Unmanned aerial vehicles (UAVs) are emerging as a promising platform for enabling integrated sensing and communication (ISAC) in multi-cell systems due to their deployment flexibility. However, this flexibility also introduces significant challenges, particularly co-channel interference at the UAV receiver. In this paper, we propose a novel adaptive co-channel interference mitigation model for UAV-enabled multi-cell systems. Specifically, the proposed model consists of two key components: a cost function and an update algorithm. First, we derive a new cost function that incorporates both magnitude and phase errors–critical metrics for guiding the estimated signal toward the desired signal. Second, the cost function is extended to formulate a parameter update algorithm, whose effectiveness is analyzed using both geometric and entropy-based approaches. Simulation results demonstrate that the proposed method outperforms state-of-the-art techniques, establishing it as a robust solution for interference mitigation in UAV-enabled multi-cell ISAC systems.

Full-duplex (FD) zero-intermediate-frequency (zero-IF) transceivers are attractive for the Internet of Things (IoT) because they reduce cost, power consumption, and hardware complexity. However, their practicality is limited by severe self-interference (SI) dominated by DC offset, frequency-selective in-phase/quadrature (IQ) imbalance, and power-amplifier (PA) nonlinearities that vary over time with thermal effects and protection logic. Conventional cancellers that rely on uniform, time-domain models perform poorly under these frequency-selective and state-dependent distortions. This paper proposes a hardware-aware, frequency-domain self-interference cancellation (SIC) framework that (i) analytically characterizes zero-IF distortion spectra to enable selective per-subcarrier basis allocation, and (ii) introduces lightweight state-adaptive processing using DC-magnitude detection to switch coefficient sets in real time. A USRP X310 prototype demonstrates mean SIC of 35.5 dB across both stable and dynamic PA states at 15 and 21 dBm transmit power, achieved with only 2.57 basis functions per subcarrier on average. Compared with polynomial, parallel Hammerstein, and neural-network baselines, the proposed design achieves superior cancellation with orders-of-magnitude lower complexity. These results establish frequency-domain, hardware-aware SIC as a practical and scalable solution for resource-constrained IoT devices operating in dynamic wireless environments.

Efficient channel estimation and beamforming in the upper mid-band spectrum pose a fundamental challenge for 6G base stations (BSs). This requires practical beam management techniques that account for the propagation characteristics of this frequency range and the increasing number of BS antennas. To address the channel estimation bottleneck in Frequency Division Duplex (FDD) systems, this paper proposes an integrated framework combining channel synthesis and AI-based beam management. We first introduce partial statistical reciprocity (PSR) by extending the concept of partial reciprocity into the statistical domain, enabling a novel model-based online channel synthesis method. The proposed approach uses uplink channel parameters collected during BS operation to generate synthetic downlink channels that reflect the spatial distribution of active user equipment (UE), enabling the creation of high-quality, site-specific datasets for AI training. Furthermore, we propose a joint optimization framework for scalable codebook design and downlink channel estimation. A U-Net Transformer model is trained to estimate full beam-domain channel state information (CSI) using a single snapshot of uplink CSI and feedback from codebook-based beam sweeping. The model iteratively updates the codebook via backpropagation to align with current UE distributions and channel conditions, while channel estimation is performed through forward inference. The proposed AI-based framework achieves accurate channel reconstruction with minimal feedback, offering a scalable and practical solution for 6G spatial multiple access in the upper mid-band FDD system.

Sixth-generation (6G) networks are rapidly becoming a focal point of global technological innovation, driven by the need to support hyper-reliable, low-latency, and intelligent connectivity for applications such as immersive extended reality, autonomous systems, and ubiquitous sensing. While 6G promises transformative advancements in wireless communication, achieving its ambitious goals poses significant fundamental challenges. One natural direction is to scale multiple-input multiple-output (MIMO) technology to unprecedented levels; however, doing so introduces substantial hardware complexity and power consumption. To overcome these limitations, recent research has explored antenna reconfigurability as a novel degree of freedom (DoF) at the physical (PHY) layer. Among these efforts, the fluid antenna system (FAS) has emerged as a compelling concept, offering reconfigurability in both spatial positioning and physical structure. This idea has inspired related innovations, including movable antennas, flexible-position MIMO, reconfigurable MIMO architectures, and adaptive antenna arrays, collectively referred to as next-generation reconfigurable antenna (NGRA) systems. While prior work has primarily focused on spatial flexibility, this article introduces a generalized model of FAS that incorporates both structural and morphological fluidity, enabling the vision of “shapeless and formless” antennas in future wireless systems. We analyze FAS’s potential to enhance key performance metrics such as coverage, energy efficiency, reliability, and spectral capacity. In addition, we outline implementation challenges and explore synergies with key 6G enablers, including reconfigurable intelligent surfaces (RIS), non-terrestrial networks (NTN), integrated sensing and communication (ISAC), and artificial intelligence (AI). This survey provides a comprehensive overview of NGRA systems and identifies promising directions for future research in reconfigurable wireless technologies.

This paper explores the unsourced/uncoordinated random access (URA) problem for integrated sensing and communication (ISAC) systems. Recent findings indicate that conventional multiple access strategies, such as treating interference as noise (TIN) and time-division multiple access (TDMA), are often overwhelmed and fail to support the rapidly increasing number of active users. To address this, the unsourced ISAC (UNISAC) system model has emerged as a promising framework for future ISAC networks. In this work, we adopt a realistic channel model and propose the use of a fluid antenna system (FAS) for UNISAC. We derive the achievable performance bounds and floor for the proposed FAS-UNISAC to demonstrate its significant potential. For communication, the randomized overlapping of channel responses from different scattering paths yields substantial gains within a compact antenna space.We prove that the joint detection and decoding error is inversely proportional to the channel gain. For line-of-sight (LOS) only channel model, leveraging covariance information through the spatial diversity of FAS effectively expands the receiving aperture, thereby enhancing sensing resolution. A universal sensing upper bound is established as a benchmark for potential sensing methods. Additionally, we investigate the benefits of asynchronous transmission, which introduces an extra degree of freedom through resource multiplexing gains via randomized timing offsets among users. Our results show that the spatial diversity inherent in FAS significantly enhances the supported user volume, as well as the sensing and communication capabilities.

Intelligent spectrum management is crucial for improving spectrum efficiency and achieving secure utilization of spectrum resources. However, existing intelligent spectrum management methods, typically based on small-scale models, suffer from notable limitations in recognition accuracy, convergence speed, and generalization, particularly in the complex and dynamic spectrum environments. To address these challenges, this paper proposes a novel spectrum foundation model, termed SpectrumFM, establishing a new paradigm for spectrum management. SpectrumFM features an innovative encoder architecture that synergistically exploits the convolutional neural networks and the multi-head self-attention mechanisms to enhance feature extraction and enable robust representation learning. The model is pre-trained via two novel self-supervised learning tasks, namely masked reconstruction and next-slot signal prediction, which leverage large-scale in-phase and quadrature (IQ) data to achieve comprehensive and transferable spectrum representations. Furthermore, a parameter-efficient fine-tuning strategy is proposed to enable SpectrumFM to adapt to various downstream spectrum management tasks, including automatic modulation classification (AMC), wireless technology classification (WTC), spectrum sensing (SS), and anomaly detection (AD). Extensive experiments demonstrate that SpectrumFM achieves superior performance in terms of accuracy, robustness, adaptability, few-shot learning efficiency, and convergence speed, consistently outperforming conventional methods across multiple benchmarks. Specifically, SpectrumFM improves AMC accuracy by up to 12.1% and WTC accuracy by 9.3%, achieves an area under the curve (AUC) of 0.97 in SS at -4 dB signal-to-noise ratio (SNR), and enhances AD performance by over 10%.

Multiple-input multiple-output (MIMO) communication has led to immense enhancements in data rates and efficient spectrum management. The evolution of MIMO, though, has been accompanied by increased hardware complexity and array sizes, causing the system power consumption to increase. Despite past advances in power-efficient hybrid architectures, new solutions are needed to enable extremely large-scale MIMO deployments for 6G and beyond. In this paper, we introduce a novel architecture that integrates low-power reconfigurable antennas with both digital and analog precoding. Thistri-hybridapproach addresses key limitations in traditional and hybrid MIMO systems by improving power consumption and adds a new layer for signal processing. We provide an analysis of the proposed architecture and compare its performance with existing solutions, including fully-digital and hybrid MIMO systems. The results demonstrate significant improvements in energy efficiency, highlighting the potential of the tri-hybrid system to meet the growing demands of future wireless networks. We conclude the paper with a summary of design and implementation challenges, including the need for technological advancements in reconfigurable array hardware and tunable antenna parameters.

This paper considers communication between a base station (BS) to two users, each from one side of a simultaneously transmitting-reflecting reconfigurable intelligent surface (STAR-RIS) in the absence of a direct link. Rate-splitting multiple access (RSMA) strategy is employed and the STAR-RIS is subjected to phase errors. The users are equipped with a planar fluid antenna system (FAS) with position reconfigurability for spatial diversity. First, we derive the distribution of the equivalent channel gain at the FAS-equipped users, characterized by at-distribution. We then obtain analytical expressions for the outage probability (OP) and average capacity (AC), with the latter obtained via a heuristic approach. Our findings highlight the potential of FAS to mitigate phase imperfections in STAR-RIS-assisted communications, significantly enhancing system performance compared to traditional antenna systems (TAS) with only modest hardware complexity and negligible training or feedback overhead at the user side. Furthermore, we quantify the impact of practical phase errors on system efficiency, emphasizing the importance of robust and energy-efficient strategies for next-generation wireless networks.

Symbiotic radio (SR) is a promising technology designed to meet the increasing demand for spectrum-efficient communication. However, the small size of backscatter devices (BDs), which are typically equipped with a single antenna, poses challenges in achieving sufficient diversity or spatial multiplexing, thereby hindering the advancement of SR. To address this issue, we introduce fluid antennas (FAs) into SR, enabling devices to dynamically adjust their positions to create a favorable wireless environment and overcome spatial constraints, thereby achieving significant diversity gains. In this paper, we investigate the uplink performance of FA-assisted SR (FA-SR). First, we propose a novel collaborative cancellation channel estimation scheme based on least squares regression (CC-LSR) for scenarios with imperfect channel state information (CSI). We then derive tight lower bound expressions for the channel capacity under both perfect and imperfect CSI cases and formulate the corresponding weighted sum channel capacity (WSCC) optimization problems. The positions of the FAs and the combining vectors are jointly optimized to maximize the lower bound of the WSCC. To solve these problems, we develop joint optimization methods for both perfect and imperfect CSI scenarios using chaotic sequence-based adaptive particle swarm optimization (CSA-PSO). Nevertheless, the high computational complexity of joint optimization poses challenges for practical implementation. To this end, we propose a progressive optimization framework (POF) tailored to both perfect and imperfect CSI scenarios, in which the original problem is divided into three subproblems that are progressively solved to find locally optimal solutions. Numerical results demonstrate that POF significantly reduces computational complexity with minimal performance loss compared to joint optimization methods, particularly under imperfect CSI conditions.

Compact ultra-massive antenna arrays (CUMA) share key characteristics with holographic communication systems, featuring densely spaced and individually controlled antenna elements that enable precise manipulation of electromagnetic waves. In this paper, we investigate the spectral efficiency (SE) of CUMA deployed within some constrained physical space. Departing from prior works that assume ideal isotropic antennas, we derive a closed-form expression for the SE assuming a line-of-sight (LoS) channel at the electromagnetic level, explicitly accounting for mutual coupling and antenna orientation. The analysis reveals that the channel gain is highly sensitive to both the array orientation and individual antenna directions. In the single-user case, our results show that the optimal orientation of the user array is either aligned parallel or perpendicular to the signal direction, depending on the inter-element spacing. Notably, near-optimal channel gain is achieved when individual antennas are oriented perpendicular to the signal direction. In the multi-user case, we further optimize transceiver configurations under mutual coupling constraints. Simulation results confirm that SE is strongly influenced by the directional alignment of user antennas and array placement in the near-field regime. CUMA significantly outperforms traditional half-wavelength spaced arrays in terms of SE when constrained to the same physical aperture.

Fluid antenna (FA) technology has emerged as a promising approach in wireless communications due to its capability of providing increased degrees of freedom (DoFs) and exceptional design flexibility. This paper addresses the challenge of direction-of-arrival (DOA) estimation for aligned received signals (ARS) and non-aligned received signals (NARS) by designing two specialized uniform FA structures under time-constrained mobility. For ARS scenarios, we propose a fully movable antenna configuration that maximizes the virtual array aperture, whereas for NARS scenarios, we design a structure incorporating a fixed reference antenna to reliably extract phase information from the signal covariance. To overcome the limitations of large virtual arrays and limited sample data inherent in time-varying channels (TVC), we introduce two novel DOA estimation methods: TMRLS-MUSIC for ARS, combining Toeplitz matrix reconstruction (TMR) with linear shrinkage (LS) estimation, and TMR-MUSIC for NARS, utilizing sub-covariance matrices to construct virtual array responses. Both methods employ Nyström approximation to significantly reduce computational complexity while maintaining estimation accuracy. Theoretical analyses and extensive simulation results demonstrate that the proposed methods achieve underdetermined DOA estimation using minimal FA elements, outperform conventional methods in estimation accuracy, and substantially reduce computational complexity.

This paper proposes a novel covert transmission framework for an unmanned aerial vehicle (UAV)-reconfigurable holographic surface (RHS)-aided full-duplex (FD) integrated sensing and communication (ISAC) system, where the aerial access point (AP) simultaneously performs target sensing and downlink covert communication. We jointly design the AP’s downlink transmit signal and uplink receive beamformers, the RHS weights, the users’ uplink transmit powers, and the UAV’s trajectory, considering imperfect knowledge of the warden’s channel state information (CSI). An optimization problem is formulated to maximize the minimum covert transmission rate (CTR) among all downlink covert users (DCUs), subject to constraints on required sensing and uplink transmission capabilities, covertness, and total power budget. To tackle the intractable non-convex problem, we leverage the Bernstein-type inequality, majorization-minimization (MM), and successive convex approximation (SCA), and propose a secure optimization framework that efficiently updates all variables using convex optimization techniques. To further understand the proposed algorithm, its convergence behavior and computational complexity are discussed. Simulation results demonstrate that integrating RHS and UAV techniques into the optimization design enhances the covert transmission performance of FD-ISAC systems while ensuring a certain level of sensing capability.

Fluid antenna system (FAS) technology can further improve the performance by changing the antenna position and shape over a given space dynamically. In this paper, a FAS-assisted orbital angular momentum (FAS-OAM) communication system is proposed, in which the base station (BS) transmits OAM signals with multiple modes to a receiver equipped with fluid antennas. In order to analyze the performance of the proposed system accurately, the block-correlation model is employed to construct the channel correlation of FAS with low complexity. Then, the outage probability and ergodic capacity are derived based on the assumption of the non-central chi-square distribution, and the Gauss-Laguerre quadrature method is adopted to obtain closed-form results. Simulation results show that the derived theoretical approximate and closed-form results of outage probability and ergodic capacity are consistent with corresponding simulation results. Compared with the conventional OAM and MIMO systems, the proposed FAS-OAM system exhibits significant performance advantages in terms of outage probability and ergodic capacity, and confirms the effectiveness of FAS-OAM over fading channels.

The advancement of nanotechnology has enabled the deployment of medical nanonetworks within the human body, further accelerated by progress in terahertz communication technologies and nanonetwork routing protocols. However, nanonodes may move in dynamic environments due to environmental influences or self-propulsion mechanisms, posing significant challenges to routing protocol design. To address this, we propose a dynamic routing protocol for nanonetworks that integrates real-time velocity vectors to account for time-varying node positions. During relay node selection, key factors such as candidate nodes' velocity vectors are comprehensively evaluated to determine the optimal relay for next-hop transmission. To ensure efficient data exchange among multiple nodes, we design a time-division multiple access (TDMA)-based media access control (MAC) protocol to prevent packet collisions and losses. The protocol assigns distinct transmission and reception time slots to different node types and implements a countdown mechanism to manage channel access and eliminate conflicts. Numerical and simulation results demonstrate that the proposed protocol significantly outperforms benchmark protocols, achieving notable improvements in both time and energy efficiency.

Integrated sensing, communications, and computation (ISCC) is becoming increasingly critical, particularly for enabling advanced intelligent applications. This paper proposes an ISCC framework for edge-intelligent networks, where edge intelligent devices (EIDs) cooperatively sense multiple mobile targets and simultaneously offload radar sensing data to a base station (BS) equipped with an edge server for processing. To address the time-varying nature of the network, we develop an online resource management strategy that maximizes the long-term average weighted sum rate (AWSR), subject to queue stability, average power constraints, and quality-of-service (QoS) requirements. Using the Lyapunov drift-plus-penalty framework, the original stochastic optimization problem is decomposed into a sequence of deterministic subproblems across time slots. At each time slot, sensing scheduling, transmit beamforming for both sensing and communications, receive beamforming for radar echoes, and computing resource allocation at the BS are jointly optimized through an efficient alternating optimization algorithm based on the current system state. Simulation results validate the effectiveness of the proposed online strategy, showing superior performance over baseline methods and revealing the influence of key parameters. In particular, a trade-off is observed between the AWSR and queue backlogs, which can be flexibly tuned via control parameters.

The fluid antenna system (FAS) enables position reconfigurability. A potential drawback of real-time FAS, however, is that it requires complete channel state information (CSI) for each FAS port at every communication time slot, an approach referred to as ideal-FAS. Recognizing the difficulties of achieving ideal-FAS, we propose a FAS scheme based on incomplete CSI, referred to as semi-blind FAS. This paper first introduces the spatial-temporal framework of FAS, upon which the proposed semi-blind FAS is developed. The proposed semi-blind FAS is lightweight and computationally efficient, scalable to an arbitrary number of ports and time slots, and operates without pre-training or deep learning structures. The scheme effectively exploits incomplete historical CSI to estimate the conditional distribution across all FAS ports at the desired time slot, thereby identifying the statistical optimal port for signal reception. Generally, the key idea of semi-blind FAS is to select the optimal port through conditional distribution analysis, from a statistical perspective, with optimality defined according to the scenario of interest. Inspired by information-theoretic entropy, we further develop the residual entropy power ratio to characterize how physical parameters influence the performance gap between semi-blind FAS and ideal-FAS. Our analysis reveals that estimation performance depends not only on the number of sampled ports and time slots, but also on the specific indices of ports with given CSI at each time slot, i.e., the port sampling strategy. This critical factor has been largely overlooked in existing port estimation studies. Numerical results demonstrate that the proposed semi-blind FAS achieves performance comparable to, and in some cases indistinguishable from, that of ideal-FAS, while requiring significantly fewer port CSI measurements and lower port switching speeds.

An auto-polarization fluid antenna (APFA) is developed for indoor wireless channel sounding and employed to reveal a novel “fluid polarization effect” (FPE) in wireless communications. Unlike conventional fluid antennas (FAs) that are steering their beams/nulls with the aid of external mechanical/electronic actuators, the APFA only relies on the natural swinging of human arms to yield a self-driven polarization switching ability. Compared with the conventional fixed circularly polarized antennas, the wrist-worn, self-driven APFA in indoor wireless channel sounding systems effectively reduces multipath clusters (MPCs), attains smaller path loss exponent (PLE), and consequently yields the FPE. Compared to the fixed circularly polarized case with PLE= 1.62, the measured PLE is reduced by 14% to 1.38, and the system packet error rate (PER) is improved by 76%. It realizes robust anti-multipath fading performance owing to the much-improved FPE. The fluid effect in polarization domain is anticipated to remarkably enhance the anti-multipath fading performance of wearable wireless communication systems. It opens a new horizon to develop self-driven, cost-effective fluid antenna systems (FASs) for universal applications.

In-band full-duplex (IBFD) systems are expected to double the spectral efficiency compared to half-duplex systems, provided that loopback self-interference (SI) can be effectively suppressed. The inherent interference mitigation capabilities of the emerging fluid antenna system (FAS) technology make it a promising candidate for addressing the SI challenge in IBFD systems. This paper thus proposes a FAS-assisted self-interference cancellation (SIC) framework, which leverages a receiver-side FAS to dynamically select an interference-free port. Analytical results include a lower bound and an approximation of the residual SI (RSI) power, both derived for rich-scattering channels by considering the joint spatial correlation amongst the FAS ports. Simulations of RSI power and forward link rates validate the analysis, showing that the SIC performance improves with the number of FAS ports. Additionally, simulations under practical conditions, such as finite-scattering environments and wideband integrated access and backhaul (IAB) channels, reveal that the proposed approach offers superior SIC capability and significant forward rate gains over conventional IBFD SIC schemes.

In this paper, we propose a novel aerial backhaul architecture that employs an aerial active reconfigurable intelligent surface (RIS) to achieve energy-efficient, full 3D coverage including UAV-BSs and ground users in 6G wireless networks. Unlike prior aerial-RIS approaches limited to 2D coverage with only servicing ground users or passive operation, the proposed design integrates an active-RIS onto a high-altitude aerial platform, enabling reliable line-of-sight links and overcoming multiplicative fading through amplification. In a scenario with UAV-BSs deployed to handle sudden traffic surges in urban areas, the aerial-active-RIS both reflects and amplifies backhaul signals to overcome blockage. We jointly optimize the aerial platform placement, array partitioning, and RIS phase configuration to maximize UAV-BS energy-efficiency. Simulation results confirm that the proposed method significantly outperforms benchmarks, demonstrating its strong potential to deliver resilient backhaul connectivity with comprehensive 3D coverage in 6G networks.

This paper introduces a unified analytical and optimization framework for fluid antenna system-active reconfigurable intelligent surface (FAS-ARIS) communications in 6G. By combining the port reconfigurability of FAS with the signal amplification of ARIS, the proposed design enables more flexible control of the propagation environment and enhanced link reliability beyond what passive solutions can offer. We first derive the optimal ARIS amplification gain under a reflection power constraint to maximize the user’s signal-to-noise ratio (SNR). Using a block-diagonal matrix approximation, we obtain a tractable outage expression and a tight independent-antenna equivalent upper-bound. Building on this, we establish the monotonic relationship between outage and effective channel gain, which enables a closed-form solution for ARIS phase optimization under limited channel state information (CSI). To further improve spectral efficiency, we propose a region-partitioned throughput optimization framework that achieves near-optimal performance without exhaustive search, thereby verifying its low computational complexity. Extensive simulations confirm the accuracy of the analysis and demonstrate consistent gains in outage and throughput compared to baselines.

In this paper, we propose a precoding framework for frequency division duplex (FDD) integrated sensing and communication (ISAC) systems with multiple-input multiple-output (MIMO). Specifically, we aim to maximize ergodic sum spectral efficiency (SE) while satisfying a sensing beam pattern constraint defined by the mean squared error (MSE). Our method reconstructs downlink (DL) channel state information (CSI) from uplink (UL) training signals using partial reciprocity, eliminating the need for CSI feedback. To obtain the error covariance matrix of the reconstructed DL CSI, we devise anobserved Fisher information-based estimation technique. Leveraging this, to mitigate interference caused by imperfect DL CSI reconstruction and sensing operations, we propose a rate-splitting multiple access (RSMA) aided precoder optimization method. This method jointly updates the precoding vector and Lagrange multipliers by solving the nonlinear eigenvalue problem with eigenvector dependency to maximize SE. The numerical results show that the proposed design achieves precise beam pattern control, maximizes SE, and significantly improves the sensing-communication trade-off compared to the state-of-the-art methods in FDD ISAC scenarios.

Cell-free massive multiple-input multiple-output (CF massive MIMO) is regarded as a promising technology for next-generation wireless communication systems. However, conventional CF massive MIMO systems typically employ fixed-position antennas (FPAs) at access points (APs), which limits the exploitation of spatial degrees of freedom (DoFs) for antenna position optimization. To address this issue, we propose the use of fluid antennas (FAs) in place of FPAs, enabling more DoFs at APs and leading to a novel FA-aided CF massive MIMO (FA-CF) architecture. In this paper, we investigate the uplink spectral efficiency (SE) of FA-CF systems with imperfect channel state information (CSI). We design a minimum mean-square error (MMSE)-based channel estimation scheme to estimate the aggregated channels between APs and user equipments (UEs). We further derive achievable SE expressions for both centralized and distributed processing schemes, including fully centralized processing (FCP), large-scale fading decoding (LSFD), and equal-gain decoding processing (EGDP). Moreover, we formulate a mean-square error (MSE) minimization problem based on the signal transmission model. To solve this problem, we develop an efficient algorithm that combines orthogonal matching pursuit (OMP) with binary search to jointly optimize FA positions and the combining matrix. In addition, we propose a protective weak-ordering (PWO) strategy to enhance the SE of the FCP scheme. Numerical results demonstrate that FA-CF significantly outperforms conventional CF systems in terms of SE, even with a limited number of antennas, and maintains strong robustness under imperfect CSI or heavy UE loads by adaptively adjusting antenna positions. These results highlight FA-CF as a promising architecture offering enhanced SE and robustness for future wireless systems, particularly in scenarios where large-scale AP deployment is infeasible or cost-constrained.

In this paper, we investigate a reconfigurable intelligent surface (RIS) aided spectrum sharing scheme between a multiple input multiple-output (MIMO) radar and MIMO multiuser communication system under non-homogeneous interference scenarios, including the interference from scattering points, the mutual interference between the two systems, and the interference among multiple users. We consider a flexibly-weighted framework for joint resource allocation, aiming at maximizing the mutual information of both the radar and the communication systems under the usual constraints on the transmit power and on the compatibility of the space spectral. To deal with the resulting triple degrees of freedom non-convex framework, a sub-optimal procedure, based on iterative alternating maximization of three suitably derived subproblems, is proposed and analyzed. Each yields a closed-form solution. In particular, to address the constant modulus constraint imposed by the RIS, we propose two different optimization strategies, based on the Minorization-Maximization framework in conjunction with the Alternating Direction Penalty Method and the Element Block Coordinate Descent formulations, namely, MM-ADPM and MM-EBCD, respectively. Finally, simulation results compare the effectiveness and advantages of the two algorithms.

Fluid antenna multiple access (FAMA) has recently emerged as a simple, promising scheme for large-scale multiuser connectivity, offering strong scalability with low implementation complexity. Nevertheless, most existing FAMA studies focus on downlink transmission under perfect channel state information (CSI) at the receiver side, while the uplink counterpart remains largely unexplored. This paper proposes a novel codebook-based port selection and combining (CPSC) FAMA framework for the uplink communications without CSI at the base station (BS). In the proposed scheme, a predefined codebook is designed and broadcast by the BS. Each user equipment (UE) employs a fluid antenna, acquires its local CSI and independently chooses the most suitable codeword, activates the corresponding fluid antenna ports, and determines the combining weights to achieve a two-way match between the selected codeword and the instantaneous effective channel. The BS then separates the superimposed user signals through codebook-guided projection operations without requiring global CSI or multiuser joint optimization. To handle potential codeword collisions, three lightweight scheduling strategies are introduced, offering flexible trade-offs between signaling overhead and collision avoidance. Simulation results demonstrate that the proposed CPSC-FAMA approach achieves substantially higher rates than fixed-antenna systems while maintaining low complexity. Moreover, the results confirm that amortizing the optimization cost over the UEs effectively reduces the BS processing burden and enhances scalability, making the proposed scheme a strong candidate for future sixth-generation (6G) networks.

The fluid antenna system (FAS) concept represents shape-flexible and position-flexible antenna technologies designed to enhance wireless communication applications. In this paper, we apply this concept to reconfigurable intelligent surfaces (RISs), introducing fluid RIS (FRIS), where each tunably reflecting element becomes afluid elementwith additional position reconfigurability. This new paradigm is referred to as fluid RIS (FRIS). We investigate an FRIS-programmable wireless channel, in which the fluid metasurface is divided into non-overlapping subareas, each acting as a fluid element that can dynamically adjust both its position and phase shift of the reflected signal. We first analyze the single-user, single-input single-output (SU-SISO) channel, in which a single-antenna transmitter communicates with a single-antenna receiver via an FRIS. The achievable rate is then maximized by optimizing the fluid elements using a particle swarm optimization (PSO)-based approach. Next, we extend our analysis to the multi-user, multiple-input single-output (MU-MISO) case, where a multi-antenna base station (BS) transmits individual data streams to multiple single-antenna users via an FRIS. In this case, the joint optimization of the positions and phase shifts of the FRIS element, as well as the BS precoding to maximize the sum-rate is studied. To solve the problem, a combination of techniques including PSO, semi-definite relaxation (SDR), and minimum mean square error (MMSE) is proposed. Numerical results demonstrate that the proposed FRIS approach significantly outperforms conventional RIS configurations in terms of achievable rate performance.

This paper presents a novel framework for enhancing physical-layer security in integrated sensing and communication (ISAC) systems by leveraging the reconfigurability of fluid antenna systems (FAS). We propose a joint precoding and port selection (JPPS) strategy that maximizes the sum secrecy rate while simultaneously ensuring reliable radar sensing. The problem is formulated using fractional programming (FP) and solved through an iterative algorithm that integrates FP transformations with successive convex approximation (SCA). To reduce computational complexity, we further develop low-complexity schemes based on zero-forcing (ZF) precoding, combined with greedy port selection and trace-inverse minimization. Simulation results show substantial improvements in both secrecy performance and sensing accuracy compared to conventional baselines, across a wide range of FAS port number, user loads, and sensing targets. These findings highlight the critical importance of FAS geometry optimization in enabling secure and efficient joint communication-sensing for next-generation wireless networks.

This paper studies the use of multiple planar fluid antennas at a full-duplex base station (BS) for integrated sensing and communication (ISAC). In this model, the BS communicates with a downlink user, an uplink user, and performs target sensing simultaneously. Our objective is to maximize the communication sum-rate of the up and downlink users while meeting the sensing and power constraints. Given that the problem is non-convex, we first reformulate the problem using the fractional programming (FP) framework. After that, we iteratively optimize the beamforming vectors of the BS, the uplink transmit power from the user, and the antenna positions of both transmit and receive fluid antenna systems (FASs) at the BS. In particular, the transmit and receive beamforming vectors are optimized by utilizing the majorization-minimization (MM) framework, and a closed-form solution for the uplink transmit power is derived. To optimize the BS antenna positions, we transform the problems into convex quadratically constrained quadratic programs (QCQP) by using Taylor series expansion. The subproblems can then be solved based on the successive convex approximation (SCA). Simulation results show that FAS can greatly improve the communication rate compared to the traditional fixed-position antenna (FPA) system.

Based on our current understanding, extreme massive access over the same physical channel is only possible if an extra-large multiple-input multiple-output (XL-MIMO) antenna is used at the base station (BS) and instantaneous channel state information (CSI) is known at the BS side for precoding design. This casts doubt on scalability and challenges in device-to-device situations in which there is not a centralized, optimized BS for transmitting the user signals. To address this problem, we revisit the massive connectivity challenge by considering the case where no CSI is available at the BS and no precoding is used. In this situation, inter-user interference (IUI) mitigation can only be performed at the user terminal (UT) side. Leveraging the position flexibility of fluid antenna system (FAS), we adopt a fluid antenna multiple access (FAMA) approach that exploits the interference signal fluctuation in the spatial domain. Specifically, we assume that we haveNspatially correlated received signals per symbol duration from FAS. Our main approach uses a simple heuristic port shortlisting method that identifies promising ports to obtain favourable received signals that can be combined via maximum ratio combining (MRC) to form the received output signal for final detection. On top of this, a pre-trained deep joint source-channel coding (JSCC) scheme is employed, which together with a diffusion-based denoising model (MixDDPM) at the UT side, can improve the IUI immunity. We refer to the proposed scheme as turbo FAMA. Simulation results show that with a physical FAS size of 20 wavelengths at each UT transmitting quaternary phase shift keying (QPSK) symbols, fast FAMA can support 50 users whileturboFAMA can handle up to 200 users if the required symbol error rate (SER) is 10-2. If a higher error tolerance is acceptable, say SER at 0.1, turbo FAMA can even serve up to 1000 users but fast FAMA is only able to handle 160 users, all remarkably achieved without CSI at the BS.

Fluid antenna systems (FAS) are emerging as a transformative enabler for sixth-generation (6G) wireless communications, providing unprecedented spatial diversity through dynamic reconfiguration of antenna ports. However, the inherent spatial correlation among ports poses significant challenges for accurate analysis. Conventional models such as Jakes are analytically intractable, while oversimplified constant-correlation models fail to capture the true behavior. In this work, we address these challenges by applying the variable block-correlation model (VBCM) -- originally proposed by Ramírez-Espinosa \textit{et al.} in 2024 -- to FAS security analysis, and by developing comprehensive optimization methods to enhance analytical accuracy. We derive new closed-form expressions for average secrecy capacity (ASC) and secrecy outage probability (SOP), demonstrating that the VBCM framework achieves simulation-aligned accuracy, with relative errors consistently below $5\%$ (compared to $10$--$15\%$ for constant-correlation models). To maximize ASC, we further design two algorithms: a grid search (GS) method and a gradient descent (GD) method. Numerical results reveal that the VBCM-based approach not only provides reliable insights into FAS security performance, but also yields substantial gains -- ASC improvements exceeding $120\%$ in high-threat scenarios and $18$--$19\%$ performance enhancements for compact antenna configurations. These findings underscore the practical value of integrating VBCM into FAS security analysis and optimization, establishing it as a powerful tool for advancing 6G communication systems.

In response to the “security blind zone” challenges faced by traditional reconfigurable intelligent surface (RIS)-aided covert communication (CC) systems, the joint distance-angle beamforming capability of frequency diverse RIS (FD-RIS) shows significant potential for addressing these limitations. Therefore, this paper initially incorporates the FD-RIS into the CC systems and proposes the corresponding CC transmission scheme. Specifically, we first develop the signal processing model of the FD-RIS, which considers effective control of harmonic signals by leveraging the time-delay techniques. The joint distance-angle beamforming capability is then validated through its normalized beampattern. Based on this model, we then construct an FD-RIS-assisted CC system under a multi-warden scenario and derive an approximate closed-form expression for the covert constraints by considering the worst-case eavesdropping conditions and utilizing the logarithmic moment-generating function. An optimization problem is formulated which aims at maximizing the covert user’s achievable rate under covert constrains by jointly designing the time delays and modulation frequencies. To tackle this non-convex problem, an iterative algorithm with assured convergence is proposed to effectively solve the time-delay and modulation frequency variables. To evaluate the performance of the proposed scheme, we consider three communication scenarios with varying spatial correlations between the covert user and wardens. Simulation results demonstrate that FD-RIS can significantly improve covert performance, particularly in angular-overlap scenarios where traditional RIS experiences severe degradation. These findings further highlight the effectiveness of FD-RIS in enhancing CC robustness under challenging spatial environments.

This paper proposes a novel pattern-reconfigurable fluid reconfigurable intelligent surface (FRIS) framework, where each fluid element can dynamically adjust its radiation pattern based on instantaneous channel conditions. To evaluate its potential, we first conduct a comparative analysis of the received signal power in point-to-point communication systems assisted by three types of surfaces: (1) the proposed pattern-reconfigurable FRIS, (2) a position-reconfigurable FRIS, and (3) a conventional RIS. Theoretical results demonstrate that the pattern-reconfigurable FRIS provides a significant advantage in modulating transmission signals compared to the other two configurations. To further study its capabilities, we extend the framework to a multiuser communication scenario. In this context, the spherical harmonics orthogonal decomposition (SHOD) method is employed to accurately model the radiation patterns of individual fluid elements, making the pattern design process more tractable. An optimization problem is then formulated with the objective of maximizing the weighted sum rate among users by jointly designing the active beamforming vectors and the spherical harmonics coefficients, subject to both transmit power and pattern energy constraints. To tackle the resulting non-convex optimization problem, we propose an iterative algorithm that alternates between a minimum mean-square error (MMSE) approach for active beamforming and a Riemannian conjugate gradient (RCG) method for updating the spherical harmonics coefficients. Simulation results show that the proposed pattern-reconfigurable FRIS significantly outperforms traditional RIS architectures based on the 3GPP 38.901 and isotropic radiation models, achieving average performance gains of 161.5% and 176.2%, respectively. Additionally, it reduces the required number of antennas and RIS elements by over 300%, offering substantial improvements in hardware efficiency.

This paper proposes a hardware-software co-design approach to efficiently optimize beamforming and port selection in fluid antenna systems (FASs). To begin with, a fluid-antenna (FA)-enabled downlink multi-cell multiple-input multiple-output (MIMO) network is modeled, and a weighted sum-rate (WSR) maximization problem is formulated. Second, a method that integrates graph neural networks (GNNs) with random port selection (RPS) is proposed to jointly optimize beamforming and port selection, while also assessing the benefits and limitations of random selection. Third, an instruction-driven deep learning accelerator based on a field-programmable gate array (FPGA) is developed to minimize inference latency. To further enhance efficiency, a scheduling algorithm is introduced to reduce redundant computations and minimize the idle time of computing cores. Simulation results demonstrate that the proposed GNN-RPS approach achieves competitive communication performance. Furthermore, experimental evaluations indicate that the FPGA-based accelerator maintains low latency while simultaneously executing beamforming inference for multiple port selections.

This paper investigates a design framework for sparse fluid antenna systems (FAS) enabling high-performance direction-of-arrival (DOA) estimation, particularly in challenging millimeter-wave (mmWave) environments. By ingeniously harnessing the mobility of fluid antenna (FA) elements, the proposed architectures achieve an extended range of spatial degrees of freedom (DoFs) compared to conventional fixed-position antenna (FPA) arrays. This innovation not only facilitates the seamless application of super-resolution DOA estimators but also enables robust DOA estimation, accurately localizing more sources than the number of physical antenna elements. We introduce two bespoke FA array structures and mobility strategies tailored to scenarios with aligned and misaligned received signals, respectively, demonstrating a hardware-driven approach to overcoming complexities typically addressed by intricate algorithms. A key contribution is a light-of-sight (LoS)-centric, closed-form DOA estimator, which first employs an eigenvalue-ratio test for precise LoS path number detection, followed by a polynomial root-finding procedure. This method distinctly showcases the unique advantages of FAS by simplifying the estimation process while enhancing accuracy. Numerical results compellingly verify that the proposed FA array designs and estimation techniques yield an extended DoFs range, deliver superior DOA accuracy, and maintain robustness across diverse signal conditions.

In a single-antenna fluid antenna system (FAS), the transceiver dynamically selects the antenna port with the strongest instantaneous channel to enhance link reliability. However, deriving accurate yet tractable performance expressions under the fully correlated fading channel remains challenging, primarily due to the absence of a closed-form distribution for the FAS channel. To address this gap, this paper develops a novel performance evaluation framework for FAS under fully correlated Rayleigh fading by modeling the FAS channel using extreme value distributions (EVDs). We first justify the suitability of EVDs and model the FAS channel as a Gumbel distribution, whose parameters, estimated via the maximum likelihood (ML) criterion, are expressed as functions of the number of ports and antenna aperture size. Closed-form approximate expressions for the outage probability (OP) and the ergodic capacity (EC) are then derived. Simulation results show that the Gumbel distribution provides simple yet sufficiently accurate expressions for EC, although slight deviations in OP accur in the low-probability region of practical interest. To further improve accuracy, the FAS channel is modeled using the generalized extreme value (GEV) distribution, yielding closed-form expressions for OP and EC based on ML-estimated parameters. Simulation results confirm that the GEV distribution provides superior accuracy compared to the Gumbel, particularly for OP, while both EVD-based approaches offer computationally efficient and analytically tractable tools for evaluating FAS performance under the fully correlated fading channel.

Hybrid satellite-unmanned aerial vehicle (UAV)-terrestrial networks (SUTNs) can provide maritime users with ubiquitous communication services. However, eavesdropping poses a significant challenge to the secure communications of SUTNs due to their wide-area coverage. In this paper, we propose a novel secure scheme for maritime communications, where a terrestrial-UAV integrated network coexists with marine satellite (MS) systems in the presence of an eavesdropper (Eve). Considering imperfect channel state information (CSI) for both the MS and Eve, we focus on the collaborative design of beamforming for the terrestrial base station (TBS), UAV, and MS, as well as the UAV’s trajectory. A robust optimization problem is formulated to maximize the worst-case secrecy rate, subject to constraints on worst-case communication quality for each user, UAV locations, and TBS backhaul throughput. To tackle this intractable non-convex problem, we leverage the S-procedure, general sign-definiteness, and successive convex approximation (SCA) to propose a security solution that efficiently optimizes all variables using convex optimization techniques. Numerical results validate the effectiveness of the proposed solution, illustrating the impact of CSI errors and the secure performance enhancements achieved through joint trajectory and beamforming optimization.

In this paper, we introduce a novel framework for multiple access code design under the finite blocklength regime in multi-input and multi-output (MIMO) systems, termed coded pattern multiple access (CPMA). CPMA involves a series of multiple access code designs facilitated by a sparse pattern mapper/demapper, enabling independent information projection onto transmission patterns. Unlike existing approaches, the mapping and demapping of patterns are completely isolated components, ensuring energy-efficient transmission. In this work, we establish and analyze practical CPMA models, thoroughly investigating the performance limits of a potential non-bijective demapper. Closed-form and integral-form solutions are provided to describe these performance limits. Additionally, we present a practical application of CPMA: the coded pattern unsourced random access (CPURA) scheme. This scheme is designed for finite blocklength transmission under a quasi-static fading channel. The proposed CPURA achieves bound-approaching performance for large user groups, outperforming existing state-of-the-art methods in the context of massive machine-type communications (mMTC). Notably, the minimum required energy-per-bit to support 1,200 active users exhibits only a 1.3 dB gap from the achievable bound, validating the potential of the proposed CPMA framework.

Fluid antenna system (FAS) revolutionizes wireless communications via utilizing position-flexible antennas that dynamically optimize channel conditions and mitigate multipath fading. This innovation is particularly valuable in indoor environments, in which signal propagation is severely degraded due to structural obstructions and complex multipath reflections. In this paper, we investigate the channel modeling and the joint optimization of antenna positioning, beamforming, and power allocation for indoor FAS. In particular, we propose a layout-specific channel model, and employ the novel group relative policy optimization (GRPO) algorithm for tackling the optimization problem. Compared to the state-of-the-art Sionna model, our model achieves an 83.3% reduction in computation time with an approximately 3 dB increase in root-mean-square error (RMSE). When simplified to a two-ray model, our model allows for a closed-form antenna position solution with near-optimal performance. For the joint optimization problem, our GRPO algorithm outperforms proximal policy optimization (PPO) and other baselines in sum-rate, while requiring only 50.8% computational resources of PPO, thanks to its group advantage estimation. Simulation results show that increasing either the group size or trajectory length in GRPO does not yield significant improvements in sum-rate, suggesting that these parameters can be selected conservatively without sacrificing performance.

Utilizing communication signals to extract motion parameters has emerged as a key direction in Vehicle-to-Everything (V2X) networks. Accurately modeling the relationship between communication signals and sensing performance is critical for the advancement of such systems. Unlike prior work that relies primarily on qualitative analysis, this paper derives the Cramér-Rao Bound (CRB) for radar parameter estimation in the context of Orthogonal Frequency Division Multiplexing (OFDM) waveforms and Uniform Planar Array (UPA) configurations. Recognizing that vehicles may act as extended targets, we propose two New Radio (NR)-V2X-compatible beamforming schemes tailored to different phases of the communication process. During the initial beam establishment phase, we develop a beamforming approach based on the union of predictive error ellipses, which enhances scatterer localization through temporally assisted beam training. In the beam adjustment phase, we introduce an adaptive narrowest-beam strategy that leverages the positions of scatterers and the communication receiver (CR), enabling effective tracking with reduced complexity. The beam design problem is addressed using the minimum enclosing ellipse algorithm and tailored antenna control methods. Simulation results validate the proposed approach, showing up to a 32.4% improvement in achievable rate with a 32×32 transmit antenna array and a 5.2% gain with an 8×8 array, compared to conventional beam sweeping under identical SNR conditions.

In this paper, we study efficientmulti-beamtraining design fornear-fieldcommunications to reduce the beam training overhead of conventional single-beam training methods. In particular, the array-division-based multi-beam training method, which is widely used in far-field communications, cannot be directly applied in the near-field scenario, since different sub-arrays may observe different user angles and there exist coverage holes in the angular domain. To address these issues, we first devise a new near-field multi-beam codebook by sparsely activating a portion of antennas to form an effectivesparse linear array(SLA), hence generating multiple beams simultaneously by exploiting the near-fieldgrating lobes. Next, atwo-stagenear-field beam training method is proposed. In the first stage, several candidate user locations are identified based on multi-beam sweeping over time, followed by the second stage to determine the true user location with a small number of pilots for single-beam sweeping. Finally, numerical results show that our proposed multi-beam training method significantly reduces the beam training overhead as compared to conventional single-beam training methods, while achieving comparable rate performance in data transmissions.

This paper develops a comprehensive framework for the performance analysis of fluid antenna system (FAS)-enabled unmanned aerial vehicle (UAV) relaying networks operating in the finite blocklength regime. This work establishes a rigorous methodology for characterizing system reliability under diverse propagation environments. Closed-form expressions for the block error rate (BLER) are derived by employing a tractable eigenvalue-based approximation of the spatially correlated UAV-to-user link, whose underlying independent diversity components are modeled as Nakagami-mfading. This approach addresses both line-of-sight (LoS) dominant rural and probabilistic non-line-of-sight (NLoS) urban scenarios. Furthermore, a high signal-to-noise ratio (SNR) asymptotic analysis is developed, revealing the fundamental diversity order of the UAV-to-user link. Based on this, we further address the practical issue of energy efficiency. A realistic energy efficiency maximization problem is formulated, which explicitly accounts for the time and energy overhead in the FAS port selection process. An efficient hierarchical algorithm is then proposed to jointly optimize the key system parameters. Extensive numerical results validate the analysis and illustrate that while FASs can yield substantial power gains, the operational overhead introduces a non-trivial trade-off, leading to an optimal number of ports and fundamentally different UAV deployment strategies in rural versus urban environments. In summary, this work provides both foundational analysis and practical design guidelines for FAS-enabled UAV communications.

The enhancement of spectrum efficiency and the realization of secure spectrum utilization are critically dependent on spectrum cognition. However, existing spectrum cognition methods often exhibit limited generalization and suboptimal accuracy when deployed across diverse spectrum environments and tasks. To overcome these challenges, we propose a spectrum foundation model, termed SpectrumFM, which provides a new paradigm for spectrum cognition. An innovative spectrum encoder that exploits the convolutional neural networks and the multi-head self attention mechanisms is proposed to effectively capture both fine-grained local signal structures and high-level global dependencies in the spectrum data. To enhance its adaptability, two novel self-supervised learning tasks, namely masked reconstruction and next-slot signal prediction, are developed for pre-training SpectrumFM, enabling the model to learn rich and transferable representations. Furthermore, low-rank adaptation (LoRA) parameter-efficient fine-tuning is exploited to enable SpectrumFM to seamlessly adapt to various downstream spectrum cognition tasks, including spectrum sensing (SS), anomaly detection (AD), and wireless technology classification (WTC). Extensive experiments demonstrate the superiority of SpectrumFM over state-of-the-art methods. Specifically, it improves detection probability in the SS task by 30% at -4 dB signal-to-noise ratio (SNR), boosts the area under the curve (AUC) in the AD task by over 10%, and enhances WTC accuracy by 9.6%.1

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

This paper investigates joint transmit beamforming and trajectory optimization techniques for an unmanned aerial vehicle (UAV)-enabled covert full-duplex (FD) integrated sensing and communication (ISAC) systems with hardware impairments (HWIs), where the aerial access point (AP) transmits and receives sensing signals while the integrated communication operates in either downlink or uplink. We jointly optimize the downlink transmit signal and the uplink receive beamformers at the AP, the transmit power at the uplink users and the trajectory of the UAV. An optimization problem is formulated for maximizing the minimum covert transmission rate (CTR) among all covert users (CUs) subject to the constraints of the required sensing and uplink transmission capabilities, system covertness, total power budget. To tackle the intractable non-convex problem, we leverage majorization-minimization (MM) and successive convex approximation (SCA), and propose a security solution that efficiently optimizes all variables by employing convex optimization approaches. Numerical results demonstrate the effectiveness of the proposed method in balancing the trade-off between covert communication and sensing performance, highlighting the UAV’s potential in adaptive ISAC deployment.

Downlink transmission of a nonorthogonal visible light communication (VLC) system, empowered by autonomous aerial vehicles (AAVs), is studied for coexisting enhanced mobile broadband (eMBB), ultrareliable low-latency communication (URLLC), and massive machine-type communication (mMTC) services. A joint resource allocation problem involving user association, transmit power, and flight trajectory of AAVs is formulated, with the goal of characterizing a multiobjective tradeoff as a weighted sum of the power consumption of each AAV and the perceived Quality of Experience (QoE) of its associated eMBB users, while ensuring the service-specific requirements for eMBB, mMTC, and URLLC are met. Assuming the imperfection of channel state information (CSI), we invoke a generalized Benders decomposition (GBD) methodology, leveraging tools from convex optimization and multiagent deep reinforcement learning to address this problem. We further analytically derive the upper and lower bounds on the reward function for each AAV as a learning agent. Extensive simulations confirm that our proposed method outperforms the single-agent counterpart in the literature, with up to a 22% reduction in power consumption and a 13% gain in perceived QoE. Additionally, compared to the globally optimal brute-force method for AAV-user association, our proposed method experiences only a trivial performance loss in a small-scale scenario.

In this paper, we investigate a multi-user downlink scheduling and power allocation strategy for urban air mobility (UAM) within a 6G non-terrestrial network (NTN) framework that integrates satellite and ground networks. We consider a system model involving multiple ground stations (GSs) and a single satellite, addressing the sum rate maximization problem with link-association, power, elevation angle, and minimum quality-of-service constraints. The proposed method initially segregates satellite-serviced users to reduce interference among the remaining GS-serviced users, taking into account the locations and movements of those UAMs. Subsequently, using a graph-theoretical approach, we convert the GS link association problem into a minimum-cost maximum-flow problem. In this process, we employ an analytical method involving polynomial approximations or a numerical method using integral approximation through the sum of time-sampled parameters. We then address the non-convex power allocation problem for scheduled links through iterative algorithms. The proposed scheduling and power allocation algorithms effectively manage interference in multi-UAM and multi-GS environments, and their performance is validated through extensive simulation results. Our study provides a comprehensive framework and strategy for efficient downlink transmission in future UAM operations, paving the way for novel applications in 6G NTN.

Surface wave communication (SWC) is an emerging technology garnering significant interest for its diverse potential applications in communications. However, accurately computing electromagnetic field strength, which is related to the path loss, in reconfigurable surface structures, particularly for long-distance transmission, presents an ongoing challenge. To address this, we introduce a novel analytical model employing surface wave ray tracing. Unlike conventional simulations, our analytical approach enables precise computation of the electromagnetic field strength attenuation in both short and long-distance transmissions, providing invaluable insights for practical SWC implementations. Our proposed model takes into account key system parameters such as surface material, thickness, cavity porosity, and other variables influencing propagation performance. This facilitates analysis of optimal reconfigurable structures. Simulation results validate the model’s accuracy in short-distance transmission, thereby endorsing its effectiveness in studying surface wave path loss over longer distances. Furthermore, our study demonstrates the SWC superiority over traditional coaxial cable and space-wave communication in mitigating path loss. Additionally, we explore the impacts of various factors such as different dielectric layers, wall materials, leakage, and pathway width on SWC performance, providing deeper insights into designing optimal reconfigurable structures for SWC applications.

This paper investigates a multiuser millimeter-wave (mmWave) uplink system in which each user is equipped with a multi-antenna fluid antenna system (FAS) while the base station (BS) has multiple fixed-position antennas. Our primary objective is to maximize the system capacity by optimizing the transmit covariance matrices and the antenna position vectors of the users jointly. To gain insights, we start by deriving upper bounds and approximations for the capacity. Then we delve into the capacity maximization problem. Beginning with the simple scenario of a single user equipped with a single-antenna FAS, we demonstrate that a closed-form optimal solution exists when there are only two propagation paths between the user and the BS. In the case where multiple propagation paths are present, a near-optimal solution can also be obtained through a one-dimensional search method. Expanding our focus to multiuser cases, in which users are equipped with either single- or multi-antenna FAS, we show that the original capacity maximization problems can be reformulated into distinct rank-one programmings. Then, we propose alternating optimization algorithms to deal with the transformed problems. Simulation results indicate that FAS can improve the capacity of the multiple access channel (MAC) greatly, and the proposed algorithms outperform all the benchmarks.

Nonlinear self-interference (SI) cancellation is essential for mitigating the impact of transmitter-side nonlinearity on overall SI cancellation performance in flexible duplex systems, including in-band full-duplex (IBFD) and sub-band full-duplex (SBFD). Digital SI cancellation (SIC) must address the nonlinearity in the power amplifier (PA) and the in-phase/quadrature-phase (IQ) imbalance from up/down converters at the base station (BS), in addition to analog SIC. In environments with rich signal reflection paths, however, the required number of delayed taps for time-domain nonlinear SI cancellation increases exponentially with the number of multipaths, leading to excessive complexity. This paper introduces a novel, low-complexity, frequency domain nonlinear SIC, suitable for flexible duplex systems with multiple-input and multiple-output (MIMO) configurations. The key approach involves decomposing nonlinear SI into a nonlinear basis and categorizing them based on their effectiveness across any flexible duplex setting. The proposed algorithm is founded on our analytical results of intermodulation distortion (IMD) in the frequency domain and utilizes a specialized pilot sequence. This algorithm is directly applicable to orthogonal frequency division multiplexing (OFDM) multi-carrier systems and offers lower complexity than conventional digital SIC methods. Additionally, we assess the impact of the proposed SIC on flexible duplex systems through system-level simulation (SLS) using 3D ray-tracing and proof-of-concept (PoC) measurement.

Free-space optical (FSO) communication is a promising solution to support wireless backhaul links in emerging 6G non-terrestrial networks. At the link level, pointing errors in FSO links can significantly impact capacity, making accurate modeling of these errors essential for both assessing and enhancing communication performance. In this paper, we introduce a novel model for FSO pointing errors in autonomous aerial vehicles (UAVs) that incorporates three-dimensional (3D) jitter, including roll, pitch, and yaw angle jittering. We derive a probability density function for the pointing error angle based on the relative position and posture of the UAV to the ground station. This model is then integrated into a trajectory optimization problem designed to maximize energy efficiency while meeting constraints on speed, acceleration, and elevation angle. Our proposed optimization method significantly improves energy efficiency by adjusting the UAV’s flight trajectory to minimize exposure to directions highly affected by jitter. The simulation results emphasize the importance of using UAV-specific 3D jitter models in achieving accurate performance measurements and effective system optimization in FSO communication networks. Using our generalized model, the optimized trajectories achieve up to 11.8% higher energy efficiency compared to those derived from conventional Gaussian pointing error models.

Fluid antenna system (FAS) has recently surfaced as a promising technology for the upcoming sixth generation (6G) wireless networks. Unlike traditional antenna system (TAS) with fixed antenna location, FAS introduces a flexible component in which the radiating element can switch its position within a predefined space. This capability allows FAS to achieve additional diversity and multiplexing gains. Nevertheless, to fully reap the benefits of FAS, obtaining channel state information (CSI) over the predefined space is crucial. In this paper, we study the system with a transmitter equipped with a traditional fixed antenna and a receiver with a fluid antenna by considering an electromagnetic-compliant channel model. We address the challenges of channel estimation and reconstruction using Nyquist sampling and maximum likelihood estimation (MLE) methods. Our analysis reveals a fundamental tradeoff between the accuracy of the reconstructed channel and the number of estimated channels, indicating that half-wavelength sampling is insufficient for perfect reconstruction and that oversampling is essential to enhance accuracy. Despite its advantages, oversampling can introduce practical challenges. Consequently, we propose a suboptimal sampling distance that facilitates efficient channel reconstruction. In addition, we employ the MLE method to bound the channel estimation error by$\epsilon $, with a specific confidence interval (CI). Our findings enable us to determine the minimum number of estimated channels and the total number of pilot symbols required for efficient channel reconstruction in a given space. Lastly, we investigate the rate performance of FAS and TAS and demonstrate that FAS with imperfect CSI can outperform TAS with perfect CSI. In contrast to existing works, we also show that there is an optimal fluid antenna size that maximizes the achievable rate when considering the energy and bandwidth overheads for full CSI acquisition.

Different from conventional reconfigurable intelligent surfaces (RIS), a recent innovation called simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) has emerged, aimed at achieving complete 360-degree coverage in communication networks. Additionally, full-duplex (FD) technology is recognized as a potent approach for enhancing spectral efficiency by enabling simultaneous transmission and reception within the same time and frequency resources. In this study, we investigate the performance of a STAR-RIS-assisted FD communication system. The STAR-RIS is strategically placed at the cell-edge to facilitate communication for users located in this challenging region, while cell-center users can communicate directly with the FD base station (BS). We employ a non-orthogonal multiple access pairing scheme and account for system impairments, such as self-interference at the BS and imperfect successive interference cancellation. We derive closed-form expressions for the ergodic rates in both the up-link and down-link communications and extend our analysis to bidirectional communication between cell-center and cell-edge users. Furthermore, we formulate an optimization problem aimed at maximizing the ergodic sum-rate. This optimization involves adjusting the amplitudes and phase-shifts of the STAR-RIS elements and allocating total transmit power efficiently. To gain deeper insights into the achievable rates of STAR-RIS-aided FD systems, we explore the impact of various system parameters through numerical results.

Reconfigurable intelligent surface (RIS) has been identified as a promising technology for future wireless communication systems due to its ability to manipulate the propagation environment intelligently. RIS is a frequency-selective device, thus it can only effectively manipulate the propagation of signals within a specific frequency band. This frequency-selective characteristic can make deploying RIS in wireless cellular networks more challenging, as adjacent base stations (BSs) operate on different frequency bands. In addition, rate-splitting multiple access (RSMA) scheme has been shown to enhance the performance of RIS-aided multi-user communication systems. Accordingly, this work considers a more practical reflection model for RIS-aided RSMA communication systems, which accounts for the responses of signals across different frequency bands. To that end, new analytical expressions for the ergodic sum-rate are derived using the moment generating function (MGF) and Jensen’s inequality. Based on these analytical sum-rate expressions, novel practical RIS reflection designs and power allocation strategies for the RSMA scheme are proposed and investigated to maximize the achievable sum-rate in RIS-assisted multi-cell, multi-band cellular networks. Simple sub-optimal designs are also introduced and discussed. The results validate the significant gains of our proposed reflection design algorithms with RSMA over conventional schemes in terms of achievable sum-rate. Additionally, the power allocation strategy for the RSMA scheme is shown to offer superior performance compared to conventional precoding schemes that do not rely on RSMA.

In contrast to conventional reconfigurable intelligent surface (RIS), simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) has been proposed recently to enlarge the serving area from 180° to 360° coverage. This work considers the performance of a STAR-RIS aided full-duplex (FD) non-orthogonal multiple access (NOMA) communication systems. The STAR-RIS is implemented at the cell-edge to assist the celledge users, while the cell-center users can communicate directly with a FD base station (BS). We first introduce new user clustering schemes for the downlink and uplink transmissions. Then, based on the proposed transmission schemes closed-form expressions of the ergodic rates in the downlink and uplink modes are derived taking into account the system impairments caused by the self interference at the FD-BS and the imperfect successive interference cancellation (SIC). Moreover, an optimization problem to maximize the total sum-rate is formulated and solved by optimizing the amplitudes and the phase-shifts of the STAR-RIS elements and allocating the transmit power efficiently. The performance of the proposed user clustering schemes and the optimal STAR-RIS design are investigated through numerical results.

This paper investigates the capacity performance of an uplink fluid antenna system (FAS), in which the base station (BS) is equipped with multiple fluid antennas and each user has a single fluid antenna. We aim to maximize the capacity of the system by optimizing the transmit power, and the user and BS antenna positions. Beginning with simple cases where the number of paths or the number of BS antennas is small, we reveal that the capacity is independent of the antenna positions. Then we give an upper bound on the capacity for the case where the BS has a single fluid antenna. After that, we show that in the optimal case, all users should transmit at the maximum power. Moreover, we propose an alternative algorithm to iteratively optimize the antenna positions at the BS and user sides. When keeping the user antenna positions fixed, the BS antenna positions are updated alternatively using a discrete exhaustive search in the single-user case. By transforming the capacity maximization problem into a difference-of-convex (DC) form, the majorization-minimization (MM) algorithm can also be applied to jointly optimize the BS antenna positions when there is a single user in the system. For the multiuser scenario, the antenna positions at the BS side are optimized utilizing the gradient descent method. We show that the user antenna positions can also be optimized using the discrete exhaustive search or the MM algorithm. Simulation results show that FAS can greatly improve the system capacity compared to traditional fixed-position antenna systems.

This paper investigates the optimization of the probabilistically robust transmit beamforming problem with channel uncertainties in the multiuser multiple-input single-output (MISO) downlink transmission. This problem poses significant analytical and computational challenges. Currently, the state-of-the-art optimization method relies on convex restrictions as tractable approximations to ensure robustness against Gaussian channel uncertainties. However, this method not only exhibits high computational complexity and suffers from the rank relaxation issue but also yields conservative solutions. In this paper, we propose an unsupervised deep learning-based approach that incorporates the sampling of channel uncertainties in the training process to optimize the probabilistic system performance. We introduce a model-driven learning approach that defines a new beamforming structure with trainable parameters to account for channel uncertainties. Additionally, we employ a graph neural network to efficiently infer the key beamforming parameters. We successfully apply this approach to the minimum rate quantile maximization problem subject to outage and total power constraints. Furthermore, we propose a bisection search method to address the more challenging power minimization problem with probabilistic rate constraints by leveraging the aforementioned approach. Numerical results confirm that our approach achieves non-conservative robust performance, higher data rates, greater power efficiency, and faster execution compared to state-of-the-art optimization methods.

Full-duplex (FD) technology is gaining popularity for integration into a wide range of wireless networks due to its demonstrated potential in recent studies. In contrast to half-duplex (HD) technology, the implementation of FD in networks necessitates considering internode interference (INI) from various network perspectives. When deploying FD technology in networks, several critical factors must be taken into account. These include self-interference (SI) and the requisite SI cancellation (SIC) processes, as well as the selection of multiple user equipment (UE) per time slot. In addition, INI, including cross-link interference (CLI) and intercell interference (ICI), becomes a crucial issue during concurrent uplink (UL) and downlink (DL) transmission and reception, similar to SI. Since most INIs are challenging to eliminate, a comprehensive investigation that covers radio resource control (RRC), medium access control (MAC), and the physical (PHY) layer is essential in the context of FD network design, rather than focusing on individual network layers and types. This article covers state-of-the-art studies, including protocols and documents from the third-generation partnership project (3GPP) for FD, MAC protocol, user scheduling, and CLI handling. The methods are also compared through a network-level system simulation based on 3-D ray tracing.

This paper investigates the performance of a single-user fluid antenna system (FAS), by exploiting a class of elliptical copulas to describe the dependence structure amongst the fluid antenna positions (ports). By expressing the well-known Jakes’ model in terms of the Gaussian copula, we consider two cases: (i) the general case, i.e., any arbitrary correlated fading distribution; and (ii) the specific case, i.e., correlated Nakagami-m fading. For both scenarios, we first derive analytical expressions for the cumulative distribution function (CDF) and probability density function (PDF) of the equivalent channel in terms of multivariate normal distribution. Then we obtain the outage probability (OP) and the delay outage rate (DOR) to analyze the performance of FAS. By employing the popular rank correlation coefficients such as Spearman’s$\rho $and Kendall’s$\tau $, we measure the degree of dependency in correlated arbitrary fading channels and illustrate how the Gaussian copula can be accurately connected to Jakes’ model in FAS. Our numerical results demonstrate that increasing the size of FAS provides lower OP and DOR, but the system performance saturates as the number of antenna ports increases. In addition, our results indicate that FAS provides better performance compared to conventional single-fixed antenna systems even when the size of fluid antenna is small.

This paper investigates the performance of physical layer security (PLS) in fluid antenna-aided communication systems under arbitrary correlated fading channels. In particular, it is considered that a single fixed-antenna transmitter aims to send confidential information to a legitimate receiver equipped with a planar fluid antenna system (FAS), while an eavesdropper, also taking advantage of a planar FAS, attempts to decode the desired message. For this scenario, we first present analytical expressions of the equivalent channel distributions at the legitimate user and eavesdropper by using copula, so that the obtained analytical results are valid for any arbitrarily correlated fading distributions. Then, with the help of Gauss-Laguerre quadrature, we derive compact analytical expressions for the average secrecy capacity (ASC), the secrecy outage probability (SOP), and the secrecy energy efficiency (SEE) for the FAS wiretap channel. Moreover, for exemplary purposes, we also obtain the compact expression of ASC, SOP, and SEE by utilizing the Gaussian copula under correlated Rayleigh fading channels as a special case. Eventually, numerical results indicate that applying the fluid antenna with only one activated port to PLS can guarantee more secure and reliable transmission, when compared to traditional antenna systems (TAS) exploiting maximal ratio combining (MRC) and antenna selection (AS) under selection combining (SC).

In this paper, we propose a novel architecture for a lens antenna array (LAA) designed to work with a small number of antennas and enable angle-of-arrival (AoA) estimation for advanced 5G vehicle-to-everything (V2X) use cases that demand wider bandwidths and higher data rates. We derive a received signal in terms of optical analysis to consider the variability of the focal region for different carrier frequencies in a wideband multi-carrier system. By taking full advantage of the beam squint effect for multiple pilot signals with different frequencies, we propose a novel reconfiguration of antenna array (RAA) for the sparse LAA and a max-energy antenna selection (MS) algorithm for the AoA estimation. In addition, this paper presents an analysis of the received power at the single antenna with the maximum energy and compares it to simulation results. In contrast to previous studies on LAA that assumed a large number of antennas, which can require high complexity and hardware costs, the proposed RAA with MS estimation algorithm is shown that meets the requirements of 5G V2X in a vehicular environment while utilizing limited RF hardware and has low complexity.

To enable innovative applications and services, both industry and academia are exploring new technologies for sixth generation (6G) communications. One of the promising candidates is fluid antenna system (FAS). Unlike existing systems, FAS is a novel communication technology where its antenna can freely change its position and shape within a given space. Compared to the traditional systems, this unique capability has the potential of providing higher diversity and interference-free communications. Nevertheless, the performance limits of FAS remain unclear as its system properties are difficult to analyze. To address this, we approximate the outage probability and diversity gain of FAS in closed-form expressions. We then propose a suboptimal FAS with$N^{\ast}$ports, where a significant gain can be obtained over FAS with$N^{\ast}-1$ports whilst FAS with$N^{\ast}+1$ports only yields marginal improvement over the proposed suboptimal FAS. In this paper, we also provide analytical and simulation results to unfold the key factors that affect the performance of FAS. Limited to systems with one active radio frequency (RF)-chain, we show that the proposed suboptimal FAS outperforms single-antenna (SISO) system and selection combining (SC) system in terms of outage probability. Interestingly, when the given space is$\frac {\lambda }{2}$, the outage probability of the proposed suboptimal FAS with one active RF-chain achieves near to that of the maximal ratio combining (MRC) system with multiple active RF-chains.

Multiple-input multiple-output (MIMO) system has been the defining mobile communications technology in recent generations. With the ever-increasing demands looming towards the sixth generation (6G), we are in need of additional degrees of freedom that deliver further gains beyond MIMO. To this goal, fluid antenna system (FAS) has emerged as a new way to obtain spatial diversity using reconfigurable position-switchable antennas. Considering the case with more than one ports activated on a 2D fluid antenna surface at both ends, we take the information-theoretic approach to study the achievable performance limits of the MIMO-FAS. First of all, we propose a suboptimal scheme, referred to as QR MIMO-FAS, to maximize the rate at high signal-to-noise ratio (SNR) via joint port selection, transmit and receive beamforming and power allocation. We then derive the optimal diversity and multiplexing tradeoff (DMT) of MIMO-FAS. From the DMT, we highlight that MIMO-FAS outperforms traditional MIMO antenna systems. Further, we introduce a new metric, namelyq-outage capacity, which can jointly consider rate and outage probability. Through this metric, our results indicate that MIMO-FAS surpasses traditional MIMO greatly.

Reconfigurable intelligent surface (RIS) has been recognized as a promising technique for the sixth generation (6G) of mobile communication networks. The key feature of RIS is to reconfigure the propagation environment via smart signal reflections. In addition, active RIS schemes have been recently proposed to overcome the deep path loss attenuation inherent in the RIS-aided communication systems. Accordingly, this paper considers the secrecy performance of up-link RIS-aided multiple users multiple-input single-output (MU-MISO) communication systems, in the presence of multiple passive eavesdroppers. In contrast to the existing works, we investigate the impact of the RIS phase shift errors on the secrecy performance. Taking into account the complex environment, where a general Rician channel model is adopted for all the communication links, closed-form approximate expressions for the ergodic secrecy rate are derived for three RIS configurations, namely, i) passive RIS, ii) active RIS, iii) active RIS with energy harvesting (EH RIS). Then, based on the derived expressions, we optimize the phase shifts at the RIS to enhance the system performance. In addition, the best RIS configuration selection is considered for a given target secrecy rate and amount of the power available at the users. Finally, Monte-Carlo simulations are provided to verify the accuracy of the analysis, and the impact of different system parameters on the secrecy performance is investigated. The results in this paper show that, an active RIS scheme can be implemented to enhance the secrecy performance of RIS-aided communication systems with phase shift errors, especially when the users have limited transmission power.

The aim of this paper is to enhance the performance of an integrated sensing and communications (ISAC) system in the multiuser multiple-input multiple-output (MIMO) downlink in which a two-dimensional (2D) fluid antenna system (FAS) with multiple activated ports is employed at the base station (BS) to maximize the sum-rate of the downlink users subject to a sensing constraint. The unique feature of this setup is that the locations of the antenna ports at the FAS can be optimized jointly with the precoding design to achieve a higher sum-rate. The required optimization problem is however NP-hard. To overcome this, we start by considering the perfect channel state information (CSI) scenario where all the port CSI is available. Deep reinforcement learning is utilized to build an end-to-end learning framework for the joint optimization problem. In particular, by fixing the activated ports, we adopt a primal-dual based learning algorithm to design a constraint-aware neural network for optimizing the ISAC precoder. Then, by using the neural precoding network to calculate the reward, we adopt the deep reinforcement learning algorithm to design the port selection and precoder jointly. An advantage actor and critic (A2C) algorithm is proposed to train the policy, in which the actor network uses the pointer network to learn the stochastic policy and the critic network adopts the Long Short-Term Memory (LSTM) encoder architecture to learn the expected reward from the observations. Afterwards, the partial CSI case is addressed, where we propose a masked autoencoder (MAE) induced channel extrapolation for predicting all the CSI to facilitate the joint design. Simulation results demonstrate the promising performance of using FAS for multiuser MIMO and also validate the proposed learning-based scheme.

Spectrum sensing and communication security are of crucial importance in cognitive radio networks (CRNs). In this paper, we utilize intelligent reflecting surfaces (IRS) to simultaneously enhance spectrum sensing accuracy and the secrecy performance of secondary users (SUs) through physical layer security (PLS) techniques. Additionally, we employ IRS as a novel approach to achieve the target probability of detection. We formulate a joint sensing and transmission security optimization problem to maximize the sum secrecy rate of SUs under both perfect and imperfect channel state information (CSI). To transform the probability of detection into a tractable expression, we adopt a safe approximation for theQ-function. We use a computationally-efficient block coordinate descent (BCD)-based algorithm to optimize the beamforming design and IRS phase shifts alternately. Specifically, we employ theS-procedure to handle the semi-infinite constraints under the imperfect CSI case. Simulation results demonstrate that by leveraging IRS for spectrum sensing, we can significantly reduce the sensing time while achieving the required probability of detection and the probability of false alarm. Furthermore, our proposed scheme improves both sensing accuracy and secrecy rate in both cases compared to the benchmark schemes.

The emergence of fluid antenna systems (FAS) offers a novel technique for obtaining spatial diversity and leveraging interference fades for spectrum sharing in multiuser scenarios—a paradigm referred to as fluid antenna multiple access (FAMA). Nevertheless, as the number of users increases, the interference mitigation capability diminishes. To overcome this, opportunistic scheduling that prioritizes robust users proves to be an effective method for enhancing FAMA. This paper introduces a resilient decentralized reinforcement learning (RL) approach for opportunistic FAMA (O-FAMA), to autonomously select robust users and the port of each chosen user’s FAS jointly to maximize the network sum-rate. In order to enhance learning efficiency in this multi-agent environment, we propose a novel team-theoretic RL framework that includes a derivative network guiding the multi-agent learning of each solution’s policy networks. Our simulation results confirm the effectiveness of the proposed methodology.

This paper aims to present a simple multiple access scheme for massive connectivity that enables a large number of mobile user equipments (UEs) to occupy the same time-frequency channel without the need of precoding and power control at the base station (BS) and interference cancellation at each UE. The proposed approach does not even require the UEs to know their signal-to-interference ratios (SIRs) and each UE also needs only two radio-frequency (RF) chains to operate. The proposed scheme is inspired by the emerging concept of fluid antenna system (FAS) which enables high-resolution position-switchable antenna to be deployed at each UE. Instead of activating only one port of FAS for reception, each UE activates an ultra massive number of ports to receive the signal. The activated ports are chosen to ensure that the in-phase and quadrature components of the desired signal at the ports are added constructively while the interference signals superimpose randomly. This approach is referred to as compact ultra massive antenna array (CUMA) which can also be realized by deploying a dense, fixed massive antenna array at each UE. We derive the exact probability density function (pdf) of the SIR of a CUMA UE which leads to the data rate analysis. Simulation results demonstrate that even with mutual coupling and under finite scattering, more than 10 UEs can be supported by having a 25×13-port FAS of size 15 cm×8 cm at each UE. Considering quadrature phase shift keying (QPSK), CUMA delivers a network data rate of 10.7 bps per channel use serving 10 UEs at 26 GHz, and the rate is risen to 15.1 bps per channel use if 20 UEs are accommodated at 40 GHz with a 40×21-port FAS at every UE. In the case without mutual coupling and under rich scattering, CUMA can even support hundreds of UEs per channel use.

Fluid antenna system (FAS) is a new flexible antenna technology that offers a new approach to multiple access, referred to as fluid antenna multiple access (FAMA). The performance of FAMA has been investigated but previous results were based on simplified spatial correlation models. In this paper, we will revisit FAMA for the two-user case and study the outage probability by characterizing the joint spatial correlation among the ports. We first derive a closed-form lower bound on the outage probability and reveal that in the absence of spatial correlation, the outage probability of the system decreases exponentially as the number of ports increases. We then show that the channel model can be greatly simplified by focusing upon a limited number of channel variables, allowing us to derive the outage probability using the approximate model. To gain insight, we further approximate the channel model and provide another approximation of the outage probability that is easier to compute. Simulation results validate the approximations and demonstrate that the outage probability decreases with the number of ports but has an error floor unless the antenna size is increased. Also, when the number of ports is fixed, the outage probability initially decreases exponentially with the size but eventually approaches the lower bound.

Fluid antenna system (FAS) represents all forms of movable and non-movable position-flexible antenna system, and opens up the possibility of a new form of modulation schemes. In this paper, we investigate the design of position index modulation (PIM) for FAS for decreasing the bit error rate (BER) while taking advantage of the rate gain in index modulation. We further derive the BER and data rate expressions to assess the achievable performance of PIM. Simulation results are provided to illustrate the performance and some insights are drawn into the impact of both channel estimation accuracy and transmission power.

This demonstration proposal presents an effective and energy-efficient reconfigurable intelligent surface (RIS) design and implementation through a real-time beamforming testbed. The RIS can dynamically control the beam tuning property based on liquid crystal (LC) cell structure, and a series of RIS prototypes are devised at 28 GHz and 140 GHz respectively. Actual fabrication and measurement, over-the-air field trial and system-level simulation are performed through this paper, and real-time testbed implementation will be shown to substantiate its feasibility.

For neurological diseases such as depression and Alzheimer's, researchers have actively studied transcranial magnetic stimulation (TMS), owing to its being noninvasive. Using a changing magnetic field, researchers can activate the corresponding area of the brain for treatment. In this paper, we propose an improved treatment for Alzheimer's using a magnetic multiple input multiple output (MIMO) system. Conventional MIMO systems within the RF communication area are based on beam steering using attenuators and phase shifters. Steering a magnetic beam, however, may be regarded as a tough task. We introduce an organized algorithm that, using magnetic MIMO, controls the magnetic field direction. For verification, we perform real-time Alzheimer's treatments. We believe this work will be the cornerstone of the brain communication technology.

In beyond 5G (BSG) and 6G, due to the paradigm shift to mm Wave, transparent antennas are emerging as a way to install many base stations and access points without compromising aesthetics. In line with these requirements, in this paper, we fabricate high-conductivity and high-optical transmission antennas and use them to propose two transparent antenna scenarios for B5G/6G. We introduce in-building network systems using a transparent window-type antenna and a mmWave augmented reality (AR) system using a glass-type antenna as use cases. These use cases are evaluated via software-defined- radio (SDR) implementation, system-level and link-level simulations, respectively, to show the possibility of future use of transparent antenna in B5G/6G.

It is our pleasure to share with you this Special Issue, which brings together a diverse set of articles dealing with various aspects of semantic and goal-oriented communications, providing a snapshot of research activities in this highly active research area. Wireless communications and networking research has traditionally focused on improving the capacity and throughput of the underlying wireless network. However, recent explosion in data-driven machine learning applications and their reliance on huge datasets collected by edge devices have raised legitimate concerns that the increasing data traffic might soon overwhelm the capacity of current networks despite ongoing efforts to increase their capacity and efficiency. Also, most of the edge intelligence applications impose stringent delay constraints, which cannot be met by naive forwarding of data samples for processing at the receiver end. This made it obvious to researchers in both academia and industry that it is essential to analyze the “value” or “relevance” of collected data, and filter and prioritize the delivery of data based on its value/relevance as well as the wireless channel and network conditions. In this context, data value will be closely connected to the underlying signals and processes that generate the data, e.g., text, image, video, or sensor data, and what the receiver intends to do with the received data. This subjectivity of data value makes semantic and goal-oriented communication a rather elusive research topic, which has led to both an increasingly rich and active area of investigation, but also a controversial one, mainly due to the lack of clear and widely agreed-upon definitions of some of the core concepts and formulations. Despite these disagreements, there is almost unanimous consensus on the importance and potential impact of this line of investigation for the design of future communication systems and networks.

Communication systems to date primarily aim at reliably communicating bit sequences. Such an approach provides efficient engineering designs that are agnostic to the meanings of the messages or to the goal that the message exchange aims to achieve. Next generation systems, however, can be potentially enriched by folding message semantics and goals of communication into their design. Further, these systems can be made cognizant of the context in which communication exchange takes place, thereby providing avenues for novel design insights. This tutorial summarizes the efforts to date, starting from its early adaptations, semantic-aware and task-oriented communications, covering the foundations, algorithms and potential implementations. The focus is on approaches that utilize information theory to provide the foundations, as well as the significant role of learning in semantics and task-aware communications.

Positioning accuracy is a critical requirement for vehicle-to-everything (V2X) use cases. Therefore, this paper derives the theoretical limits of estimation for the position and orientation of vehicles in a cooperative vehicle-to-vehicle (V2V) scenario, using a lens-based multiple-input multiple-output (lens-MIMO) system. Following this, we analyze the Cramér-Rao lower bounds (CRLBs) of the position and orientation estimation and explore a received signal model of a lens-MIMO for the particular angle of arrival (AoA) estimation with a V2V geometric model. Further, we propose a lower complexity AoA estimation technique exploiting the unique characteristics of the lens-MIMO for a single target vehicle; as a result, its estimation scheme is effectively extended by the successive interference cancellation (SIC) method for multiple target vehicles. Given these AoAs, we investigate the lens-MIMO estimation capability for the positions and orientations of vehicles. Subsequently, we prove that the lens-MIMO outperforms a conventional uniform linear array (ULA) in a certain configuration of a lens’s structure. Finally, we confirm that the proposed localization algorithm is superior to ULA’s CRLB as the resolution of the lens increases in spite of the lower complexity.

The capability of nodes to transmit and receive data simultaneously within the same frequency band, referred to as in-band FD, disrupts the conventional assumptions underlying wireless network design. This new feature enhances spectral efficiency and reduces latency, which are essential drivers in advancing next-generation networks. In the past few years, full-duplex (FD) has evolved from being a laboratory idea to being incorporated into telecommunications standards and proof of concepts. From 2010 to 2020, considerable research and development efforts were devoted to advancing FD wireless communications. By 2015, the cable modem industry had already implemented in-band FD technology to establish the DOCSIS 4.0 standard, enabling next-generation cable modems to operate in FD mode. By 2020, FD wireless products started to emerge in the market.

The use of in-band full-duplex (FD) enables nodes to simultaneously transmit and receive on the same frequency band, which challenges the traditional assumption in wireless network design. The full-duplex capability enhances spectral efficiency and decreases latency, which are two key drivers pushing the performance expectations of next-generation mobile networks. In less than ten years, in-band FD has advanced from being demonstrated in research labs to being implemented in standards, presenting new opportunities to utilize its foundational concepts. Some of the most significant opportunities include using FD to enable wireless networks to sense the physical environment, integrate sensing and communication applications, develop integrated access and backhaul solutions, and work with smart signal propagation environments powered by reconfigurable intelligent surfaces. However, these new opportunities also come with new challenges for large-scale commercial deployment of FD technology, such as managing self-interference, combating cross-link interference in multi-cell networks, and coexistence of dynamic time division duplex, subband FD and FD networks.

Molecular communication (MC) is an emerging field aiming at realizing information exchange via chemical signals between nanomachines in nanonetworks. Information transmission that is energy-efficient and relies on relatively low-complexity transceiver techniques is of practical importance for MC systems. Based on the fact that constellation shaping can improve energy efficiency, in this paper, we propose a molecular shell mapping (MSM) scheme to implement the probabilistic constellation shaping for MC. The MSM method is designed to exploit the concentration sequences with the lowest sum sequence weight, which results in an energy-efficient signal constellation. Furthermore, we propose an algorithm for selecting and sorting the concentration sequences to mitigate inter-symbol interference. For information detection, we design a genie-aided maximum-likelihood (ML) detector and a realistic ML detector to leverage the constructive effect of intra-sequence interference, as well as derive their bit error rates and achievable rates. Additionally, for the applications using large blocklength sequences, a low-complexity ML detection method is proposed. Numerical simulation results confirm that the shaped signaling using the MSM method is more energy-efficient than the conventional equiprobable signaling, achieving shaping gains of up to 1.5 dB at an ultra-short blocklength of 4.

Traditional channel capacity based on the discrete spatial dimensions mismatches the continuous electromagnetic fields. For the wireless communication system in a limited region, the spatial discretization may results in information loss because the continuous field can not be perfectly recovered from the sampling points. Therefore, electromagnetic information theory based on spatially continuous electromagnetic fields becomes necessary to reveal the fundamental theoretical capacity bound of communication systems. In this paper, we propose analyzing schemes for the performance limit between continuous transceivers. Specifically, we model the communication process between two continuous regions by random fields. Then, for the white noise model, we use Mercer expansion to derive the mutual information between the source and the destination. For the close-form expression, an analytic method is introduced based on autocorrelation functions with rational spectrum. Moreover, the Fredholm determinant is used for the general autocorrelation functions to provide the numerical calculation scheme. Further works extend the white noise model to colored noise and discuss the mutual information under it. Finally, we build an ideal model with infinite-length source and destination which shows a strong correpsondence with the time-domain model in classical information theory. The mutual information and the capacity are derived through the spatial spectral density.

Fluid antennas offer a novel way to achieve massive connectivity by enabling each user to find a ‘port’ in space where the instantaneous interference undergoes a deep null for multiple access. While this unprecedented capability permits hundreds of users to share the same radio channel, each user needs to switch its best port on a symbol-by-symbol basis, which is impractical. Motivated by this, this paper considers the scenario in which the fluid antenna of each user updates its best port only if the fading channel changes. We refer to this approach asslowfluid antenna multiple access ($s$-FAMA). In this paper, we first investigate the interference immunity of$s$-FAMA through analyzing the outage probability. Then an outage probability upper bound is obtained, from which we shed light on the achievable multiplexing gain of the system and unpack the impacts of various system parameters on the performance. Numerical results reveal that despite having a weaker multiplexing power than the symbol-based,fastFAMA (i.e.,$f$-FAMA), spatial multiplexing of 4 users or more is possible if the users’ fluid antennas have large numbers of ports.

Massive multiple-input multiple-output (MIMO) and full-duplex (FD) are promising candidates for achieving the spectral efficiency to meet the needs of 5G communications. One essential key to realizing practical FD massive MIMO systems is how to effectively mitigate the self-interference (SI). Conventionally, however, the performance comparison of different SI methods by reflecting the actual channel characteristics was insufficient in the literature. Accordingly, this paper presents a performance analysis of SI cancellation (SIC) methods in FD massive MIMO systems. Analytical and numerical results confirm that, in an imperfect channel-estimation case, the ergodic rates performance of the spatial suppression in the uplink outperforms those of the SI subtraction, due to the correlation between the precoder and the estimation error of the SI channel. In addition, we discuss which method performs better under different given system constraints such as uplink and downlink sum rates, the total transmit power, and the power scaling law.

One of the main obstacles in full-duplex radios is analog-to-digital converter (ADC) saturation on a receiver due to the strong self-interference (SI). To solve this issue, researchers have proposed two different types of analog self-interference cancellation (SIC) methods—i) passive suppression and ii) regeneration-and-subtraction of SI. For the latter case, the tunable RF component, such as a multi-tap circuit, reproduces and subtracts the SI. The resolutions of such RF components constitute the key factor of the analog SIC. Indeed, they are directly related to how well the SI is imitated. Another major issue in analog SIC is the inaccurate estimation of the SI channel due to the nonlinear distortions, which mainly come from the power amplifier (PA). In this paper, we derive a closed-form expression for the SIC performance of the multi-tap circuit; we consider how the RF components must overcome such practical impairments as digitally-controlled attenuators, phase shifters, and PA. For a realistic performance analysis, we exploit the measured PA characteristics and carry out a 3D ray-tracing-based, system-level throughput analysis. Our results confirm that the non-idealities of the RF components significantly affect the analog SIC performance. We believe our study provides insight into the design of the practical full-duplex system.

As a way of providing users flexible computing services, networks exist that can make full use of air and ground computing resources. Such networks are called air-ground collaborative mobile edge computing (AGC-MEC) networks. AGC-MEC supports numerous emerging real-time applications for which timely computed results are critical. Researchers have developed a novel metric “age of information (AoI)” that can capture the freshness of computed results. This is the first paper to study the problem of AoI-aware scheduling forAir-groundCollaborative mobileEdge computing (i.e., IACE). So as to minimize the weighted AoI of all the terrestrial user equipments (UEs), we have jointly optimized task scheduling, computing resource allocation, and unmanned aerial vehicle (UAV) trajectory taking into account the constraints on the computing resources and the available energy of the UAV. The formulated problem, which is a challenge to solve, is a mixed-integer nonlinear programming (MINLP) problem. To obtain an effective solution, we propose an iterative algorithm based on the alternating optimization approach, which entails dividing the considered problem into three subproblems. Extensive simulations show that the proposed algorithm can achieve lower weighted AoI than five benchmark algorithms, while satisfying the resource constraints. Furthermore, simulation results demonstrate two interesting insights. First, the introduction of an aerial MEC server facilitates a flexible offloading design of the UEs which is critical to guaranteeing the freshness of computed results. Second, by optimizing the scheduling, the proposed design can unlock performance gains, especially in the resource-limited regime.

Multiple access can be realized by utilizing the spatial moments of deep fades, using fluid antennas. The interference immunity for fluid antenna multiple access (FAMA), nevertheless, comes with the requirement of a large number of ports at each user. To alleviate this, we study the synergy between opportunistic scheduling and FAMA. A large pool of users permits selection of favourable users for FAMA and decreases the outage probability at each selected user. Our objective is to characterize the benefits of opportunistic scheduling in FAMA. In particular, we derive the multiplexing gain of the opportunistic FAMA network in closed form and upper bound the required number of users in the pool to achieve a given multiplexing gain. Also, we find a lower bound on the required outage probability at each user for achieving a given network multiplexing gain, from which the advantage of opportunistic scheduling is illustrated. In addition, we investigate the rate of increase of the multiplexing gain with respect to the number of users in the pool, and derive a tight approximation to the multiplexing gain, expressed in closed form. As a key result of our analysis, we obtain an operating condition on the product of the number of users in the pool and the number of ports at each fluid antenna that ensures a high multiplexing gain. Numerical results demonstrate clear benefits of opportunistic scheduling in FAMA networks, and corroborate our analytical results.

The recent attempts to realize full-duplex (FD) communications in millimeter wave (mmWave) systems have garnered a significant amount of interest for its potential. In this paper, we present a cost-efficient design of mmWave FD systems, where the system dynamically reduces the RF beam codebook in a computationally efficient manner, so that it is comprised of the RF beams that will prevent the Rx receive chain from saturating due to the self-interference (SI). The analog beamformer will suppress the SI to the level that the residual SI can be completely removed with digital SI cancellation, allowing the digital beamformer to concentrate on the desired channel, free of the SI. To reduce the computation required for the proposed method, we propose two sufficient conditions that prevent the Rx side saturations, which are practically tight. Through performance evaluations conducted in realistically modeled mmWave FD scenarios, we demonstrate that the proposed design achieves comparable performance with the ideal FD and other benchmarks with significantly lower costs.

In this paper, we propose a novel wireless architecture, mounted on a high-altitude aerial platform, which is enabled by reconfigurable intelligent surface (RIS). By installing RIS on the aerial platform, rich line-of-sight and full-area coverage can be achieved, thereby, overcoming the limitations of the conventional terrestrial RIS. We consider a scenario where a sudden increase in traffic in an urban area triggers authorities to rapidly deploy unmanned-aerial vehicle base stations (UAV-BSs) to serve the ground users. In this scenario, since the direct backhaul link from the ground source can be blocked due to several obstacles from the urban area, we propose reflecting the backhaul signal using aerial-RIS so that it successfully reaches the UAV-BSs. We jointly optimize the placement and array-partition strategies of aerial-RIS and the phases of RIS elements, which leads to an increase in energy-efficiency of every UAV-BS. We show that the complexity of our algorithm can be bounded by the quadratic order, thus implying high computational efficiency. We verify the performance of the proposed algorithm via extensive numerical evaluations and show that our method achieves an outstanding performance in terms of energy-efficiency compared to benchmark schemes.

In this paper, we propose a novel wireless backhaul architecture, mounted on a high-altitude aerial platform, which is enabled by reconfigurable intelligent surface (RIS). We assume a sudden increase in traffic in an urban area, and to serve the ground users therein, authorities rapidly deploy unmanned-aerial-vehicle base-stations (UAV-BSs). In this scenario, since the direct backhaul link from the ground source can be blocked due to several obstacles from the urban area, we propose reflecting the backhaul signal using aerial-RIS and the phase of each RIS element, which leads to an increase in energy-efficiency ensuring the reliable backhaul link for every UAV-BS. We optimize the placement and array-partitioning strategy of aerial-RIS and the phase of each RIS element, which leads to an increase of energy-efficiency under guaranteeing the reliable backhaul link for every UAV-BS. We show that the complexity of our algorithm is upper-bounded by the quadratic order, thus implying high computational efficiency. We verify the performance of the proposed algorithm via extensive numerical evaluations and show that our method achieves an outstanding performance in terms of energy-efficiency compared to benchmark schemes.

In molecular communication via diffusion (MCvD), information is conveyed by diffusing molecules. MCvD enjoys high energy efficiency but suffers from low date rates due to the long tail of the channel impulse response. To this end, the multiple-input multiple-output (MIMO) technique has been introduced to MCvD. However, the inter-symbol interference (ISI) and inter-link interference (ILI) deteriorate the bit error rate (BER) performance of MIMO MCvD systems. In this paper, a novel molecular modulation scheme, called molecular type permutation shift keying in the spatio-temporal domain, is proposed for MIMO MCvD systems to improve the BER performance by combating ILI and ISI. A low-complexity detector without requiring channel impulse response information is proposed. Furthermore, a complementary coding scheme that can effectively reduce ILI is designed. Additionally, the BER upper bound is analyzed. Numerical simulations on BER corroborate the analysis and show that the proposed scheme is a promising multi-molecule modulation alternative, which outperforms the existing MIMO MCvD modulation schemes.

Communication between nanomachines is still an important topic in the construction of the Internet of Bio-Nano Things (IoBNT). Currently, molecular communication (MC) is expected to be a promising technology to realize IoBNT. To effectively serve the IoBNT composed of multiple nanomachine clusters, it is imperative to study multiple-access MC. In this article, based on the molecular division multiple access technology, we propose a novel multiuser MC system, where information molecules with different diffusion coefficients are first employed. Aiming at the user fairness in the considered system, we investigate the optimization of molecular resource allocation, including the assignment of the types of molecules and the number of molecules of a type. Specifically, three performance metrics are considered, namely, min-max fairness for error probability, max-min fairness for achievable rate, and weighted sum-rate maximization. Moreover, we propose two assignment strategies for types of molecules, i.e., best-to-best (BTB) and best-to-worst (BTW). Subsequently, for a two-user scenario, we analytically derive the optimal allocation for the number of molecules when types of molecules are fixed for all users. In contrast, for a three-user scenario, we prove that the BTB and BTW schemes with the optimal allocation for the number of molecules can provide the lower and upper bounds on system performance, respectively. Finally, numerical results show that the combination of BTW and the optimal allocation for the number of molecules yields better performance than the benchmarks.

Molecular communication (MC) is a bio-inspired communication paradigm, which lays the foundation for the Internet of Bio-NanoThings (IoBNT) in the medical field. As a high energy-efficient information transfer method, MC via diffusion (MCvD) is envisioned as a promising candidate for IoBNT but suffers from low date rates due to the long tail of the channel impulse response (CIR). To this end, the multiple-input–multiple-output (MIMO) technique has been introduced to MCvD. However, the intersymbol interference (ISI) and interlink interference (ILI) deteriorate the bit error rate (BER) performance of MIMO MCvD systems. In this article, molecular type permutation shift keying in the space domain (MTPSK-SD) and time-interleaved MTPSK-SD are proposed for MIMO MCvD systems to improve the BER performance by reducing ILI. The principle of MTPSK-SD is further generalized to the spatiotemporal domain, yielding three spatiotemporal modulation schemes, which can provide desirable BER performance without requiring any CIR information in the communication scenarios affected by different levels of ISI and ILI. Two low-complexity detectors are proposed to obtain different tradeoffs between anti-ILI and anti-ISI performance. Furthermore, a complementary coding scheme, which can effectively reduce the ILI under the considered symmetrical system topology, is designed and applied to all the proposed modulation schemes. Additionally, the BER upper bound is analyzed. Numerical simulations on BER corroborate the analysis and show that the proposed schemes are promising multimolecule modulation alternatives, which outperform the existing MIMO MCvD modulation schemes.

In this paper, we propose a novel nano-molecular communication system, including a nano receiver design and detection strategies. We show how machine intelligence can be incorporated into the practical implementation of nano communications. We introduce a testbed employing a biosensor chip as a receiver. The chip is made to be sufficiently small to be implanted under the human skin with no harm while detecting concentrations of glucose molecules over time. Molecules are released by a transmitter, to convey information through a thin pipe. For this configuration, the channel model is unknown, and the sensor dynamics can differ with according to the manufacturing process. Therefore, it is more desirable to find a universal strategy than using closed-form channel expressions so that it can be less sensitive to the channel and sensor variation. Learning-based approaches are likely to solve the problem. Therefore, in this paper, we suggest detection strategies with and without machine learning. We first describe our intuitions of nanomachine design from observations, and we show how the learning-based techniques can benefit the system by reducing the design burden and enhancing the accuracy of data detection. The study concludes by showing sample results of real data transmission.

For nano-scale communications, there must be cooperation and simultaneous communication between nano devices. To this end, in this paper, we investigate two-way (a.k.a. bi-directional) molecular communications between nano devices. If different types of molecules are used for the communication links, the two-way system eliminates the need to consider self-interference. However, in many systems, it is not feasible to use a different type of molecule for each communication link. Thus, we propose a two-way molecular communication system that uses a single type of molecule. We derive a channel model for this system and use it to analyze the proposed system's bit error rate, throughput, and self-interference. Moreover, we propose analog- and digital- self-interference cancellation techniques. The enhancement of link-level performance using these techniques is confirmed with both particle-based simulations and analytical results.

In this paper, we present the space shift keying based molecular communication (SSK-MC), where the space symbol is of interest. For micro-scale SSK-MC, the information is embedded into the index of a single activated transmitter nanomachine, while the index of the sensor is considered for the macro-scale SSK-MC. The cancellation of the strongest inter-link interference in multiple-input multiple-output (MIMO) based MC is available with the implementation of SSK-MC, in which only one transmitter is activated during each symbol duration. We derive the symbol error rate (SER) of SSK-MC, which shows a perfect match with its numerical simulations. Apart from the theoretical analysis, we further design an underwater prototype under the 4×4 MIMO-MC configuration for SSK-MC, demonstrating its SER performance with different detection algorithms.

This paper presents novel ultrareliable and low-latency communication (URLLC) techniques for URLLC services, such as Tactile Internet services. Among typical use cases of URLLC services are teleoperation, immersive virtual reality, cooperative automated driving, and so on. In such URLLC services, new kinds of traffic such as haptic information including kinesthetic information and tactile information need to be delivered in addition to high-quality video and audio traffic in traditional multimedia services. Furthermore, such a variety of traffic has various characteristics in terms of packet sizes and data rates with a variety of requirements of latency and reliability. Furthermore, some traffic may occur in a sporadic manner but requires reliable delivery of packets of medium to large sizes within a low latency, which is not supported by current state-of-the-art wireless communication systems and is very challenging for future wireless communication systems. Thus, to meet such a variety of tight traffic requirements in a wireless communication system, novel technologies from the physical layer to the network layer need to be devised. In this paper, some novel physical layer technologies such as waveform multiplexing, multiple-access scheme, channel code design, synchronization, and full-duplex transmission for spectrally efficient URLLC are introduced. In addition, a novel performance evaluation approach, which combines a ray-tracing tool and system-level simulation, is suggested for evaluating the performance of the proposed schemes. Simulation results show the feasibility of the proposed schemes providing realistic URLLC services in realistic geographical environments, which encourages further efforts to substantiate the proposed work.

In this paper, a new channel model is presented for molecular communications (MC), where a point source emitted by the transmitter undergoes three phases, with effect of convection dominating in the first two phases, whereas diffusion prevailing in the final phase. The point source obtains its initial velocity and passes through the nozzle of the nanomachine transmitter in the first phase, followed by a deceleration process in the second phase. The free diffusion model is considered in the third phase. Based on this channel model, the energy transfer issue for two-way MC system is also taken into account, in which one of the transceivers is assumed to have abundant information molecules from its ambient environment, whereas the other one obtains the information molecules by implementing the simultaneous molecular information and energy transfer (SMIET). Finally, analytical bit error rate (BER) expressions are validated by computer simulations. Our results suggest that the symbol duration and the SMIET order significantly influence the BER performance in our two-way MC system.

In this paper, we propose a low complexity direction of arrival (DoA) estimation algorithm that operates in radio frequency (RF) lens-embedded millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems. In mmWave system, to compute the beamforming weighting coefficients in real-time, precise DoA estimation with low complexity is essential. We first investigate the conventional DoA estimation algorithms in mmWave MIMO systems and evaluate its performance. Then we introduce the Power pROfile Based Estimation (PROBE) algorithm for DoA estimation that operates in the RF lens-embedded system. We elaborate on the computational complexity of each algorithm in terms of the number of multiplication operations. We show that the proposed algorithm has a significant performance advantage in terms of estimation accuracy over other conventional algorithms in a complexity-constrained environment.

Inspired by the eye diagram in classical radio frequency (RF) based communications, the MOL-Eye diagram is proposed for the performance evaluation of a molecular signal within the context of molecular communication. Utilizing various features of this diagram, three new metrics for the performance evaluation of a molecular signal, namely the maximum eye height, standard deviation of received molecules, and counting SNR (CSNR) are introduced. The applicability of these performance metrics in this domain is verified by comparing the performance of binary concentration shift keying (BCSK) and BCSK with consecutive power adjustment (BCSK-CPA) modulation techniques in a vessel-like environment with laminar flow. The results show that, in addition to classical performance metrics such as biterror rate and channel capacity, these performance metrics can also be used to show the advantage of an efficient modulation technique over a simpler one.

This paper concerns the feasibility of full-duplex large-scale multiple-input-multiple-output cellular systems. We first derive the analytic model of the ergodic achievable sum-rate for cell-boundary users. The model is derived by applying a simple linear filter, i.e., matched filter or zero-forcing filter, to the base-station (BS). In the analytic model, we consider large-scale fading, pilot contamination, transmitter noise, and receiver distortion. In addition, to solve the critical pilot overhead problem induced by self-interference channel estimation, we propose a pilot transmission scheme-the simultaneous pilot transmission (SPT)-and assess its performance, in terms of the ergodic sum-rate. In considering two multicell scenarios, cooperative and non-cooperative multicell systems, we obtain the ergodic achievable sum-rate by reflecting the characteristic of each scenarios, such as limited front-haul capacity and procedures of channel estimation. With all derived results, to investigate the feasibility, we observe the tradeoffs between the full- and half-duplex systems, between the SPT and conventional scheme, and between the two multicell scenarios with respect to various system parameters and environment. In the end, we confirm the tightness of our analytic model and advantages of full-duplex, SPT, and cooperation of BSs in our system model.

This paper presents spatial diversity techniques applied to multiple-input multiple-output (MIMO) diffusion-based molecular communications (DBMC). Two types of spatial coding techniques, namely Alamouti-type coding and repetition MIMO coding are suggested and analyzed. In addition, we consider receiver-side equal-gain combining, which is equivalent to maximum-ratio combining in symmetrical scenarios. For numerical analysis, the channel impulse responses of a symmetrical 2 × 2 MIMO-DBMC system are acquired by a trained artificial neural network. It is demonstrated that spatial diversity has the potential to improve the system performance and that repetition MIMO coding outperforms Alamouti-type coding.

In molecular communication, the heavy tail nature of molecular signals causes inter-symbol interference (ISI). Because of this, it is difficult to decrease symbol periods and achieve high data rate. As a probable solution for ISI mitigation, enzymes were proposed to be used since they are capable of degrading ISI molecules without deteriorating the molecular communication. While most prior work has assumed an infinite amount of enzymes deployed around the channel, from a resource perspective, it is more efficient to deploy a limited amount of enzymes at particular locations and structures. This paper considers carrying out such deployment at two structures-around the receiver (Rx) and/or the transmitter (Tx) site. For both of the deployment scenarios, channels with different system environment parameters, Tx-to-Rx distance, size of enzyme area, and symbol period, are compared with each other for analyzing an optimized system environment for ISI mitigation when a limited amount of enzymes are available. \n

This work studies the impact of time-synchronization in molecular timing (MT) channels by analyzing three different modulation techniques. The first requires transmitter-receiver synchronization and is based on modulating information on the release timing of information particles. The other two are asynchronous and are based on modulating information on the relative time between two consecutive releases of information particles using indistinguishable or distinguishable particles. All modulation schemes result in a system that relate the transmitted and the received signals through an additive noise, which follows a stable distribution. As the common notion of the variance of a signal is not suitable for defining the power of stable distributed signals (due to infinite variance), we derive an expression for the geometric power of a large class of stable distributions, and then use this result to characterize the geometric signal-to-noise ratio (G-SNR) for each of the modulation techniques. In addition, for binary communication, we derive the optimal detection rules for each modulation technique. Numerical evaluations indicate that the bit error rate (BER) is constant for a given G-SNR, and the performance gain obtained by using synchronized communication is significant. Yet, it is also shown that by using two distinguishable particles per bit instead of one, the BER of the asynchronous technique can approach that of the synchronous one.

This paper presents the performance analysis of the self-interference cancellation (SIC) methods in full-duplex large-scale multiple-input multiple-output systems. To mitigate self-interference (SI), we assume that the full duplex-base station (BS) uses SI-subtraction or spatial suppression. Analytical and numerical results confirm that the SI-subtraction outperforms the spatial suppression for SIC in a perfect channel estimation case. It is also concluded that the uplink and the overall ergodic rates performance of the spatial suppression are respectively better than those of the SI-subtraction in an imperfect channel estimation case under a given system constraint such as uplink and downlink sum rates.

In active transport molecular communication (ATMC), information particles are actively transported from a transmitter to a receiver using special proteins. Prior work has demonstrated that ATMC can be an attractive and viable solution for on-chip applications. The energy consumption of an ATMC system plays a central role in its design and engineering. In this work, an energy model is presented for ATMC and this model is used to provide guidelines for designing energy efficient systems. The channel capacity per unit energy is analyzed and maximized. It is shown that based on the size of the symbol set and the symbol duration, there is a vesicle size that maximizes the rate per unit energy. It is also demonstrated that maximizing the rate per unit energy yields very different system parameters compared to maximizing the rate only.

Due to the limited environments of radio frequency resources, the frequency assignment problem (FAP) is a crucial issue. We especially focus on the system that requires re-assignment of the frequencies often, so that the operation time is a limited resource as well as the radio resource. The FAP is closely related to the graph coloring problem, which is NP-hard problem. In this paper, we investigate a frequency assignment problem from a graph theory perspective for those limited system. We propose an effective algorithm based on the graph coloring theory and randomization incorporation into greedy heuristics. Main concern of the frequency assignments to clustered nodes is to reduce the number of used frequencies and the range. This paper has its another novelty in considering realistic interference constraints by considering net filter discrimination and measurement data from the field. Performance analysis is done by synthetic and measured data, and we observe a significant improvements by employing proposed algorithms in both cases.

In diffusion-based molecular communication, information transport is governed by diffusion through a fluid medium. The achievable data rates for these channels are very low compared to the radio-based communication system, since diffusion can be a slow process. To improve the data rate, a novel multiple-input multiple-output (MIMO) design for molecular communication is proposed that utilizes multiple molecular emitters at the transmitter and multiple molecular detectors at the receiver (in RF communication these all correspond to antennas). Using particle-based simulators, the channel's impulse response is obtained and mathematically modeled. These models are then used to determine interlink interference (ILI) and intersymbol interference (ISI). It is assumed that when the receiver has incomplete information regarding the system and the channel state, low complexity symbol detection methods are preferred since the receiver is small and simple. Thus, four detection algorithms are proposed-adaptive thresholding, practical zero forcing with channel models excluding/including the ILI and ISI, and Genie-aided zero forcing. The proposed algorithms are evaluated extensively using numerical and analytical evaluations.

In this work, we consider diffusion-based molecular communication timing channels. Three different timing channels are presented based on three different modulation techniques, i.e., i) modulation of the release timing of the information particles, ii) modulation on the time between two consecutive information particles of the same type, and iii) modulation on the time between two consecutive information particles of different types. We show that each channel can be represented as an additive noise channel, where the noise follows one of the subclasses of stable distributions. We provide expressions for the probability density function of the noise terms, and numerical evaluations for the probability density function and cumulative density function. We also show that the tails are longer than Gaussian distribution, as expected.

In this paper, we propose an end-to-end channel model for molecular communication systems with metal-oxide sensors. In particular, we focus on the recently developed table top molecular communication platform. The system is separated into two parts: the propagation and the sensor detection. There is derived, based on this, a more realistic end-to-end channel model. However, since some of the coefficients in the derived models are unknown, we collect a great deal of experimental data to estimate these coefficients and evaluate how they change with respect to the different system parameters. Finally, a noise model is derived for the system to complete an end-to-end system model for the tabletop platform.

In this paper, we propose a novel design for molecular communication in which both the transmitter and the receiver have, in a 3-dimensional environment, multiple bulges (in RF communication this corresponds to antenna). The proposed system consists of a fluid medium, information molecules, a transmitter, and a receiver. We simulate the system with a one-shot signal to obtain the channel's finite impulse response. We then incorporate this result within our mathematical analysis to determine interference. Molecular communication has a great need for low complexity, hence, the receiver may have incomplete information regarding the system and the channel state. Thus, for the cases of limited information set at the receiver, we propose three detection algorithms, namely adaptive thresholding, practical zero forcing, and Genie-aided zero forcing.

In molecular communication information is transferred with the use of molecules. Molecular multiple-input multiple- output (MIMO) system with drift (positive velocity) at macro- scale will be presented and the improvement against single- input single-output (SISO) molecular communication systems will be verified via our testbed. Until now it was unclear whether MIMO techniques, which are extensively used in modern radio frequency (RF) communications, could be applied to molecular communication. In the demonstration, using our MIMO testbed we will show that we can achieve nearly 1.7 times higher data rate than SISO molecular communication systems. Moreover, signal-to-inter-link-interfeence metric for one-shot signal will be depicted for a given symbol duration.

In this paper, we consider massive multiple-input-multiple-output systems for both downlink and uplink scenarios, where three radio units connected via one digital unit support multiple user equipments at the cell-boundary through the same radio resource, i.e., the same time-frequency slot. For downlink transmitter options, the study considers zero forcing (ZF) and maximum ratio transmission (MRT), whereas for uplink receiver options, it considers ZF and maximum ratio combining (MRC). For the sum rate of each of these, we derive simple closed-form formulas. In the simple but practically relevant case where uniform power is allocated to all downlink data streams, we observe that, for the downlink, vector normalization is better for ZF whereas matrix normalization is better for MRT. For a given antenna and user configuration, we also analytically derive the signal-to-noise-ratio level below which MRC should be used instead of ZF. Numerical simulations confirm our analytical results.

In this paper, we propose a realistic channel model for a table-top molecular communication platform that is capable for transmitting short text messages across a room. The observed system response for this experimental platform does not match the theoretical results in the literature. This is because many simplifying assumptions regarding the flow, the sensor, and environmental conditions, which were used in derivations of previous theoretical models do not hold in practice. Therefore, in this paper, based on experimental observations, theoretical models are modified to create more realistic channel models.

In this paper, we investigate information-theoretic scaling laws, independent from communication strategies, for point-to-point molecular communication, where it sends/receives information-encoded molecules between nanomachines. Since the Shannon capacity for this is still an open problem, we first derive an asymptotic order in a single coordinate, i.e., i) scaling time with constant number of molecules m and ii) scaling molecules with constant time t. For a single coordinate case, we show that the asymptotic scaling is logarithmic in either coordinate, i.e., Θ(log t) and Θ(log m), respectively. We also study asymptotic behavior of scaling in both time and molecules and show that, if molecules and time are proportional to each other, then the asymptotic scaling is linear, i.e., Θ(t) = Θ(m).

In this paper, we investigate the effectiveness of radio resource allocation with H.264/AVC scalable video coding (SVC) in massive multiple-input multiple-output (MIMO) systems. During transmission of SVC-encoded videos in error prone network environments, packet losses of a certain layer may cause a severe reduction of quality or even prevent 1n2 correct decoding of other layers, since SVC layers are highly interdependent. It is generally said that the most important information should be preserved with the highest priority from packet losses. To investigate the validity of such a scheme, we apply unequal radio power allocation for SVC layers in massive MIMO systems. We first show that the error rate changes drastically with respect to transmit power in massive MIMO systems. Then, we conduct simulations to analyze the overall quality in terms of both the traditional peak signal-to-noise ratio (PSNR) and the structural similarity (SSIM) considering perceived quality. Our results show that layer prioritization in massive MIMO systems is not always beneficial in terms of quality and the content characteristics need to be considered for effective power allocation.

In cognitive radio, current trend is to utilize geolocation database for TV bands. Considering more dynamic bands in terms of primary user activity, however, necessitates the use of Radio Environment Map (REM), which is an advanced knowledge base that stores live multidomain information on the entities in the network and the environment. In Cognitive Radio Networks (CRNs), mobile nodes that are capable of measuring the energy of the frequency bands are less capable compared to dedicated sensing nodes in the network. Therefore, deployment algorithm of the dedicated sensor nodes is of great importance and affects the constructed REM interference map quality. We propose a novel deployment algorithm for CRNs that considers user distribution probabilities. Numerical results confirm that the proposed deployment algorithm significantly improves the REM performance. The proposed algorithm is compared with random deployments and it is applied on Kriging and LIvE REM construction techniques.

Recently, a tabletop molecular communication platform has been developed for transmitting short text messages across a room. The end-to-end system impulse response for this platform does not follow previously published theoretical works because of imperfect receiver, transmitter, and turbulent flows. Moreover, it is observed that this platform resembles a nonlinear system, which makes the rich body of theoretical work that has been developed by communication engineers not applicable to this platform. In this work, we first introduce corrections to the previous theoretical models of the end-to-end system impulse response based on the observed data from experimentation. Using the corrected impulse response models, we then formulate the nonlinearity of the system as noise and show that through simplifying assumptions it can be represented as Gaussian noise. Through formulating the system's nonlinearity as the output a linear system corrupted by noise, the rich toolbox of mathematical models of communication systems, most of which are based on linearity assumption, can be applied to this platform.

In this paper, we propose three novel modulation techniques, i.e., concentration-based, molecular-type-based, and molecular-ratio-based, using isomers as messenger molecules for nano communication networks via diffusion. To evaluate achievable rate performance, we compare the proposed techniques with conventional insulin-based concepts under practical scenarios. Analytical and numerical results confirm that the proposed modulation techniques using isomers achieve higher data transmission rate performance than the insulin based concepts. We also investigate the tradeoff between messenger sizes and modulation orders and provide guidelines for selecting from among several possible candidates.

In this paper, we propose novel modulation techniques using isomers as messenger molecules for nano communication via diffusion. To evaluate achievable rate performance, we compare the proposed techniques with concentration-based and molecular-type-based methods. Analytical and numerical results confirm that the proposed modulation techniques achieve higher data transmission rate performance than conventional insulin based concepts.

In this paper, we propose jointly-optimized beamforming algorithms for cognitive networks to maximize the achievable rates, where primary and cognitive users share the same spectrum and are equipped with multiple antennas. We consider the transmission of a single information stream in both primary and secondary links. No coordination is required between the primary and cognitive users and the interference cancellation is done at the cognitive user. Specifically, the beamforming vectors of the cognitive link are designed to maximize the achievable rate under the condition that the interference both at the primary and cognitive receivers is completely nullified. Furthermore, it is proved that the achievable rate of a general N_t^{C}x 2 (N_t^{C} transmit antennas at the cognitive transmitter and 2 receive antennas at the cognitive receiver) cognitive multiple-input multiple-output (MIMO) link employing the optimal proposed beamformers (which completely nullifies the interference to and from the primary link while maximizing its own achievable rate) is the same as the rate of an interference-free (N_t^{C}-1)x 1 multiple-input single-output (MISO) link employing an optimal maximal ratio transmission beamforming vector. The sum rate performance of the proposed algorithms is evaluated by Monte Carlo simulations. The impact of the number of transmit and receive antennas on the proposed algorithm is also discussed.

In this paper, we propose jointly optimized linear transceiver algorithms called interference aware-coordinated beamforming (IA-CBF) for a two-cell system where each base station is equipped with multiple transmit antennas. To generalize IA-CBF to more than two-cell scenarios, a new beam-switching mechanism combined with IA-CBF is proposed. For a two-cell system, we derive a minimum-mean-square-error-type IA-CBF algorithm based on a lower bound on the achievable sum rate. We propose optimal (under an assumption of zero other-cell interference) and suboptimal transmit/receive beamforming vectors through zero-forcing IA-CBF algorithms. We also investigate the optimality of the proposed IA-CBF algorithms with respect to the number of receive antennas. Numerical results confirm that the proposed system with two transmit/receive antennas achieves the full degrees of freedom (a.k.a. multiplexing gain) of the two-cell multiple-input multiple-output channel while showing a better sum rate performance than competitive solutions such as non-cooperative eigen-beamforming and interference nulling. A three-dimensional ray tracing tool is also used to evaluate the proposed multi-cell IA-CBF algorithm.

Spectrum leasing via cooperation refers to the possibility for primary users to lease part of the spectral resources to secondary users in exchange for cooperation. This paper proposes a novel implementation of this concept in which secondary cooperation aims at improving the secrecy of the primary link. In particular, a secondary transmission with multiple antennas creates interference on both primary and eavesdropping receivers but an appropriately designed beamformer may impair more the eavesdropper's reception and thus enhance primary secrecy. Design of the secondary beamformer that maximizes the primary secrecy rate while guaranteeing a minimal secondary rate is studied. It is proved that the problem can be solved in a domain that includes only two real numbers irrespective of the number of antennas. Numerical results show that the proposed spectrum leasing strategy increases the primary secrecy rate compared to the case of no spectrum leasing for a wide range of secondary minimum rate constraints.

The capacity of a wireless network could be considerably improved by employing directional antennas that are capable of illuminating multiple beams toward different directions. However, more beams from the same BS may lead to stronger inter-cell interference. In this paper, we consider coordinated beam scheduling schemes to mitigate the inter-cell interference. We first formulate this problem as a combinatorial optimization problem. We then reveal that the complexity of this problem hinges upon a single scalar termed as degree of constraint (DoC), which is related to the degree of conflict a beam is subject to. If the DoC is at least three, the general beam scheduling problem is NP-hard. If DoC is smaller than three, which corresponds to a relevant subclass of the beam scheduling problem arising in practice, this problem can be solved in polynomial time. We propose an optimal beam scheduling algorithm based on the auction method to this particular subclass of problem. This algorithm is of low complexity and is well suited for distributed implementations. We then extend the auction algorithm to solve the general multi-cell beam scheduling problem. The performance of the proposed algorithms is finally assessed through extensive simulation studies.

This paper investigates the theoretical limits of white space sensing in a cognitive radio (CR) network limited by channel correlation. In a log-normal shadowing channel, the received signal power is correlated based on the distance between the sensors and this makes sensing the presence of a signal difficult, even with several cooperative sensors. In the proposed system, each sensor uses the spectral covariance sensing (SCS) algorithm to detect the primary signal and then sends its decision statistic to the base station (BS). The BS, using the Neyman Pearson log-likelihood ratio test, makes the final decision. We analyze the probability of a false alarm (PFA) and compare it with that of the cooperative energy detector. We show that an asymptotic lower bound on the PFAis an order of magnitude lower than that of the energy detector. We also demonstrate improvements in the cooperation gain in terms of the effective number of independent sensors, and the required number of sensors for a given detection metric. The results of this paper show that cooperative SCS detection has far better white space sensing properties than cooperative energy detection in correlated channels.

We propose transceiver algorithms in cognitive radio networks where the cognitive users are equipped with multiple antennas. Prior work has focused on the design of precoding matrices to suppress interference to the primary receivers. This work considers designs of precoding and decoding matrices for spatial sensing to achieve two objectives: (i) to prevent interference to the primary receivers and (ii) to remove the interference, due to primary transmissions, at the secondary receiver. With single antenna primary terminals and two antenna cognitive terminals, a linear transceiver design has been introduced under a global channel state information (CSI) assumption . In this letter, multiple antenna primary and cognitive terminals and three different CSI scenarios depending upon the amount of CSI are studied: (i) local CSI, (ii) global CSI, and (iii) local CSI with side information. When local CSI is available, we leverage prior work and employ the projected-channel singular value decomposition (P-SVD). In the global CSI scenario, we propose a joint transmitter-receiver design under the assumption of full CSI of all the users at the secondary transceiver. To reduce the feedback overhead, we also propose a new iterative algorithm that exploits only local CSI with side information. In this algorithm, the secondary transmitter and receiver iteratively update precoding and decoding matrices based on the local CSI and side information (precoding/decoding matrices at the previous iteration step) to maximize the rate of the secondary link while maintaining the zero-interference constraint. Convergence is established in the special case of single stream beamforming. Numerical results confirm that the proposed joint design and the iterative algorithm show better achievable rate performance than the P-SVD technique at the expense, respectively, of CSI knowledge and side information.

The asymptotic optimality of a jointly optimized linear multiuser beamforming system with two transmit and many receive antennas for the fading multiple-input multiple-output (MIMO) Gaussian broadcast channel is investigated. We derive the asymptotic sum capacity of the independent and identically-distributed (i.i.d.) MIMO broadcast channel and the asymptotic sum rate of a linear multiuser beamforming system with respect to the number of receive antennas per user, where two users are served with a zero inter-user interference constraint. Based on this result, we show that linear multiuser beamforming with many receive antennas can asymptotically achieve the sum capacity of the two-user i.i.d. MIMO broadcast channel even without opportunistic scheduling gain. Numerical results confirm that as the number of receive antennas increases the sum rate of linear multiuser beamforming system converges to the sum capacity.

In this paper, we propose a mechanism to switch between multiple-input multiple-output (MIMO) with two transmit antennas and single-input multiple-output (SIMO) to conserve mobile terminals' energy. We focus on saving uplink RF transmission energy of mobile terminals in cellular systems supporting best effort traffic. The key idea is to judiciously slow down transmission rates when a base station is underutilized. We show that there exists a crossover point on the transmission rate below which SIMO consumes less power than MIMO when circuit power is included. The crossover point is an increasing function of the circuit power, the number of receive antennas and channel correlation, all of which increase the potential energy savings resulting from mode switching. We propose an adaptive mode switching algorithm combined with rate selection to maintain a user's target throughput while achieving energy efficiency. Extensive flow-level simulations under dynamic loads confirm that the proposed technique can reduce the transmission energy by more than 50% and enables an effective tradeoff between file transfer delay and energy conservation.

This paper proposes non-iterative coordinated beamforming algorithms for a multiuser MIMO (multiple input multiple output) system with multiple antennas at the transmitter and multiple users, each with multiple receive antennas. The transmitter uses linear beamforming to convey information to each user, while each receiver uses a quantized combining vector, sent from the transmitter via a low-rate feedforward control channel. Two different algorithms for optimizing transmit beamformers and receive combining vectors are proposed: a joint optimization and a greedy search. Simulations show that the proposed methods using quantized codebooks approach the sum capacity of the MIMO broadcast channel.

In this paper, we propose a new joint optimization of linear transmit beamforming and receive combining vectors for the multiple-input multiple-output (MIMO) broadcast channel. We consider the transmission of a single information stream to two users with two or more receive antennas. Unlike past work in which iterative computation is required to design the beamformers, we derive specific formulations for the transmit beamformers for two active users via a power iteration and a generalized eigen analysis. To enable practical implementation, a new limited feedback algorithm is proposed that exploits the structure of the algorithm to avoid full channel quantization. The feedback overhead of the proposed algorithm is independent of the number of receive antennas. Monte Carlo simulations are used to evaluate the bit error rate and the sum rate performances of the proposed algorithm. Simulation results show that the proposed method performs close to the sum capacity of the MIMO broadcast channel even with limited feedback.

Precoding with block diagonalization (BD) is an attractive technique for approaching the sum capacity in the multiuser multiple-input multiple-output (MIMO) broadcast channel. Unfortunately, BD requires either global channel state information at every receiver or an additional training phase, which demands additional control overhead and additional system planning. In this paper we propose a new multiuser MIMO algorithm that combines BD with vector perturbation (VP). The proposed algorithm avoids the second training phase, reduces each user is receiver complexity thanks to pre-equalization with VP at the transmitter, and has comparable diversity performance to BD with maximum likelihood decoding algorithm. A bound on the achievable sum rate for the proposed technique is derived and used to show that BD with VP approaches the achievable sum rate of BD with water-filling. Numerical simulations confirm that the proposed technique provides better bit error rate and diversity performance than BD with a zero-forcing receiver as well as BD with zero-forcing precoding.

This paper considers the joint optimization of the transmitter beamforming filters and receiver combining filters for the multiple-input multiple-output (MIMO) broadcast channel. A low-complexity iterative algorithm to compute the filters is proposed and then a closed-form expression of the filters for the downlink with two transmit antennas is derived, avoiding the need for iterative computation in this case. To enable practical implementation, a new limited feedback algorithm is proposed that exploits the channel structure in the closed form solution, and is independent of the number of receive antennas. Performance is evaluated by Monte Carlo simulations as a function of the amount of feedback. With two receive antennas, the proposed method performs close to the sum capacity of the MIMO broadcast channel, without the need for multiuser diversity and with only limited feedback.

This paper presents a derivation of an expression for the ergodic capacity of spatial multiplexing multiple-input-multiple-output (MIMO) systems with zero-forcing (ZF) receivers and independent substream detection. Assuming that the channel is unknown at the transmitter but known at the receiver, the ergodic capacity is formulated as a function of log-normal shadowing and Rayleigh fading. Gauss-Hermite quadrature integration is used to approximate the ergodic capacity expression in a concise form. The proposed analytical approach allows investigation of the effects of the shadowing standard deviation and the transmit correlation. Numerical and simulation results confirm that under various composite channel scenarios, the analytical results match well with the simulation results.

Preceding with block diagonalization is an attractive approach for approaching sum capacity in multiuser MIMO (multiple input multiple output) broadcast channels. This method though requires either global channel state information at every receiver or an additional training phase, which requires additional system planning. This paper proposes a lattice based multi-user precoder that uses block diagonalization combined with pre-equalization and perturbation for the multiuser MIMO broadcast channel. Achievable rates are computed and used to show that the proposed technique approaches the capacity with block diagonalization and water-filling but does not require the additional channel state information at the receiver. Monte Carlo simulations under the case of equal power allocation show that the proposed method provides better diversity and BER (bit error rate) performance than block diagonalization with a zero-forcing receiver.

In this paper, we investigate a multiple input multiple output (MIMO) multiuser relay channel, where a source with multiple antennas sends data to multiple users via a relay with multiple antennas. The relay applies linear processing to the received signal and forwards the processed signal to multiple users. In our system model, the direct links from the source to the users are neglected. We propose algorithms to compute achievable sum rates of this system based on dirty paper coding. An achievable sum rate defines a sum rate that can be achieved in the MIMO multiuser relay channel with zero error probability for any user, hence it is also a lower bound of the capacity of this channel. These algorithms also produce coefficients of the precoder at the source node and the coefficients of the linear processing unit at the relay. Simulations show that the proposed system architecture and algorithms achieve sum rate performance that is close to the derived performance upper bound

We propose a multichannel feedback protocol to enable local scheduling with channel state information for wireless networks with orthogonal frequency division multiplexing (OFDM). In our proposed protocol, the frequency subcarrier domain is shared by multiple control channels, on which request-to-send (RTS) and clear-to-send (CTS) are exchanged. These control channels are created using random spreading signatures. Channel state information of the simultaneous transmissions, which defines the gains of these channels, is exchanged on these control channels and channel state information tables that contain the channel information of both the desired link and the interfering links are created at the transmit nodes, then scheduling decisions are made based on the channel information tables. We show that the proposed protocol improves the network throughput compared to IEEE 802.11 style protocols for a wireless local area network topology

System level performance of OFDM/ OFDMA is evaluated for various system parameters, such as frequency reuse factor, scheduling algorithm and sub-channel structures. Specifically, the generalized two step sub-channel/power allocation algorithm, with either aggregated or distributed sub-channel structure, is first extended to provide proportional fairness. Then, by simulation, the sector throughput is evaluated in practical OFDMA based cellular system environment with frequency reuse of 1 and 3, respectively.

In this paper we propose and analyze an adaptive spatial modulation scheme for MIMO-OFDM systems which adaptively and optimally selects one of the following transmission modes: diversity, spatial multiplexing and a hybrid combination of these two modes. Two criterias are used for mode selection, namely, the minimum Euclidean distance and a simple threshold based stochastic method exploiting channel quality estimations. We consider practically implementable antenna configuration with four transmit antennas and two or four receive antennas. Simulation results show that considerable BER performance gains can be obtained by the adaptive spatial modulation system, as compared with systems based on fixed modulation schemes.