This paper presents the first bit error rate (BER) analysis of a pinching-antenna (PA)-based non-orthogonal multiple access (NOMA) communication system. The PA is assumed to be able to be placed anywhere along the waveguide and serves two NOMA user equipment (UEs) in both uplink (UL) and downlink (DL) scenarios. Exact closed-form expressions for the average BER of each user are derived under practical imperfect successive interference cancellation (SIC). These expressions are then used to optimize the PA location for minimizing the overall average BER of both UEs. In the UL case, the interference between the users’ channels introduces phase-dependent fluctuations in the BER cost function, making it highly non-convex with many local extrema. To address this challenge, a smoothing technique is applied to extract the lower envelope of the BER function, effectively suppressing ripples and enabling a reliable identification of the global minimum. In the DL case, a joint optimization of the PA location and NOMA power allocation coefficients is proposed to minimize the average BER. Simulation results verify the accuracy of the analytical derivations and the effectiveness of the proposed optimization methods. Notably, the UL results demonstrate that an optimally positioned PA can create the required received power difference between two equally powered UEs for reliable power-domain NOMA decoding under imperfect SIC.

A pinching-antenna system (PASS)-enhanced mobile edge computing (MEC) architecture is investigated to improve the task offloading efficiency and latency performance in dynamic wireless environments. By leveraging dielectric waveguides and flexibly adjustable pinching antennas, PASS establishes short-distance line-of-sight (LoS) links while effectively mitigating the significant path loss and potential signal blockage, making it a promising solution for high-frequency MEC systems. We formulate a network latency minimization problem to joint optimize uplink PASS beamforming and task offloading. The resulting problem is modeled as a Markov decision process (MDP) and solved via the deep reinforcement learning (DRL) method. To address the instability introduced by the max operator in the objective function, we propose a load balancing-aware proximal policy optimization (LBPPO) algorithm. LBPPO incorporates both node-level and waveguide-level load balancing information into the policy design, maintaining computational and transmission delay equilibrium, respectively. Simulation results demonstrate that the proposed PASS-enhanced MEC with adaptive uplink PASS beamforming exhibit stronger convergence capability than fixed-PA baselines and conventional MIMO-assisted MEC, especially in scenarios with a large number of UEs or high transmit power.

This paper investigates a multi-user pinching-antenna (PA) system, where a single PA is activated on each waveguide. With the maximum ratio transmission (MRT) beamforming, the system spectral efficiency (SE) is studied, where the inter-user interference term complicates the analysis of the SE. To overcome this obstacle, the stationary phase point method (SPPM) is applied to obtain an analytically tractable form of the SE. The analysis reveals that the average inter-user interference can be negligible with a large waveguide spacing even using the MRT. This insight makes the simple MRT appealing for PA-based multi-user communications. Finally, the theoretical analysis is verified through simulations. Our numerical results confirm that 1) with the aid of SPPM, the approximation of the system SE is accurate; 2) and while increasing the waveguide spacing helps reduce the average inter-user interference, it might degrade the SE due to the increased signal propagation path loss.

Direct-sequence spread spectrum (DSSS) communication, as a key technology in code-division multiple access networks, demonstrates excellent performance in multiple-access capability and interference suppression. However, its effectiveness significantly degrades in mobile underwater acoustic (UWA) networks, where the platform mobility induces severe Doppler effect and exacerbates the instability of multipath propagation. These impairments are further exacerbated by the inherently low data rates of UWA DSSS systems, where the prolonged signal duration leads to time-varying Doppler effect and fast-fading multipath structure within a single frame, collectively degrading the UWA DSSS receiver performance. To address these limitations, this paper proposes an improved DSSS receiver to achieve time-varying UWA multipath tracking, which employs Kalman filtering for channel parameter estimation. A multipath management technique is implemented to determine the appearance and disappearance of paths, followed by a coherent combination of all valid paths. Comprehensive simulations demonstrate the superior performance of the proposed receiver under various time-varying channel scenarios. The effectiveness of the proposed receiver is further validated by experimental data collected from Danjiangkou Lake and Zhang River experiments.

Multiple simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) aided cell-free massive multiple-input multiple-output (CF mMIMO) network is investigated. A long-term sum-rate maximization problem is formulated to jointly optimize the active beamforming at each access point (AP) and the passive beamforming at each STAR-RIS while satisfying the quality of service (QoS) requirements. To address this non-convex problem with challenges caused by user mobility and high-dimensional optimization variables, two deep reinforcement learning (DRL)-based beamforming algorithms are proposed. Firstly, a soft actor-critic (SAC)-based centralized joint beamforming algorithm is proposed, which adds maximum entropy term to the objective function and provides an exceptional exploration-exploitation trade-off. However, since the centralized scheme might suffer from high communication loads and latency, we generalize the proposed approach into a distributed control. A multi-agent SAC (MASAC)-based distributed beamforming algorithm is further proposed, which employs the centralized training and decentralized execution (CTDE) framework, where each AP plays a role of an agent and makes decisions based on local observations. Simulation results demonstrate that: 1) The multiple STAR-RISs can effectively improve the performance of the CF mMIMO networks compared to other baseline schemes; 2) Both SAC-based and MASAC-based beamforming algorithms can maximize the sum-rate and guarantee the QoS of users in the long term; and 3) The MASAC-based beamforming algorithm performs better and converges faster than the SAC-based beamforming algorithm as it reduces the overall complexity and alleviates pressure on the fronthaul link transmission by making decentralized decisions.

The novel concept of switch-controlled dynamic metasurface antenna (DMA)-empowered near-field integrated sensing and communication (ISAC) is investigated, where a switch network is incorporated between the DMA and RF chains. By sharing the hardware and spectrum, the proposed DMA-based ISAC transmitter enables simultaneous communication and sensing with a reduced number of RF chains. For the single-user ISAC scenario, a joint microstrip selection and beamforming design is proposed for the system with one communication user and one sensing target. Based on this framework, the sensing signal-to-interference-plus-noise ratio (SINR) maximization problem is formulated by jointly optimizing the digital precoder, radar sensing covariance matrix, DMA weighting matrix, and the DMA microstrip selection matrix, while satisfying the communication SINR of the user. The resultant mixed-integer programming (MIP) problem is solved by a block coordinate descent (BCD)-based penalty dual decomposition (PDD) algorithm to find a high-quality near-optimal solution. Then, the system is extended to the multi-user scenario with the presence of clutters and scatterers. A radar signal-to-clutter-plus-noise ratio (SCNR) maximization problem is formulated, subject to the individual communication SINR constraint for each user. The resultant joint optimization problem is also efficiently solved via the proposed BCD-based PDD algorithm. Simulation results demonstrate that the proposed scheme can achieve a better balance between communication and sensing performance over the benchmark schemes.

A reconfigurable intelligent surface (RIS)-assisted non-orthogonal multiple access (NOMA) system is investigated, where the transmitter (Alice) is a dual-functional radar-communication (DFRC) base station (BS) that aims to sense the location of a potential warden (Willie), while simultaneously transmitting public and covert signals to the legitimate users, Carol and Bob, respectively. Both cases of known and unknown Willie locations are considered. For the known-location case, assuming perfect channel state information (CSI) at Willie, a covert rate maximization is formulated with the joint optimization of active and passive beamforming, which is solved using successive convex approximation (SCA), penalty method, and semidefinite relaxation (SDR). For the unknown-location case, we propose to estimate Willie’s location via radar sensing and develop a sensing-based imperfect CSI model. In particular, the CSI error uncertainty is bounded by the sensing accuracy, which is characterized by the Cramér-Rao bound (CRB). Subsequently, a robust communication rate maximization problem is formulated under the constraints on quality-of-service (QoS) of Carol, sensing accuracy, and covertness level. The Schur complement and S-procedure are employed to handle the non-convex constraints. Numerical results compare the system performance under the two cases, and demonstrate the significant covert performance superiority of the sensing-based imperfect CSI model and NOMA over the general norm-bounded imperfect CSI model and the orthogonal multiple access scheme. Furthermore, the dual yet contradictory effects of sensing on covert communications are revealed. It is also found that Alice primarily utilizes Carol’s signal for sensing, while allocating almost all of Bob’s signal for communication.

A novel near-field integrated sensing and communications framework for secure unmanned aerial vehicle (UAV) networks with high time efficiency is proposed. A ground base station (GBS) with large aperture size communicates with one communication UAV (C-UAV) under the existence of one eavesdropping UAV (E-UAV), where the artificial noise (AN) is employed for both jamming and sensing purpose. Given that the E-UAV’s motion model is unknown at the GBS, we first propose a near-field localization and trajectory tracking scheme. Specifically, exploiting the variant Doppler shift observations over the spatial domain in the near field, the E-UAV’s three-dimensional (3D) velocities are estimated from echo signals. To provide the timely correction of location prediction errors, the extended Kalman filter (EKF) is adopted to fuse the predicted states and the measured ones. Subsequently, based on the real-time predicated location of the E-UAV, we further propose a joint GBS beamforming and C-UAV trajectory design scheme for maximizing the instantaneous secrecy rate, while guaranteeing the sensing accuracy constraint. To solve the resultant non-convex problem, an alternating optimization approach is developed, where the near-field GBS beamforming and the C-UAV trajectory design subproblems are iteratively solved by exploiting the successive convex approximation method. Finally, our numerical results unveil that: 1) the E-UAV’s 3D velocities and location can be accurately estimated in real time with our proposed framework by exploiting the near-field spherical wave propagation; and 2) the proposed framework achieves superior secrecy rate compared to benchmark schemes and closely approaches the performance when the E-UAV trajectory is perfectly known.

Non-orthogonal multiple access (NOMA) is widely recognized for enhancing the energy and spectral efficiency through effective radio resource sharing. However, uplink NOMA systems face greater challenges than their downlink counterparts, as their bit error rate (BER) performance is hindered by an inherent error floor due to error propagation caused by imperfect successive interference cancellation (SIC). This paper investigates the BER performance improvements enabled by reconfigurable intelligent surfaces (RISs) in multi-user uplink NOMA transmission. Specifically, we propose a novel RIS-assisted uplink NOMA design, where the RIS phase shifts are optimized to enhance the received signal amplitudes while mitigating the phase rotations induced by the channel. To achieve this, we first develop an accurate channel model for the effective user channels, which facilitates our BER analysis. We then introduce a channel alignment scheme for a two-user scenario, enabling efficient SIC-based detection and deriving closed-form BER expressions. We further extend the analysis to a generalized setup with an arbitrary number of users and modulation orders for quadrature amplitude modulation signaling. The analysis is also extended to consider imperfect channel state information (CSI) knowledge and the multi-antenna base station (BS) cases. Using the derived BER expressions, we develop an optimized uplink NOMA power allocation (PA) scheme to minimize the average BER while satisfying the user transmit power constraints. It will be shown that the proposed NOMA detection scheme, in conjunction with the optimized PA strategy, eliminate SIC error floors at the base station. The theoretical BER expressions are validated using simulations, which confirms the effectiveness of the proposed design in eliminating BER floors.

As a novel antenna array architecture, continuous aperture arrays (CAPAs) have garnered wide attention in recent years. While existing CAPA-based research has predominantly addressed unicast scenarios, the critical area of multicast beamforming remains unexplored. In this paper, a CAPA-based multi-group multicast communication system is investigated. An integral-based CAPA multi-group multicast beamforming design is formulated for the maximization of the system energy efficiency (EE), subject to a minimum multicast SE constraint of each user group and a total transmit power constraint. To address this non-convex fractional programming problem, we employ Dinkelbach’s method, such that the non-convex group-wise multicast spectral efficiency (SE) constraint is first equivalently transformed into a tractable form using auxiliary variables. Then, an efficient block coordinate descent (BCD)-based algorithm is developed to solve the reformulated problem. The CAPA beamforming design subproblem can be optimally solved via the Lagrangian dual method and the calculus of variations (CoV) theory. It reveals that the optimized CAPA beamformer should be a combination of all the groups’ user channels. To further reduce the computational complexity, a low-complexity zero-forcing (ZF)-based approach is proposed. The closed-form ZF CAPA beamformer is derived using each group’s most representative user channel to mitigate the inter-group interference while ensuring the intra-group multicast performance. Then, the beamforming design subproblem in the BCD-based algorithm becomes a convex power allocation subproblem, which can be efficiently solved. Numerical results demonstrate that 1) the CAPA can significantly improve the EE compared to conventional spatially discrete arrays (SPDAs); 2) due to the enhanced spatial resolutions, increasing the aperture size of CAPA is not always beneficial for EE enhancement in multicast scenarios; and 3) wider user distributions of each group cause a significant EE degradation of CAPA compared to SPDA.

This paper investigates a pinching-antenna (PA)-enabled cognitive radio network, where both the primary transmitter (PT) and secondary transmitter (ST) are equipped with a single waveguide and multiple PAs to facilitate simultaneous spectrum sharing. Under a general Ricean fading channel model, a closed-form analytical expression for the average spectral efficiency (SE) achieved by PAs is first derived. Based on this, a sum- SE maximization problem is formulated to jointly optimize the primary and secondary pinching beamforming, subject to system constraints on the transmission power budgets, minimum antenna separation requirements, and feasible PA deployment regions. To address this non-convex problem, a two-stage optimization algorithm is developed, in which stage 1 designs the PT/ST pinching beamforming and stage 2 updates the ST transmit power. For the PT and ST pinching beamforming optimization, the coarse positions of PA are first determined at the waveguide-level. Then, wavelength-level refinements achieve constructive signal combination at the intended user and destructive superposition at the unintended user. For the ST power control, a closed-form solution is derived. Simulation results demonstrate that i) PAs can achieve significant SE improvements over conventional fixed-position antennas; ii) the proposed pinching beamforming design achieves effective interference suppression and superior performance for both even and odd numbers of PAs; and iii) the developed two-stage optimization algorithm enables nearly orthogonal transmission between the primary and secondary networks.

The fundamental capacity limits of pinching-antenna systems (PASS)-based multi-user communication network are investigated. Two practical pinching-antenna configuration strategies are considered, namelymultiple-time discrete activationandone-off continuous sliding. For each strategy, the capacity and rate regions are characterized under the non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA) schemes, respectively. 1) For NOMA, the capacity region achieved by the discrete activation is first characterized. In particular, the ideal case with the infinite number of activation times is considered, where the optimal PA activation and resource allocation scheme is derived. It is shown that different user groups or decoding orders are served via time-sharing. Inspired by this result, an inner bound of capacity region is obtained for the practical case with a finite number of activation times. Then, for the continuous sliding case, the inner bound of capacity region is characterized by alternately optimizing the resource allocation and PAs’ continuous positions. 2) For OMA, the rate region is first obtained for the discrete activation case. It is unveiled that multiple users are successively served. Then, by alternately optimizing the resource allocation and PAs’ continuous positions, the inner bound of rate region is obtained for the continuous sliding case. Numerical results demonstrate that i) PASS can significantly improve the capacity performance compared with the conventional fixed-antenna systems; ii) the capacity gain can be further enhanced by using the proposed PA activation and sliding and resource allocation schemes; and iii) the continuous sliding has the potential to outperform the discrete activation in NOMA, whereas the latter performs better in OMA.

Pinching-antenna systems (PASS) improve wireless links by configuring the locations of activated pinching antennas along dielectric waveguides, namely pinching beamforming. In this paper, a novel adjustable power radiation model is proposed for PASS, where power radiation ratios of pinching antennas can be flexibly controlled by tuning coupling spacing between pinching antennas and waveguides. The closed-form coupling spacings are derived to achieve flexible and equal-power radiation. Based on the commonly-assumed equal-power radiation, a practical PASS framework relying on discrete activation is considered, where pinching antennas can only be activated among a set of predefined locations. A transmit power minimization problem is formulated, which jointly optimizes the transmit beamforming, pinching beamforming, and the numbers of activated pinching antennas, subject to each user’s minimum rate requirement. (1) To obtain globally optimal solutions of the resulting highly coupled mixed-integer nonlinear programming (MINLP) problem, branch-and-bound (BnB)-based algorithms are proposed for both single-user and multi-user scenarios. (2) A low-complexity many-to-many matching algorithm is further developed. Combined with the Karush-Kuhn-Tucker (KKT) theory, locally optimal and pairwise-stable solutions are obtained within polynomial-time complexity. Simulation results demonstrate that: (i) PASS significantly outperforms conventional multi-antenna architectures, particularly when the number of users and the spatial range increase; and (ii) The proposed matching-based algorithm achieves near-optimal performance, resulting in only a slight performance loss while significantly reducing computational overheads. Code is available at https://github.com/xiaoxiaxusummer/PASS_Discrete.

Pinching-antenna systems (PASS) have emerged as a promising technology due to their ability to dynamically reconfigure wireless propagation environments. A novel PASS-based multi-user non-orthogonal multiple access (NOMA) framework is proposed by exploiting the waveguide-division (WD) transmission characteristic. Specifically, each NOMA user cluster is served by one dedicated waveguide, and the corresponding pinching beamforming is exploited to enhance the intra-cluster performance while mitigating the inter-cluster interference. Based on this framework, a sum-rate maximization problem is formulated for jointly optimizing power allocation, pinching beamforming, and user scheduling. To solve this problem, a two-step algorithm is developed, which decomposes the original problem into two subproblems. For the joint power allocation and pinching beamforming design, a penalty dual decomposition (PDD) algorithm is proposed to obtain the locally optimal solutions. Specifically, the coupling constraints are alleviated through augmented Lagrangian relaxation, and the resulting augmented Lagrangian (AL) problem is decomposed into four subproblems, which are solved by the block coordinate descent (BCD) method. For the user scheduling, a low-complexity matching algorithm is developed to solve the user-to-waveguide assignment problem. Simulation results demonstrate that 1) the proposed PASS-based NOMA framework under the WD transmission structure achieves significant sum-rate gain over conventional fixed-position antenna systems and orthogonal multiple access (OMA) scheme; and 2) the proposed matching-based user scheduling algorithm achieves near-optimal user-waveguide association with low computational complexity.

Pinching-antenna systems (PASS) represent an innovative advancement in flexible-antenna technologies, aimed at significantly improving wireless communications by ensuring reliable line-of-sight connections and dynamic antenna array reconfigurations. To employ multi-waveguide PASS in multi-user communications, three practical transmission structures are proposed, namely waveguide multiplexing (WM), waveguide division (WD), and waveguide switching (WS). Based on the proposed structures, the joint baseband signal processing and pinching beamforming design is studied for a general multi-group multicast communication system, with the unicast communication encompassed as a special case. A max-min fairness (MMF) problem is formulated for each proposed transmission structure, subject to the maximum transmit power constraint. For WM, to solve the highly-coupled and non-convex MMF problem with complex exponential and fractional expressions, a penalty dual decomposition (PDD)-based algorithm is invoked for obtaining locally optimal solutions. Specifically, the augmented Lagrangian relaxation is first applied to alleviate the stringent coupling constraints, which is followed by the block decomposition over the resulting augmented Lagrangian function. Then, the proposed PDD-based algorithm is extended to solve the MMF problem for both WD and WS. Furthermore, a low-complexity algorithm is proposed for the unicast case employing the WS structure, by simultaneously aligning the signal phases and minimizing the large-scale path loss at each user. Finally, numerical results reveal that: 1) the MMF performance is significantly improved by employing the PASS compared to conventional fixed-position antenna systems; 2) WS and WM are suitable for unicast and multicast communications, respectively; 3) the performance gap between WD and WM can be significantly alleviated when the users are geographically isolated.

A novel pinching-antenna systems (PASS)-enabled secure wireless communication framework is proposed. By dynamically adjusting the positions of dielectric particles, namely pinching antennas (PAs), along the waveguides, PASS introduces a novel concept of pinching beamforming to enhance the performance of physical layer security. A fundamental PASS-enabled secure communication system is considered with one legitimate user and one eavesdropper. Both single-waveguide and multiple-waveguide scenarios are studied. 1) For the single-waveguide scenario, the secrecy rate (SR) maximization is formulated to optimize the pinching beamforming. A PA-wise successive tuning (PAST) algorithm is proposed, which ensures constructive signal superposition at the legitimate user while inducing a destructive legitimate signal at the eavesdropper. 2) For the multiple-waveguide scenario, artificial noise (AN) is employed to further improve secrecy performance. A pair of practical transmission architectures are developed:waveguide division (WD)andwaveguide multiplexing (WM). The key difference lies in whether each waveguide carries a single type of signal or a mixture of signals with baseband beamforming. For the SR maximization problem under the WD case, a two-stage algorithm is developed, where the pinching beamforming is designed with the PAST algorithm and the baseband power allocation among AN and legitimate signals is solved using successive convex approximation (SCA). For the WM case, an alternating optimization algorithm is developed, where the baseband beamforming is optimized with SCA and the pinching beamforming is designed employing particle swarm optimization. Numerical results demonstrate that i) PASS can significantly improve the secrecy performance over conventional antenna systems in both scenarios; ii) the proposed PAST algorithm for the single-waveguide scenario is efficient, especially when the number of PAs is even or large; iii) WM provides higher and more stable performance at the cost of increased complexity, while WD serves as a simple yet scalable alternative, which is effective when a large number of PAs are deployed.

A novel non-orthogonal multiple access (NOMA) based low-delay service framework is proposed for fog radio access networks (F-RANs). Fog access points (FAPs) leverage NOMA for local delivery of cached content, while the cloud access point employs NOMA to simultaneously push content to FAPs and directly serve users. Based on this model, a delay minimization problem is formulated by jointly optimizing user association, cache placement, and power allocation. To address this non-convex mixed-integer nonlinear programming problem, an alternating optimization (AO) algorithm is developed, which decomposes the original problem into two subproblems, namely joint user association and cache placement, and power allocation. In particular, a low-complexity algorithm is designed to optimizing the user association and cache placement strategy using the McCormick envelope theory and Lagrangian partial relaxation. The power allocation is optimized by invoking the successive convex approximation. Simulation results reveal that: 1) the proposed AO-based algorithm effectively balances between the achieved performance and computational efficiency, and 2) the proposed NOMA-based F-RANs framework significantly outperforms orthogonal multiple access-based F-RANs systems in terms of average transmission delay in different scenarios.

An extremely large-scale intelligent reflecting surface (XL-IRS) aided communication system is studied. Although XL-IRS can effectively combat the double path-loss attenuation, the large aperture size introduces significant near-field effects. The complex near-field propagation and large number of XL-IRS elements can lead to optimality and complexity challenges in beam focusing design. Focusing on a spectral efficiency (SE) maximization problem, two unsupervised learning based algorithms are conceived for the joint optimization of base station and XL-IRS beam focusing, which operate without pre-training and exhibit strong robustness. Specifically, a dense-connected dilated autoencoder meta learning (DDAML) algorithm is proposed to achieve high SE by utilizing dense connections, while reasonably designing an autoencoder to reduce computational complexity. Furthermore, considering a need for low execution time in practical applications, a convolutional dilated autoencoder meta learning (CDAML) algorithm is also proposed to further reduce computational complexity. Simulation results show that the proposed DDAML algorithm achieves the highest SE, while the proposed CDAML algorithm significantly reduces computational complexity at the cost of limited SE loss. Moreover, the two proposed algorithms also demonstrate remarkable robustness in XL-IRS-aided near-field communications.

We propose a hybrid simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) architecture with dynamically switched active and passive elements to support joint localization, communication, and wireless power transfer (WPT). We first pursue a parallel factor analysis with the alternating least squares (PARAFAC-ALS)-based tensor decomposition approach that decouples the base station (BS)-reconfigurable intelligent surface (RIS) and RIS-user channels, thereby enabling low-overhead channel acquisition. Based on this, we formulate a system energy efficiency (EE) maximization problem, subject to the spectral efficiency (SE) requirements of communication users, sensing signal-to-interference-plus-noise ratio constraints, and the nonlinear energy harvesting requirements of energy-harvesting users. The optimization problem is nonconvex since the transmit power allocation, STAR-RIS coefficients, and active/passive mode assignments are tightly coupled in both the objective and constraints. We address this issue by alternating between two subproblems, and solving them via fractional programming, successive convex approximation and a multi-seed greedy strategy employed as an initialization step. Numerical results demonstrate that selectively activating a small, well-chosen subset of STAR-RIS elements achieves 1.5 to 3 times EE improvements compared with fully passive/active architectures, while satisfying communication, sensing, and power-transfer requirements.

A novel mobile edge generation (MEG) framework is proposed to efficiently operate large models at edge networks for low-latency image generation. The generation of large-scale image content is split into two parts, namely primary and secondary regions, with an adjustable generation splitting ratio. The primary region is generated by a large generative model (LGM) at the edge cloud and then transmitted to the mobile device, whereas the remaining secondary regions is created by a tiny generative model (TinyGM) at the mobile device, thus reducing transmission and computation overheads. Both single-user and multi-user cases are considered to characterize the tradeoff between mobile energy consumption and generation delay. For the single-user case, a multi-objective programming (MOP) is formulated for the joint optimization of generation splitting and mobile power control, which simultaneously minimizes the generation delay and mobile energy consumption. This MOP is transferred into single-objective optimization using the ϵ-constraint method. The closed-form optimal solution is derived to obtain Pareto-optimal energy-delay (E-D) region. It is revealed that MEG achieves significant performance gains then conventional fully edge generation (FEG) when signal-to-noise ratio (SNR) or mobile generative cost is low. For the multi-user case, a joint generation splitting and resource allocation problem is formulated, which minimizes the maximum generation delay subject to ϵ-bounded mobile energy consumption and resource constraints. An McCormick-relaxation branch-and-bound (M-BnB) algorithm is proposed to obtain the globally optimal solution. Simulation results demonstrate the Pareto-optimal E-D region in single-user and multi-user cases. Furthermore, MEG flexibly reduces delay compared to conventional FEG and model split schemes while maintaining generative quality.

A novel pinching antenna system (PASS)-enabled downlink multi-user multiple-input single-output (MISO) framework is proposed. PASS consists of multiple waveguides spanning over thousands of wavelength, which equip numerous low-cost dielectric particles, named pinching antennas (PAs), to radiate signals into free space. The positions of PAs can be reconfigured to change both the large-scale path losses and phases of signals, thus facilitating the novelpinching beamformingdesign. A sum rate maximization problem is formulated, which jointly optimizes the transmit and pinching beamforming to adaptively achieve constructive signal enhancement and destructive interference mitigation. To solve this highly coupled and nonconvex problem, both optimization-based and learning-based methods are proposed. 1) For the optimization-based method, a majorization-minimization and penalty dual decomposition (MM-PDD) algorithm is developed, which handles the nonconvex complex exponential component using a Lipschitz surrogate function and then invokes PDD for problem decoupling. 2) For the learning-based method, a novel Karush-Kuhn-Tucker (KKT)-guided dual learning (KDL) approach is proposed, which enables KKT solutions to be reconstructed in a data-driven manner by learning dual variables. Following this idea, a KDL-Transformer algorithm is developed, which captures both inter-PA/inter-user dependencies and channel-state-information (CSI)-beamforming dependencies by attention mechanisms. Simulation results demonstrate that: i) The proposed PASS framework significantly outperforms conventional massive multiple input multiple output (MIMO) system even with a few PAs. ii) The proposed KDL-Transformer can improve over 20% system performance than MM-PDD algorithm, while achieving a millisecond-level response on modern GPUs.

A simultaneously transmitting and reflecting surface (STARS) assisted near-field (NF) integrated sensing and communication (ISAC) framework is proposed, where the radio sensors are installed on the STARS to directly conduct the distance-domain sensing by exploiting the spherical wavefront. A new squared position error bound (SPEB) expression is derived to reveal the dependence on beamforming (BF) design and sensor deployment. To balance the trade-off between the SPEB and the sensor deployment cost, a cost function minimization problem is formulated to jointly optimize the sensor deployment, the active and passive BF, subject to communication and power consumption constraints. For the sensor deployment optimization, a joint sensor deployment algorithm is proposed by invoking the successive convex approximation. Under a specific relationship between the sensor numbers and BF design, we derive the optimal sensor interval in a closed-form expression. For the joint BF optimization, a penalty-based method is invoked. Simulation results validated that the derived SPEB expression is close to the exact SPEB, which reveals the Fisher Information Matrix of position estimation in NF can be approximated as a diagonal matrix. Furthermore, the proposed algorithms achieve the best SPEB performance compared to the benchmark schemes accompanying the lowest deployment cost.

A novel movable-element enabled simultaneously transmitting and reflecting surface (ME-STARS) communication system is proposed, where ME-STARS elements positions can be adjusted to enhance the degress-of-freedom for transmission and reflection. For each ME-STARS operating protocols, namely energy-splitting (ES), mode switching (MS), and time switching (TS), a weighted sum rate (WSR) maximization problem is formulated to jointly optimize the active beamforming at the base station (BS) as well as the elements positions and passive beamforming at the ME-STARS. An alternative optimization (AO)-based iterative algorithm is developed to decompose the original non-convex problem into three subproblems. Specifically, the gradient descent algorithm is employed for solving the ME-STARS element position optimization subproblem, and the weighted minimum mean square error and the successive convex approximation methods are invoked for solving the active and passive beamforming subproblems, respectively. It is further demonstrated that the proposed AO algorithm for ES can be extended to solve the problems for MS and TS. Numerical results unveil that: 1) the ME-STARS can significantly improve the WSR compared to the STARS with fixed position elements and the conventional reconfigurable intelligent surface with movable elements, thanks to the extra spatial-domain diversity and the higher flexibility in beamforming; and 2) the performance gain of ME-STARS is significant in the scenarios with larger number of users or more scatterers.

A simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted simultaneous wireless information and power transfer (SWIPT) system is investigated. Both active and passive STAR-RISs are considered. Passive STAR-RISs can be cost-efficiently fabricated to large aperture sizes with significant near-field regions, but the design flexibility is limited by the coupled phase-shifts. Active STAR-RISs can further amplify signals and have independent phase-shifts, but their aperture sizes are relatively small due to the high cost. To characterize and compare their performance, a power consumption minimization problem is formulated by jointly designing the beamforming at the access point (AP) and the STAR-RIS, subject to both the power and information quality-of-service requirements. To solve the resulting highly-coupled non-convex problem, the original problem is first decomposed into simpler subproblems and then an alternating optimization framework is proposed. For the passive STAR-RIS, the coupled phase-shift constraint is tackled by employing a vector-driven weighted penalty method. While for the active STAR-RIS, the independent phase-shift is optimized with AP beamforming via matrix-driven semidefinite programming, and the amplitude matrix is updated using convex optimization techniques in each iteration. Numerical results show that: 1) given the same aperture sizes, the active STAR-RIS exhibits superior performance over the passive one when the aperture size is small, but the performance gap decreases with the increase in aperture size; and 2) given identical power budgets, the passive STAR-RIS is generally preferred, whereas the active STAR-RIS typically suffers performance loss for balancing between the hardware power and the amplification power.

Non-orthogonal multiple access (NOMA) offers enhanced energy and spectral efficiency through effective resource sharing, yet uplink NOMA suffers from bit error rate (BER) degradation due to error propagation from imperfect successive interference cancellation (SIC). This paper investigates the potential BER performance enhancement via reconfigurable intelligent surfaces (RISs) in uplink NOMA systems. A novel RIS-assisted design is proposed, wherein the RIS phase shifts are optimized to amplify the received signals and mitigate channel-induced phase distortions. An accurate effective statistical channel modeling is developed to facilitate our closed-form BER analysis, supported by a two-user channel alignment scheme for efficient SIC detection. Based on the derived BER expressions, an optimized power allocation (PA) strategy is formulated to minimize the average BER under transmit power constraints. Simulation results validate the theoretical analysis, demonstrating that the proposed PA scheme effectively eliminates the BER floors associated with uplink NOMA at the base station.

A continuous aperture array (CAPA)-based multicast communication system is investigated in this paper, where a base station (BS) employs a CAPA to serve a set of multicast users. Under a transmit power constraint, the problem of maximizing the system multicast spectral efficiency (SE) by designing the CAPA beamformer is formulated, where the involved beamformer is a continuous current density function across the CAPA surface. By introducing auxiliary variables, the non-convex multicast SE objective function is first transformed into a tractable form. Then, to address the reformulated problem, an efficient block coordinate descent (BCD)-based algorithm is developed. The CAPA beamforming design subproblem can be optimally solved via the Lagrangian dual method and the calculus of variations (CoV) theory. Numerical results demonstrate that the considered CAPA can significantly improve the multicast SE compared to a conventional spatially discrete array (SPDA).

A novel mobile edge generation (MEG) framework is proposed to enable large generative model (LGM) capabilities at the edge, which offers low-latency, power-saving, and languageguided generation on mobile device. Specifically, our framework splits the generation of large-scale content (e.g., high-definition image) into two parts, namely primary and secondary regions. Only the primary region is generated by the LGM at the edge cloud and transmitted via downlink, while the secondary region is generated by the tiny generative model (TinyGM) at the mobile device. By configuring generation splitting ratio between edge and mobile devices, the transmission and computation overheads can be reduced. We formulate a joint generation splitting and mobile power control optimization problem. The formulated problem is a multi-objective optimization programming, which simultaneously minimizes the generation latency and the mobile energy consumption. To explore the performance limits, we first transfer the multi-objective programming into a single-objective programming based on the$\epsilon$-constraint method. Then, we derive the closed-form Pareto-optimal solution of generation splitting and mobile power control. Thereby, the performance boundary of energy-delay (E-D) tradeoff region is obtained. Furthermore, we also identify the conditions under which the proposed MEG strictly outperforms the fully edge generation (FEG) scheme, and demonstrates that performance gains increase as signal-tonoise ratio (SNR) and mobile generation cost decrease. Numerical results demonstrate the optimal E-D tradeoff of the proposed MEG and verify that it can significantly reduce the latency compared to FEG while achieving satisfactory generation.

This paper investigates near-field multi-user downlink communications over Ricean fading channels underpinned by the holographic multiple-input multiple-output (HMIMO) technology. We first establish the mutual coupling and radiation efficiency models to characterize the effect of mutual coupling and then formulate the practical input-output relationship. Based on this, the achievable spectral efficiency (SE) is derived for maximum ratio transmission (MRT). By further investigating the special cases of pure line-of-sight (LoS) and Rayleigh fading, our analysis reveals that for a moderate number of antenna elements, the system's SE with mutual coupling might outperform that without mutual coupling, especially in the low transmit power regime. Moreover, the additional distance degrees-of-freedom (DoF) introduced by the near-filed channel can enable the inter-user interference mitigation, even for the worst case when the users have similar angular directions. Finally, the obtained theoretical analysis is validated through simulations.

The real-time near-field beamforming framework with the aided of 3D velocity sensing and localization for unmanned aerial vehicle (UAV) communications is proposed. Exploiting the variant Doppler shift over the spatial domain in the near field, the three-dimensional (3D) velocities are estimated with the echo signals. To provide timely correction of the location prediction errors with the estimated velocities, the extended Kalman filter (EKF) is adopted to fuse the predicted states and the estimated ones. Subsequently, the near-field beamforming can be conducted with the predicted locations of the UAV, thereby realizing zero-pilot and low-latency transmission. Numerical results unveil that, the proposed scheme can achieve the accurate tracking of the UAV's flying route.

A novel framework of the uncrewed aerial vehicle (UAV)-mounted active simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) communications with the nonorthogonal multiple access (NOMA) is proposed for Internet of Things (IoT) networks. In particular, an active STAR-RIS is deployed onboard to enhance the communication link between the base station (BS) and the IoT devices, and NOMA is utilized for supporting the multidevice connectivity. Based on the proposed framework, a system sum rate maximization problem is formulated for the joint optimization of the active STAR-RIS beamforming, the UAV trajectory design, and the power allocation. To solve the nonconvex problem with highly coupled variables, an alternating optimization (AO) algorithm is proposed to decouple the original problem into three subproblems. Specifically, for the active STAR-RIS beamforming, the amplification coefficient, the power-splitting ratio, and the phase shift are incorporated into a combined variable to simplify the optimization process. Afterward, the penalty-based method is invoked for handling the nonconvex rank-one constraint. For the UAV trajectory design and the power allocation subproblems, the successive convex optimization method is applied for iteratively approximating the local-optimal solution. Numerical results demonstrate that: 1) the proposed algorithm achieves superior performance compared to the benchmarks in terms of the sum rate and 2) the UAV-mounted active STAR-RIS can effectively enhance the channel gain from the BS to the IoT devices by the high-quality channel construction and the power compensation.

Mobile-edge generation (MEG) is an emerging technology that allows the network to meet the challenging traffic load expectations posed by the rise of generative artificial intelligence (GAI). A novel MEG model is proposed for deploying GAI models on edge servers (ESs) and user equipment (UE) to jointly complete text-to-image generation tasks. In the generation task, the ES and UE will cooperatively generate the image according to the text prompt given by the user. To enable the MEG, a pretrained latent diffusion model (LDM) is invoked to generate the latent feature, and an edge-inferencing MEG protocol is employed for data transmission exchange between the ES and the UE. A compression coding technique is proposed for compressing the latent features to produce seeds. Based on the above seed-enabled MEG model, an image quality optimization problem with energy constraint is formulated. The transmitting power of the seed is dynamically optimized by a deep reinforcement learning (DRL) agent over the fading channel. The proposed MEG-enabled text-to-image generation system is evaluated in terms of image quality and transmission overhead. The numerical results indicate that, compared to the conventional centralized generation-and-downloading scheme, the symbol number of the transmission of MEG is materially reduced. In addition, the proposed compression coding approach can improve the quality of generated images under low signal-to-noise ratio (SNR) conditions, and the DRL-enabled dynamic power control further improves the image quality under the energy constraint compared to static transmit power control.

A simultaneously transmitting and reflecting reconfigurable-intelligent-surface (STAR-RIS)-assisted wireless powered communication network (WPCN) is proposed, where two energy-limited devices first harvest energy from a hybrid access point (HAP) and then use that energy to transmit information back. To fully eliminate thedoubly-near-far-effect in WPCNs, two STAR-RIS operating protocol-driven transmission strategies, namely energy splitting nonorthogonal multiple access (ES-NOMA) and time switching time division multiple access (TS-TDMA) are proposed. For each strategy, the corresponding optimization problem is formulated to maximize the minimum throughput by jointly optimizing time allocation, user transmit power, active HAP beamforming, and passive STAR-RIS beamforming. For ES-NOMA, the resulting intractable problem is solved via a two-layer algorithm, which exploits the 1-D search and block coordinate descent methods in an iterative manner. For TS-TDMA, the optimal active beamforming and passive beamforming are first determined according to the maximum-ratio transmission beamformer. Then, the optimal solution of the time allocation variables is obtained by solving a standard convex problem. Numerical results show that: 1) the STAR-RIS can achieve considerable performance improvements for both strategies compared to the conventional RIS; 2) TS-TDMA is preferred for single-antenna scenarios, whereas ES-NOMA is better suited for multiantenna scenarios; and 3) the superiority of ES-NOMA over TS-TDMA is enhanced as the number of STAR-RIS elements increases.

A novel movable-element simultaneously transmitting and reflecting surface (ME-STARS)-assisted near-field wideband communication framework is proposed. In particular, the position of each STARS element can be adjusted to combat the significant wideband beam squint issue in the near field instead of using costly true-time delay components. Four practical ME-STARS element movement modes are proposed, namely region-based (RB), horizontal-based (HB), vertical-based (VB), and diagonal-based (DB) modes. Based on this, a near-field wideband multi-user downlink communication scenario is considered, where a sum rate maximization problem is formulated by jointly optimizing the base station (BS) precoding, ME-STARS beamforming, and element positions. To solve this intractable problem, a two-layer algorithm is developed. For the inner layer, the block coordinate descent optimization framework is utilized to solve the BS precoding and ME-STARS beamforming in an iterative manner. For the outer layer, the particle swarm optimization-based heuristic search method is employed to determine the desired element positions. Numerical results show that: 1) the ME-STARSs can effectively address the beam squint for near-field wideband communications compared to conventional STARSs with fixed element positions; 2) the RB mode achieves the most efficient beam squint effect mitigation, while the DB mode achieves the best trade-off between performance gain and hardware overhead; and 3) an increase in the number of ME-STARS elements or BS subcarriers substantially improves the system performance.

In this work, we investigate an active reconfigurable intelligent surface (RIS)-aided non-orthogonal multiple access (NOMA)-enabled space-air-ground integrated network (SAGIN) with cognitive radio, leveraging the flexible deployment of an unmanned aerial vehicle (UAV) and the ubiquitous coverage of satellite networks. The UAV serves uplink and downlink users in the secondary network via NOMA and time division multiple access mechanisms, respectively, while satellites provide wireless backhaul for the UAV and primary users. We aim to maximize the weighted sum mean rate and energy efficiency for the secondary network by jointly the optimizing power allocation, the RIS reflection coefficients (RC), the user matching factors, and the UAV trajectory. We propose an alternating optimization framework based on the block coordinate ascent (BCA) technique, which decouples the problem into multiple variable blocks for alternating optimization until convergence. Moreover, we investigate the performance of energy-efficient active RIS with a sub-connected architecture, decoupling the RIS RC optimization into amplification factor and phase shift subproblems to be solved separately. Finally, simulation results validate the effectiveness of the proposed schemes, and demonstrate weakness of passive RIS and rationality and economics of sub-connected active RIS architecture.

A reconfigurable intelligent surface (RIS) assisted semi-grant-free (SGF) non-orthogonal multiple access (NOMA) system is investigated. Unlike existing works that only focus on short-term resource allocation, we study a long-term power-saving optimization problem under queue stability constraints and utilize Lyapunov stability theory to deal with delay-aware resource allocation. We first transform the long-term problem into a series of per-time-slot problems by exploiting the Lyapunov theory. Then, the objective function is minimized by alternatingly optimizing the power allocation, channel assignment, and RIS reflection coefficients. In particular, the channel assignment subproblem is solved by invoking a many-to-one matching algorithm. The power allocation sub-problem is addressed by the developed fractional programming algorithm. The reflection coefficients design sub-problem is solved by a penalty-based method, which tackles the rank one constraint and optimizes reflection coefficients. The numerical results validate the effectiveness and show that it can achieve queue stability by setting the Lyapunov parameters. It also shows that the proposed RIS-assisted SGF NOMA system outperforms without RIS and random RIS phase-shift baselines.

The core idea of holographic MIMO (HMIMO) is to densely deploy numerous antenna elements within a given aperture size. However, with the denser distribution of antenna elements, stronger mutual coupling effects would kick in among antenna elements, which would eventually affect the communication performance. Meanwhile, as the holographic array usually has large physical size, the possibility of near-field communication increases. This paper investigates a near-field multi-user downlink HMIMO system and characterizes the spectral efficiency (SE) under the mutual coupling effect over Ricean fading channels. Both perfect and imperfect channel state information (CSI) scenarios are considered. (i) For the perfect CSI case, the mutual coupling and radiation efficiency model are first established. Then, a closed-form SE expression is derived under maximum ratio transmission (MRT). By comparing the SE between the cases with and without mutual coupling, it is unveiled that the system SE with mutual coupling might outperform that without mutual coupling in the low transmit power regime for a given aperture size. Moreover, it is also unveiled that the inter-user interference cannot be eliminated unless the physical size of the array increases to infinity. Fortunately, the additional distance term in the near-field channel can be exploited for the inter-user interference mitigation, especially for the worst case, where the users’ angular positions overlap to a great extent. (ii) For the imperfect CSI case, the channel estimation error is considered for the derivation of the closed-form SE under MRT. It shows that in the low transmit power regime, the system SE can be enhanced by increasing the pilot power and the antenna element density, the latter of which will lead to severe mutual coupling. In the high transmit power regime, increasing the pilot power has a limited effect on improving the system SE. However, increasing the antenna element density remains highly beneficial for enhancing the system SE. Finally, both analytical and simulation results confirm that reducing the antenna spacing will be accompanied by significant mutual coupling effects, which may potentially enhance the system SE. However, this enhancement is ultimately limited by the radiation efficiency of the antennas and the physical size of the array.

A near-field wideband communication system is investigated in which a base station (BS) employs an extra-large scale antenna array (ELAA) to serve multiple users in its near-field region. To facilitate near-field multi-user beamforming and mitigate the spatial wideband effect, the BS employs a hybrid beamforming architecture based on true-time delayers (TTDs). In addition to the conventional fully-connected TTD-based hybrid beamforming architecture, a new sub-connected architecture is proposed to improve energy efficiency and reduce hardware requirements. Two wideband beamforming optimization approaches are proposed to maximize spectral efficiency for both architectures. 1) Fully-digital approximation (FDA) approach: In this method, the TTD-based hybrid beamformer is optimized by the block-coordinate descent and penalty method to approximate the optimal digital beamformer. This approach ensures convergence to the stationary point of the spectral efficiency maximization problem. 2) Heuristic two-stage (HTS) approach: In this approach, the analog and digital beamformers are designed in two stages. In particular, two low-complexity methods are proposed to design the high-dimensional analog beamformers based on approximate and exact line-of-sight channels, respectively. Subsequently, the low-dimensional digital beamformer is optimized based on the low-dimensional equivalent channels, resulting in reduced computational complexity and channel estimation complexity. Our numerical results show that 1) the proposed approach effectively eliminates the spatial wideband effect, and 2) the proposed sub-connected architecture is more energy efficient and has fewer hardware constraints on the TTD and system bandwidth compared to the fully-connected architecture.

The Pinching-Antenna SyStem (PASS) is a revolutionary flexible antenna technology designed to enhance wireless communication by establishing strong line-of-sight (LoS) links, reducing free-space path loss and enabling antenna array reconfigurability. PASS uses dielectric waveguides with low propagation loss for signal transmission, radiating via a passive pinching antenna, which is a small dielectric element applied to the waveguide. This paper first proposes a physics-based hardware model for PASS, where the pinching antenna is modeled as an open-ended directional coupler, and the electromagnetic field behavior is analyzed using coupled-mode theory. A simplified signal model characterizes the coupling effect between multiple antennas on the same waveguide. Based on this, two power models are proposed: equal power and proportional power models. Additionally, a transmit power minimization problem is formulated/studied for the joint optimization of transmit and pinching beamforming under both continuous and discrete pinching antenna activations. Two algorithms are proposed to solve this multimodal optimization problem: the penalty-based alternating optimization algorithm and a low-complexity zero-forcing (ZF)-based algorithm. Numerical results show that 1) the ZF-based low-complexity algorithm performs similarly to the penalty-based algorithm, 2) PASS reduces transmit power by over 95% compared to conventional and massive MIMO, 3) discrete activation causes minimal performance loss but requires a dense antenna set to match continuous activation, and 4) the proportional power model yields performance comparable to the equal power model.

A novel hard-latency guaranteed cluster-free multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) framework is proposed to deal with burst traffics that commonly occur in real-world scenarios. The hard-latency constrained effective throughput (HLC-ET) maximization problem is formulated, which jointly optimizes the beamforming and cluster-free success interference cancellation (SIC) operations. To address the resultant problem, a two-stage reinforcement learning (RL)-based algorithm is developed to capture system uncertainty, where the large-dimension optimization is decoupled into two stages to reduce the action space and fasten convergence of RL. In the long-term stage, we aim to maximize the HLC-ET, and a hybrid RL algorithm with policy reuse is adoped to control the priority weights to construct the weighted sum rate (WSR) function of users. In the short-term stage, a branch-and-bound (BB) based algorithm is further developed to obtain the optimal solution of the WSR maximization problem. The BB-based algorithm is proved to guarantee the convergence to an ϵ-optimal solution of the WSR maximization problem within a finite number of steps. To accelerate computation in the short-term stage, a channel correlation based two-loop greedy (CC-TLG) algorithm is proposed to significantly reduce the complexity with almost no performance loss compared to the BB-based algorithm. Finally, simulations demonstrate the advantages of the proposed two-stage RL based joint beamforming and SIC optimization (TSRL-JBSO) algorithm over conventional RL-based and non-RL based algorithms.

A continuous aperture array (CAPA)-based secure communication system is investigated, where a base station (BS) equipped with a CAPA transmits signals to a legitimate user under the existence of an eavesdropper. For improving the secrecy performance, the artificial noise (AN) is employed at the BS for the jamming purpose. We aim at maximizing the secrecy rate by jointly optimizing the information-bearing and AN source current patterns, subject to the maximum transmit power constraint. To solve the resultant non-convex integral-based functional programming problem, a channel subspace-based approach is first proposed via exploiting the result that the optimal current patterns always lie within the subspace spanned by all users’ channel responses. Then, the intractable CAPA continuous source current pattern design problem with an infinite number of optimization variables is equivalently transformed into the channel-subspace weighting factor optimization problem with a finite number of optimization variables. A penalty-based successive convex approximation method is developed for iteratively optimizing the finite-size weighting vectors. To further reduce the computational complexity, we propose a two-stage source current patterns design scheme. Specifically, the information-bearing and AN patterns are first designed using the maximal ration transmission (MRT) and zero-forcing (ZF) transmission, respectively. Then, the remaining power allocation is addressed via the one-dimensional search method. Numerical results unveil that 1) the CAPA brings in significant secrecy rate gain compared to the conventional discrete multiple-input multiple-output (MIMO); 2) the proposed channel subspace-based algorithm outperforms the conventional Fourier-based approach, while sustaining much lower computational complexity; and 3) the two-stage ZF-MRT approach has negligible performance loss for the large transmit power regime.

This paper investigates the design of jointly supporting physical layer security (PLS) and covert communications (CCs) in an active simultaneously transmitting and reflecting reconfigurable intelligent surface (a-STAR-RIS) assisted integrated sensing and communication (ISAC) system. Due to the unified waveform design of ISAC signals, we consider a challenging scenario with two targets being suspicious attackers, where one warden target potentially detects the confidential transmission behavior of covert users and another eavesdropper target attempts to intercept the broadcasted confidential information of security users. We investigate the joint beamforming design at the base station (BS) and the a-STAR-RIS to achieve a high-quality sensing beampattern while meeting covertness and security communication requirements. (1) For the ideal scenario with perfect channel state information (CSI) and precise target locations, we propose an alternative optimization (AO) method to address the optimization problem involving highly coupled variables. Specifically, the optimal beamforming design at the BS is handled using the semi-definite relaxation (SDR) technique, while the beamforming design at the a-STAR-RIS is addressed through a penalty-based iterative algorithm. (2) A more practical case with uncertain target locations and imperfect CSI is considered to achieve a robust beamforming design, where the non-deterministic outage probability constraints are effectively transformed by employing the Bernstein-type inequality. Numerical results demonstrate the superiority of the a-STAR-RIS over the baseline cases and certify that the proposed algorithms can effectively balance the tradeoff among the sensing quality, covert and secure communication requirements. Besides, results also show that the proposed robust beamforming scheme can construct adequate sensing beampattern, even with imperfect CSI and uncertain target locations.

The performance of near-field sensing (NISE) in a legacy wideband multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) communication system is analyzed. The maximum likelihood estimates (MLE) for the target’s distance and angle relative to the antenna array are derived. To evaluate the estimation error, closedform analytical expressions of Cram´er-Rao bounds (CRBs) are derived for both uniform linear arrays (ULAs) and uniform circular arrays (UCAs). The asymptotic CRBs are then analyzed to reveal the scaling laws of CRBs with respect to key system parameters, including array size, bandwidth, and target distance. Our results reveal that 1) the mean-squared error achieved by MLEs approaches CRBs in the high signal-to-noise ratio regime; 2) a larger array aperture does not necessarily improve NISE performance, especially with ultra-large bandwidth; 3) large bandwidth sets an estimation error ceiling for NISE as target distance increases; 4) array aperture and bandwidth, rather than the number of antennas and subcarriers, are the key factors affecting wideband NISE performance; and 5) UCAs offer superior, angle-independent wideband NISE performance compared to ULAs with the same aperture.

A hybrid non-orthogonal multiple access (HNOMA) empowered integrated sensing and communications (ISAC) framework is proposed, which adaptively manages the additional sensing-to-communication (S2C) interference to save the transmit power. Two scenarios with different numbers of communication users (CUs) are investigated. For the first scenario where the number of CUs does not exceed the number of transmit antennas, a mixed integer problem is formulated to optimize the beamforming (BF) design and successive interference cancellation (SIC) options. An ideal case is primarily inspected, which unveils an insight into the required number of dedicated sensing beams. Inspired by this insight, the SIC options are determined while the remaining BF design is solved via semidefinite relaxation (SDR). For the second scenario where the number of CUs exceeds the number of transmit antennas, the CUs are further grouped into NOMA clusters to mitigate the communication-to-communication interference. An alternating optimization-based algorithm is developed, where the BF design, SIC options and power allocation are alternatively optimized. Simulation results reveal that: 1) the proposed algorithm achieves power-saving gain compared to the conventional ISAC; 2) the proposed algorithm can further exploit the benefits of NOMA to save transmission power while maintaining the least beampattern mismatch in the second scenario.

The novel concept of mobile edge generation (MEG) is investigated, where the generative artificial intelligence (GAI) model is partitioned into sub-models to be distributed in the network edge, thus enabling latent feature exchange between the edge server and user equipments (UEs). A seed coding module is introduced to encode the intermediate latent features generated by the GAI sub-model at the edge server into flexibly-sized seed for transmission to UEs, instead of transmitting large-size raw data. A weighted energy consumption minimization problem is formulated by jointly optimizing the seed coding ratio (SCR), transmit power, and computing frequencies while guaranteeing the quality-of-generation requirements including total latency and peak signal-to-noise ratio (PSNR). To enhance the resilience of the MEG models against the channel noise, a joint fine-tuning scheme based on low-rank adaption is proposed to train the introduced rank-reduced bypass matrices and seed coding module. Based on the fine-tuned results, a PSNR model regarding SCR and communication signal-to-noise ratio is established to overcome the optimization difficulty due to the lack of the explicit PSNR model. A proximal policy optimization-based MEG energy consumption optimization (MEG-ECO) algorithm is proposed to solve the formulated problem, where the order of magnitude balancing on state and penalty shaping are exploited for more efficient learning. Numerical results reveal that 1) the fine-tuned MEG models have superior resilience against the channel noise; 2) the proposed MEG-ECO algorithm can significantly reduce energy consumption by up to 87.4% compared to conventional centralized generation and up to 33.5% against MEG without seed coding module; and 3) the energy consumption decreases when more partial models are assigned to the edge server, whereas this impact diminishes as the latency threshold is relaxed.

Mobile edge computing (MEC) enables the provision of high-reliability and low-latency applications by offering computation and storage resources in close proximity to end-users. Different from traditional computation task offloading in MEC systems, the large data volume and complex task computation of artificial intelligence involved intelligent computation task offloading have increased greatly. To address this challenge, we propose a MEC system for multiple base stations and multiple terminals, which exploits semantic transmission and early exit of inference. Based on this, we investigate a joint semantic transmission and resource allocation problem for maximizing system reward combined with analysis of semantic transmission and intelligent computation process. To solve the formulated problem, we decompose it into communication resource allocation subproblem, semantic transmission subproblem, and computation capacity allocation subproblem. Then, we use 3D matching and convex optimization method to solve subproblems based on the block coordinate descent (BCD) framework. The optimized feasible solutions are derived from an efficient BCD based joint semantic transmission and resource allocation algorithm in MEC systems. Our simulation demonstrates that: 1) The proposed algorithm significantly improves the delay performance for MEC systems compared with benchmarks; 2) The design of transmission mode and early exit of inference greatly increases system reward during offloading; and 3) Our proposed system achieves efficient utilization of resources from the perspective of system reward in the intelligent scenario.

A large-scale simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted simultaneous wireless information and power transfer (SWIPT) system is investigated. In contrast to conventional single-field (near or far) models, a mixed near- and far-field SWIPT is considered, where a STAR-RIS with coupled phase-shift is utilized to support near-field energy devices and far-field information users. Under this setup, a transmit power minimization problem is formulated by jointly designing the access point beamforming and the STARRIS beamforming, subject to the quality of service requirements for power and information. To solve this intractable problem, a weight penalty based alternating optimization algorithm is proposed. Finally, numerical results validate the effectiveness of the proposed scheme. Moreover, the coupled phase-shift associated with the STAR-RIS has a more pronounced effect on far-field information users than on near-field energy devices.

A novel multiple-input multiple-output (MIMO) cognitive radio (CR) system is proposed in this work. Specifically, the underly secondary network in the proposed CR system reuses the same frequency resources occupied by the primary network with the help of the simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). The secondary base station (SBS) beamformers and the STAR-RIS coefficients are jointly designed to maximize the sum rate of secondary users (SUs) considering the power limitation at the SBS, interference limitation at the primary users (PUs), and the coefficients constraint for the STAR-RIS. The block coordinate descent method is invoked to tackle the formulated optimization problem. In each iteration, the beamformers at the SBS are optimized by solving a quadratically constrained quadratic program problem, and the passive STAR beamforming problem is solved with the successive convex approximation-based algorithm. Simulation results show that the proposed STAR-RIS aided CR communication framework can significantly enhance the sum rate of the secondary system.

The cell-free massive multiple-input multiple-output (CF mMIMO) network is emerging as an innovative technology that utilizes numerous access points (APs) distributed across a region to provide services for users through coherent transmission and reception. To enhance the coverage of the CF mMIMO network, we introduce simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) into the CF mMIMO network. In the STAR-RIS aided CF mMIMO network, beamforming for the downlink of mobile users becomes exceptionally complex and challenging. Therefore, we propose a beamforming algorithm based on soft actor-critic (SAC), which can jointly optimize the beamforming of APs and phase shifts and amplitude coefficients of STAR-RISs. Simulation results demonstrate that the proposed SAC-based beamforming algorithm significantly maximizes the sum-rate of the STAR-RIS aided CF mMIMO network, outperforming the no STAR-RIS aided CF mMIMO network in terms of meeting quality of service (QoS) requirement.

The performance bounds of near-field sensing are studied for circular arrays, focusing on the impact of bandwidth and array size. The closed-form Cramér-Rao bounds (CRBs) for angle and distance estimation are derived, revealing the scaling laws of the CRBs with bandwidth and array size. Contrary to expectations, enlarging array size does not always enhance sensing performance. Furthermore, the asymptotic CRBs are analyzed under different conditions, unveiling that the derived expressions include the existing results as special cases. Finally, the derived expressions are validated through numerical results.

A novel reconfigurable intelligent surfaces (RIS) assisted near-field (NF) ISAC system is investigated, where the spherical wave propagation environment is utilized to elevate the radio sensing performance. Except for the conventional RIS, the sensor elements are embedded on the RIS surface to conduct the radios sensing functionality. By exploiting the symmetry property of the steering vector, a new expression of position error bound (PEB) is derived to unveil the impact of the sensor deployment. To balance the radio sensing performance and the sensor deployment cost, a cost function minimization problem is formulated to jointly optimize the number of sensor elements and the passive beamforming (BF). To solve this non-convex problem, a joint geometric programming element-wise (JGPE) algorithm is proposed. The successive convex approximation for geometric programming is invoked to optimize the number of sensor elements while the element-wise algorithm is adopted to optimize the passive BF. Numerical results demonstrated that the proposed algorithm reach the least PEB and cost function value among the benchmarks.

Mobile edge generation (MEG) is an emerging technology that allows the network to meet the challenging traffic load expectations posed by the rise of generative artificial intelligence (GAI). A novel MEG model is proposed for deploying GAI models on edge servers (ES) and user equipment (UE) to jointly complete test-to-image generation tasks. In the generaation task, the user uploads the text prompt and the ES and UE will cooperatively generate the image for the user. To enable the data transmission exchange between the ES and the UE, a seed based MEG protocol is employed, where a coded latent feature is created as a generation seed. A pre-trained latent diffusion model (LDM) is invoked to generate the latent feature, and a compression coding technique is proposed for compressing the latent features. The proposed MEG enabled text-to-image generation system is evaluated in terms of image quality and transmission overhead. The numerical results indicate that, compared to the conventional centralized generation-and-downloading scheme, the symbol number of the transmission of MEG is materially reduced. In addition, the proposed compression coding approach can improve the quality of generated images under low signal-to-noise ratio (SNR) conditions.

A downlink near-field integrated sensing and communication (ISAC) framework is proposed. A novel double-array structure at the BS is proposed, where an assisting receiver (AR) is attached to the main transmitter (MT) to enable the near-field communication (NFC) system with the ability of target positioning. The joint angle and distance Cramér-Rao bound (CRB) is derived and then minimized subject to the communication quality of ser-vice (QoS) requirement and the hybrid-analog-and-digital (HAD) structure constraint. A double-loop iterative algorithm utilizing the penalty dual decomposition (PDD) framework is proposed to tackle the non-convex problem. The numerical results show that: 1) The proposed ISAC system can locate the target in both angle and distance domains; 2) The performance of the HAD ISAC approaches the performance of fully digital (FD) ISAC when the communication QoS requirement is not stringent.

A novel semantic communication (SC)-assisted secrecy transmission framework is proposed. In particular, the legitimate transmitter (Tx) sends the superimposed semantic and bit stream to the legitimate receiver (Rx), where the information may be eavesdropped by the malicious node (EVE). As the EVE merely has the conventional bit-oriented communication structure, the semantic signal acts as the type of beneficial information-bearing artificial noise (AN), which not only keeps strictly confidential to the EVE but also interferes with the EVE. The ergodic (equivalent) secrecy rate over fading wiretap channels is maximized by jointly optimizing the transmit power, semantic-bit power splitting ratio, and the successive interference cancellation decoding order at the Tx, subject to both the instantaneous peak and long-term average power constraints. To address this non-convex problem, both the optimal and suboptimal algorithms are developed by employing the Lagrangian dual method and the successive convex approximation method, respectively. Numerical results show that the proposed SC-assisted secrecy transmission scheme can significantly enhance the physical layer security compared to the baselines using the conventional bit-oriented communication and no-information-bearing AN. It also shows that the proposed suboptimal algorithm can achieve a near-optimal performance.

True-time delayers (TTDs) are popular components for hybrid beamforming architectures to combat the spatial-wideband effect in wideband near-field communications. A se-rial and a hybrid serial-parallel TTD configuration are inves-tigated for hybrid beamforming architectures. Compared to the conventional parallel configuration, the serial configuration exhibits a cumulative time delay through multiple TTDs, which potentially alleviates the maximum delay requirements on the TTDs. However, independent control of individual TTDs becomes impossible in the serial configuration. In this context, a hybrid TTD configuration is proposed as a compromise solution. More-over, the wideband near-field beamforming design for different configurations is studied for maximizing the spectral efficiency in single-user systems. In particular, a closed-form solution for the beamforming design is derived. The preferred user locations and the required maximum time delay of each TTD configuration are characterized. Our numerical results confirm the effectiveness of the proposed designs.

A near-field simultaneous wireless information and power transfer (SWIPT) network is investigated, where the hybrid beamforming architecture is employed at the base station to send the information beams for information transmission while charging energy harvesting users. A transmit power minimization problem is formulated by jointly optimizing the analog beamformer and the baseband digital beamformers. To tackle the non-convex optimization problem, a penalty-based two-layer (PTL) algorithm is proposed to optimize the analog beamformer and baseband digital information beamformers. By employing the block coordinate descent method, the optimal analog beamformer, and baseband digital information beamformers are obtained in the closed-form expressions. Moreover, a low-complexity two-stage algorithm to reduce the high computational complexity caused by the large number of antennas is proposed. Numerical results illustrate that: 1) the proposed PTL algorithm can achieve near-optimal performance; and 2) in contrast to the far-field SWIPT, a single near-field beamformer can focus the energy on multiple locations.

A near-field multi-input multi-output (MIMO) multi-user downlink system is investigated. To achieve efficient beam tracking in near field, the trajectories of mobile users (MUs) are first predicted, and then successive hybrid beamfocusing with data stream allocation is performed to maximize the throughput. Specifically, a digit-aware location prediction framework based on Transformer is proposed to predict the subsequent locations of MUs. Through attending to the individual decimal digits of the MUs' locations, the prediction error can be reduced from the digit perspective. We then propose a dual-tiered proximal policy optimization algorithm to learn the adaptive hybrid beam-focusing and data stream allocation according to the predicted MUs' movement. The policy of agent is hierarchically designed for effective dimensionality reduction of the large-scale action space. The numerical results demonstrate that 1) Our proposed algorithms outperform the baselines in terms of throughput with high predictive accuracy and beamfocusing gain; 2) The proposed beam tracking scheme can achieve a similar throughput to the perfect CSI scheme, while the performance gap of the non-tracking scheme is 53.2%; 3) Compared to the fixed data stream allocation, the proposed adaptive data stream allocation benefits a performance gain which escalates with an increasing number of total data streams.

A novel downlink hybrid reconfigurable intelligent surface (RIS) transmitter enabled multi-user communication framework is studied. Specifically, elements on the RIS can flexibly switch between active and passive modes to deliver information to multiple users. The system energy efficiency is maximized by jointly optimizing RIS element mode scheduling, transmission beamforming vector, and power allocation coefficients, subject to the user's individual rate requirement and the maximum RIS amplification power constraint. We first exploit the Dinkelbach approach to transform the original mixed-integer nonlinear programming problem into a nonfractional optimization problem. Then, an alternating optimization based algorithm is developed to address this problem. In particular, the optimal RIS element operating mode is determined by the exhaustive search method. Then, the RIS beamforming and power allocation coefficients are alternately designed. Finally, numerical results show that a significant performance improvement can be reaped by the proposed scheme compared to the baseline schemes employing full-active RIS or full-passive RIS.

A novel non-orthogonal semantic communication (NSC) framework is proposed for facilitating high-efficiency multi-user semantic communications. The NSC technique enables non-orthogonal semantic streams among users by sharing the same resource block. A semantic superposition coding (SSC) and a semantic interference tolerated (SIT) decoding paradigm are proposed for the NSC transmitters and receivers, respectively. SSC encoders are a type of joint source-channel encoder enabled by deep learning (DL), which aims to superpose the semantic information for different users to a piece of semantic feature sequence. An SSC encoder at the access point (AP) is paired with several SIT decoders at different user equipment. By jointly training the SSC encoder and all SIT decoders, the SIT decoders can identify the desired semantic information for each user, and the semantic interferences introduced by SSC are mitigated. Simulation results reveal that the proposed NSC scheme considerably improves transmission efficiency. Meanwhile, at high compression ratios, the NSC scheme outperforms conventional orthogonal semantic communications in terms of accuracy gains.

The rapid development of next-generation Internet of Things (IoT) applications, including integrated-sensing-and-communication (ISAC), smart grids, smart cities, intelligent transport networks, etc., enables at least tens of billions of bandwidth-thirsty IoT devices, which consume a deluge of data in the sixth-generation (6G) communication systems. In addition, future challenging heterogeneous services and applications, such as Industry 4.0, require the provisioning of unprecedented massive device access, heterogeneous data traffic, high spectral efficiency, and low latency. As a result, there is an urgent demand to pay more attention to IoT networks for high-reliable and low-delay massive access.

A simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) aided integrated sensing, computing, and communication (ISCC) Internet of Robotic Things (IoRT) framework is proposed. Specifically, the full-duplex (FD) base station (BS) simultaneously receives the offloading signals from decision robots (DRs) and carries out target robot (TR) sensing. A computation rate maximization problem is formulated to optimize the sensing and receive beamformers at the BS and the STAR-RIS coefficients under the BS power constraint, the sensing signal-to-noise ratio constraint, and STAR-RIS coefficients constraints. The alternating optimization (AO) method is adopted to solve the proposed optimization problem. With fixed STAR-RIS coefficients, the subproblem with respect to sensing and receiving beamformer at the BS is tackled with the weighted minimum mean-square error method. Given beamformers at the BS, the subproblem with respect to STAR-RIS coefficients is tacked with the penalty method and successive convex approximation method. The overall algorithm is guaranteed to converge to at least a stationary point of the computation rate maximization problem. Our simulation results validate that the proposed STAR-RIS aided ISCC IoRT system can enhance the sum computation rate compared with the benchmark schemes.

A near-field simultaneous wireless information and power transfer (SWIPT) network is investigated, where the hybrid beamforming architecture is employed at the base station (BS) for information transmission while charging the energy harvesting users. A transmit power minimization problem is formulated by jointly optimizing the analog beamformer, the baseband digital information/energy beamformers, and the number of dedicated energy beams. To tackle the uncertain number of dedicated energy beams, a semidefinite relaxation-based rank-one solution construction method is proposed to obtain the optimal baseband digital beamformers under the fixed analog precoder. Based on the structure of the optimal baseband digital beamformers, it is proved that no dedicated energy beam is required in the near-field SWIPT. To further exploit this insight, a penalty-based two-layer (PTL) algorithm is proposed to optimize the analog beamformer and baseband digital information beamformers. By employing the block coordinate descent method, the optimal analog beamformer, and baseband digital information beamformers are obtained in the closed-form expressions. Moreover, to reduce the high computational complexity caused by the large number of antennas, a low-complexity two-stage algorithm is proposed. Numerical results illustrate that: 1) the proposed PTL algorithm can achieve near-optimal performance and 2) in contrast to the far-field SWIPT, a single near-field beamformer can focus the energy on multiple locations.

A near-field integrated sensing, positioning, and communication (ISPAC) framework is proposed, where a base station (BS) simultaneously serves multiple communication users and carries out target sensing and positioning. A novel double-array structure is proposed to enable the near-field ISPAC at the BS. Specifically, a small-scale assisting transceiver (AT) is attached to the large-scale main transceiver (MT) to empower the communication system with the ability of sensing and positioning. Based on the proposed framework, the joint angle and distance Cramér-Rao bound (CRB) is first derived. Then, the CRB is minimized subject to the minimum communication rate requirement in both downlink and uplink ISPAC scenarios: 1) For downlink ISPAC, a downlink target positioning algorithm is proposed and a penalty dual decomposition (PDD)-based double-loop algorithm is developed to tackle the non-convex optimization problem. 2) For uplink ISPAC, an uplink target positioning algorithm is proposed and an efficient alternating optimization algorithm is conceived to solve the non-convex CRB minimization problem with coupled user communication and target probing design. Both proposed optimization algorithms can converge to a stationary point of the CRB minimization problem. Numerical results show that: 1) The proposed ISPAC system can locate the target in both angle and distance domains merely relying on single BS and limited bandwidths; and 2) the positioning performance achieved by the hybrid-analog-and-digital ISPAC approaches that achieved by fully digital ISPAC when the communication rate requirement is not stringent.

Abstract Near-field technology is increasingly recognized due to its transformative potential in communication systems, establishing it as a critical enabler for sixth-generation (6G) telecommunication development. This paper presents a comprehensive survey of recent advancements in near-field technology research. First, we explore the near-field propagation fundamentals by detailing definitions, transmission characteristics, and performance analysis. Next, we investigate various near-field channel models—deterministic, stochastic, and electromagnetic information theory based models, and review the latest progress in near-field channel testing, highlighting practical performance and limitations. With evolving channel models, traditional mechanisms such as channel estimation, beamtraining, and codebook design require redesign and optimization to align with near-field propagation characteristics. We then introduce innovative beam designs enabled by near-field technologies, focusing on non-diffractive beams (such as Bessel and Airy) and orbital angular momentum (OAM) beams, addressing both hardware architectures and signal processing frameworks, showcasing their revolutionary potential in near-field communication systems. Additionally, we highlight progress in both engineering and standardization, covering the primary 6G spectrum allocation, enabling technologies for near-field propagation, and network deployment strategies. Finally, we conclude by identifying promising future research directions for near-field technology development that could significantly impact system design. This comprehensive review provides a detailed understanding of the current state and potential of near-field technologies.

The ultimate goal of next generation multiple access (NGMA) is to support massive terminals and facilitate multiple functionalities over the limited radio resources of wireless networks in the most efficient manner possible. However, the random and uncontrollable wireless radio environment is a major obstacle to realizing this NGMA vision. Given the prominent feature of achieving a 360° smart radio environment, simultaneously transmitting and reflecting surfaces (STARS) are emerging as one key enabling technology among the family of reconfigurable intelligent surfaces for NGMA. This article provides a comprehensive overview of the recent research progress of STARS, focusing on fundamentals, performance analysis, and full-space beamforming design, as well as promising employments of STARS in NGMA. In particular, we first introduce the basics of STARS by elaborating on the foundational principles and operating protocols as well as discussing different STARS categories and prototypes. Moreover, we systematically survey the existing performance analysis and beamforming design for STARS-aided wireless communications in terms of diverse objectives and different mathematical approaches. Given the superiority of STARS, we further discuss advanced STARS applications as well as the attractive interplay between STARS and other emerging techniques to motivate future works for realizing efficient NGMA.

Multiple-antenna technologies are advancing toward the development of extremely large aperture arrays and the utilization of extremely high frequencies, driving the progress of next-generation multiple access (NGMA). This evolution is accompanied by the emergence of near-field communications (NFCs), characterized by spherical-wave propagation, which introduces additional range dimensions to the channel and enhances system throughput. In this context, a tutorial-based primer on NFC is presented, emphasizing its applications in multiuser communications and multiple access (MA). The following areas are investigated: 1) the commonly used near-field channel models are reviewed along with their simplifications under various near-field conditions; 2) building upon these models, the information-theoretic capacity limits of NFC-MA are analyzed, including the derivation of the sum-rate capacity and capacity region, and their upper limits for both downlink and uplink scenarios; and 3) a detailed investigation of near-field multiuser beamforming design is presented, offering low-complexity and effective NFC-MA design methodologies in both the spatial and wavenumber (angular) domains. Throughout these investigations, near-field MA is compared with its far-field counterpart to highlight its superiority and flexibility in terms of interference management, thereby laying the groundwork for achieving NGMA.

This paper proposes a novel simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted unmanned aerial vehicle (UAV) non-orthogonal multiple access (NOMA) emergency communication network. Multiple STAR-RISs are deployed to provide additional and intelligent transmission links between trapped users and UAV-mounted base station (BS). Each user selects the nearest STAR-RIS for uploading data, and NOMA is employed for users located at the same side of the same STAR-RIS. Considering practical requirements of post-disaster emergency communications, we formulate a throughput maximization problem subject to constraints on minimum average rate and maximum energy consumption, where the UAV trajectory, STAR-RIS passive beamforming, and time and power allocation are jointly optimized. Furthermore, we propose a Lagrange based reward constrained proximal policy optimization (LRCPPO) algorithm, which provides an adaptive method for solving the long-term optimization problem with cumulative constraints. Specifically, using Lagrange relaxation, the original problem is transformed into an unconstrained problem with a two-layer structure. The inner layer is solved by penalized reward based proximal policy optimization (PPO) algorithm. In the outer layer, Lagrange multipliers are updated by gradient descent. Numerical results show the proposed algorithm can effectively improve network performance while satisfying the constraints well. It also demonstrates the superiority of the proposed STAR-RIS assisted UAV NOMA network architecture over the benchmark schemes employing reflecting-only RISs and orthogonal multiple access.

True-time delayers (TTDs) are popular components for hybrid beamforming architectures to combat the spatial-wideband effect in wideband near-field communications. In this paper, aserialand ahybrid serial-parallelTTD configuration are investigated for hybrid beamforming architectures. Compared to the conventional parallel configuration, the serial configuration exhibits acumulativetime delay caused by multiple TTDs, which potentially alleviates the maximum delay requirements on the individual TTDs. However, independent control of individual TTDs becomes impossible in the serial configuration. Therefore, a hybrid TTD configuration is proposed as a compromise solution. Furthermore, a power equalization approach is proposed to address the cumulative insertion loss of the serial and hybrid TTD configurations. Moreover, the wideband near-field beamforming design for different configurations is studied to maximize the spectral efficiency in both single-user and multiple-user systems. 1) For single-user systems, a closed-form solution for the beamforming design is derived. The preferred user locations and the required maximum time delay of each TTD configuration are characterized. 2) For multi-user systems, a penalty-based iterative algorithm is developed to obtain a stationary point of the spectral efficiency maximization problem for the considered TTD configurations. In addition, a hybrid-forward-and-backward (HFB) implementation is proposed to enhance the performance of the serial configuration. Our numerical results confirm the effectiveness of the proposed designs and unveil that i) compared to the conventional parallel configuration, both the serial and hybrid configurations can significantly reduce the maximum time delays required for the individual TTDs and ii) the hybrid configuration excels in single-user systems, while the HFB serial configuration is preferred in multi-user systems.

In the multiple unmanned aerial vehicle (UAV) mobile edge computing (MEC) systems, the cooperative computation among multiple UAVs can improve the overall computation service capability. Multi-UAV MEC systems can meet the quality of service requirements for computation intensive applications of ground terminals (GTs) in complex field environments, emergency disaster relief and other special scenarios. In this paper, a multi-UAV cooperative computation framework is proposed while taking the GT movement and random arrival of computation tasks into consideration. A long-term optimization problem is formulated for the joint optimization of UAV trajectory and resource allocation, subject to minimizing the total GT computation task completion time and the total system energy consumption. To solve this problem, a joint multiple time-scale optimization algorithm is proposed. In particular, the optimization problem is decomposed into a long time-scale multi-UAV trajectory planning subproblem and a short time-scale resource allocation subproblem. The proximal policy optimization algorithm is invoked to solve the long time-scale subproblem. The greedy algorithm and the successive convex approximation (SCA) method are employed to solve the short time-scale subproblem. Finally, a joint multiple time-scale optimization algorithm with a two-layer loop structure is proposed. Simulation results show that: 1) the proposed multi-UAV cooperative computation MEC system outperforms the conventional MEC system without collaboration among UAVs; and 2) the proposed algorithm can quickly adapt to different degrees of environmental dynamics and outperforms the benchmark algorithm for different network sizes, task requirements, and available resources.

A novel hybrid active-passive reconfigurable intelligent surface (RIS) transmitter enabled downlink multi-user communication system is investigated. Specifically, RISs are exploited to serve as transmitter antennas, where each element can flexibly switch between active and passive modes to deliver information to multiple users. The system energy efficiency (EE) maximization problem is formulated by jointly optimizing the RIS element scheduling and beamforming coefficients, as well as the power allocation coefficients, subject to the user’s individual rate requirement and the maximum RIS amplification power constraint. Using the Dinkelbach relaxation, the original mixed-integer nonlinear programming problem is transformed into a nonfractional optimization problem with a two-layer structure, which is solved by the alternating optimization approach. In particular, an exhaustive search method is proposed to determine the optimal operating mode for each RIS element. Then, the RIS beamforming and power allocation coefficients are properly designed in an alternating manner. To overcome the potentially high complexity caused by exhaustive searching, we further develop a joint RIS element mode and beamforming optimization scheme by exploiting the Big-M formulation technique. Numerical results validate that: 1) The proposed hybrid RIS scheme yields higher EE than the baseline multi-antenna schemes employing fully active/passive RIS or conventional radio frequency chains; 2) Both proposed algorithms are effective in improving the system performance, especially the latter can achieve precise design of RIS elements with low complexity; and 3) For a fixed-size hybrid RIS, maximum EE can be reaped by setting only a minority of elements to operate in the active mode.

The development of sixth-generation (6G) communication technologies is confronted with the significant challenge of spectrum resource shortage. To alleviate this issue, we propose a novel simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) aided multiple-input multiple-output (MIMO) cognitive radio (CR) system. Specifically, the underlying secondary network in the proposed CR system reuses the same frequency resources occupied by the primary network with the help of the STAR-RIS. The secondary network sum rate maximization problem is first formulated for the STAR-RIS aided MIMO CR system. The adoption of STAR-RIS necessitates an intricate beamforming design for the considered system due to its large number of coupled coefficients. The block coordinate descent method is employed to address the formulated optimization problem. In each iteration, the beamformers at the secondary base station (SBS) are optimized by solving a quadratically constrained quadratic program (QCQP) problem. Concurrently, the STAR-RIS passive beamforming problem is resolved using tailored algorithms designed for the two phase-shift models: 1) For theindependent phase-shift model, a successive convex approximation-based algorithm is proposed; 2) For thecoupled phase-shift model, a penalty dual decomposition-based algorithm is conceived, in which the phase shifts and amplitudes of the STAR-RIS elements are optimized using closed-form solutions. Simulation results show that: 1) The proposed STAR-RIS aided CR communication framework can significantly enhance the sum rate of the secondary system; 2) The coupled phase-shift model results in limited performance degradation compared to the independent phase-shift model.

A non-orthogonal multiple access (NOMA)-based spatial modulation system operating over multiple subchannels is investigated. For scheduled users of each subchannel, a mixed multicast and unicast transmission is delivered. The multicast content is transmitted via the transmit antenna domain, while unicast contents are transmitted through the amplitude-phase modulated symbols using NOMA via the active antenna. Firstly, the unicast rate for each user and an upper bound for the multicast rate are derived. Secondly, a joint subchannel and power allocation problem for weighted sum rate maximization is formulated. To solve this challenging mixed-integer non-linear problem, we decompose it into three subproblems, namely the decoding order design, the subchannel assignment, and the power allocation. A heuristic scheme is developed to solve the first one by investigating the characteristics of the decoding order constraint. To avoid the high complexity caused by exhaustive search, the subchannel assignment is reformulated as a many-to-one matching with peer effect, and the Gale-Shapley method and swap operation are designed to solve it. The power allocation is solved by employing the successive convex approximation. Moreover, a joint subchannel and power allocation algorithm is proposed to further boost the performance, and a robust power allocation algorithm is proposed under channel uncertainties.

A bidirectional integrated sensing and communication (ISAC) system is proposed, in which a pair of transceivers carry out two-way communication and mutual sensing. Both full-duplex and half-duplex operations in narrowband and wideband systems are conceived for the bidirectional ISAC. 1) For the narrowband system, the conventional full-duplex and half-duplex operations are redesigned to take into account sensing echo signals. Then, the transmit beamforming design of both transceivers is proposed for addressing the sensing and communication (S&C) tradeoff. A one-layer iterative algorithm relying on successive convex approximation (SCA) is proposed to obtain Karush-Kuhn-Tucker (KKT) optimal solutions. 2) For the wideband system, the new full-duplex and half-duplex operations are proposed for the bidirectional ISAC. In particular, the frequency-selective fading channel is tackled by delay pre-compensation and path-based beamforming. By redesigning the proposed SCA-based algorithm, the KKT optimal solutions for path-based beamforming for characterizing the S&C tradeoff are obtained. Finally, the numerical results show that: i) For both bandwidth scenarios,full-duplex mode may not always be preferable to half-duplex modedue to the presence of the sensing interference; and ii) For both duplex operations, it is sufficient to reuse communication signals for sensing in the narrowband system, while an additional dedicated sensing signal is required in the wideband system.

The reconfigurable intelligent surface (RIS) is a promising technology to provide smart radio environment. In contrast to the well-studied patch-array-based RISs, this work focuses on the metasurface-based RISs and simultaneously transmitting and reflecting (STAR)-RISs where the elements have millimeter or even molecular sizes. For these meticulous metasurface structures, near-field effects are dominant and a continuous electric current distribution should be adopted for capturing their electromagnetic response instead of discrete phase-shift matrices. Exploiting the electric current distribution, a Green’s function method based channel model is proposed. Based on the proposed model, performance analysis is carried out for both transmitting/reflecting-only RISs and STAR-RISs. 1) For the transmitting/reflecting-only RIS-aided single-user scenario, closed-formed expressions for the near-field/far-field boundary and the end-to-end channel gain are derived. Then, degrees-of-freedom (DoFs) and the power scaling laws are obtained. It is proved that the near-field channel exhibits higher DoFs than the far-field channel. It is also confirmed that when communication distance increases beyond the field boundary, the near-field power scaling law degrades to the well-known far-field result. 2) For the STAR-RIS-aided multi-user scenario, three practical STAR-RIS configuration strategies are proposed, namely power splitting (PS), selective element grouping (SEG), and random element grouping (REG) strategies. The channel gains for users are derived within both the pure near-field regime and the hybrid near-field and far-field regime. Finally, numerical results confirm that: 1) metasurface-based RISs are able to to outperform patch-array-based RISs, 2) the received power scales quadratically with the number of elements within the far-field regime and scales linearly within the near-field regime, and 3) for STAR-RISs, SEG has the highest near-field channel gain among the three proposed strategies and PS yields the highest DoFs for the near-field channel.

A novel non-orthogonal multiple access (NOMA) assisted full space integrated sensing and communication (ISAC) framework is proposed to elevate the radio sensing performance. Exploiting the simultaneously transmitting and reflecting RIS (STAR-RIS) to extend the half-space into full-space ISAC coverage intensifies the competition for wireless resources. To alleviate this fierce competition as well as ensure ISAC performance, the cluster-based NOMA (CB-NOMA) technique is employed to save the joint communication and sensing (C&S) beams. Furthermore, the dedicated sensing beam accompanied by the joint C&S beams supports the radio sensing functionality. A minimum beampattern gain maximization problem is formulated to jointly optimize the power allocation, active and passive beamformer (BF) design, subject to communication requirements. To solve this non-convex problem, a block coordinate descent (BCD) based integral matrix algorithm is proposed to reach a suboptimal solution. For the joint power allocation and active BF block, the semidefinite relaxation and successive convex approximation are employed to optimize the coupled variables. For the passive BF block, the penalty-based method is invoked. To further reduce the complexity of the passive BF design, a BCD-based element-wise algorithm is proposed, where the joint phase shift and amplitude coefficients of each STAR-RIS element are optimized one by one. Simulation results verified that our proposed algorithms achieve higher beampattern gain towards the intended targets than the benchmark schemes accompanying less mismatch error.

Simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) are gaining great attention for their ability to achieve full-space coverage. In this paper, the queue-aware STAR-RIS assisted non-orthogonal multiple access (NOMA) communication system is investigated to ensure system stability. To tackle the challenge of infinite time periods for stability, the long-term stability-oriented problem is reformulated as a per-slot queue-weighted sum rate (QWSR) maximization problem using Lyapunov drift theory. Particularly, the allocated rate weight for each user is determined by the corresponding data queue at the base station (BS). By jointly optimizing the NOMA decoding order, the active beamforming coefficients at the BS, and the passive transmission and reflection coefficients at the STAR-RIS, three STAR-RIS operating protocols are considered, namely energy splitting (ES), mode switching (MS), and time switching (TS). An equivalent-combined channel gain based scheme is proposed to obtain the desired decoding order. For ES, the highly coupled and non-convex problem is solved iteratively and alternatively by invoking the blocked coordinate descent and the successive convex approximation methods. This approach is further expanded to a penalty-based two-loop algorithm to solve the binary amplitude constrained problem for MS. For TS, the problem is decomposed into two subproblems, each of which is solved similarly as ES. Simulation results show that: i) our proposed STAR-RIS assisted NOMA communication achieves superior performance to the conventional schemes; ii) the reformulated QWSR maximization problem is proven to ensure the system stability; and iii) TS performs best in both the QWSR and the average queue length.

A multi-user heterogeneous semantic communication (SemCom) and bit communication (BitCom) system is investigated. Each user can be served via either SemCom or BitCom for demanding semantic or bit data. Orthogonal/non-orthogonal multiple access (OMA/NOMA) techniques are employed to provide access for multiple users. Channel-based and user demand-based transmission protocols are proposed, where a joint optimization problem of the communication mode selection, frequency bandwidth and power allocation, and NOMA user pairing is formulated to maximize the long-term (equivalent) semantic throughput and user satisfaction, respectively. To solve the formulated problems: 1) For channel-based transmission, a twin-delayed deep deterministic policy gradient with reference neuron enhanced Softmax (TD3-RNS) algorithm is proposed, where a fixed-value neuron is invoked to improve the training efficiency; 2) For user demand-based transmission, a transfer TD3-RNS (T2D3-RNS) algorithm is proposed, where the learned policy is transferred to address the sparse rewards and perverse incentive problem caused by the optimization objective and reward shaping, respectively. Simulation results demonstrate that: i) The proposed heterogeneous scheme outperforms the baselines which merely use SemCom or BitCom; ii) Compared to OMA, NOMA is more compatible with the proposed heterogeneous scheme; and iii) The proposed algorithms outperform the benchmarks in channel-based and user demand-based transmission, respectively.

A simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted simultaneous wireless information and power transfer (SWIPT) system is proposed. More particularly, an STAR-RIS is deployed to assist in the information/power transfer from a multi-antenna access point (AP) to multiple single-antenna information users (IUs) and energy users (EUs), where two practical STAR-RIS operating protocols, namely energy splitting (ES) and time switching (TS), are employed. Under the imperfect channel state information (CSI) condition, a multi-objective optimization problem (MOOP) framework, that simultaneously maximizes the minimum data rate and minimum harvested power, is employed to investigate the fundamental rate-energy trade-off between IUs and EUs. To obtain the optimal robust resource allocation strategy, the MOOP is first transformed into a single-objective optimization problem (SOOP) via the ϵ-constraint method, which is then reformulated by approximating semi-infinite inequality constraints with the S-procedure. For ES, an alternating optimization (AO)-based algorithm is proposed to jointly design AP active beamforming and STAR-RIS passive beamforming, where a penalty method is leveraged in STAR-RIS beamforming design. Furthermore, the developed algorithm is extended to optimize the time allocation policy and beamforming vectors in a two-layer iterative manner for TS. Numerical results reveal that: 1) deploying STAR-RISs achieves a significant performance gain over conventional RISs, especially in terms of harvested power for EUs; 2) the ES protocol obtains a better user fairness performance when focusing only on IUs or EUs, while the TS protocol yields a better balance between IUs and EUs; 3) the imperfect CSI affects IUs more significantly than EUs, whereas TS can confer a more robust design to attenuate these effects.

A simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) aided near-field multiple-input multiple-output (MIMO) communication framework is proposed. A weighted sum rate maximization problem for the joint optimization of the active beamforming at the base station (BS) and the transmission/reflection-coefficients (TRCs) at the STAR-RIS is formulated. The resulting non-convex problem is solved by the developed block coordinate descent (BCD)-based algorithm. Numerical results illustrate that the near-field beamforming for the STAR-RIS aided MIMO communications significantly improve the achieved weighted sum rate.

A wideband simultaneously transmitting and reflecting surface (STARS) aided terahertz (THz) communication system is proposed. The spatial wideband effect at the base station (BS) and STARS leads to significant performance degradation due to the beam split issue. To address this, true time delayers (TTDs) are introduced into the conventional hybrid beamforming structure for facilitating wideband beamforming. The hybrid beamforming at the BS and the passive beamforming at the STARS are jointly designed to maximize the spectral efficiency of the proposed system. A double-loop iterative algorithm based on penalty dual decomposition is proposed to solve the resulting optimization problem. Finally, the numerical results confirm the effectiveness of exploiting STARS in wideband THz systems.

A near-field wideband multi-user communication system is studied. To eliminate the near-field beam split caused by the wideband spatial effect, a hybrid beamforming architecture based on true-time delayers (TTDs) is exploited. A heuristic two-stage approach is proposed for optimizing the analog and digital beamformers of the TTD-based hybrid beamforming architecture to facilitate near-field wideband beamfocusing. In particular, in the first stage, a closed-form analog beamformer design based on a piecewise-near-field approximation is proposed to maximize the array gain at users. Next, in the second stage, the digital beamformers are optimized by exploiting the successive convex approximation. Finally, our numerical results demonstrate that the proposed approach can effectively eliminate the near-field beam split and outperforms the existing approach in terms of spectral efficiency.

A simultaneously transmitting and reflecting RIS (STAR-RIS) empowered integrated sensing and communications (ISAC) framework is proposed, where the STAR-RIS establishes an additional link to compensate for the insufficient LoS link. To alleviate the conflicts between the limited wireless resources and the multifunctionality requirements, a cluster-based NOMA transmission scheme is adopted, where the communication functionality is employed by the joint communication and sensing (C&S) beam in a NOMA approach. A minimum beampattern gain maximization problem is formulated to jointly optimize the power allocation, active and passive beamformer (BF) design. We propose a block coordinate descent (BCD) based iterative algorithm, which splits the optimization variables into two blocks. For the joint power allocation and active BF block, the semidefinite relaxation and successive convex approximation are employed. For the passive BF block, the penalty-based method is invoked to deal with the non-convex constraints. Simulation results verified that our proposed algorithm achieves higher beampattern gain at the intended targets than the other baselines accompanying the least mismatch error.

The novel concept of near-field non-orthogonal multiple access (NF-NOMA) communications is proposed. By exploiting the analog beamformers focusing on specific locations, the far-to-near successive interference cancellation order can be further facilitated. In the proposed NF-NOMA, the two NOMA users in different angular directions with distinct quality of service (QoS) requirements can be grouped into one cluster and are served by one analog beamformer focusing on multiple locations. To maximize the sum rate of higher QoS (H-QoS) users, the analog beamformer is first designed using the beam-splitting technique, which focuses the energy on both two NOMA users at two different locations. Then, a singular value decomposition based zero-forcing (SVD-ZF) digital beamformer is designed to mitigate the inter-cluster interference. Furthermore, an antenna allocation algorithm is proposed by employing the many-to-one matching method. Finally, an iterative algorithm is proposed to obtain suboptimal power allocation solutions via the fractional programming. Numerical results demonstrate that: i) in contrast to the conventional far-field NOMA, the proposed NF-NOMA schemes can achieve a higher spectral efficiency even if the HQoS users are far located; and ii) NF-NOMA transmission always outperforms near-field orthogonal multiple access transmission.

The coexistence of semantic communication (SemCom) and bit-based communication (BitCom) towards next-generation wireless networks is investigated. First, a semantic and bit uplink communication framework is proposed, where a near user (N-user) and a far user (F-user) upload information to the access point employing BitCom and SemCom, respectively. For effectively accommodating the two different users, orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) schemes are proposed. The semantic-versus-bit rate region achieved by each scheme is characterized. The analytical performance comparison shows that NOMA is always superior to OMA, but for the F-user, employing SemCom may not always outperform BitCom. The presented numerical examples verify the analytical result.

An integrated simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) and full-duplex (FD) relay aided downlink system is investigated. We formulate an optimization problem to maximize the downlink system achievable sum rate, by jointly optimizing the receive beamforming at the relay, the transmit beamforming at both the base station and the relay, and the passive beamforming at the STAR-RIS. To tackle the formulated non-convex problem, we decompose this problem into three subproblems by invoking alternating optimization. In particular, a penalty-based algorithm is developed to solve each subproblem via successive convex approximation technique. Finally, numerical results reveal that the proposed scheme can achieve significant performance gains compared to baselines.

A novel opportunistic semantic and bit communication strategy is proposed for uplink non-orthogonal multiple access (NOMA). Specifically, a secondary far user (F-user) employs either semantic communication (SemCom) or bit-based communication (BitCom) to participate in NOMA with a primary near user (N-user) employing the BitCom. For each fading channel state, the secondary F-user has to select the most suitable communication method, thus striking a good tradeoff between its own achieved performance and the interference imposed on the primary N-user. The optimal communication policy at the F-user over fading channels is derived for maximizing the ergodic (equivalent) semantic rate achieved at the F-user, subject to the minimum ergodic bit rate constraint of the N-user. Numerical results show that the proposed opportunistic scheme can achieve higher communication performance for NOMA than the baseline schemes merely employing SemCom or BitCom. In addition, SemCom can better guarantee the performance of the F-user admitted in NOMA than BitCom when the communication requirement of the primary N-user is high.

A simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted simultaneous wireless information and power transfer (SWIPT) framework is proposed. More particularly, an STAR-RIS is deployed to assist in SWIPT from a multi-antenna access point (AP) to multiple single-antenna information users (IUs) and energy users (EUs). Due to the near-passive operation of the STAR-RIS, a more practical setup under the assumption of imperfect channel state information is investigated. The max-min fairness optimization problem is formulated to maximize the minimum power harvested by EUs, subject to the signal-to-interference-plus-noise ratio (SINR) constraints for IUs. To tackle this non-convex problem, an alternating optimization (AO) based algorithm is proposed for robust beamforming design. We first approximate the semiinfinite inequality constraints with S-procedure, then the AP active beamforming and the STAR-RIS passive beamforming are alternatively designed, where a penalty based approach is leveraged for STAR-RIS reconfiguration. Numerical results demonstrate that: i) the significant performance gains can be achieved by the proposed scheme over the baseline schemes; and ii) more STAR-RIS elements and higher SINR requirements weaken the robustness of the EU performance in terms of energy harvesting.

A large reconfigurable intelligent surface (LRIS) assisted wireless communication system is investigated in this paper. The increased aperture size and reconfigurable element number of LRIS bring new challenges, including limited incident beam coverage on LRIS, near-field signal propagation, and high beamforming complexity. Against these challenges, a two-step low-complexity beamforming approach is proposed, where a deep reinforcement learning (DRL) algorithm is invoked for determining the optimal beam direction, and a codebook based on the geometric channel state information is designed to map the direction to the beamforming matrixes. The proposed approach not only reduces the computational complexity, but also exploits the geometric channel of BS-LRIS to reduce the channel estimation complexity caused by LRIS. Simulation results indicate that the LRIS can further reduce power consumption compared to the small-size RIS. Meanwhile, the proposed joint codebook-DRL approach achieves a counterbalance compared to the sheer DRL algorithm with lower complexity.

A reconfigurable intelligent surface (RIS)-assisted downlink nonorthogonal multiple access (NOMA) Internet of Things (IoT) network is proposed, where a Quality-of-Service (QoS)-based NOMA clustering scheme is conceived to effectively utilize the limited wireless resources among IoT devices. A throughput maximization problem is formulated by jointly optimizing the phase shifts of the RIS and the power allocation of the base station (BS) from the short-term and long-term perspectives. We aim to investigate and compare the performance of deep learning (DL) and deep reinforcement learning (DRL) algorithms for solving the formulated problems. In particular, the DL method utilizes model-agnostic-metalearning (MAML) to enhance the generalization capability of the neural network and to accelerate the convergence rate. For the DRL method, the deep deterministic policy gradient (DDPG) algorithm is employed to incorporate continuous phase-shift variables. It shows that the DL method only focuses on the maximization of the instantaneous throughput, whereas the DRL method can coordinate the power consumption over different time slots to maximize the long-term throughput. Numerical results demonstrate that: 1) the proposed QoS-based NOMA clustering scheme achieves higher IoT throughput than the conventional channel-based scheme; 2) the implementation of RISs induces approximately 5%–25% throughput gain as the number of RIS elements increases from 8 to 64; 3) DL and DRL achieve a similar throughput performance for the short-term optimization, while DRL is superior for the long-term optimization, especially when the total transmit power is limited.

A novel semantics-empowered two-user uplink non-orthogonal multiple access (NOMA) framework is proposed for resource efficiency enhancement. More particularly, a secondary far user (F-user) employs the semantic communication (SemCom) while a primary near user (N-user) employs the conventional bit-based communication (BitCom). The fundamental performance limit, namely semantic-versus-bit (SvB) rate region, of the proposed semantics-empowered NOMA framework is characterized. The equivalent SvB rate region achieved by the conventional BitCom-based NOMA is provided as the baseline scheme. It unveils that, compared to BitCom, SemCom can significantly improve the F-user’s performance when its permitted transmit power is strictly capped, but may perform worse when its permitted transmit power is high. Guided by this result, the proposed semantics-empowered NOMA framework is investigated over fading channels. An opportunistic SemCom and BitCom scheme is proposed, which enables the secondary F-user to participate in NOMA via the most suitable communication method at each fading state, thus striking a good tradeoff between its own achieved performance and the interference imposed on the primary N-user. Two scenarios are considered for employing the opportunistic scheme, namely on-off resource management and continuous resource management. For each scenario, the optimal communication policy over fading channels is derived for maximizing the ergodic semantic rate achieved at the secondary F-user, subject to the minimum ergodic bit rate constraint of the primary N-user. Numerical results show that: 1) proposed opportunistic scheme in both scenarios can achieve higher communication performance for NOMA than the baseline schemes merely employing SemCom or BitCom; 2) SemCom can better guarantee the performance of the F-user admitted in NOMA than BitCom when the communication requirement of the primary N-user is high; and 3) continuous power control at the F-user is necessary for ensuring high performance over fading channels, while the on-off time scheduling is sufficient.

Multiple access (MA) design is investigated to facilitate the coexistence of the emerging semantic transmission and the conventional bit-based transmission in future networks. Thesemantic rateis adopted for measuring the performance of the semantic transmission. However, a key challenge is that there is no closed-form expression for a key parameter, namely thesemantic similarity, which characterizes the sentence similarity between an original sentence and the corresponding recovered sentence. To overcome this challenge, we propose a data regression method, where the semantic similarity is approximated by ageneralized logistic function. Using the obtained tractable function, we propose a heterogeneous semantic and bit communication framework, where an access point simultaneously sends the semantic and bit streams to one semantics-interested user (S-user) and one bit-interested user (B-user). To realize this heterogeneous semantic and bit transmission in multi-user networks, three MA schemes are proposed, namely orthogonal multiple access (OMA), non-orthogonal multiple access (NOMA), and semi-NOMA. More specifically, the bit stream in semi-NOMA is split into two streams, one is transmitted with the semantic stream over the shared frequency sub-band and the other is transmitted over the separate orthogonal frequency sub-band. To study the fundamental performance limits of the three proposed MA schemes, thesemantic-versus-bit (SvB) rate regionand thepower regionare defined. An optimal resource allocation procedure is then derived for characterizing the boundary of the SvB rate region and the power region achieved by each MA scheme. The structures of the derived solutions demonstrate that semi-NOMA is superior to both NOMA and OMA given its highly flexible transmission policy. Our numerical results: 1) confirm that the proposed semi-NOMA is the optimal MA scheme as compared to OMA and NOMA even under the symmetric channel case, and 2) reveal that the superiority of semi-NOMA is more prominent when the channel condition of the S-user is better than that of the B-user.

A non-orthogonal multiple access (NOMA)-aided joint communication, sensing, and multi-tier computing (JCSMC) framework is proposed. In this framework, a multi-functional base station (BS) simultaneously carries out target sensing and provide edge computing services to the nearby users. To enhance the computation efficiency, the multi-tier computing structure is exploited, where the BS can further offload the computation tasks to a powerful Cloud server (CS). The potential benefits of employing NOMA in the proposed JCSMC framework are investigated, which can maximize the computation offloading capacity and suppress inter-functionality interference. Based on the proposed framework, the transmit beamformer of the BS and computing resource allocation among the BS and CS are jointly optimized to maximize the computation rate subject to the communication-computation causality and the sensing quality constraints. Both partial and binary computation offloading modes are considered: 1) For the partial offloading mode, a weighted minimum mean square error based alternating optimization algorithm is proposed to solve the corresponding non-convex optimization problem. It is proved that a Karush–Kuhn–Tucker optimal solution can be obtained; 2) For the binary offloading mode, the resultant highly-coupled mixed-integer optimization problem is first transformed to an equivalent but more tractable form. Then, the reformulated problem is solved by utilizing the alternating direction method of multipliers approach to obtain a nearly optimal solution. Finally, numerical results verify the effectiveness of the proposed algorithms and reveal that: i) the computation rate can be significantly enhanced by exploiting the multi-tier computing architecture when the BS is resource-limited, and ii) the proposed NOMA-aided JSCMC framework is superior in inter-functionality interference management and can achieve high-quality sensing and computing performance simultaneously compared with other benchmark schemes.

A multi-cell cluster-free NOMA framework is proposed, where both intra-cell and inter-cell interference are jointly mitigated via flexible cluster-free successive interference cancellation (SIC) and coordinated beamforming design. The joint design problem is formulated to maximize the system sum rate while satisfying the SIC decoding requirements and users’ minimum data rate requirements. To address this highly complex and coupling non-convex mixed integer nonlinear programming (MINLP), a novel distributed auto-learning graph neural network (AutoGNN) architecture is proposed to alleviate the overwhelming information exchange burdens among base stations (BSs). The proposed AutoGNN can train the GNN model weights whilst automatically optimizing the GNN architecture, namely the GNN network depth and message embedding sizes, to achieve communication-efficient distributed scheduling. Based on the proposed architecture, a bi-level AutoGNN learning algorithm is further developed to efficiently approximate the hypergradient in model training. It is theoretically proved that the proposed bi-level AutoGNN learning algorithm can converge to a stationary point. Numerical results reveal that: 1) the proposed cluster-free NOMA framework outperforms the conventional cluster-based NOMA framework in the multi-cell scenario; and 2) the proposed AutoGNN architecture significantly reduces the computation and communication overheads compared to the conventional convex optimization-based methods and the conventional GNNs with fixed architectures.

Abstract Simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) have been attracting significant attention in both academia and industry for their advantages of achieving 360° coverage and enhanced degrees-of-freedom. This article first identifies the fundamentals of STAR-RIS, by discussing the hardware models, channel models, and signal models. Then, three representative categorizing approaches for STAR-RISs are introduced from the phase-shift, directional, and energy consumption perspectives. Furthermore, the beamforming design of STAR-RISs is investigated for both independent and coupled phase-shift cases. As a recent advance, a general optimization framework, which has high compatibility and provable optimality regardless of the application scenarios, is proposed. As a further advance, several promising applications are discussed to demonstrate the potential benefits of applying STAR-RISs in sixth-generation wireless communication. Lastly, a few future directions and research opportunities are highlighted.

Simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted non-orthogonal multiple access (NOMA) communication systems are investigated in its vicinity, where a STAR-RIS is deployed within a predefined region for establishing communication links for users. Both beamformer-based NOMA and cluster-based NOMA schemes are employed at the multi-antenna base station (BS). For each scheme, the STAR-RIS deployment location, the passive transmitting and reflecting beamforming (BF) of the STAR-RIS, and the active BF at the BS are jointly optimized for maximizing the weighted sum-rate (WSR) of users. To solve the resultant non-convex problems, an alternating optimization (AO) algorithm is proposed, where successive convex approximation (SCA) and semi-definite programming (SDP) methods are invoked for iteratively addressing the non-convexity of each sub-problem. Numerical results reveal that 1) the WSR performance can be significantly enhanced by optimizing the specific deployment location of the STAR-RIS; 2) both beamformer-based and cluster-based NOMA prefer asymmetric STAR-RIS deployment.

A generalized downlink multi-antenna non-orthogonal multiple access (NOMA) transmission framework is proposed with the novel concept of cluster-free successive interference cancellation (SIC). In contrast to conventional NOMA approaches, where SIC is successively carried out within the same cluster, the key idea is that the SIC can be flexibly implemented between any arbitrary users to achieve efficient interference elimination. Based on the proposed framework, a sum rate maximization problem is formulated for jointly optimizing the transmit beamforming and the SIC operations between users, subject to the SIC decoding conditions and users’ minimal data rate requirements. To tackle this highly-coupled mixed-integer nonlinear programming problem, an alternating direction method of multipliers-successive convex approximation (ADMM-SCA) algorithm is developed. The original problem is first reformulated into a tractable biconvex augmented Lagrangian (AL) problem by handling the non-convex terms via SCA. Then, this AL problem is decomposed into two subproblems that are iteratively solved by the ADMM to obtain the stationary solution. Furthermore, to reduce the computational complexity and alleviate the parameter initialization sensitivity of ADMM-SCA, a Matching-SCA algorithm is proposed. The intractable binary SIC operations are solved through an extended many-to-many matching, which is jointly combined with an SCA process to optimize the transmit beamforming. The proposed Matching-SCA can converge to an enhanced exchange-stable matching that guarantees the local optimality. Numerical results demonstrate that: i) the proposed Matching-SCA algorithm achieves comparable performance and a faster convergence compared to ADMM-SCA; ii) the proposed generalized framework realizes scenario-adaptive communications and outperforms traditional multi-antenna NOMA approaches in various communication regimes.

A novel reconfigurable intelligent surface-aided multi-robot network is proposed, where multiple mobile robots are served by an access point (AP) through non-orthogonal multiple access (NOMA). The goal is to maximize the sum-rate of whole trajectories for the multi-robot system by jointly optimizing trajectories and NOMA decoding orders of robots, phase-shift coefficients of the RIS, and the power allocation of the AP, subject to predicted initial and final positions of robots and the quality of service (QoS) of each robot. To tackle this problem, an integrated machine learning (ML) scheme is proposed, which combines long short-term memory (LSTM)-autoregressive integrated moving average (ARIMA) model and dueling double deep Q-network ($\text{D}^{3}$QN) algorithm. For initial and final position prediction for robots, the LSTM-ARIMA is able to overcome the problem of gradient vanishment of non-stationary and non-linear sequences of data. For jointly determining the phase shift matrix and robots’ trajectories,$\text{D}^{3}$QN is invoked for solving the problem of action value overestimation. Based on the proposed scheme, each robot holds an optimal trajectory based on the maximum sum-rate of a whole trajectory, which reveals that robots pursue long-term benefits for whole trajectory design. Numerical results demonstrated that: 1) LSTM-ARIMA model provides high accuracy predicting model; 2) The proposed$\text{D}^{3}$QN algorithm can achieve fast average convergence; and 3) RIS-NOMA networks have superior network performance compared to RIS-aided orthogonal counterparts.

A novel coexisting passive reconfigurable intelligent surface (RIS) and active decode-and-forward (DF) relay assisted non-orthogonal multiple access (NOMA) transmission framework is proposed. In particular, two communication protocols are conceived, namely Hybrid NOMA (H-NOMA) and Full NOMA (F-NOMA). Based on the proposed two protocols, both the sum rate maximization and max-min rate fairness problems are formulated for jointly optimizing the power allocation at the access point and relay as well as the passive beamforming design at the RIS. To tackle the non-convex problems, an alternating optimization (AO) based algorithm is first developed, where the transmit power and the RIS phase-shift are alternatingly optimized by leveraging the two-dimensional search and rank-relaxed difference-of-convex (DC) programming, respectively. Then, a two-layer penalty based joint optimization (JO) algorithm is developed to jointly optimize the resource allocation coefficients within each iteration. Finally, numerical results demonstrate that: i) the proposed coexisting RIS and relay assisted transmission framework is capable of achieving a significant user performance improvement than conventional schemes without RIS or relay; ii) compared with the AO algorithm, the JO algorithm requires less execution time at the cost of a slight performance loss; and iii) the H-NOMA and F-NOMA protocols are generally preferable for ensuring user rate fairness and enhancing user sum rate, respectively.

A simultaneously transmitting and reflecting surface (STARS) enabled integrated sensing and communications (ISAC) framework is proposed, where the entire space is partitioned by STARS into a sensing space and a communication space. A novel sensing-at-STARS structure is proposed, where dedicated sensors are mounted at STARS to address the significant path loss and clutter interference of sensing. The Cramér-Rao bound (CRB) of the two-dimensional (2D) direction-of-arrivals (DOAs) estimation of the sensing target is derived, which is then minimized subject to the minimum communication requirement. A novel approach is proposed to transform the complicated CRB minimization problem into a trackable modified Fisher information matrix (FIM) optimization problem. Both independent and coupled phase-shift models of STARS are investigated: 1) For the independent phase-shift model, to address the coupling problem of ISAC waveform and STARS coefficient, an efficient double-loop iterative algorithm based on the penalty dual decomposition (PDD) framework is conceived; 2) For the coupled phase-shift model, based on the PDD framework, a low complexity alternating optimization algorithm is proposed to tackle the coupled phase-shift constraint by alternately optimizing the amplitude and phase-shift coefficients of STARS with closed-form expressions. Finally, the numerical results demonstrate that: 1) STARS significantly outperforms conventional RIS in terms of CRB under the communication constraints; 2) coupled phase-shift model achieves comparable performance to the independent one for low communication requirements or sufficient STARS elements; 3) it is more efficient to increase the number of passive elements of STARS than the active elements of the sensor; 4) higher sensing accuracy can be achieved by STARS using the practical 2D maximum likelihood estimator compared with the conventional RIS.

A non-orthogonal multiple access (NOMA)-aided joint communication, sensing, and multi-tier computing (JCSMC) framework is proposed. In this framework, a multi-functional base station (BS) simultaneously carries out target sensing and provide edge computing services to the nearby users. To enhance the computation efficiency, the multi-tier computing structure is exploited, where the BS can further offload the computation tasks to a powerful Cloud server (CS). The potential benefits of employing NOMA in the proposed JCSMC framework are investigated. Based on the proposed framework, the transmit beamformer of the BS and computing resource allocation among the BS and CS are jointly optimized to maximize the computation rate subject to the communication-computation causality and the sensing quality constraints. A weighted minimum mean square error based alternating optimization algorithm is proposed to solve the corresponding non-convex optimization problem. Fi-nally, numerical results show the significant performance gain achieved by the proposed schemes over the benchmark schemes.

Simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) have received great attention due to the capability of achieving full-space coverage. In this paper, the queue-stable STAR-RIS assisted non-orthogonal multiple access (NOMA) communication system is investigated to ensure the stability of queueing systems. To tackle the challenge of infinite time slots required for stability, the long-term stability-oriented problem is reformulated to maximize the per-slot queue-weighted sum rate (QWSR) of users. In particular, the rate weight allocated for each user is determined by the length of a data queue, which is maintained at the base station (BS) and pending to be delivered to each user. Then, the QWSR is maximized by jointly optimizing the NOMA decoding order, the active beamforming coefficients (ABCs) at the BS, and the passive transmission and reflection coefficients (PTRCs) at the STAR-RIS. To handle the highly-coupled and non-convex problem, the blocked coordinate descent and the successive convex approximation methods are invoked to iteratively and alternatively optimize the problem. Simulation results revel that: i) our proposed STAR-RIS assisted NOMA communication achieves better performance than the conventional schemes; ii) the reformulated per-slot QWSR maximization problem is proven to ensure the system stability.

This paper investigates an integrated passive reconfigurable intelligent surface (RIS) and active relay assisted non-orthogonal multiple access (NOMA) system. To unleash the potential of distant user based on the downlink NOMA protocol, we identify a two-stage transmission strategy. In both stages, RISs provide coverage for paired users by actively modifying the channel response, and in the second stage, we introduce a dedicated active relay to realize the communication between the base station (BS) and distant user. Our goal is to maximize the system sum rate by jointly optimizing the power allocation at the BS and the passive beamforming at the RIS. To tackle the formulated non-convex problem, we propose an alternating penalty-based based algorithm. In particular, for the RIS phase-shift reconfiguration, a difference-of-convex (DC) approach is utilized to accurately detect the feasibility of the rank-one constraint. Numerical results demonstrate that: i) the integrated transmission scheme outperforms other baseline schemes; ii) the proposed scheme is capable of significantly enhancing the performance of distant NOMA users.

A simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) aided communication system is investigated, where an access point sends information to two users located on each side of the STAR-RIS. Different from current works assuming that the phase-shift coefficients for transmission and reflection can be independently adjusted, which is non-trivial to realize for purely passive STAR-RISs, a coupled transmission and reflection phase-shift model is considered. Based on this model, a power consumption minimization problem is formulated for both non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA). In particular, the amplitude and phase-shift coefficients for transmission and reflection are jointly optimized, subject to the rate constraints of the users. To solve this non-convex problem, an efficient element-wise alternating optimization algorithm is developed to find a high-quality suboptimal solution, whose complexity scales only linearly with the number of STAR elements. Finally, numerical results are provided for both NOMA and OMA to validate the effectiveness of the proposed algorithm by comparing its performance with that of STAR-RISs using the independent phase-shift model and conventional reflecting/transmitting-only RISs.

The novel concept of non-orthogonal multiple access (NOMA) aided joint radar and multicast-unicast communication (Rad-MU-Com) is investigated. Employing the same spectrum resource, a multi-input-multi-output (MIMO) dual-functional radar-communication (DFRC) base station detects the radar-centric user (R-user), while transmitting mixed multicast-unicast messages both to the R-user and to the communication-centric user (C-user). In particular, the multicast information is intended for both the R- and C-users, whereas the unicast information is only intended for the C-user. More explicitly, NOMA is employed to facilitate this double spectrum sharing, where the multicast and unicast signals are superimposed in the power domain and the superimposed communication signals are also exploited as radar probing waveforms. A beamformer-based NOMA-aided joint Rad-MU-Com framework is proposed for the system having a single R-user and a single C-user. Based on this framework, the unicast rate maximization problem is formulated by optimizing the beamformers employed, while satisfying the rate requirement of multicast and the predefined accuracy of the radar beam pattern. The resultant non-convex optimization problem is solved by a penalty-based iterative algorithm to find a high-quality near-optimal solution. Finally, our numerical results reveal that significant performance gains can be achieved by the proposed scheme over the benchmark schemes.

A non-orthogonal multiple access (NOMA) inspired integrated sensing and communication (ISAC) system is investigated. A dual-functional base station (BS) serves multiple communication users while sensing multiple targets, by trans-mitting the non-orthogonal superposition of the communication and sensing signals. A NOMA inspired interference cancellation scheme is proposed, where part of the dedicated sensing signal is treated as the virtual communication signals to be mitigated at each communication user via successive interference cancellation (SIC). Based on this framework, the transmitted communication and sensing signals are jointly optimized to match the desired sensing beampattern, while satisfying the minimum rate requirement and the SIC condition at the communication users. Then, the formulated non-convex optimization problem is solved by invoking the successive convex approximation (SCA) to obtain a near-optimal solution. The numerical results show the proposed NOMA-inspired ISAC system can achieve better performance than the conventional ISAC system and comparable performance to the ideal ISAC system where all sensing interference is assumed to be removed unconditionally.

WiFi-based human action recognition (HAR) has been regarded as a promising solution in applications such as smart living and remote monitoring due to the pervasive and unobtrusive nature of WiFi signals. However, the efficacy of WiFi signals is prone to be influenced by the change in the ambient environment and varies over different sub-carriers. To remedy this issue, we propose an end-to-end Gabor residual anti-aliasing sensing network (GraSens) to directly recognize the actions using the WiFi signals from the wireless devices in diverse scenarios. In particular, a new Gabor residual block is designed to address the impact of the changing surrounding environment with a focus on learning reliable and robust temporal-frequency representations of WiFi signals. In each block, the Gabor layer is integrated with the anti-aliasing layer in a residual manner to gain the shift-invariant features. Furthermore, fractal temporal and frequency self-attention are proposed in a joint effort to explicitly concentrate on the efficacy of WiFi signals and thus enhance the quality of output features scattered in different subcarriers. Experimental results throughout our wireless-vision action recognition dataset (WVAR) and three public datasets demonstrate that our proposed GraSens scheme outperforms state-of-the-art methods with respect to recognition accuracy.

A simultaneously transmitting and reflecting intelligent surface (STARS) enabled integrated sensing and communications (ISAC) framework is proposed, where the whole space is divided by STARS into a sensing space and a communication space. A novel sensing-at-STARS structure, where dedicated sensors are installed at the STARS, is proposed to address the significant path loss and clutter interference for sensing. The Cramér-Rao bound (CRB) of the 2-dimension (2D) direction-of-arrivals (DOAs) estimation of the sensing target is derived, which is then minimized subject to the minimum communication requirement. A novel approach is proposed to transform the complicated CRB minimization problem into a trackable modified Fisher information matrix (FIM) optimization problem. Moreover, to address the coupled issue in the modified FIM, an efficient double-loop iterative algorithm based on the penalty dual decomposition method is conceived. The numerical results demonstrate that: 1) STARS significantly outperforms the conventional transmitting/reflecting-only intelligent surface; 2) High sensing accuracy can be achieved by STARS using the practical 2D maximum likelihood estimator.

In this work, we focus on simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) with coupled transmission and reflection phase shifts. We demonstrate how to achieve full diversity for users on both sides by proposing a practical phase-shift design, namely, the diversity preserving design. To evaluate the performance, a STAR-RIS-aided two-user downlink communication system is investigated for both orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA). The outage probabilities, diversity orders, and power scaling laws under this design are studied and compared with the performance upper and lower bounds. Numerical simulations show that the proposed diversity preserving phase-shift configuration strategy for the STAR-RIS achieves the same diversity order as the STAR-RISs assuming independent phase-shift, and achieves a comparable power scaling law with only 4 dB power reduction.

Due to the explosive growth in the number of wireless devices and diverse wireless services, such as virtual/augmented reality and Internet-of-Everything, next generation wireless networks face unprecedented challenges caused by heterogeneous data traffic, massive connectivity, and ultra-high bandwidth efficiency and ultra-low latency requirements. To address these challenges, advanced multiple access schemes are expected to be developed, namely next generation multiple access (NGMA), which are capable of supporting massive numbers of users in a more resource- and complexity-efficient manner than existing multiple access schemes. As the research on NGMA is in a very early stage, in this paper, we explore the evolution of NGMA with a particular focus on non-orthogonal multiple access (NOMA), i.e., the transition from NOMA to NGMA. In particular, we first review the fundamental capacity limits of NOMA, elaborate on the new requirements for NGMA, and discuss several possible candidate techniques. Moreover, given the high compatibility and flexibility of NOMA, we provide an overview of current research efforts on multi-antenna techniques for NOMA, promising future application scenarios of NOMA, and the interplay between NOMA and other emerging physical layer techniques. Furthermore, we discuss advanced mathematical tools for facilitating the design of NOMA communication systems, including conventional optimization approaches and new machine learning techniques. Next, we propose a unified framework for NGMA based on multiple antennas and NOMA, where both downlink and uplink transmissions are considered, thus setting the foundation for this emerging research area. Finally, several practical implementation challenges for NGMA are highlighted as motivation for future work.

The novel concept of non-orthogonal multiple access (NOMA) aided joint radar and multicast-unicast communication (Rad-MU-Com) is investigated. Employing the same spectrum resource, a multi-input-multi-output (MIMO) dual-functional radar-communication (DFRC) base station detects the radar-centric users (R-user), while transmitting mixed multicast-unicast messages both to the R-user and to the communication-centric user (C-user). In particular, the multicast information is intended for both the R- and C-users, whereas the unicast information is only intended for the C-user. More explicitly, NOMA is employed to facilitate thisdouble spectrum sharing, where the multicast and unicast signals are superimposed in the power domain and the superimposed communication signals are also exploited as radar probing waveforms. First, abeamformer-basedNOMA-aided joint Rad-MU-Com framework is proposed for the system having a single R-user and a single C-user. Based on this framework, the unicast rate maximization problem is formulated by optimizing the beamformers employed, while satisfying the rate requirement of multicast and the predefined accuracy of the radar beam pattern. The resultant non-convex optimization problem is solved by a penalty-based iterative algorithm to find a high-quality near-optimal solution. Next, the system is extended to the scenario of multiple pairs of R- and C-users, where acluster-basedNOMA-aided joint Rad-MU-Com framework is proposed. A joint beamformer design and power allocation optimization problem is formulated for the maximization of the sum of the unicast rate at each C-user, subject to the constraints on both the minimum multicast rate for each R&C pair and on accuracy of the radar beam pattern for detecting multiple R-users. The resultant joint optimization problem is efficiently solved by another penalty-based iterative algorithm developed. Finally, our numerical results reveal that significant performance gains can be achieved by the proposed schemes over the benchmark schemes employing conventional transmission strategies.

With the accelerated development of immersive applications and the explosive increment of internet-of-things (IoT) terminals, 6G would introduce terahertz (THz) massive multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) technologies to meet the ultra-high-speed data rate and massive connectivity requirements. Nevertheless, the unreliability of THz transmissions and the extreme heterogeneity of device requirements pose critical challenges for practical applications. To address these challenges, we propose a novel smart reconfigurable THz MIMO-NOMA framework, which can realize customizable and intelligent communications by flexibly and coordinately reconfiguring hybrid beams through the cooperation between access points (APs) and reconfigurable intelligent surfaces (RISs). The optimization problem is formulated as a decentralized partially-observable Markov decision process (Dec-POMDP) to maximize the network energy efficiency, while guaranteeing the diversified users’ performance, via a joint RIS element selection, coordinated discrete phase-shift control, and power allocation strategy. To solve the above non-convex, strongly coupled, and highly complex mixed integer nonlinear programming (MINLP) problem, we propose a novel multi-agent deep reinforcement learning (MADRL) algorithm, namelygraph-embedded value-decomposition actor-critic (GE-VDAC), that embeds the interaction information of agents, and learns a locally optimal solution through a distributed policy. Numerical results demonstrate that the proposed algorithm achieves highly customized communications and outperforms traditional MADRL algorithms.

A novel air-to-ground communication paradigm is conceived, where an unmanned aerial vehicle (UAV)-mounted base station (BS) equipped with multiple antennas sends information to multiple ground users (GUs) with the aid of a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). In contrast to the conventional RIS whose main function is to reflect incident signals, the STAR-RIS is capable of both transmitting and reflecting the impinging signals from either side of the surface, thereby leading to full-space 360 degree coverage. However, the transmissive and reflective capabilities of the STAR-RIS require more complex transmission/reflection coefficient design. Therefore, in this work, a sum-rate maximization problem is formulated for the joint optimization of the UAV’s trajectory, the active beamforming at the UAV, and the passive transmission/reflection beamforming at the STAR-RIS. This cutting-edge optimization problem is also subject to the UAV’s flight safety, to the maximum flight duration constraint, as well as to the GUs’ minimum data rate requirements. Given the unknown locations of obstacles prior to the UAV’s flight, we provide an online decision making framework employing reinforcement learning (RL) to simultaneously adjust both the UAV’s trajectory as well as the active and passive beamformer. To enhance the system’s robustness against the associated uncertainties caused by limited sampling of the environment, a novel “distributionally-robust” RL (DRRL) algorithm is proposed for offering an adequate worst-case performance guarantee. Our numerical results unveil that: 1) the STAR-RIS assisted UAV communications benefit from significant sum-rate gain over the conventional reflecting-only RIS; and 2) the proposed DRRL algorithm achieves both more stable and more robust performance than the state-of-the-art RL algorithms.

A reconfigurable intelligent surface (RIS)-assisted multi-user downlink communication system over fading channels is investigated, where both non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA) schemes are employed. In particular, the time overhead for configuring the RIS reflective elements at the beginning of each fading channel is considered. The optimization goal is maximizing the effective throughput of the entire transmission period by jointly optimizing the phase shift of the RIS and the power allocation of the AP for each channel block. In an effort to solve the formulated problem and fill the research vacancy of the performance comparison between different machine learning tools in wireless networks, a deep learning (DL) approach and a reinforcement learning (RL) approach are proposed and their representative superiority and inferiority are investigated. The DL approach can locate the optimal phase shifts with the deep neural network fitting as well as the corresponding power allocation for each user. From the perspective of long-term reward, the phase shift control with configuration overhead can be regarded as a Markov decision process and the RL algorithm is proficient in solving such problems with the assistance of the Bellman equation. The numerical results indicate that: 1) From the perspective of the wireless network, NOMA can achieve a throughput gain of about 42% compared with OMA; 2) The well-trained RL and DL agents are able to achieve the same performance in Rician channel, while RL is superior in the Rayleigh channel; 3) The DL approach has lower complexity and faster convergence, while the RL approach has preferable strategy flexibility.

A simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted multi-user downlink multiple-input single-output (MISO) communication system is investigated. In contrast to the existing ideal STAR-RIS model assuming an independent transmission and reflection phase-shift control, a practical coupled phase-shift model is considered. Then, a joint active and passive beamforming optimization problem is formulated for minimizing the long-term transmission power consumption, subject to the coupled phase-shift constraint and the minimum data rate constraint. Despite the coupled nature of the phase-shift model, the formulated problem is solved by invoking a hybrid continuous and discrete phase-shift control policy. Inspired by this observation, a pair of hybrid reinforcement learning (RL) algorithms, namely the hybrid deep deterministic policy gradient (hybrid DDPG) algorithm and the joint DDPG & deep-Q network (DDPG-DQN) based algorithm are proposed. The hybrid DDPG algorithm controls the associated high-dimensional continuous and discrete actions by relying on the hybrid action mapping. By contrast, the joint DDPG-DQN algorithm constructs two Markov decision processes (MDPs) relying on an inner and an outer environment, thereby amalgamating the two agents to accomplish a joint hybrid control. Simulation results demonstrate that the STAR-RIS has superiority over other conventional RISs in terms of its energy consumption. Furthermore, both the proposed algorithms outperform the baseline DDPG algorithm, and the joint DDPG-DQN algorithm achieves a superior performance, albeit at an increased computational complexity.

A novel intelligent reconfigurable surface (IRS) assisted coordinated multi-point (CoMP) system is proposed. Our objective is to maximize the energy efficiency (EE) of this system by jointly optimizing base station (BS) clustering, user association, sub-carrier assignment, power allocation, and optimal design of the IRS, while satisfying the users’ quality of service requirements. Considering the amplitude and phase shift characteristics, both ideal and non-ideal IRS are investigated. The formulated problem is proved to be NP-hard. By analyzing its structure, we decouple it into the power allocation sub-problem, the BS clustering, UE association, and sub-carrier assignment sub-problem, and the reflection coefficients design sub-problem. For the power allocation sub-problem, we invoke the fractional programming to find the optimal solution. For the reflection coefficients design sub-problem of ideal IRS, the optimal solution is derived with the Lagrangian dual method. Whereas quantization-based method is employed to find the discrete phase shifts for non-ideal IRS. We finally propose a genetic algorithm (GA) to represent the potential solutions of sub-carrier assignment, and combine the other two optimization algorithms as fitness estimator in GA. Numerical results validate the feasibility, fast convergence, and the flexibility of the proposed algorithm. It shows that the proposed scheme outperform the system without IRS and that with a random initialized IRS.

The novel concept of simultaneously transmitting and reflecting (STAR) reconfigurable intelligent surfaces (RISs) is investigated, where the incident wireless signal is divided into transmitted and reflected signals passing into both sides of the space surrounding the surface, thus facilitating a full-space manipulation of signal propagation. Based on the introduced basic signal model of `STAR', three practical operating protocols for STAR-RISs are proposed, namely energy splitting (ES), mode switching (MS), and time switching (TS). Moreover, a STAR-RIS aided downlink communication system is considered for both unicast and multicast transmission, where a multi-antenna base station (BS) sends information to two users, i.e., one on each side of the STAR-RIS. A power consumption minimization problem for the joint optimization of the active beamforming at the BS and the passive transmission and reflection beamforming at the STAR-RIS is formulated for each of the proposed operating protocols, subject to communication rate constraints of the users. For ES, the resulting highly-coupled non-convex optimization problem is solved by an iterative algorithm, which exploits the penalty method and successive convex approximation. Then, the proposed penalty-based iterative algorithm is extended to solve the mixed-integer non-convex optimization problem for MS. For TS, the optimization problem is decomposed into two subproblems, which can be consecutively solved using state-of-the-art algorithms and convex optimization techniques. Finally, our numerical results reveal that: 1) the TS and ES operating protocols are generally preferable for unicast and multicast transmission, respectively; and 2) the required power consumption for both scenarios is significantly reduced by employing the proposed STAR-RIS instead of conventional reflecting/transmiting-only RISs.

Simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) is a promising technology that aids in achieving full-space coverage on both sides of the surface, by splitting the incident signal into transmitted and reflected signals. This paper investigates the resource allocation problem in a STAR-RIS-assisted multi-carrier communication networks. To maximize the system sum-rate, a joint optimization problem comprising of the channel assignment, power allocation, and transmission and reflection beamforming at the STAR-RIS for orthogonal multiple access (OMA) is first formulated. To solve this challenging problem, we first propose a channel assignment scheme utilizing matching theory and then invoke the alternating optimization-based method to optimize the resource allocation policy and beamforming vectors iteratively. Furthermore, the sum-rate maximization problem for non-orthogonal multiple access (NOMA) with flexible decoding orders is investigated. To efficiently solve it, we first propose a location-based matching algorithm to determine the sub-channel assignment, where a transmitted user and a reflected user are grouped on a sub-channel. Based on thistransmission-and-reflectionsub-channel assignment strategy, a three-step approach is proposed, which involves the optimization of decoding orders, beamforming-coefficient vectors, and power allocation, by employing semidefinite programming, convex upper bound approximation, and geometry programming, respectively. Numerical results unveil that: 1) For OMA, a general design that includes the same-side user-pairing for channel assignment is preferable, whereas for NOMA, the proposed transmission-and-reflection scheme can achieve comparable performance to the exhaustive search-based algorithm. 2) The STAR-RIS-aided NOMA network significantly outperforms networks employing conventional RISs and OMA.

A novel intelligent reflecting surface (IRS)-aided multi-robot network is proposed, where multiple mobile wheeled robots are served by an access point (AP) through non-orthogonal multiple access (NOMA). The goal is to maximize the sum-rate of all robots by jointly optimizing trajectories and NOMA decoding orders of robots, reflecting coefficients of the IRS, and the power allocation of the AP, subject to the quality of service (QoS) of each robot. To tackle this problem, a dueling double deep Q-network (D3QN) based algorithm is invoked for jointly determining the phase shift matrix and robots’ trajectories. Specifically, the trajectories for robots contain a set of local optimal positions, which reveals that robots make the optimal decision at each step. Numerical results demonstrated that the proposed D3QN algorithm outperforms the conventional algorithm, while the performance of IRS-NOMA network is better than the orthogonal multiple access (OMA) network.

This paper investigates intelligent reflecting surface (IRS)-assisted systems, where an access point sends independent information to multiple users with the aid of one IRS. Our goal is to characterize the capacity region of the IRS-assisted multiuser communication systems. We jointly optimize the discrete phase-shift matrix of the IRS and resource allocation with the capacity-achieving non-orthogonal multiple access (NOMA) transmission scheme. The Pareto boundary of the capacity region is characterized by maximizing the average sum rate of all users, subject to a set of rate-profile constraints, total transmit power and discrete IRS phase shift constraints. Though the formulated problem is non-convex, we derive the globally optimal solutions by invoking the Lagrange duality method. It is shown that the optimal transmission strategy is alternating transmission among different user groups by dynamically adjusting the IRS phase shifts. We further propose a Hadamard codebook based scheme, which serves as a lower bound on the optimal performance gains. Numerical results demonstrate that: i) the IRS is capable of significantly improving the capacity region; ii) the capacity region achieved by the Hadamard codebook based scheme is close to that of discrete phase shifts for a small number of IRS elements.

Due to the mobility and flexibility of unmanned aerial vehicle (UAV), it has been widely used in data collection. This article considers multiple UAVs to collect data from multiple sensor nodes (SNs) with the interference. In order to ensure the timeliness of the collected data, we jointly optimize the trajectory of the UAVs and the wake-up time allocation as well as the transmit power of the SNs for minimization of the mission completion time. The formulated problem is non-convex with continuous variables which is difficult to solve. In order to solve this problem, we consider time discretization directly, then use bisection search to turn it into a finite variable problem and use the successive convex approximation and alternating optimization techniques to solve it. In our research, we initialized the UAV's trajectory with tangent circles to speed up the convergence of algorithm. The simulation results show that the proposed method complete the missions of data collection better than benchmark schemes.

Intelligent reflecting surface (IRS) enhanced multi-unmanned aerial vehicle (UAV) non-orthogonal multiple access (NOMA) networks are investigated. A new transmission framework is proposed, where multiple UAV-mounted base stations employ NOMA to serve multiple groups of ground users with the aid of an IRS. The three-dimensional (3D) placement and transmit power of UAVs, the reflection matrix of the IRS, and the NOMA decoding orders among users are jointly optimized for maximization of the sum rate of considered networks. To tackle the formulated mixed-integer non-convex optimization problem with coupled variables, a block coordinate descent (BCD)-based iterative algorithm is developed. Specifically, the original problem is decomposed into three subproblems, which are alternately solved by exploiting the penalty-based method and the successive convex approximation technique. The proposed BCD-based algorithm is demonstrated to be able to obtain a stationary point of the original problem with polynomial time complexity. Numerical results show that: 1) the proposed NOMA-IRS scheme for multi-UAV networks achieves a higher sum rate compared to the benchmark schemes, i.e., orthogonal multiple access (OMA)-IRS and NOMA without IRS; 2) the use of IRS is capable of providing performance gain for multi-UAV networks by both enhancing channel qualities of UAVs to their served users and mitigating the inter-UAV interference; and 3) optimizing the UAV placement can make the sum rate gain brought by NOMA more distinct due to the flexible decoding order design.

The fundamental capacity limits of intelligent reflecting surface (IRS)-assisted multi-user wireless communication systems are investigated in this article. Specifically, the capacity and rate regions for both capacity-achieving non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA) transmission schemes are characterized by jointly optimizing the IRS reflection matrix and wireless resource allocation under the constraints of a maximum number of IRS reconfiguration times. In NOMA, all users are served in the same resource blocks by employing superposition coding and successive interference cancelation techniques. In OMA, all users are served by being allocated orthogonal resource blocks of different sizes. For NOMA, the ideal case with an asymptotically large number of IRS reconfiguration times is firstly considered, where the optimal solution is obtained by employing the Lagrange duality method. Inspired by this result, an inner bound of the capacity region for the general case with a finite number of IRS reconfiguration times is derived. For OMA, the optimal transmission strategy for the ideal case is to serve each individual user alternatingly with its effective channel power gain maximized. Based on this result, a rate region inner bound for the general case is derived. Finally, numerical results are provided to show that: i) a significant capacity and rate region improvement can be achieved by using IRS; ii) the capacity gain can be further improved by dynamically configuring the IRS reflection matrix.

Integrating robots into cellular networks creating connected robotic users has emerged as a promising technology for future smart cities and smart factories due to their low cost and high maneuverability. However, the requirement of establishing stable and high-quality communication links to the robotic users greatly restricts their applicability, especially in indoor environments where obstacles may block the wireless link. To tackle this challenge, in this paper, an indoor robot navigation system is investigated, where an intelligent reflecting surface (IRS) is employed to enhance the connectivity between the access point (AP) and robotic users. Both single-user and multiple-user scenarios are considered. In the single-user scenario, one mobile robotic user (MRU) communicates with the AP. In the multiple-user scenario, the AP serves one MRU and one static robotic user (SRU) employing either non-orthogonal multiple access (NOMA) or orthogonal multiple access (OMA) transmission. The considered system is optimized for minimization of the travelling time/distance of the MRU from a given starting point to a predefined final location, while satisfying constraints on the communication quality of the robotic users. To this end, a radio map based approach is proposed to exploit location-dependent channel propagation knowledge. For the single-user scenario, a channel power gain map is constructed, which characterizes the spatial distribution of the maximum expected effective channel power gain of the MRU for the optimal IRS phase shifts. Based on the obtained channel power gain map, the communication-aware robot path planing problem is solved by exploiting graph theory. For the multiple-user scenario, a communication rate map is constructed, which characterizes the spatial distribution of the maximum expected rate of the MRU for the optimal power allocation at the AP and the optimal IRS phase shifts subject to a minimum rate requirement for the SRU. The joint optimization problem is efficiently solved by invoking bisection search and successive convex approximation methods. Then, a graph theory based solution for the robot path planning problem is derived by exploiting the obtained communication rate map. Our numerical results show that: 1) the required travelling distance of the MRU can be significantly reduced by deploying an IRS; 2) NOMA yields a higher communication rate for the MRU than OMA; 3) the IRS performance gain is significantly more pronounced for NOMA than for OMA.

The fundamental intelligent reflecting surface (IRS) deployment problem is investigated for IRS-assisted networks, where one IRS is arranged to be deployed in a specific region for assisting the communication between an access point (AP) and multiple users. Specifically, three multiple access schemes are considered, namely non-orthogonal multiple access (NOMA), frequency division multiple access (FDMA), and time division multiple access (TDMA). The weighted sum rate maximization problem for joint optimization of the deployment location and the reflection coefficients of the IRS as well as the power allocation at the AP is formulated. The non-convex optimization problems obtained for NOMA and FDMA are solved by employing monotonic optimization and semidefinite relaxation to find a performance upper bound. The problem obtained for TDMA is optimally solved by leveraging thetime-selectivenature of the IRS. Furthermore, for all three multiple access schemes, low-complexity suboptimal algorithms are developed by exploiting alternating optimization and successive convex approximation techniques, where alocal region optimizationmethod is applied for optimizing the IRS deployment location. Numerical results are provided to show that: 1) near-optimal performance can be achieved by the proposed suboptimal algorithms; 2)asymmetricandsymmetricIRS deployment strategies are preferable for NOMA and FDMA/TDMA, respectively; 3) the performance gain achieved with IRS can be significantly improved by optimizing the deployment location.

In this paper, a multi-scale convolutional neural network-based (MSN) method is proposed for robust automatic modulation classification (AMC). The classifier directly utilizes in-phase and quadrature (I/Q) samples to identify the modulation type of received signal without any data preprocessing, thereby reducing the computational complexity. Further, the network architecture employs one-dimensional convolution (Conv1D) to extract multi-scale feature maps due to its merits of low computational complexity. Then these multi-scale feature maps are merged together by repeated multi-scale fusions, in order to improve the classification accuracy performance and the robustness to varying SNR environment. Repeated multi-scale fusions can make better use of amplitude-phase information because it can learn the local changes brought by modulation as well as the timing characteristics of the samples. Simulation results show that proposed MSN achieves classification rate of 97.38% classification accuracy at high SNR regimes for 24 different modulation types on the public well-known over-the-air (OTA) dataset. Moreover, MSN still can recognize the modulation types of received signals with the accuracy rates of about 95% under varying SNR scenarios. Compared to the methods proposed in other papers, our classifier not only shows a better performance in terms of classification accuracy, but also is the most robust in varying SNR environment.

In millimeter-wave (mmWave) communication systems, hybrid beamforming is regarded as an effective way to increase the spectral efficiency of the massive multiple-input multiple-output (MIMO) system. Assuming perfect channel state information (CSI) is known at the transmitter, we focus on a downlink massive multi-user MIMO system which supports multi-stream per user. In the above scenario, we investigate the hybrid beamforming problem with strong correlation between users' channels, where the existing schemes have performance loss. To tackle this problem, this paper proposes the channel correlation cancelation-based hybrid beamforming (CCCHB) algorithm which considers the correlation between channels and decomposes the optimization of overall spectrum efficiency of the users to a series of sub-rate optimization problems. And the block diagonalization (BD) technique is used in the equivalent channel to eliminate inter-user interference. Simulation results illustrate that the performance of the proposed scheme outperforms the existing algorithm, especially significant when there exists high correlation between users' channels.

This paper investigates ground-aerial uplink non-orthogonal multiple access (NOMA) cellular networks. A rotary-wing unmanned aerial vehicle (UAV) user and multiple ground users (GUEs) are served by ground base stations (GBSs) by utilizing the uplink NOMA protocol. The UAV is dispatched to upload specific information bits to each target GBSs. Specifically, our goal is to minimize the UAV mission completion time by jointly optimizing the UAV trajectory and UAV-GBS association order while taking into account the UAV's interference to non-associated GBSs. The formulated problem is a mixed integer non-convex problem and involves infinite variables. To tackle this problem, we efficiently check the feasibility of the formulated problem by utilizing graph theory and topology theory. Next, we prove that the optimal UAV trajectory needs to satisfy the fly-hover-fly structure. With this insight, we first design an efficient solution with predefined hovering locations by leveraging graph theory techniques. Furthermore, we propose an iterative UAV trajectory design by applying successive convex approximation (SCA) technique, which is guaranteed to coverage to a locally optimal solution. We demonstrate that the two proposed designs exhibit polynomial time complexity. Finally, numerical results show that: 1) the SCA based design outperforms the fly-hover-fly based design; 2) the UAV mission completion time is significantly minimized with proposed NOMA schemes compared with the orthogonal multiple access (OMA) scheme; 3) the increase of GUEs' quality of service (QoS) requirements will increase the UAV mission completion time.

This paper investigates a downlink multiple-input single-output intelligent reflecting surface (IRS) aided non-orthogonal multiple access (NOMA) system, where a base station (BS) serves multiple users with the aid of IRSs. Our goal is to maximize the sum rate of all users by jointly optimizing the active beamforming at the BS and the passive beamforming at the IRS, subject to successive interference cancellation decoding rate conditions and IRS reflecting elements constraints. In term of the characteristics of reflection amplitudes and phase shifts, we consider ideal and non-ideal IRS assumptions. To tackle the formulated non-convex problems, we propose efficient algorithms by invoking alternating optimization, which design the active beamforming and passive beamforming alternately. For the ideal IRS scenario, the two subproblems are solved by invoking the successive convex approximation technique. For the non-ideal IRS scenario, constant modulus IRS elements are further divided into continuous phase shifts and discrete phase shifts. To tackle the passive beamforming problem with continuous phase shifts, a novel algorithm is developed by utilizing the sequential rank-one constraint relaxation approach, which is guaranteed to find a locally optimal rank-one solution. Then, a quantization-based scheme is proposed for discrete phase shifts. Finally, numerical results illustrate that: i) the system sum rate can be significantly improved by deploying the IRS with the proposed algorithms; ii) 3-bit phase shifters are capable of achieving almost the same performance as the ideal IRS; iii) the proposed IRS-aided NOMA systems achieve higher system sum rate than the IRS-aided orthogonal multiple access system.

This paper investigates ground-aerial uplink non- orthogonal multiple access (NOMA) cellular networks. A unmanned aerial vehicle (UAV) user and ground users (GUEs) are served by ground base stations (GBSs) by utilizing uplink NOMA protocol. The goal is to minimize the UAV mission completion time by jointly designing the UAV trajectory and UAV-GBS association vectors while considering the interference of UAV to other non-associated GBSs. The formulated problem is a mixed integer non-convex problem and involves infinite number of variables, which is difficult to be directly solved. To tackle this challenge, we first prove the optimal UAV trajectory satisfies \emph{fly-hover-fly} communication policy. With this insight, we propose an efficient algorithm to solve the original problem based on a properly constructed graph by invoking graph theory and convex optimization techniques. Numerical results show that the UAV mission completion time is significantly minimized with proposed NOMA scheme compared with conventional orthogonal multiple access (OMA) communication and reveal a tradeoff between the UAV mission completion time and GUEs' quality-of-service (QoS) requirements.

With the development of wireless communication, one of the critical demands is secure transmission especially in downlink communication. In this paper, downlink secure transmission in two-cell base station cooperation multiuser network is studied. The two base stations are fully cooperated and an artificial noise (AN) is added to degrade the eavesdropper(EVE)'s channel. Perfect channel state information (CSI) of all users is known by the base stations and regularized channel inversion (RCI) precoding is used. The close form expression of secrecy sum rate is derived in the large system regime. The regularization parameter and the power allocation ratio are optimized based on larger system regime results. From analysis, base station cooperation network could serve more users per cell in secure transmission than single cell network. The numerical results show large system regime results are accurate even in finite case. In the simulation figures, the analytical optimal results can well approximate to the simulation results.