The evolution of 6G has brought distributed multi-input multi-output (D-MIMO) near-field communications into the spotlight, owing to their potential to significantly enhance wireless capacity and spectral efficiency. This study delves into exploring the optimal characteristics of distributed antenna deployment in D-MIMO systems through the optimization of access point (AP) placement and subarray topology. To guarantee the stability of system configuration, we derive an approximate ergodic sum rate with high accuracy under near-field spherical wavefront propagation that serves as the optimization criterion. Confronting the non-convex challenges, we adopt an approach integrating successive convex approximation (SCA), gradient descent, and alternating optimization methods, which enables us to attain a near-optimal solution with low complexity. Numerical simulations highlight the significant superiority of our proposal to traditional baseline approaches, revealing key characteristics and providing crucial insights for AP deployment and array layout optimization. In densely clustered user scenarios, APs should be deployed near the user aggregation centroid, which may not align with the geometric center. For dispersed user distributions, AP deployment positions should be adjusted more flexibly to regional statistical characteristics rather than confined to central points to accommodate specific user distribution features. Moreover, the array topology exhibits a pattern of alternating dense and sparse distribution, diverging from the attributes observed in centralized MIMO (C-MIMO) architectures, where a consistent medium density is flanked by regions of sparsity.

In the realm of fifth-generation new radio (5G-NR) communication technology, how to use limited resources and low-complexity algorithms to provide high transmission rates for massive multiple-input multiple-output (mMIMO) systems with differentiated service demands remains a challenge. This paper explores a novel scenario-specific beamforming design tailored to user distributions and propagation environments. The proposed method dynamically adjusts beam orientations and widths, enabling precise coverage of users’ active regions. To mitigate inter-beam interference in spatial multiplexing, we derive a strict orthogonality condition and propose a corresponding beam grouping strategy to enforce it. Furthermore, to address the nonconvex optimization problem of time-frequency-space resource allocation (RA), we build upon the idea of the greedy algorithm to balance transmission rate and computational complexity. Building on this foundation, we propose a traffic-aware RA strategy and introduce a novel beam selection criterion by incorporating both channel characteristics and user-specific traffic demands. These improvements jointly enhance the system’s utilization of time-frequency resources and spatial reuse capability. Simulation results validate the proposed algorithms, demonstrating notable improvements in computational efficiency, system adaptability, spectral efficiency, and average achievable rate.

The deployment of extremely large antenna arrays (ELAAs) in extremely large-scale multiple-input multiple-output (XL-MIMO) systems introduces significant near-field effects, such as spherical wavefront propagation and spatially non-stationary (SnS) properties. When combined with the dual-wideband effects inherent to wideband systems, these phenomena fundamentally alter the channel’s sparsity patterns in the angular-delay domain, rendering existing estimation methods insufficient. To address these challenges, this paper reconsiders the channel estimation problem for wideband XL-MIMO systems. Leveraging the spatial-chirp property of array responses, we first quantitatively characterize the angular-delay domain sparsity of wideband XL-MIMO channels, revealing both global block sparsity and local common-delay sparsity. To effectively capture this structured sparsity, we then propose a novel column-wise hierarchical prior model that integrates a precision sharing mechanism and a Markov random field (MRF) structure. Building on this prior model, the channel estimation task is formulated as a multiple measurement vector (MMV)-based Bayesian inference problem. Tailored to the complex factor graph induced by this hierarchical prior, we develop a MMV-based hybrid message passing (MMV-HMP) algorithm. This algorithm performs message updates along the edges of the factor graph, and selectively applies either the variational message passing (VMP) or sum-product (SP) rules, depending on the factor-node structure and message tractability. Simulation results validate the effectiveness of the proposed column-wise hierarchical prior model through ablation studies and demonstrate that the MMV-HMP algorithm, while maintaining moderate computational complexity, consistently outperforms existing baselines which fail to capture the structured sparsity of wideband XL-MIMO channels.

Kronecker product-based codewords, constructed from 2D DFT bases, are fundamental to constructing Type I, Type II, and enhanced Type II codebooks in 5G New Radio (NR). While these codewords are conventionally interpreted as directed orthogonal beams, this paper reveals that a significant portion of these codebooks is associated with evanescent waves, rendering them redundant for practical array beamforming and channel representation. This redundancy is rigorously proven using mathematical and electromagnetic models and validated by full-waveform and system-level simulations. Leveraging this redundancy, we propose a method to compress these codebooks, typically reducing their size by 21.5%. This compression significantly decreases signaling and pilot overhead, enhancing the efficiency of channel state information feedback and beam training without adding algorithmic complexity. We also propose codebooks for irregular arrays that are compatible with existing NR feedback frameworks, potentially accelerating the standardization of irregular array deployment. Inspired by the revelation of codebook redundancy, we extend the discussions to the properties of near-field channel and classical Rayleigh channels, and offer practical suggestions for future standard designs.

This paper analyzes the spatial multiplexing capability in the line-of-sight (LoS) extremely large-scale multiple-input multiple-output (XL-MIMO) systems, where the impacts of array geometry (such as the shape, size, position, and orientation) on spatial degrees of freedom (DoF) is presented, resulting the validation of promising performance gain of the fluid antenna system (FAS). To this end, we first provide an exact closed-form expression for the local spatial bandwidth at the center of the receive array. Then, we analyze the maximum local spatial bandwidth at different spatial positions. An approximate closed-form expression for the achievable spatial DoF is obtained based on the derived local spatial bandwidth. Simulation results are presented for validation.

Existing channel estimation algorithms for ex-tremely large-scale multiple-input multiple-output (XL-MIMO) systems are predominantly designed for single-user scenarios and often overlook inter-user correlations. To address this limitation, this paper reformulates the joint multiuser channel estimation problem as a multiple-measurement vector (MMV)-based sparse signal recovery task. To solve this, we propose a novel row-wise hierarchical prior model that captures the structured sparsity of the joint multiuser channel in the angular-delay domain. Specifically, shared precision parameters for each row of the angular-delay domain channel are introduced to model common-row sparsity, while a Markov random field (MRF) is employed to encourage cluster sparsity. Building on this structured prior, we develop a computationally effi-cient channel estimation algorithm using variational message passing. Simulation results demonstrate that the proposed method significantly outperforms existing single-user-based approaches.

Simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) offers a promising solution to conquer the challenge of severe signal attenuation through buildings for seamless indoor-outdoor connectivity in 6G networks. This paper focuses on hybrid active-passive STAR-RIS assisted indoor-outdoor communications and delves into the comparative impact of diverse non-ideal factors on system performance, such as transceiver hardware impairments, imperfect channel state information (CSI), and STAR-RIS phase noise. Under these system imperfections, our objective is to maximize the minimum user rate by optimizing user grouping, non-orthogonal multiple access (NOMA) decoding order, power allocation coefficients, active beamforming, and transmission and reflection beamforming, thereby enhancing system robustness and user fairness. Given the complexity of the formulated problem, characterized by interwoven optimization variables, we propose a collaborative convex optimization iterative (CCOI) algorithm by blending successive convex approximation (SCA), semidefinite relaxation (SDR), Gaussian randomization, and convex upper bound approximation. This methodology is capable of adeptly striking a balance between system performance and computational efficiency. Simulation results demonstrate the significant advantages of our proposed CCOI algorithm and provides guiding insights on system design for various defects. Additionally, the hybrid active-passive STAR-RIS structure offers enhanced flexibility and effectively balances the performance trade-offs between conventional active and passive models.

This paper investigates the spatial bandwidth of line-of-sight (LoS) channels in extra-large MIMO (XL-MIMO) systems. For linear large-scale antenna arrays (LSAAs) with transceivers randomly positioned in 3D space, a simple but accurate closed-form expression is derived to characterize the local spatial bandwidth. Based on this analysis, we examine the properties of local spatial bandwidth and further derive expressions for the effective spatial bandwidth and the achievable degrees of freedom (i.e., theKnumber) for LSAAs. We also conduct case studies for both coplanar and non-coplanar transmitting and receiving arrays, providing more concise and intuitive expressions for local spatial bandwidth and achievable spatial degrees of freedom. Finally, the impact of array geometry on LoS XL-MIMO channel capacity is explored. When the transmitting and receiving arrays are coplanar and perpendicular to the line connecting their centers, the effective degree of freedom of the LoS channel is found to be approximately maximized. This orientation also maximizes the channel capacity in near-field high-SNR scenarios.

Different types of intelligent reflecting surfaces (IRS) are exploited for assisting wireless communications. The joint use of passive IRS (PIRS) and active IRS (AIRS) emerges as a promising solution owing to their complementary advantages. They can be integrated into a single hybrid active-passive IRS (HIRS) or deployed in a distributed manner, which poses challenges in determining the IRS element allocation and placement for rate maximization. In this paper, we investigate the capacity of an IRS-aided wireless communication system with both active and passive elements. Specifically, we consider three deployment schemes: 1) base station (BS)$\rightarrow $HIRS$\rightarrow $user (BHU); 2) BS$\rightarrow $AIRS$\rightarrow $PIRS$\rightarrow $user (BAPU); 3) BS$\rightarrow $PIRS$\rightarrow $AIRS$\rightarrow $user (BPAU). Under the line-of-sight channel model, we formulate a rate maximization problem via a joint optimization of the IRS element allocation and placement. We first derive the optimized number of active and passive elements for BHU, BAPU, and BPAU schemes, respectively. Then, low-complexity HIRS/AIRS placement strategies are provided. To obtain more insights, we characterize the system capacity scaling orders for the three schemes with respect to the large total number of IRS elements, amplification power budget, and BS transmit power. Finally, simulation results are presented to validate our theoretical findings and show the performance difference among the BHU, BAPU, and BPAU schemes with the proposed joint design under various system setups.

With the growing demand for virtual reality (VR) applications, mobile wireless networks should support the connections of a massive number of VR users. To support ultra-high data rates of multiple simultaneously transmitted VR streamings, we propose a mobile edge computing (MEC)-assisted rate splitting (RS) VR streaming transmission system. In the proposed system, RS technology exploits the shared interests of multiple VR users and MEC offloads the partial rendering tasks to achieve a better quality of experience (QoE) for VR users. Aiming to minimize the total weighted energy consumption, we formulate a joint communication and computing resource optimization problem while ensuring the required distortion and latency of VR users. To deal with the formulated intractable problem, we propose a joint rendering offloading and resource allocation algorithm that alternately solves the subproblems of quantization parameters selection, rendering offloading decision, transmit precoding design, rate allocation of RS transmission, and computing resource allocation. The simulation results demonstrate the effectiveness of the proposed algorithm in saving energy consumption. Specially, the performance of the proposed algorithm is 22.1% higher than that of the multicast-unicast scheme and can achieve 95.9% of the exhaustive search based algorithm.

In this paper, we investigate the channel estimation problem for extremely large-scale multiple-input multiple-output (XL-MIMO) systems with a hybrid analog-digital architecture, implemented within a decentralized baseband processing (DBP) framework with a star topology. Existing centralized and fully decentralized channel estimation methods face limitations due to excessive computational complexity or degraded performance. To overcome these challenges, we propose a novel two-stage channel estimation scheme that integrates local sparse reconstruction with global fusion and refinement. Specifically, in the first stage, by exploiting the sparsity of channels in the angular-delay domain, the local reconstruction task is formulated as a sparse signal recovery problem. To solve it, we develop a graph neural networks-enhanced sparse Bayesian learning (SBL-GNNs) algorithm, which effectively captures dependencies among channel coefficients, significantly improving estimation accuracy. In the second stage, the local estimates from the local processing units (LPUs) are aligned into a global angular domain for fusion at the central processing unit (CPU). Based on the aggregated observations, the channel refinement is modeled as a Bayesian denoising problem. To efficiently solve it, we devise a variational message passing algorithm that incorporates a Markov chain-based hierarchical sparse prior, effectively leveraging both the sparsity and the correlations of the channels in the global angular-delay domain. Simulation results show the effectiveness and superiority of the proposed SBL-GNNs algorithm over existing methods, demonstrating improved estimation performance and reduced computational complexity.

To cope with the unendurable delay of maritime wireless networks (MWNs), this paper proposes a collaborative transmission framework utilizing a multi-antenna uncrewed surface vessel (USV) and multiple cache-aided buoys to satisfy the on-demand file requirements for remote users (RUs). Specifically, a direct transmission scheme is adopted for hit-requested files and a multi-hop transmission scheme is devised to handle cache misses. To fully exploit the local cache and signal processing capabilities, we integrate two schemes into a collaborative transmission framework, where the USV dynamically supports buoys in uncached file fetching, and buoys collaborate to forward both cached and fetched files to RUs through a cooperative beamforming policy. We aim to minimize the overall transmission completion time by jointly optimizing the USV trajectory, cooperative beamforming, and transmission duration under the constraints of USV kinetic, transmit power, and file requirements. By leveraging the completion condition analysis, the original problem is transformed into a sequence of one-slot problems and a finite-horizon problem, where the closed-form solution for the local caching beamforming at each buoy is derived. Due to the complexity of the multivariable coupling, we propose an equivalent rate transformation method for transmission strategy design. Numerical results validate the effectiveness of the proposed scheme and algorithm.

Extremely Large-scale Array (ELAA) promises to deliver ultra-high data rates with increased antenna elements. However, increasing antenna elements leads to a wider realm of near-field, which challenges the traditional design of codebooks. In this paper, we propose novel near-field codebook schemes based on the fitting formula of codewords’ quantization performance. First, we analyze the quantization performance properties of uniform linear array (ULA) and uniform planar array (UPA) codewords. Our findings reveal an intriguing property: the correlation formula for ULA codewords can be represented by the elliptic formula, while the correlation formula for UPA codewords can be approximated using the ellipsoid formula. Building on this insight, we propose a ULA uniform codebook that maximizes the minimum correlation based on the derived formula. Moreover, we introduce a ULA dislocation codebook to further reduce quantization overhead. Continuing our exploration, we propose UPA uniform and dislocation codebook schemes. Our investigation demonstrates that oversampling in the angular domain offers distinct advantages, achieving heightened accuracy while minimizing overhead in quantifying near-field channels. Numerical results demonstrate the appealing advantages of the proposed codebook over existing methods in decreasing quantization overhead and increasing quantization accuracy.

There has been a growing interest in applying machine learning to real-world tasks. However, due to the black-box nature of machine learning models, it is crucial to 1) verify important properties of a model and 2) understand the reasons behind a model’s prediction before deploying them in a production environment. Existing approaches typically handle them as two separate and sometimes orthogonal topics. In this paper, we show that the verification and interpretability of machine learning models are tightly related and can be unified by satisfiability modulo theories (SMT). Our key insight is: not only a wide range of properties of machine learning models can be formulated as SMT problems and verified accordingly, but many commonly studied interpretability questions can also be answered by iteratively checking the satisfiability and related properties of multiple SMT problems. Leveraging this insight, we design UINT, a general verification and interpretability framework for learning-based networking systems. UINT 1) allows operators to specify verification and interpretability problems as SMT formulas, 2) encodes the target machine learning models into SMT constraints, and 3) automatically simplifies and solves the corresponding verification and interpretability problems using commodity SMT solvers. We implement a prototype of UINT and evaluate it on real-world learning-based networking systems. Results demonstrate the efficiency and efficacy of UINT in verifying and interpreting key questions for these systems.

The integration of intelligent reflecting surfaces (IRS) onto structures takes advantage of its electrically thin and flexible properties, effectively transforming original obstructions into potential serving nodes. However, existing works model IRS as a planar array, which is not well coupled with the infrastructural shape, posing challenges to future large-scale deployment. Therefore, this paper introduces conformal IRS and proposes a reflection constraint model owing to the curvature imparted by the conformal deployment of IRS, based on which the conformal IRS-assisted network is modeled and system-level performance analysis is derived via stochastic geometry. Specifically, considering infrastructure facades non-planar feature, to balance the accuracy and analytical tractability, randomly located infrastructures are modeled as cylinders based on the Boolean scheme, and conformal IRSs are deployed around a fraction ρ of them. In addition, the surface curvature of conformal IRS modulates the distribution of Line-of-Sight paths within the system and its effective illumination. We obtain the successfully reflecting probability and the effective reflecting elements respectively under two different states of IRS perfect reflecting and random scattering, and further derive the coverage probability. Numerical results show that conformal IRS significantly improves network connectivity, though it slightly reduces coverage compared to 2D planar IRS.

While Distributed Machine Learning (DML) has been widely used to achieve decent performance, it is still challenging to take full advantage of data and devices distributed at multiple vantage points to adapt and learn, especially it is non-trivial to address dynamic and divergence challenges based on the linear aggregation framework as follows: (1) heterogeneous learning data at different devices (i.e., non-IID data) resulting in model divergence and (2) in the case of time-varying communication links, the limited ability for devices to reconcile model divergence. In this paper, we contribute a non-linear class aggregation framework HyperPrism that leverages distributed mirror descent with averaging done in the mirror descent dual space and adapts the degree of Weighted Power Mean (WPM) used in each round. Moreover, HyperPrism could adaptively choose different mapping for different layers of the local model with a dedicated hypernetwork per device, achieving automatic optimization of DML in high divergence settings. We perform rigorous analysis and experimental evaluations to demonstrate the effectiveness of adaptive, mirror-mapping DML. In particular, we extend the generalizability of existing related works and position them as special cases within HyperPrism. Our experimental results show that HyperPrism can improve the convergence speed up to 98.63% and scale well to more devices compared with the state-of-the-art, all with little additional computation overhead compared to traditional linear aggregation.

With the combination of extra-large arrays and high frequencies, near-field transmissions have become prevalent, challenging the validity of classical channel representations typically derived under the plane wavefront assumption. In this paper, we investigate the angular-domain representation of line-of-sight (LoS) extra-large MIMO (XL-MIMO) channels, considering the impact of spherical wavefront effects. First, we demonstrate the structured sparsity of LoS XL-MIMO channels in the angular domain. Leveraging this sparsity, we propose an effective spatial bandwidth channel representation method, which characterizes near-field LoS XL-MIMO channels as a superposition of multiple plane wave components, enabling us to capture the spherical wavefront effect in a low-dimensional angular channel. Subsequently, we introduce an angular-domain transceiver architecture based on this low-dimensional channel representation. This architecture could significantly facilitate the implementation of LoS XL-MIMO systems. Finally, simulation results confirm the effectiveness of the effective spatial bandwidth identification method and analyze the impact of various array geometries on the effective spatial bandwidth. Additionally, the availability of the angular-domain processing architecture is validated.

In this work, we investigate the joint visibility region (VR) detection and channel estimation (CE) problem for extremely large-scale multiple-input-multiple-output (XL-MIMO) systems considering both the spherical wavefront effect and spatial non-stationary (SnS) property. Unlike existing SnS CE methods that rely on the statistical characteristics of channels in the spatial or delay domain, we propose an approach that simultaneously exploits the antenna-domain spatial correlation and the wavenumber-domain sparsity of SnS channels. To this end, we introduce a two-stage VR detection and CE scheme. In the first stage, the belief regarding the visibility of antennas is obtained through a VR detection-oriented message passing (VRDO-MP) scheme, which fully exploits the spatial correlation among adjacent antenna elements. In the second stage, leveraging the VR information and wavenumber-domain sparsity, we accurately estimate the SnS channel employing the belief-based orthogonal matching pursuit (BB-OMP) method. Simulations show that the proposed algorithms lead to a significant enhancement in VR detection and CE accuracy as compared to existing methods, especially in low signal-to-noise ratio (SNR) scenarios.

Due to long-term reliability, wireless control systems (WCSs) have attracted significant interest recently. However, mission-critical control requires stringent ultra-reliability and low-latency communication (URLLC) with massive data delivery, which are major challenges for conventional wireless networks. This paper investigates downlink URLLC in WCS, where the semantic communication is adopted at the control center to extract task-oriented semantic information from original large-sized data. To efficiency, the control center utilizes the rate splitting policy to deliver semantic information through private messages, while the semantic knowledge is transmitted through one common message. We aim to maximize the weighted sum semantic information transmission rate by jointly optimizing the semantic information extraction, delivery duration, rate splitting, and transmit beamforming, subject to several practical constraints, including recovery accuracy, quality of service requirements, communication latency and computation delay. By the problem decomposition, two sub-problems are obtained, where the closed-form solution for the semantic information extraction is derived at each step. Due to the complexity of the multivariable coupling in the channel dispersion, we propose fractional transformation methods for rate splitting design. Numerical results confirm that the RSMA and semantic communication design can complement each other for multiplexing gains enhancement and latency reduction to achieve overloaded connections.

In this paper, satellite-terrestrial assisted multi-tier computing networks (STMTCN) are proposed to satisfy the growing computation demands of user terminals (UTs) in next generation wireless networks. In the STMTCN, UT’s computation task can be processed at different computing entities and a multi-tier computation model named computing depth is proposed to better reflect the multi-tier computing process. Then, we formulate a weighted sum energy consumption minimization problem via jointly optimizing UT-satellite association, computing depth, multiple-input multiple-out (MIMO) precoding, and computation resource allocation. The non-convex optimization problem is decomposed into four subproblems, each of which is solved iteratively. Specifically, the UT-satellite association subproblem is solved by quadratic transform based fractional programming and Lagrangian dual method and a closed-form expression is obtained. The computing depth for local tier and the satellite tier is solved respectively with first-order Taylor expansion. Then, MIMO precoding subproblem for UT and satellite offloading is solved by quadratic transform and interior point method (IPM). Finally, the computation resource allocation for UT and satellite is obtained in a closed-form expression and the GW computation resource allocation is solved by using IPM. Simulation results show that the proposed STMTCN and algorithms can fulfill the UT’s computing demands with low energy consumption.

There has been a growing interest in applying machine learning to real-world tasks. However, due to the blackbox nature of machine learning models, it is crucial to (1) verify important properties of a model and (2) understand the reasons behind a model's prediction before deploying them in a production environment. Existing approaches typically handle them as two separate and sometimes orthogonal topics. In this paper, we show that the verification and interpretability of machine learning models are tightly related and can be unified by satisfiability modulo theories (SMT). Our key insight is: not only a wide range of properties of machine learning models can be formulated as SMT problems and verified accordingly, but many commonly studied interpretability questions can also be answered by iteratively checking the satisfiability and related properties of multiple SMT problems. Leveraging this insight, we design UINT, a general verification and interpretability framework for learning-based networking systems. UINT (1) allows operators to specify verification and interpretability problems as SMT formulas, (2) encodes the target machine learning models into SMT constraints, and (3) automatically solves the corresponding verification and interpretability problems using commodity SMT solvers. We implement a prototype of UINT and evaluate it on real-world learning-based networking systems. Results demonstrate the efficiency and efficacy of UINT in verifying and interpreting key questions for these systems.

In this paper, we investigate the optimization of non-uniform planar array (NUPA) for massive multi-input multi-output (MIMO) near-field wireless communications with line-of-sight (LOS) channels. In particular, the NUPAs at both the transmitter and receiver are misaligned placed with certain placement angles. Our focus is on the maximization of the system channel capacity, by optimizing the antenna elements position in the transmit/receive NUPAs. With the near-field spherical wave channel modeling, and by taking into account the geometric structure relationship of the arrays, the mathematically expression of channel capacity with this NUPA deployment is derived. Then, the relationship between the eigenvalues and the misaligned angles is analyzed, which further reveals the effect of the misalignment angle on the channel capacity. We show that channel capacity under specific transmission distance is related to the offset angle but irrelevant to the placement angles. Numerical results demonstrate that our theoretical analysis are consistent with the simulation results and the superiority of our optimized NUPA topology in system capacity.

Reconfigurable intelligent Surface (RIS) are often proposed as a means for restoring non-line-of-sight (NLoS) links in wireless communication. In this paper, we propose a joint design strategy for multi-RIS deployment locations and passive beamforming in multi-RIS assisted multi-BS networks with blockages. To ensure a stable network topology and low feedback overhead, the ergodic capacity is analyzed using Jensen’s inequality and the binomial expansion theorem, and then maximized as a performance metric. Taking into account the minimum spacing constraint for multi-RIS in real-world scenarios, we optimize the multi-RIS deployment locations using a genetic algorithm, where a Markov chain model can be applied to obtain the global optimal solution. Additionally, we implement the passive beamforming design for multi-RIS using the Riemannian conjugate gradient (RCG) method based on manifold space. Numerical results demonstrate the superiority of our proposed deployment strategy over the benchmark deployment schemes.

With the increasing demand of virtual reality (VR) applications, wireless systems need to provide ultra-high data rate to support VR streaming for multiple users simultaneously. In this paper, we propose a mobile edge computing-assisted rate splitting (RS) VR streaming transmission scheme to pursue better quality of experience (QoE) and alleviate the computing burden of VR users (VUs). We formulate an optimization problem to minimize the weighted energy consumption while ensuring the required QoE. The quantization parameters selection, rendering offloading decision, transmit precoding, rate allocation, and computing resource allocation are optimized and a joint Wrendering offloading and resource allocation algorithm is proposed. Simulation results validate the efficiency of the proposed algorithm and reveal the performance gain obtained from RS.

With the combination of extra-large arrays and high frequencies, near-field transmissions have become increasingly prevalent. In this paper, we investigate the angular-domain representation of near-field line-of-sight (LoS) extra-large multiple-input-multiple-output (XL-MIMO) channels. Specifically, we first demonstrate the structured sparsity of the near-field LoS channel in the angular domain. By leveraging this sparsity property, we propose an effective spatial bandwidth channel representation method. This method characterizes near-field LoS XL-MIMO channels as a superposition of multiple plane wave components within the effective spatial band between transceiver arrays. Finally, simulation results validate the equivalence between the proposed representation and the existing antenna domain channel model and demonstrate the effects of array geometries on the effective spatial bandwidth.

We investigate the downlink secrecy performance of reconfigurable intelligent surface (RIS)-aided massive multiple-input multiple-output (MIMO) systems in the presence of a multi-antenna eavesdropper. We first derive a tight closed-form expression for characterizing the lower bound of the achievable ergodic secrecy rate under spatially correlated channels, taking into account low-resolution digital-to-analog converters (DACs) and RIS phase noise. Subsequently, building upon the derived results, we optimize the power allocation among the information signal and artificial noise in closed form and the RIS phase shifts by developing a projected gradient ascent algorithm, which requires only statistical channel state information of the aggregated channel with low implementational complexity. All theoretical analyses and the effectiveness of the proposed algorithm are corroborated by simulation experiments. Our results reveal that low-resolution DAC can be beneficial with equal power allocation when the number of RIS elements is small. Besides, the detrimental influence attributed to low-resolution DACs gains prominence as the number of RIS elements grows substantially.

Reconfigurable intelligent surface (RIS) is able to manipulate the wireless environment smartly and has been exploited for assisting the wireless communications, especially at high frequency band. However, it suffers from hardware impairments (HWIs) in practical design, manufacturing and deployment, which inevitably degrades its performance and thus limits its full potential. To address this practical issue, we first propose a new RIS reflection model involving phase-shift errors, which is verified by the measurement results from field trials. With this beamforming model, various phase-shift errors caused by different HWIs can be analyzed. The phase-shift errors are classified into three categories: 1) globally independent and identically distributed errors; 2) grouped independent and identically distributed errors; and 3) grouped fixed errors. The impact of typical HWIs, including frequency mismatch, PIN diode failures and panel deformation, on RIS beamforming ability are studied with the theoretical model and are verified with numerical and field test data. The impact of frequency mismatch are discussed separately for narrow-band and wide-band beamforming. Finally, useful insights and guidelines on the RIS design and its deployment are highlighted for practical wireless sytsems.

Eye-tracking technology for mobile devices has made significant progress. However, owing to limited computing capacity and the complexity of context, the conventional image feature-based technology cannot extract features accurately, thus affecting the performance. This study proposes a novel approach by combining appearance- and feature-based eye-tracking methods. Face and eye region detections were conducted to obtain features that were used as inputs to the appearance model to detect the feature points. The feature points were used to generate feature vectors, such as corner center-pupil center, by which the gaze fixation coordinates were calculated. To obtain feature vectors with the best performance, we compared different vectors under different image resolution and illumination conditions, and the results indicated that the average gaze fixation accuracy was achieved at a visual angle of 1.93° when the image resolution was 96 × 48 pixels, with light sources illuminating from the front of the eye. Compared with the current methods, our method improved the accuracy of gaze fixation and it was more usable.

As one of the potential 6G technologies, terahertz positively affects the data rate, supports ultra-dense connection, and realizes low-latency transmission. How to make better use of the terahertz frequency is one focus of the future 6G research. Moreover, orbital angular momentum (OAM) is promised to effectively increase the system capacity with reduced hardware complexity. The application of OAM in the terahertz frequency band is investigated in this paper, giving the pros and cons. First, the characteristics of the terahertz channel are analyzed, and the feasibility of applying terahertz OAM is confirmed. Then, the investigation of terahertz OAM proves that working at terahertz frequency can effectively solve the beam divergence, and a novel receiving antenna design is proposed to reduce the receiving aperture size further. Finally, the capacity characteristics of OAM and MIMO in the terahertz frequency band are analyzed to prove the effectiveness and the necessity of applying terahertz OAM.

Reconfigurable intelligent surface (RIS) is a low-cost device envisioned to achieve substantial promotion in both spectrum and energy efficiency in the future wireless communication systems. In this paper, we investigate the downlink transmission of the RIS-enabled orthogonal frequency division multiplexing (OFDM) system, and propose a low-complexity passive beamforming optimization algorithm to maximize the achievable sum-rate over all subcarriers. The passive beam-forming optimization is a NP-hard problem due to the RIS-induced non-convex unit modulus constraints. We conquer this difficulty by exploiting a fractional programming (FP) based approach combined with an efficient manifold optimization (MO) method. With the proposed low-complexity passive beamforming optimization algorithms and water-filling power allocation, the achievable sum rate can then be maximized through alternating optimization (AO). Simulation results indicate that the proposed AO based algorithm performs well in achieving high average sum-rate with a fast convergence rate.

This paper investigates the downlink OFDM transmission assisted by reconfigurable intelligent surface (RIS). With single antenna implemented at both the base station (BS) and each user, we focus on the design of the phase shifts for the RIS, as well as power allocation on each subcarrier to improve the spectrum efficiency. To reduce the computation delay, we propose a deep reinforcement learning (DRL) based algorithm to optimize the RIS phase shift parameters, while allocating power on each subcarrier via water filling. Numerical results reveal that the proposed DRL-based framework can achieve a performance almost the same with that of successive convex approximation (SCA), while the computation delay can be greatly reduced.

The Internet of Medical Things (IoMT) is an important application of the Internet of Things (IoT) in the health field, including remote health monitoring and remote medical diagnosis. This not only brings convenience to the patient but also reduces the cost of the patient. However, the surge of data brought by mobile health monitoring equipment challenges the traditional centralized data processing model. In particular, medical data are closely related to patient privacy. Therefore, only part of the specific medical data should be provided to the medical institutions in need, rather than all the data, to ensure the confidentiality of the data to the greatest extent. But curious data processing centers can easily lead to data leakage. To tackle these challenges, we use edge computing and blockchain to build a new framework. In particular, the trusted execution environment, namely, software guard extension (SGX) technology, is introduced into edge computing to ensure the confidentiality of the data analysis process. The blockchain authenticates the IoMT devices and cloud service providers that are added to the network and provides an access policy management mechanism for IoMT data. Moreover, a prototype of the proposed framework is implemented using Hyperledger Fabric and Intel SGX, and the analysis of the blockchain and SGX performance are also presented.

Pervasive edge computing (PEC) is an emerging paradigm for the industrial Internet of Things (IIoT), and software-defined networks (SDN) offer lower latency services, and massive intelligent devices connectivity for the IIoT. However, the PEC has some issues with data security, and privacy while PEC devices sharing data among edges. What's more, the centralized SDN suffers from single point of attacks such as distributed denial of service (DDoS) from IIoT devices, and has the challenge of data leakage. In this article, we use blockchain, and proxy reencryption (PRE) technologies to tackle these challenges. The blockchain authorizes all devices in the network to improve their credibility, and authenticity. In addition, a blockchain-based data sharing framework that combines a PRE scheme is introduced for secure device-to-device communication in PEC environments. A series of smart contracts are designed for flexible operations of searching, and updating records on the blockchain. The experiments reveal that our design is highly efficient, and has high performance.

Integrating massive MIMO and simultaneous wireless information and power transfer (SWIPT) technology over the Rician fading channel is an effective method to achieve green communication under 5G background. This paper investigates system that the base station (BS) transmits both wireless energy and information simultaneously to the users. The users send pilots by utilizing the harvested energy from the BS to estimate the channel state, and then obtain the downlink channel state through the channel’s reciprocity to complete uplink and downlink information transmission. We propose an iterative algorithm to jointly optimize the downlink transmitting power of the BS and the power splitting (PS) ratios for minimizing the sum of transmitting power with uplink and downlink signal-to-interference-plus-noise ratio (SINR) constraints. Simulation results prove that the proposed algorithm outperforms over existing related algorithms in terms of transmitting power.

This paper is investigating and comparing three channel models for the fifth generation (5G) wireless communications: the quasi deterministic radio channel generator (QuaDRiGa), the NYUSIM developed by New York University (NYU) and the more general 5G (MG5G) channel model. The three channel models employ different approaches to model the time non-stationary characteristics of the 5G wireless communications, such as the geometry-based drifting modeling and cluster birth-death methodology. Simulations are carried out using the three channel models to analyze the statistical properties including angle power spectrum (APS), power delay profile (PDP), temporal autocorrelation function (ACF), and spatial cross-correlation function (CCF). Simulation results show that the calculation of angular parameters contributes to the performance of APS, generation approaches of path powers and cluster parameters have significant impact on PDP performance, the performance of ACF and CCF varies mainly due to the calculations of path delays and angle spreads for the three channel models in urban macrocell (UMa) mobile NLOS environments.

For partially-connected massive multiple-input multiple-output (MIMO) millimeter wave (mmWave) communications, an iterative precoding and combining algorithm is proposed to maximize the system spectrum efficiency. At the transmitter, the optimal unconstrained full digital precoder is firstly determined as a benchmark by singular value decomposition (SVD) of the channel matrix, and then resolved to achieve the hybrid analog and digital precoding in an iterative manner. Specifically, the initially formulated non-convex matrix decomposition problem is decomposed into a series of convex optimization sub-problems, with the amplitude of each element in the analog matrix restricted and the phase increment in adjacent iteration processes varies only in a small range. Moreover, with the unconstrained linear minimum mean square error (MMSE) combiner as the benchmark, similar iterations can be executed at the receiver. Numerical results show that the proposed algorithm acquires superior spectrum efficiency than the other classical scheme with partially-connected architecture. Furthermore, in spite of much lower hardware complexity and power consumption, the performance of our algorithm is almost identical to that of the full digital scheme as well as other mechanisms with fully-connected structure.

We study the precoding for the high-rank MIMO in the LTE-A Pro systems. Unlike the low-rank precoding, layer mapping has a relatively large impact on the performance for high-rank precoding. First, we construct a model on the relationship between layer mapping and precoding. Then we derive the system throughput with the resulting model, so that the performance of the layer mapping and precoding can be evaluated. Further, we operate a sub-space optimization for the codebook-based precoding to maximize the system throughput. Simulation results show that after optimizing layer mapping, high-rank precoding achieves much better performance.

Massive MIMO is a promising technique to provide high data rate with good energy efficiency for the future wireless cellular communication. However, its performance benefit often can be realized only when accurate channel state information (CSI) is available at the transmitter to perform accurate beamforming. With large number of antennas, full CSI consumes too much overhead to feed back without compression. To reduce CSI feedback overhead, CSI feedback scheme with dual stage precoding structure is designed to quantize the long term spatial channel correlation information and short term linear precoder information. In this paper, we discuss how to optimize this dual stage precoding scheme in the typical dual-polarized massive MIMO system. The eigenvalues of spatial correlation matrix are used to improve feedback efficiency. By relaxing the constant modulus constraint in codebook design, more flexible long term precoding can be used and adapt to the channel. A specific structure of long term precoding matrix for dual-polarized MIMO system is proposed to ensure the orthogonality of the final precoder for multi-layer transmission.

Massive MIMO is one of the promising techniques to improve the spectral efficiency and network performance in future 5G networks. Compared to FDD, it is relatively easier to realize downlink massive MIMO for TDD as downlink channel information can be obtained via uplink-downlink channel reciprocity. This paper provides our field test results of massive MIMO system with a base station prototype equipped with 64 transmit antennas. Significant throughput gain is observed by performing 3D-beamforming to current LTE-Advanced handsets by using standard-transparent Multiuser(MU) MIMO techniques. With the massive MIMO base station prototype, MU-MIMO is realized by multiplexing maximum of eight handsets in spatial domain considering both azimuth and elevation directions. In addition to the field trial test results, future potential enhancements for TDD massive MIMO system are discussed. Evaluation results of evaluating some enhancements on uplink reference signal are provided.