cell-free massive MIMO. The detector is composed of two modules, a nonlinear module at the central processing unit (CPU) and a linear module at the access point (AP). The turbo principle in iterative decoding is utilized to compute and pass the extrinsic information between modules. An analytical framework is then provided to characterize the asymptotic performance of the proposed EP detector with a large number of antennas. Simulation results will show that the proposed method outperforms the distributed detectors in terms of bit-error rate.

Channel estimation and beamforming play critical roles in frequency-division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems. However, these two modules have been treated as two stand-alone components, which makes it difficult to achieve a global system optimality. In this paper, we propose a deep learning-based approach that directly optimizes the beamformers at the base station according to the received uplink pilots, thereby, bypassing the explicit channel estimation. Different from the existing fully data-driven approach where all the modules are replaced by deep neural networks (DNNs), a neural calibration method is proposed to improve the scalability of the end-to-end design. In particular, the backbone of conventional time-efficient algorithms, i.e., the least-squares (LS) channel estimator and the zero-forcing (ZF) beamformer, is preserved and DNNs are leveraged to calibrate their inputs for better performance. The permutation equivariance property of the formulated resource allocation problem is then identified to design a low-complexity neural network architecture. Simulation results will show the superiority of the proposed neural calibration method over benchmark schemes in terms of both the spectral efficiency and scalability in large-scale wireless networks.

Federated Learning is a collaborative machine learning framework to train a deep learning model without accessing clients’ private data. Previous works assume one central parameter server either at the cloud or at the edge. The cloud server can access more data but with excessive communication overhead and long latency, while the edge server enjoys more efficient communications with the clients. To combine their advantages, we propose a client-edge-cloud hierarchical Federated Learning system, supported with a HierFAVG algorithm that allows multiple edge servers to perform partial model aggregation. In this way, the model can be trained faster and better communication-computation trade-offs can be achieved. Convergence analysis is provided for HierFAVG and the effects of key parameters are also investigated, which lead to qualitative design guidelines. Empirical experiments verify the analysis and demonstrate the benefits of this hierarchical architecture in different data distribution scenarios. Particularly, it is shown that by introducing the intermediate edge servers, the model training time and the energy consumption of the end devices can be simultaneously reduced compared to cloud-based Federated Learning.

Cellular networks are promising to support effective wireless communications for unmanned aerial vehicles (UAVs), which will help to enable various long-range UAV applications. However, these networks are optimized for terrestrial users, and thus do not guarantee seamless aerial coverage. In this paper, we propose to overcome this difficulty by exploiting controllable mobility of UAVs, and investigate connectivity-aware UAV path planning. To explicitly impose communication requirements on UAV path planning, we introduce two new metrics to quantify the cellular connectivity quality of a UAV path. Moreover, aerial coverage maps are used to provide accurate locations of scattered coverage holes in the complicated propagation environment. We formulate the UAV path planning problem as finding the shortest path subject to connectivity constraints. Based on graph search methods, a novel connectivity-aware path planning algorithm with low complexity is proposed. The effectiveness and superiority of our proposed algorithm are demonstrated using the aerial coverage map of an urban section in Virginia, which is built by ray tracing. Simulation results also illustrate a tradeoff between the path length and connectivity quality of UAVs.

Caching popular content at mobile devices, accompanied by device-to-device (D2D) communications, is one promising technology for effective mobile content delivery. User mobility is an important factor when investigating such networks, which unfortunately was largely ignored in most previous works. Preliminary studies have been carried out but the effect of mobility on the caching performance has not been fully understood. In this paper, by explicitly considering users’ contact and inter-contact durations via an alternating renewal process, we first investigate the effect of mobility with a given cache placement. A tractable expression of the data offloading ratio, i.e., the proportion of requested data that can be delivered via D2D links, is derived, which is proved to be increasing with the user moving speed. The analytical results are then used to develop an effective mobility-aware caching strategy to maximize the data offloading ratio. Simulation results are provided to confirm the accuracy of the analytical results and also validate the effect of user mobility. Performance gains of the proposed mobility-aware caching strategy are demonstrated with both stochastic models and real-life data sets. It is observed that the information of the contact durations is critical to design cache placement, especially when they are relatively short or comparable to the inter-contact durations.

Caching at mobile devices, accompanied by device-to-device (D2D) communications, is one promising technique to accommodate the exponentially increasing mobile data traffic. While most previous works ignored user mobility, there are some recent works taking it into account. However, the duration of user contact times has been ignored, making it difficult to explicitly characterize the effect of mobility. In this paper, we adopt the alternating renewal process to model the duration of both the contact and inter-contact times, and investigate how the caching performance is affected by mobility. The data offloading ratio, i.e., the proportion of requested data that can be delivered via D2D links, is taken as the performance metric. We first approximate the distribution of the communication time for a given user by beta distribution through moment matching. With this approximation, an accurate expression of the data offloading ratio is derived. For the homogeneous case where the average contact and intercontact times of different user pairs are identical, we prove that the data offloading ratio increases with the user moving speed, assuming that the transmission rate remains the same. Simulation results are provided to show the accuracy of the approximate result, and also validate the effect of user mobility.

Mobile-edge computing (MEC) has recently emerged as a prominent technology to liberate mobile devices from computationally intensive workloads, by offloading them to the proximate MEC server. To make offloading effective, the radio and computational resources need to be dynamically managed, to cope with the time-varying computation demands and wireless fading channels. In this paper, we develop an online joint radio and computational resource management algorithm for multi-user MEC systems, with the objective of minimizing the long-term average weighted sum power consumption of the mobile devices and the MEC server, subject to a task buffer stability constraint. Specifically, at each time slot, the optimal CPU-cycle frequencies of the mobile devices are obtained in closed forms, and the optimal transmit power and bandwidth allocation for computation offloading are determined with the Gauss-Seidel method; while for the MEC server, both the optimal frequencies of the CPU cores and the optimal MEC server scheduling decision are derived in closed forms. Besides, a delay-improved mechanism is proposed to reduce the execution delay. Rigorous performance analysis is conducted for the proposed algorithm and its delay-improved version, indicating that the weighted sum power consumption and execution delay obey an [O (1/V) , O (V)] tradeoff with V as a control parameter. Simulation results are provided to validate the theoretical analysis and demonstrate the impacts of various parameters.

Caching at mobile devices can facilitate device-to-device (D2D) communications, which may significantly improve spectrum efficiency and alleviate the heavy burden on backhaul links. However, most previous works ignored user mobility, thus having limited practical applications. In this paper, we take advantage of the user mobility pattern by the inter-contact times between different users, and propose a mobility-aware caching placement strategy to maximize thedata offloading ratio, which is defined as the percentage of the requested data that can be delivered via D2D links rather than through base stations. Given the NP-hard caching placement problem, we first propose an optimal dynamic programming algorithm to obtain a performance benchmark with much lower complexity than exhaustive search. We then prove that the problem falls in the category of monotone submodular maximization over a matroid constraint, and propose a time-efficient greedy algorithm, which achieves an approximation ratio as$\frac {1}{2}$. Simulation results with real-life data sets will validate the effectiveness of our proposed mobility-aware caching placement strategy. We observe that users moving at either a very low or very high speed should cache the most popular files, while users moving at a medium speed should cache less popular files to avoid duplication.

Mobile-edge computing (MEC) has recently emerged as a promising paradigm to liberate mobile devices from increasingly intensive computation workloads, as well as to improve the quality of computation experience. In this paper, we investigate the tradeoff between two critical but conflicting objectives in multi-user MEC systems, namely, the power consumption of mobile devices and the execution delay of computation tasks. A power consumption minimization problem with task buffer stability constraints is formulated to investigate the tradeoff, and an online algorithm that decides the local execution and computation offloading policy is developed based on Lyapunov optimization. Specifically, at each time slot, the optimal frequencies of the local CPUs are obtained in closed forms, while the optimal transmit power and bandwidth allocation for computation offloading are determined with the Gauss-Seidel method. Performance analysis is conducted for the proposed algorithm, which indicates that the power consumption and execution delay obeys an [0(1/V), 0(V)] tradeoff with V as a control parameter. Simulation results are provided to validate the theoretical analysis and demonstrate the impacts of various parameters to the system performance.

As a promising technique to meet the drastically growing demand for both high throughput and uniform coverage in the fifth generation (5G) wireless networks, massive multiple-input multiple-output (MIMO) systems have attracted significant attention in recent years. However, in massive MIMO systems, as the density of mobile users (MUs) increases, conventional uplink training methods will incur prohibitively high training overhead, which is proportional to the number of MUs. In this paper, we propose a selective uplink training method for massive MIMO systems, where in each channel block only part of the MUs will send uplink pilots for channel training, and the channel states of the remaining MUs are predicted from the estimates in previous blocks, taking advantage of the channels' temporal correlation. We propose an efficient algorithm to dynamically select the MUs to be trained within each block and determine the optimal uplink training length. Simulation results show that the proposed training method provides significant throughput gains compared to the existing methods, while much lower estimation complexity is achieved. It is observed that the throughput gain becomes higher as the MU density increases.

Caching popular content at base stations is a powerful supplement to existing limited backhaul links for accommodating the exponentially increasing mobile data traffic. Given the limited cache budget, we investigate the cache size allocation problem in cellular networks to maximize the user success probability (USP), taking wireless channel statistics, backhaul capacities and file popularity distributions into consideration. The USP is defined as the probability that one user can successfully download its requested file either from the local cache or via the backhaul link. We first consider a single-cell scenario and derive a closed-form expression for the USP, which helps reveal the impacts of various parameters, such as the file popularity distribution. More specifically, for a highly concentrated file popularity distribution, the required cache size is independent of the total number of files, while for a less concentrated file popularity distribution, the required cache size is in linear relation to the total number of files. Furthermore, we study the multi-cell scenario, and provide a bisection search algorithm to find the optimal cache size allocation. The optimal cache size allocation is verified by simulations, and it is shown to play a more significant role when the file popularity distribution is less concentrated.

Underlaying device-to-device (D2D) communications to a cellular network is considered as a key technique to improve spectral efficiency in 5G networks. For such D2D systems, mode selection and resource allocation have been widely utilized for managing interference. However, previous works allowed at most one D2D link to access the same channel, while mode selection and resource allocation are typically separately designed. In this paper, we jointly optimize the mode selection and channel assignment in a cellular network with underlaying D2D communications, where multiple D2D links may share the same channel. Meanwhile, the QoS requirements for both cellular and D2D links are guaranteed, in terms of Signal-to-Interference-plus-Noise Ratio (SINR). We first propose an optimal dynamic programming (DP) algorithm, which provides a much lower computation complexity compared to exhaustive search and serves as the performance bench mark. A bipartite graph based greedy algorithm is then proposed to achieve a polynomial time complexity. Simulation results will demonstrate the advantage of allowing each channel to be accessed by multiple D2D links in dense D2D networks, as well as, the effectiveness of the proposed algorithms.

In this paper, we propose effective channel assignment algorithms for network utility maximization in a cellular network with underlaying device-to-device (D2D) communications. A major innovation is the consideration of partial channel state information (CSI), i.e., the base station (BS) is assumed to be able to acquire “partial” instantaneous CSI of the cellular and D2D links, as well as, the interference links. In contrast to the existing works, multiple D2D links are allowed to share the same channel, and the quality of service (QoS) requirements for both the cellular and D2D links are enforced. We first develop an optimal channel assignment algorithm based on dynamic programming, which enjoys a much lower complexity compared with exhaustive search and will serve as a performance benchmark. To further reduce complexity, we propose a cluster-based sub-optimal channel assignment algorithm. New closed-form expressions for the expected weighted sum rate and the successful transmission probabilities are also derived. Simulation results verify the effectiveness of the proposed algorithms. Moreover, by comparing different partial CSI scenarios, we observe that the CSI of the D2D communication links and the interference links from the D2D transmitters to the BS significantly affects the network performance, while the CSI of the interference links from the BS to the D2D receivers only has a negligible impact.

The 5th generation (5G) wireless networks are conceived in the form of heterogeneous networks (HetNets), where small cells are deployed over the conventional macrocell networks to improve the spectral efficiency. In HetNets, the interference between different tiers is the main bottleneck for achieving high spectral efficiency. Many spectrum access schemes have been proposed to manage the cross-tier interference. Unfortunately, the optimal spectrum access scheme remains unknown. In this paper, we propose an F-ALOHA based cognitive spectrum access scheme for macro-femto HetNets, where the femtocells can access the idle macro-tier spectrum with a certain probability. Therefore, besides the degrees of freedom from the conventional spectrum deployment and co-tier spectrum access, the proposed scheme obtains a new degree of freedom from cross-tier spectrum access for interference management and spectral efficiency optimization. Simulation results will show that the proposed scheme outperforms existing F-ALOHA based spectrum access schemes in terms of the area spectral efficiency (ASE). More importantly, it is observed that the maximum ASE is achieved when the number of active links per unit area, which governs the interference level, reaches a certain value. The advantage of the proposed scheme comes from its ability to offload the traffic between two tiers through the cross-tier spectrum access probability, which flexibly manages the cross-tier interference.

Underlaying device-to-device (D2D) communication links to a cellular network is a promising way to improve spectrum efficiency, for which the cross- link interference should be carefully controlled. Resource allocation has been widely utilized for managing interference in D2D networks. However, most previous works made simple assumptions by either ignoring the reliability requirement of D2D links or not allowing multiple D2D links to share the same channel. In this paper, we propose effective channel assignment algorithms to maximize the weighted sum-rate in a cellular network with underlaying D2D communications, where multiple D2D links are allowed to share the same channel. Meanwhile, the minimum Signal-to- Interference-plus-Noise Ratio (SINR) requirements for both cellular and D2D links are guaranteed. We first provide an optimal algorithm based on dynamic programming (DP) to serve as the performance benchmark, which enjoys much lower complexity compared to exhaustive search. To further reduce complexity, we then propose a cluster-based near-optimal channel assignment algorithm. Simulation results will demonstrate the advantage of allowing multiple D2D links to share the same channel in dense D2D networks, as well as verifying the effectiveness of the proposed algorithms.

Heterogeneous networks (HetNets) provide an effective way to meet the explosive growth of mobile data traffic. Previous studies have revealed that the successful transmission probability, i.e., the link reliability, of a SISO HetNet is invariant to the base station (BS) density. This indicates that the area spectral efficiency (ASE) can be increased by densifying the network, without sacrificing the link performance. However, in this paper, we shall show that the above invariance property no longer holds in multi-antenna HetNets. More specifically, changing the BS density will affect both the link reliability and the ASE, and there exists a tradeoff between these two important performance metrics. By adopting the Poisson point process to model the BS positions, we develop an exact expression of the successful transmission probability for general multiuser MIMO HetNets. We then use this result to evaluate the tradeoff between the ASE and the link reliability. It is analytically shown that the maximum successful transmission probability of the network is achieved by activating only the tier of BSs with the largest number of antennas per BS, while the maximum ASE is achieved by activating all the BSs. By adjusting the density of each tier of the HetNet, a tradeoff between the link reliability and ASE can be achieved.

In practical multimedia systems, the content of coded video streams often needs to be re-edited at the nodes of transmitting networks. For example, logo insertion is always required for copyright protection at different local transmitting nodes. This kind of video stream editing is denoted as video content editing transcoding (VCET) in this paper. Though some techniques have been suggested for VCET, these methods cannot meet the requirement of dynamic transmitting bandwidth in practical applications. In this paper, we proposed an interface macroblock-based transcoding scheme for VCET, which can reuse the variable length codes of the original video streams as much as possible to achieve the best VCET quality. In order to ensure that the edited video streams can be transmitted by the original bandwidth, we also proposed a rate control algorithm for VCET, which can accurately control the bitrate of edited video streams according to the frame level coding bits of the original video streams. Experimental results showed that the proposed scheme achieved substantially better results in bitrate accuracy, computational complexities, and video quality than many other existing schemes.

Energy harvesting (EH) has recently been attracting significant attention because of its ability to scavenge environmentally friendly energy. In this paper, we investigate the use of EH relay nodes to improve the quality of service (QoS) for relaying networks. To simplify the hardware design, we adopt a half-duplex selective decode-and-forward (SDF) relay. We propose a joint link selection and relay power allocation strategy to minimize the average outage probability. Both offline and online policies, i.e., with non-causal or causal side information about the energy state and the decoding result at the relay, are investigated by utilizing deterministic and stochastic dynamic programming (DP) algorithms, respectively. Furthermore, to reduce the complexity of the optimal online solution, we propose two low-complexity suboptimal online policies. Simulation results will show that the proposed suboptimal policies outperform the existing policies and achieve near optimal performance.

Deploying femtocells over the conventional macrocell network is a promising way to increase network capacity, whereas the main bottleneck is the interference between the femtocell and macrocell tiers. Recent research has proposed many effective methods for cross-tier interference mitigation, but unfortunately, the optimal spectrum access scheme remains unknown. In this paper, a cognitive spectrum access scheme is proposed, where each femtocell can dynamically explore and access the idle macro-tier spectrum besides its dedicated femto-tier spectrum. The optimum probabilities for each femtocell to access the femto-tier and idle macro-tier spectrum which maximize the area spectral efficiency (ASE) are investigated. With perfect spectrum sensing, the ratio between the optimum probabilities to access the femto-tier and macro-tier spectrum is equal to the amounts of available subchannels in the femto-tier and macro-tier spectrum for most cases. With non-perfect spectrum sensing, a lower bound of the optimum probability to access the femto-tier spectrum is determined, which is a piecewise function of the femtocell intensity. Simulation results validate the accuracy of our analytical results and reveal the advantages of the proposed scheme over existing schemes.

The deployment of femtocells in a conventional cellular network is a promising way to increase network capacity, whereas the main bottleneck is the interference between and within tiers. Previous work proposed channel splitting and F-ALOHA to manage the cross-tier and co-tier interference, respectively. However, such spectrum allocation scheme is not efficient given the often scenarios where part of the macro-tier spectrum is vacant but the femto-tier spectrum is overused. In this paper, a cognitive spectrum access scheme is proposed, where femtocells can access both femto-tier and macro-tier spectrum with certain probabilities, to increase the area spectral efficiency (ASE). The closed-form expressions of the optimum spectrum access probabilities in maximizing the ASE are derived for two scenarios where macrocell base stations (MBSs) are modeled as Poisson point process (PPP) and periodic grid. Analytical results reveal that for most cases, the ratio between the optimum probabilities for femtocells to access the femto-tier and macro-tier spectrum is equal to the ratio between the number of subchannels in the femto-tier and idle macro-tier spectrum. Simulation results show that with both models, the proposed scheme outperforms previous work in terms of the ASE.

Random spatial network models have been recently utilized in the performance analysis and system design for multi-cell networks. Such an approach has been mainly adopted to investigate the outage based system performance, such as the outage probability and outage throughput. However, these performance metrics are defined with a fixed-rate transmission, and cannot characterize the performance of data traffic, which normally adopts rate adaptation. In this paper, we will evaluate the average throughput of a space division multiple access (SDMA) based cellular network by considering stochastically distributed base stations (BSs) and mobile terminals (MTs). The major difficulty for the performance analysis is the complicated distribution of the interference links. We shall provide an analytical framework for evaluating the average throughput by using the Moment Generating Function (MGF) based method. Simulations will show that the proposed method is very accurate. In particular, the analytical result can be utilized to determine the optimum number of MTs to be served in SDMA networks that can maximize the network throughput.

This paper considers a conditional decode-and-forward (DF) based cooperative system where a source (S) with multiple (M) antennas transmits information to a single-antenna destination (D) with the help of multiple (L ≤ M - 1) single-antenna relays ({Ri}). The optimal transmit weighting vector at the source is not available in the literature due to the non-linear conditional DF operation, which renders the problem non-convex. To solve this problem, we first show that the optimal transmit vector for the single-relay system can be determined by comparing the S-D beamformer (maximum-ratio-transmit beamforming vector for the S-D link) with the one that utilizes “just sufficient” energy to activate the relay-link. However, it is difficult to directly apply the above idea to the multi-relay system, due to the fact that the S-R and S-D links are normally not orthogonal. To tackle this issue, we propose to utilize basis functions that are orthogonal to the S-D and S-R links, respectively, which enables the activating of one S-R link without considering the S-D link and the other S-R links. We then apply the new basis functions to the multi-relay system and propose a selective relay-activation algorithm, where the optimal solution is obtained by comparing the S-D beamformer with schemes that selectively activate different combinations of the relay-links. The selective relay-activation algorithm is different from the conventional water-filling in the sense that the energy is filled to discrete levels to activate the S-R links, a unique feature arising from the conditional DF operation.

With interference alignment in the spatial domain, the achievable degrees of freedom (DoF) of a single-hop multiple-input multiple-output (MIMO) interference channel (IC) are limited by the number of antennas at the sources and destinations. The use of relays introduces additional freedoms to manage the interference and can enhance the DoF performance. However, the characterization of the DoF regions with relays is much more complicated and is not available in the literature. In this paper, we shall investigate the DoF of the dual-hop MIMO IC via interference alignment. Based on the solvability of the alignment conditions, the upper bound for the maximum achievable DoF tuple is obtained. To evaluate the tightness of the derived bound, we further propose an iterative algorithm to determine the processing matrices at the sources, relays, and destinations for a given feasible DoF tuple. It is shown that the proposed algorithm can achieve the upper bound for the sum DoF in the low and high DoF regions, where the achievability indicates that the upper bound indeed gives the maximum sum DoF. It is also found that despite the DoF loss caused by the half-duplexity assumption, the dual-hop IC with sufficient number of relays can still outperform the conventional single-hop IC under most circumstances.

It has been shown that, with the instantaneous power constraint and joint power allocation, two-way relaying (TWR) outperforms one-way relaying (OWR) in terms of system throughput. However, the conclusion is not clear for the case with the average power constraint and the case with the instantaneous power constraint but separate power allocation. In this paper, we shall first investigate the optimality of TWR with the average power constraint where we consider both of the joint power allocation among three nodes and the separate power allocation between the source and relay nodes. It will be shown that, with the average power constraint, TWR is not always better than OWR. The conditions with which one scheme outperforms the other are derived and utilized to indicate the operating regions for the two schemes. It is observed that the operating region for TWR, where TWR outperforms OWR, increases as the transmit SNR increases. A similar conclusion is obtained for the case with the instantaneous power constraint and separate power allocation.

The optimal DMT (diversity and multiplexing tradeoff) of interference channel is still unknown. In this paper, we investigate the maximum diversity gain of interference channel and try to answer two questions. Firstly, it is known that no two links can achieve their maximum DoF (degree of freedom) simultaneously in an interference channel. The question is whether two links can achieve their maximum diversity gains simultaneously over MIMO or diagonal interference channel? Secondly, it has been shown in the literature that, with IA (interference alignment) and single-beam transmission, only one-side diversity gain (transmit or receive) is achievable in MIMO interference channel. The question is whether IA is optimal in achieving diversity gain and whether both of the transmit and receive diversity gains of MIMO interference channel can be obtained simultaneously? The major contribution of this paper is to show that, although two links in MIMO interference channel can not obtain their maximum diversity gains at the same time, both the transmit and receive diversity gains are achievable. On the other hand, two links in diagonal interference channel can obtain their maximum channel freedoms at the same time, which correspond to the maximum diversity gains.

In this paper, we investigate the benefits of relaying by comparing the transmission rates of both direct transmission (DT) and relaying. It is shown that relaying achieves SNR (signal-to-noise power ratio) gain over DT due to less pathloss, but with several relaying penalties, including a lower multiplexing gain (due to half-duplex), a lower transmit power and a higher outage requirement at each hop (due to multi-hop). We determine the conditions over which relaying outperforms DT, where the SNR gain is greater than the loss due to relaying penalties. The result is applied to the LTE-advanced networks (LTE-A) where the relay nodes (RNs) are implemented to relay information between the user equipment (UE) and the evolutional NodeB (eNB). The major difference between LTE-A and a general relay system lies in that the UE-RN hop consists of multiple frequency-division access links, while the RN-eNB hop is a point-to-point link. By investigating the effects of diversity gain on the transmission rate, we propose an outage-capacity based adaptive relaying (OCA-R) scheme to replace the conventional same-carrier relaying (SC-R). It is shown that the transmission rates of both SC-R and OCA-R are one half of the harmonic means between the outage-capacities for two hops, where the advantage of OCA-R over SC-R comes from a higher diversity gain in the RN-eNB link.

Relaying primary signals by cognitive base-stations (CBSs) can help the primary system and thus win CBSs a higher chance to transmit their own signals. For this purpose, conventional zero-forcing (CZF) beamforming is a straightforward solution where the primary and cognitive signals are transmitted from a multi-antenna CBS without causing interference to each other. However, with CZF, no priority is given to the primary user (PU), which is not consistent with the idea of cognitive radio. In this paper, we shall propose a prior ZF (PZF) scheme which gives priority to the PU by transmitting primary signals without considering their interference to the cognitive users (CUs), while cognitive signals are not allowed to generate interference to the PU. As a result, PZF provides a better channel for the CBS-PU link than CZF but the same channel gain for the CBS-CU links as CZF. We compare PZF and CZF by considering both the transmit power with given target rates and the outage performance with given transmit power, where closed-form conditions are derived to indicate their respective advantages. One of the important contributions of this paper is to prove that, with one CU, a target rate of 1 bit/s/Hz for the CU is the key point that differentiates PZF and CZF, which is independent of the number of CBS-antennas, the channel distributions, and the signal-to-noise power ratio (SNR).

Interference alignment (IA) is a powerful technique to handle interference between links that share the same wireless channel. It has been shown that IA can asymptotically help each link to achieve half of the degrees of freedom (DoF). This result solves one endpoint of the optimal diversity and multiplexing tradeoff (DMT) for interference channels, namely, the maximum multiplexing gain. In this paper, we shall focus on the other endpoint to investigate the maximum diversity gain. For an interference channel with K links where each link has a channel freedom (independent channels) of L, zero-forcing algorithm can achieve a diversity gain of L-K +1 for each link and a sum diversity gain of K(L - K + 1) for the whole network. The question we want to answer is whether interference alignment can provide a higher diversity gain, which will help determine the optimal DMT of interference channels. It will be shown that IA can increase the sum diversity gain by K - 1, where at most K - 2 of them can be utilized by one link.

The degrees of freedom (DoF) has been recognized as a powerful metric to characterize the capacity of interference channels in the high signal-to-noise (SNR) region. In this paper, by utilizing linear interference alignment, we investigate the DoF of multiple-input and multiple-output (MIMO) interference relay channels without symbol extensions. An innovative algorithm is presented to align the interference, where the filter matrices at the sources, relays and destinations are determined in an iterative manner. Based on the assumption that improperness of the alignment condition implies its unsolvability, an upper bound for the achievable DoF tuple by linear interference alignment is derived, and then utilized to examine the performance of the proposed alignment algorithm. Simulation results show that the iterative algorithm can achieve the upper bound in medium to high DoF regions.

Given the growing concern over energy consumption and associated global warming, green communication is becoming more and more important. Lots of efforts have been put into investigating energy efficiency based design in wireless systems. Unfortunately, the maximum energy efficiency of a point-to-point link is normally achieved when the transmit power approaches zero, which, however, is not desirable in practical systems due to the low achievable data rate. In this paper, we consider the energy efficiency optimization with a practical power consumption model, where, besides a maximum transmit power constraint, we set a rate constraint (r0) to guarantee an average throughput requirement (Rth). Due to the possible outage in transmission, r0is in general different from Rth, and determining r0for a given Rthis not trivial. We shall derive a closed-form solution for the optimal transmit power that maximizes energy efficiency. We will also demonstrate that a carefully selected rate constraint r0can guarantee the required average throughput Rthand provide the freedom to achieve different tradeoffs between energy efficiency and average throughput.

Large-scale propagation effect plays an important role in multiple-input multiple-output (MIMO) relay systems. In particular, the position of the relay (between the source and destination) determines the path-loss effects of adjacent hops, which will further affect the performance of each hop and thus the relay system. In this paper, by minimizing the outage probability of a decode-and-forward (DF) MIMO relay system with orthogonal space-time block coding, we show that relay position optimization improves the finite-SNR (signal-to-noise ratio) diversity gain of a relay system whose adjacent hops have different diversity orders (unbalanced system). Specifically, with relay position optimization, the diversity gain is no longer bounded by that of the weaker hop, i.e., the hop with a lower diversity order, but approaches the diversity order of the stronger hop. This diversity improvement provides a dramatic improvement for the end-to-end outage probability. It will also be shown that although power allocation has no effects on the achievable diversity order, it provides some SNR gains.

In a two-hop system with multiple amplify-and-forward (AF) relays, the instantaneous channel state information (CSI) is usually required at the relays to achieve a coherent combining at the destination. In this paper, we investigate the design of non-coherent relaying systems, where no CSI is available at the relays. It will be shown that non-coherent relaying can achieve the optimal diversity and multiplexing tradeoff (DMT) of coherent relaying systems with no penalty in the coding length, but incurs a loss of combining gain. To illustrate the practicality of non-coherent relaying, we propose a simple spreading-based non-coherent relaying scheme which achieves the optimal diversity gain. However, the spreading scheme demonstrates a unique non-coherent penalty, namely, the inclusion of one non-coherent relay may introduce "negative" contribution to the receive SNR. To handle this penalty, a distributed relay selection scheme, which can achieve a better outage performance with less energy, is proposed.

Relaying of the primary signals by cognitive base- stations (CBSs) can help the primary system and thus win CBSs a higher chance to transmit their own signals. For this purpose, conventional zero- forcing (CZF) beamforming is a straightforward solution where the primary and cognitive signals are transmitted from a multi-antenna CBS without causing interference to each other. However, with CZF, no priority is given to the primary signals, which is not consistent with the idea of cognitive radio. In this paper, we shall propose a prior ZF (PZF) method which gives priority to the primary users (PUs) by allowing the primary signals to be transmitted without considering their interference to the cognitive users (CUs), while the cognitive signals are not allowed to generate any interference to the PU. As a result, PZF provides a higher effective channel gain for the CBS-PU link and thus is preferred by the PU. The comparison of CZF and PZF with respect to the CU's performance is dictated by a tradeoff between a higher cognitive signal power (with PZF) and a lower primary interference (with CZF). It is shown that, with one CU, a target rate of 1 bit/s/Hz for the CU is the key point that differentiates the advantage of PZF and CZF, and this point is independent of the number of CBS- antennas, channel conditions, and transmit SNR.

In a multi-relay system, the instantaneous channel state information (CSI) is usually required at relays to achieve the space diversity. In this paper, we propose a delayed noncoherent relaying scheme to achieve space diversity without requiring knowledge of the instantaneous CSI at the relays. Specifically, each relay will non-coherently amplify and forward its received signals with different delays such that a virtual multipath channel is created between the source and destination. We will first determine the most efficient way to achieve the optimal diversity gain. We will then show that, with spreading or coding, the proposed non-coherent relaying can provide the same diversity order as its coherent counterpart but with a noncoherent penalty. With the simplest spreading scheme, the noncoherent penalty results in a loss of both multiplexing gain and finite-SNR diversity gain. To recover the loss due to this noncoherent penalty, a distributed relay selection algorithm, which can achieve a better outage performance with less energy, is proposed. On the other hand, with the Gaussian-coded noncoherent relaying, no multiplexing gain loss will be incurred but a loss of SNR gain will occur.

Single carrier frequency division multiple access (SC-FDMA) has been receiving much attention as the uplink multiple access technology in the next generation communication systems due to its lower peak-to-average power ratio (PAPR) compared to OFDMA. However, it was shown that PAPR is still an issue for SCFDMA, especially with the localized subcarrier allocation (SC-LFDMA) scheme. The precoding method has been shown to be effective in reducing the peak power. However, the construction of the codewords is a nondeterministic polynomial-time hard (NP-hard) problem. In this paper, we first formulate the problem of PAPR reduction by precoding as a combinatorial problem, and then propose the semidefinite relaxation approach with which the problem can then be solved in polynomial time. It will be shown that the proposed scheme can efficiently reduce the peak power with much lower complexity. By taking the transmit power limit into consideration, we further demonstrate the existence of a tradeoff between the transmit power increase and the peak power reduction. Specifically, less stringent power constraint will lead to more significant PAPR reduction.

It has been shown in the literature that, with zero-padding prefix (ZPP), the optimum diversity gain of frequency selective channels can be obtained by uncoded signals and zero forcing (ZF) equalizers. In this paper, we derive two important results about linear equalizers over frequency selective channels: 1) With cyclic prefix and any rate-1 unitary precoding, which includes the uncoded/coded-OFDM/SCFDMA systems as special cases, linear equalizers can only obtain order-1 diversity; and 2) Although linear equalizers can achieve the optimum diversity order with zero-padding prefix and uncoded signals, the required SNR (signal-to-noise ratio) is an increasing function of the symbol-length. The second result implies that we may not be able to take advantage of the diversity gain in practice due to the high SNR requirements. Special cases are also investigated to gain some physical insights about the effects of different prefixes on the performance of linear equalizers. The results in this paper can be utilized in the design of up/down-link LTE systems with linear equalizers.

Carrier frequency offset (CFO) damages the orthogonality between sub-carriers and thus causes multiuser interference in uplink OFDMA/SC-FDMA systems. For a given CFO, such multiuser interference is mainly dictated by channel (sub-carrier) allocation, which also specifies the diversity gain of one user over multi-path channels. In particular, the positions of one user's sub-channels will determine its diversity gain, while the distances between sub-channels of the concerned user and those of others will govern the CFO interference. Two popular channel allocation methods are the localized and interleaved (distributed) schemes where the former has less CFO interference but the latter achieves more diversity gain. In this paper, we will consider the channel allocation scheme for uplink LTE systems by investigating the effects of channel allocation on both of the diversity gain and CFO interference. By combining these two effects, we will propose a semi-interleaved scheme, which achieves full diversity gain with minimum CFO interference.

Cooperative communication has been receiving significant attention in recent years because of the great potential for performance improvement. In this paper, we will consider the joint design of transmit and cooperative beamforming vectors in cooperative systems with a multi-antenna source and multiple single-antenna relays. The cooperative beamforming, performed by distributed relay nodes, is different from the conventional transmit beamforming in multiple-input and multiple-output (MIMO) systems because each node only has access to the channel information of its own links. With instantaneous channel state information (CSI), the optimal beamforming vectors (transmit and cooperative) will maximize the received SNR by allocating power among different relay links, and each relay can determine the optimal operation in a distributed manner. If only statistical CSI is available, it is shown that the optimal transmit scheme degenerates to a relay selection algorithm where only one relay link is active.

Spectrum sensing is critical for cognitive systems to locate spectrum holes. In the IEEE 802.22 proposal, short quiet periods are arranged inside frames to perform a coarse intra-frame sensing as a pre-alarm for fine inter-frame sensing. However, the limited sample size of the quiet periods may not guarantee a satisfying performance and an additional burden of quiet-period synchronization is required. To improve the sensing performance, we first propose a quiet-active sensing scheme in which inactive customer-provided equipments (CPEs) will sense the channels in both the quiet and active periods. To avoid quiet-period synchronization, we further propose to utilize (optimized) active sensing, in which the quiet periods are replaced by 'quiet samples' in other domains, such as quiet sub-carriers in OFDMA systems. By doing so, we not only save the need for synchronization, but also achieve selection diversity by choosing quiet sub-carriers based on channel conditions. The proposed active sensing scheme is also promising for spectrum sharing applications where both the cognitive and primary systems can be active simultaneously.

Spectrum sensing is inevitable for cognitive systems to locate the spectrum holes. In the IEEE 802.22 proposal, very short quiet periods are arranged inside frames to perform a coarse intra-frame sensing whose results are then utilized to decide whether a fine inter-frame sensing is needed. The purpose of intra-frame sensing is to make an early alarm about the presence of primary users. However, the placement of short quiet periods in intra-sensing requires quiet period synchronization and degrades the transmission rate. In this paper, we propose active sensing as an alternative for intra-frame sensing to save the short quiet periods so that the system throughput is increased. In active sensing scheme, the sensing is performed by some inactive CPEs when the cognitive system is active. The motivation comes from the facts that we have much more received samples for active sensing and we only need a coarse result.

The optimal transmit-beam number and power allocation over generic correlated multiple-input multiple-output (MIMO) Rayleigh fading channels are investigated. By fully exploiting the algebraic structures and properties of the optimization problem, we obtain a procedure for determining the thresholds of optimal beam number and derive a set of elementary equations for optimal power allocation, which can be efficiently solved by using a Broyden-like method. A closed-form criterion for the optimality of beamforming is also proposed. Numerical results are presented for illustration.

Analysis of mutual information is important to wireless transmission over fading channels, and is usually done by assuming perfect channel state information (CSI) available at the receiver. In many practical applications, however, the CSI must be estimated by sending a limited number of pilot symbols and thus, can suffer from considerable inaccuracy. The influence of CSI inaccuracy on achievable mutual information, though analyzed in the past, is not well understood. The difficulty arises from the presence of a product term of signal and channel estimation error in the received signal model, and the lack of appropriate tools to determine its probability density function. This situation forces the adoption of a bounding technique in current literature by treating the product term either as a signal component or as a noise component. In this paper, we take a different methodology by accurately fitting the received signal with the multivariate Pearson's type-7 (MPT-7) distribution. The new results are simple in expression, very accurate as evidenced by simulation, and capable of directly revealing the dependence of the achievable mutual information on a particular channel estimator.

Distributed systems with multiple amplify-andforward (AF) relays are very appealing, due to their ease of implementing space diversity. Although their performance on synchronous flat-fading channels was well understood, the corresponding design and optimization in asynchronous frequency selective fading (AFSF) channels remains unsolved. In this paper, we tackle the problem in the information-theoretic framework, revealing that multi-relay amplify-and-forward (MR-AF) systems over AFSF channels can be better understood through the concept of virtual sub-channels. Each relay node virtually performs two functions, appropriately amplifying sub-channel signals on one hand and serving as a local switching center on the other. System design, therefore, reduces to the determination of optimal amplification factors, switching matrices, and power allocation among the source, relays and relevant sub-channels. The optimization is implemented in a layered structure. The effects of asynchronism and knowledge of channel information on mutual information are also investigated.

Gaussian signals are widely assumed in various design and analysis since the classical work of Shannon. However, the optimality of Gaussian signals is valid only when perfect channel information is available at the receiver. In many practical applications, only a channel estimate is available with certain estimation errors. It is therefore of practical importance to ask what is the optimal input distribution for such a system. The optimization can theoretically be formulated in the framework of variational calculus which, however, is in general mathematically intractable. This letter therefore takes a compromise between the optimality and tractability by confining our optimal search within the popular Pearson's distribution system. The result is Pearson's type-VII (PT-7) distribution and its superiority over the Gaussian distribution is confirmed by both theoretical analysis and computer simulations.

Gaussian inputs and nearest neighbor decoder are optimal choices for the transceiver when perfect channel information is available. However, channel uncertainty is inevitable in practical systems due to additive noise and channel variation. Under such circumstances, Gaussian inputs are no longer optimal whereas the nearest neighbor decoder even loses part of the mutual information provided by Gaussian codebooks. In this letter, we tackle the issue of decoder optimization for channels with Gaussian inputs and channel estimation errors. The result is a normalized nearest neighbor decoder which is proved, by a semi-analytical method, capable of fully obtaining the mutual information with Gaussian inputs. We further show that the proposed decoder is capable of achieving the performance improvement provided by a better non-Gaussian codebook.

Analysis of mutual information for wireless systems is usually performed by assuming perfect channel state information (CSI). In many practical applications, however, channel uncertainty occurs due to channel variation and additive noise. Such uncertainty introduces a product term between signal and channel estimation error in the received signal whose probability density function is thus difficult to determine. This situation makes the direct evaluation of mutual information difficult and forces the adoption of a bounding technique in current literature by treating the product term either as a signal component or as a noise component. In this paper, we solve the problem by accurately fitting the received signal with the multivariate Pearson's type-7 (MPT-7) distribution. The new results are simple in expression, very accurate as evidenced by simulation.

The use of ultra wideband (UWB) technology for high-resolution ranging is widely addressed in the literature, and most these studies assume the use of a correlation detector to determine the arrival time of the first path component. The correlation detector well performs in environments without signal distortion. However, dense multi-path propagation and frequency selective fading, as encountered in a typical UWB environment, force the received UWB signal to behave like a random waveform. Such a waveform is multi-dimensional in the sense that any single preset template can only capture partial signal energy for ranging. In coping with this situation, we show in this paper how to use multi-dimensional detectors for enhancing the ranging performance. The superiority of the new detectors to the conventional one is theoretically analyzed and confirmed through simulations based on real UWB data.

Ultra wideband (UWB) impulse radio (IR) is a prospective transmission technology for low-rate indoor communications, as described in the physical-layer proposals for IEEE 802.15.4a wireless personal area networks (WPAN). Time hopping (TH) code division multiple access (CDMA) is considered as an access scheme' for multiuser UWB-IR systems. The TH- CDMA system widely addressed in the literature adopts binary phase-shift keying (BPSK) with coherent detection, which requires accurate channel estimation and thus increasing the implementation complexity. In this correspondence, we suggest using TH-CDMA-PPM (pulse position modulation) with non-coherent detection to simplify the receiver structure. The influence of different combinations of TH and CDMA processing gains on error performance of the new scheme is analyzed and numerical results are presented for illustration.

Due to its finite-time resolution, the ultra wideband (UWB) technology has been proposed for use in high-resolution ranging. Such ranging systems employ several pingers each having a correlator to determine the direct-path component in the received signal. The problem with a correlation detector is that the ranging performance can significantly drop as the UWB signal experiences distortion, caused by dense multipath propagation. In this paper, we therefore use, instead, a multi- template detector, whose templates are derived from the covariance structure of distorted UWB signals. The superiority of the new detectors to the conventional one is theoretically analyzed and confirmed through simulations, on the basis of real UWB data.

Determination of the coherence bandwidth for multipath fading channels is of great importance for wireless systems design. However, the calculation of the coherence bandwidth so far has relied on an empirical formula. In this paper, we derive an exact coherence bandwidth formula for Rayleigh fading channels. The use of the new formula is illustrated by numerical results.

Optimizing the input covariance matrix of a multiple-antenna transmit system with partial channel-structure feedback is an important issue to fully exploit the channel capacity. Efficient design of the optimal input covariance matrix, however, remains unavailable although its eigenvector structure was clearly revealed in a recent publication. In this paper, we obtain an explicit derivative function forming a solid basis for optimizing the optimal input covariance matrix. This new derivative expression enables us to further develop an efficient iterative algorithm for determining the optimal eigenvalues. The technique is illustrated through numerical examples.

As described in the physical-layer proposals for IEEE 802.15.4a wireless personal area networks (WPAN), ultra wideband (UWB) impulse radio (IR) is a prospective transmission technology for low-rate indoor communications. Time hopping (TH) code division multiple access (CDMA) is considered as an access scheme for multiuser UWB-IR systems. The TH-CDMA system widely addressed in the literature adopts binary phase-shift keying (BPSK) with coherent detection, which requires accurate channel estimation and thus increasing the implementation complexity. This paper suggests using TH-CDMA-PPM (pulse position modulation) with non-coherent detection to simplify the receiver structure. The error performance of the new scheme is analyzed and numerical results are presented for illustration.

Coherence bandwidth is an important characteristic of multipath fading channels, serving as a useful tool for wireless systems design. Regardless of its importance, the determination of the coherence bandwidth, so far, has relied on an empirical formula. In this correspondence, we derive an exact coherence-bandwidth formula for Rayleigh fading channels. The use of the new formula is illustrated by numerical results.

Channel estimation is a key issue to OFDM systems no matter for inter-carrier interference cancellation or for symbol detection. The difficulty with a fast-fading OFDM channel lies in that the channel over one OFDM symbol interval is characterized by a channel matrix whose dimensions much exceeds the total number of subcarriers. Consequently, the channel estimation techniques developed for slowly fading channels are of little use. In this paper, we show how to parsimoniously parameterize an OFDM channel matrix by representing each path-coefficient vector through its eigen structure. The resulting unknown parameters are much less than that of the original channel matrix making their estimation possible. The new technique is superior to its conventional counterpart as evidenced by simulation results.

Received ultra wideband (UWB) signals in most indoor environments are stochastic in nature, typically composed of abundant multi-path components with random attenuation and arrival time. A commonly adopted detection strategy is to use a chosen deterministic template to correlate the received signal segment by segment, and only those segments having significant cross-correlation with the template are selected, temporally synchronized and co-phased in their projections for coherent combining. Correlating a deterministic template with a random signal usually results in only partial energy captured by the receiver since a random signal is of multidimensions. In this paper, we construct a receiver with multiple orthogonal branches to fully capture the signal energy, in much the same way as a quadrature receiver does in conventional digital communications. The local reference signals are derived from the orthogonal expansion of UWB signals. The random projections of a UWB signal onto different orthogonal basis functions are non-coherently combined, leading to the concept of dimension diversity. Dimension diversity can be either used alone, or alongside a Rake receiver to further exploit path diversity. The error performance of proposed receivers is analyzed, and their superiority over the conventional ones is examined by numerical results based on real UWB data

Lognormal approximation to a sum of lognormal variables is widely addressed in wireless communications. Its discrepancy from simulation results, however, is often reported in the literature. In this correspondence, we reexamine the issue of fitting a lognormal sum in a more rigorous framework of model selection in the Pearson system. It is found that over a general parameter setting for lognormal sums, a much better approximation is the Pearson type IV distribution whose parameters can be easily determined through simple arithmetic operations. Numerical examples are presented for illustration

Wireless ultra-wideband (UWB) receivers usually employ a deterministic clean pulse or pulses derived from a Gaussian monocycle as a template for correlation. A deterministic template cannot be expected to have high correlation with random UWB signals at all times. Such a mismatch can significantly degrade the system error performance. In this letter, a deterministic template is designed to maximize the energy capture capability on ensemble average, ending up with the eigen-based correlator. The eigen-based correlator can be used alone or alongside path diversity to further enhance signal detection. The new receivers outperform their counterparts using a clean pulse, as shown by theoretical analysis, and demonstrated by numerical results based on real UWB data

Wireless ultra-wideband (UWB) receivers usually employ a deterministic clean pulse or a pulse derived from a Gaussian waveform as a template for correlation. A deterministic template is not expected to have the capability of correlating highly with random UWB signals all the time. The resulting mismatch, however, can significantly degrade the system error performance. We therefore optimize a deterministic template to achieve the maximum energy capture capability in the sense of ensemble average. The UWB correlator using the optimal deterministic template can be employed along with combining to enhance signal detection. The resulting receivers outperform their counterparts using a clean pulse, as evidenced by theoretical analysis and numerical results based on real UWB data.

Modelling ultrawideband (UWB) received signals is an indispensable step to the UWB receiver design and UWB data regeneration. A popular framework for UWB modelling stems from the discrete multipath channel model whose path gains and time delays are random variables and thus, must be specified by their probability density function (pdf)- Besides, various partial characterization is used in the literature by virtue of second-order statistics (such as power delay profile), nonparametric characteristics (such as zero-crossing rate), or their combination. So far, little UWB models have the capability to account the correlation structure existing among received UWB data and little work directly addresses the original UWB data. In this paper, we take a different philosophy which believes that the information in the received UWB data itself, as long as fully exploited, plus some simple physical intuition should suffice for the model identification and its parameter estimation. The model so obtained is directly for the original data while having the capability to account for the correlation structure and nonstationarity of UWB data. The application of the new model to data regeneration is illustrated by using the real UWB data provided by the TimeDomain Corporation.