Recent research on Unmanned Aerial Vehicle (UAV) localization has focused on enhancing accuracy by incorporating angle information. However, this requires array antennas on UAVs, which increases costs and reduces flight endurance. To address this, we propose a distributed, collaborative localization method for UAV swarms, integrating Reconfigurable Intelligent Surface(RIS) with Multidimensional Scaling (MDS). This method relies solely on inter-UAV ranging and a subset of UAVs equipped with Global Positioning System (GPS). The proposed algorithm improves localization by leveraging inter-UAV ranging, RIS, and GPS-equipped UAVs. After swarm patching, an improved node selection algorithm, based on the Nyström approximation, directs RIS-equipped UAVs to designated areas. The UAV distance matrix is refined using the 3σ Gaussian principle and Rice distribution criteria, while MDS computes relative coordinates within each patch. Simulations show that the algorithm reduces localization error, achieving over 10% improvement using multiple RIS-equipped UAVs. Furthermore, the proposed algorithm reduces GPS-equipped UAV requirements by deploying passive RIS-equipped UAVs, extending swarm operation duration and enhancing localization accuracy.

Fluid antenna-enabled multiple-input multiple-output (FA-MIMO) systems hold significant application potential; however, they also introduce substantial costs in channel state information (CSI) acquisition. In this paper, we propose a novel FA-assisted rectangular differential index modulation (FA-RDIM) scheme for MIMO systems, aiming to achieve high spectral efficiency while addressing the problem of the increased CSI acquisition cost. The transmitted information is mapped to modulation symbols and cyclic shifts of FA pattern indices. A multi-stage detection method is introduced, which reduces computational complexity by identifying the most likely candidates for FA pattern indices. We derive a closed-form expression for the bit error rate (BER) theoretical performance considering error propagation, and present an optimization algorithm based on rank and determinant criterion (RDC), Hamming distance (HD), and gradient descent (GD) to optimize the FA pattern vector set. Simulation results demonstrate that the proposed scheme exhibits a minimal performance loss compared to conventional coherent modulation schemes under static channel conditions, while offering a performance advantage in time-varying channels with outdated CSI.

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.

This paper proposes a unified sparse transmission framework for coded modulation-based unsourced random access (URA) systems, referred to as sparse linear precoded URA (SLP-URA). The proposed design generalizes and unifies a variety of existing URA schemes including random spreading, sparse interleave division multiple access (IDMA), and on-off division multiple access (ODMA) as special cases of a broader design space characterized by tunable sparsity structures. To support this flexible framework, we derive a consistent generalized log-likelihood ratio (LLR)-based active user detection (AUD) method, and further propose message passing (MP)-based AUD and multi-user detection (MUD) algorithms that accommodate arbitrary sparsity patterns. An approximate low-complexity implementation is also introduced to reduce computational burden. Through rigorous symbol-level signal-to-interference-plus-noise ratio (SINR) analysis, we establish that increasing the column weight of the SLP matrix reduces SINR variance and outage probability, providing rigorous theoretical grounding for the performance gains. Simulations demonstrate that the proposed SLP-URA framework consistently achieves improved performance over conventional ODMA schemes, even when the latter are equipped with enhanced detection algorithms. These results suggest that SLP-URA provides a robust foundation for future URA system design and optimization.

In this paper, we investigate a reconfigurable intelligent surface (RIS)-aided integrated sensing and communication (ISAC) system, where the RIS is modeled using multiport network theory based on theZ-parameter representation. Unlike conventional RIS models based on reflection-coefficient matrices, we characterize RIS reconfigurability with tunable circuit impedances, thus capturing the electromagnetic mutual coupling (MC) effects among RIS elements. Specifically, we jointly optimize the transmit covariance matrix at the base station (BS) and the RIS tunable load impedance matrix to maximize the radar signal-to-noise ratio (SNR). The power budget at the BS and the quality of service (QoS) constraints for the communication users are also satisfied. To highlight the impact of electromagnetic MC on the system, both the no-MC and the MC-aware cases are considered. For the non-convex MC-aware problem, we propose an alternating optimization (AO) algorithm that integrates Neumann series approximation, semidefinite relaxation (SDR), and sequential rank-one constraint relaxation (SROCR) techniques. As a simplified form of the MC-aware case, the no-MC case can be considered as a sub-algorithm embedded in the proposed solution framework. Numerical results show that electromagnetic MC significantly affects the system performance, especially under sub-wavelength spacing.

Optical reconfigurable intelligent reflecting surface (ORIS), as a new type of programmable optical communication equipment, can reconstruct the optical channel environment and expand the application scenarios of optical wireless communications (OWC), which have attracted widespread attention. However, with the increasing demand for OWC scale in B5G and even 6G scenarios, the coverage range of a single ORIS (below 300 m) can no longer meet communication needs. Cascaded ORISs’ technology has become an inevitable trend in the development of ORIS, which has not been deeply studied in existing work. To address the above challenges, in this paper we set up cascaded ORISs in a typical long-distance free space optcis (FSO) scenario and conduct a detailed analysis of their channels and performance. This system employs cascaded ORISs to extend ORIS coverage and expand the overall scale of the FSO system, thereby enabling the FSO link to evolve from a traditional point-to-point configuration to a broader, spatially distributed communication layout. The physical model of multiple ORISs’ cascades and the effect on their communication performance are analyzed in detail. Meanwhile, the closed-form expressions of the bit error rate (BER) and outage probability of the multi-hop cascaded ORISs-assisted FSO system are derived and verified by simulations. Based on the theoretical results and simulation results, the influence of each parameter on the system performance is carefully analyzed, which provides guidance for the design of the actual system.

The vision of 6G communication demands autonomous and resilient networking in environments without fixed infrastructure. Yet most multi-agent reinforcement learning (MARL) approaches focus on isolated stages—exploration, relay formation, or access—under static deployments and centralized control, limiting adaptability. We propose the AI Agent Access (A3) Network, a unified, embodied intelligence-driven framework that transforms multi-agent networking into a dynamic, decentralized, and end-to-end system. Unlike prior schemes, the A3Network integrates exploration, target user access, and backhaul maintenance within a single learning process, while supporting on-demand agent addition during runtime. Its decentralized policies ensure that even a single agent can operate independently with limited observations, while coordinated agents achieve scalable, communication-optimized coverage. By embedding link-level communication metrics into actor–critic learning, the A3Network couples topology formation with robust decision-making. Numerical simulations demonstrate that the A3Network not only balances exploration and communication efficiency but also delivers system-level adaptability absent in existing MARL frameworks, offering a new paradigm for 6G multi-agent networks.

In this paper, a two-layer game based joint time and power allocation method for an underlay Energy Harvesting Cognitive Radio (EH-CR) network is proposed. The method first models the interplay between the Primary User (PU) and the Secondary Users (SUs) as a Stackelberg game and then models the interplay among the SUs as a Supermodel game in the underlay EH-CR network. Later, a coefficient for evaluating fair ness is introduced in order to promote fairness among the SUs. Subsequently, the utility function of the primary network and the utility function of the secondary network are defined based on their individual profits. By maximizing the secondary network's utility function, the Supermodel game's Nash Equilibrium (NE) solution is achieved. Then, by substituting the NE solution of the Supermodel game into the utility function of the primary network and then maximizing the utility function of the primary network, the NE solution of the Stackelberg game is obtained. Finally, a deterministic strategy can be obtained, which is the time coefficient of equalized spectrum sensing and the equalized power allocation scheme instead of a probabilistic strategy. Simulation outcomes demonstrate that, under the condition of maintaining the communication quality of the PU, the PU's revenue when PH0 = 0.8 can be improved by 18.2% and when PH0 = 0.6 can be improved by 13.3% compared with the conventional method.

In this paper, a precoding vector based index modulation (PV-IM) scheme is proposed. In the proposed scheme, precoding vectors are designed to maximize the Euclidean distance between precoded patterns, and their indices are used for information transmission. The proposed PV-IM scheme achieves superior performance than the constellation modulation scheme in high-rate scenarios, and overcomes the shortcomings of spatial index modulation (SIM) schemes. In addition, for the reconfigurable intelligent surface (RIS)-assisted multi-user (MU) scenario, a RIS-MU-PV-IM scheme is proposed based on the PV-IM scheme, where inter-user interference (IUI) is eliminated. The achievable rate, bit error rate (BER), and computational complexity of the proposed two schemes are analyzed. The impacts of channel estimation errors to the proposed two schemes are discussed. Extensive simulations are conducted to demonstrate the superiority of the proposed schemes.

Optical reconfigurable intelligent reflecting surface (ORIS), as a new type of programmable optical communication device, can deflect, split, and shape incident light, thereby intelligently reconstructing the optical wireless communication (OWC) environment. The application of ORIS has effectively expanded the application scenarios of OWC and improved system performance and robustness, which has attracted widespread attention. However, the existing ORIS is limited by its basic structure, and has problems such as low stability and poor signal quality in long-distance transmission. Aiming at the pain points of the existing ORIS, this paper proposes an optical reconfigurable distributed sources and reflecting surface (ORDSS) for distributed OWC. By arranging distributed sources in partial areas of the metasurface, users’ performance optimization is achieved based on power compensation and beam adaptive optimization algorithms. In this work, we performed physical modeling of an ORDSS-assisted OWC system, incorporating factors such as the characteristics and spatial distribution of ORDSS sources and reflection units, surface jitter of the ORDSS, pointing errors, and atmospheric attenuation. We derived the expression for the receiving performance of the ORDSS-assisted OWC system and compared it with that of conventional ORIS-assisted OWC systems to evaluate the performance gains brought by ORDSS. Building on the physical model and performance analysis, we investigated an adaptive power control algorithm for the distributed sources in ORDSS. In addition, we explored power compensation mechanisms based on ORDSS distributed sources to enhance system performance in mobile communication scenarios.

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.

In this work, the unsourced random access problem over a quasi-static fading channel is discussed with a massive number of antennas at the receiver. A novel transmission protocol called the Less-Is-More (LIM) strategy is proposed. Only a small portion of the data needs to be encoded and transmitted into the channel, while the remaining data bits are embedded into the patterns of sparse frames and implicitly transmitted without requiring power assumptions. Specifically, LIM divides the entire blocklength into several chunks and split the transmission in each chunk into two phases: one for pilot signals and the other for sparsely scattered modulated constellation symbols. Each user selects a single chunk to send its frame into the channel, and the frame consists of multiple scattering patterns determined by a large portion of the message bits. Consequently, the proposed scheme can implement strong channel coding with a relatively long code length, sparse frames with tolerable multi-user interference, and a high level of constellation power. Furthermore, we analyze the performance of the proposed scheme using normal approximations and provide a detailed complexity analysis of the major decoder component. Numerical results indicate that the proposed scheme performs competitively compared to state-of-the-art methods, achieving near-theoretical bound performance.

This paper investigates fluid antenna systems (FASs) subject to finite-blocklength (FBL) constraints, motivated by the strict reliability-latency and ultra-massive connectivity requirements of future wireless networks. While FAS performance has been widely studied in the asymptotic regime, its behavior under FBL remains largely unexplored. Our objective is to develop a unified set of analytical tools for evaluating FASs under FBL that remains applicable across different spatial-correlation models. First, to establish accurate benchmarks for non-orthogonal finite-length user signature design, we characterize both the average and the worst-case correlation coefficients via extreme value theory (EVT) and derive closed-form predictions of the achievable correlation levels. Second, taking block error rate (BLER) as the fundamental FBL metric, we study joint detection and decoding in FAS-assisted links and derive a closed-form BLER expression that is universally applicable across channel models. Additionally, we revisit outage probability (OP) in the FBL regime and obtain tractable OP characterizations for both FASs and conventional multiple fixed-position antenna (FPA) systems. In order to reduce the computational burden for multi-fold integrals in correlated fading models, we further propose a Taylor-expansion-assisted mean value theorem for integrals (MVTI), thus enabling efficient performance evaluation with marginal accuracy loss. Numerical results validate the analysis and reveal that even single-antenna FASs can have superior spatial diversity relative to conventional multi-FPA systems. Moreover, under both FBL and interference-limited environments, FASs provide improved energy, spectral, and hardware efficiencies, hence highlighting FAS as a promising enabler for next-generation wireless networks.

Angle of arrival (AOA) estimation with tilted photodiodes (PDs) is fast, accurate and low-cost for visible light positioning (VLP) systems. However, the most popular component assembly technology, surface-mount technology (SMT), is not able to orient the PDs towards their desired tilting angles, making it challenging to manufacture the AOA estimators massively and efficiently. In this work, we propose a novel AOA estimator based on an array of horizontally placed PDs with different lenses, where the incident light angles can be estimated by the ratios of the received optical signal intensities. Such an AOA estimator can be manufactured with the SMT lines in factories and we derive closed-form expressions for the involved AOA estimation algorithm. The proposed scheme provides an SMTcompatible solution for PD-based AOA estimation, and lays theoretical foundations for the error analysis.

This paper propose a novel three-dimensional continuous fluid antenna system-enabled index modulation (3D-CFAS-IM) scheme, where the fluid antenna can move continuously within a 3D region to fully exploit spatial resources. The optimal positions of the fluid antenna are determined by maximizing the minimum Euclidean distance between different receive patterns, and their indices are used to implement IM. The achievable rate, bit error rate (BER) performance, and computational complexity of the proposed scheme are analyzed. Simulation results demonstrate that the proposed scheme achieves better BER performance and a higher achievable rate compared to existing schemes, with acceptable complexity. Additionally, the impact of channel estimation errors on system performance is investigated.

In existing uplink multi-user multiple-input multiple-output (MU-MIMO) system designs, centralized transceiver has better performance due to their full utilization of channel state information (CSI). However, it entails prohibitive implementation costs due to excessive computational complexity and feedback overhead. In this paper, we propose a distributed precoding algorithm for uplink MU-MIMO systems to address the limitation. Specifically, user equipments (UEs) only share statistical CSI, while the base station (BS) is assumed to have access to full real-time CSI to align with practical systems. Moreover, unlike previous distributed precoding scheme, we take into account the indispensable multi-user interference (MUI). For each UE, we formulate a minimum mean square error (MMSE) optimization framework with terminal power constraint for bit error rate (BER) reduction. By judiciously integrating real-time CSI and statistical information, the algorithm is divided into two categories: purely distributed (PD) scheme and combined distributed (CD) scheme, achieving complexity and link overhead reduction. Simulations demonstrate the effectiveness of the proposed scheme in achieving favorable BER performance.

In this paper, we propose an integrated sensing and communication (ISAC) target recognition method based on radar cross section (RCS) profile estimation. Compared with traditional recognition methods such as inverse synthetic aperture radar (ISAR), the estimation of target’s RCS features does not require a large signal bandwidth to provide high resolution nor complex signal processing methods. We first study the variation of RCS with the observation angle during the target’s movement. Then, we use Periodogram algorithm for position and RCS estimation based on OFDM echo signal. After that, we create the POFACETS-based RCS database. The result of target recognition is obtained by matching the sampling RCS sequence with the database. Finally, we choose correlation coefficient as the criterion to reduce the complexity of data matching. Simulation results show that an accuracy of approximately 98.7% is achieved in target recognition.

The heading direction of an unmanned aerial vehicle (UAV) is determined by the yaw angle sensors, which are traditionally known as the inertial measurement units (IMU). IMUs have the problem of error accumulation when estimating the yaw angle, and thus magnetometers and data fusion algorithms can be embedded to counter the accumulated error. However, when the magnetometer is absent or fails in a strong electromagnetic interference environment, the estimated yaw angle will drift, and this could cause UAV collision or crash especially when the speed is high. This paper proposes to use photodiode arrays as angle of arrival (AOA) estimators to aid yaw angle estimation. By fusing the IMU data with the AOA data using the maximum likelihood estimation (MLE), the accumulated yaw angle error can be corrected. Simulations and experiments verify that the yaw angle samples obtained by the MLE are more accurate for a long period of time.

In this work, we discuss the problem of unsourced random access (URA) over a Gaussian multiple access channel (GMAC). To address the challenges posed by emerging massive machine-type connectivity, URA reframes multiple access as a coding-theoretic problem. The sparse code-oriented schemes are highly valued because they are widely used in existing protocols, making their implementation require only minimal changes to current networks. However, drawbacks such as the heavy reliance on extrinsic feedback from powerful channel codes and the lack of transmission robustness pose obstacles to the development of sparse codes. To address these drawbacks, a novel sparse code structure based on a universally applicable power division strategy is proposed. Comprehensive numerical results validate the effectiveness of the proposed scheme. Specifically, by employing the proposed power division method, which is derived analytically and does not require extensive simulations, a performance improvement of approximately 2.8 dB is achieved compared to schemes with identical channel code setups.

This paper addresses the unsourced/uncoordinated random access problem in an integrated sensing and communications (ISAC) system, with a focus on uplink multiple access code design. Recent theoretical advancements highlight that an ISAC system will be overwhelmed by the increasing number of active devices, driven by the growth of massive machine-type communication (mMTC). To meet the demands of future mMTC network, fundamental solutions are required that ensure robust capacity while maintaining favorable energy and spectral efficiency. One promising approach to support emerging massive connectivity is the development of systems based on the unsourced ISAC (UNISAC) framework. This paper proposes a spectrum-sharing compressive sensing-based UNISAC (SSCS-UNISAC) and offers insights into the practical design of UNISAC multiple access codes. In this framework, both communication signals (data transmission) and sensing signals (e.g., radar echoes) overlap within finite channel uses and are transmitted via the proposed UNISAC protocol. The proposed decoder exhibits robust performance, providing 20-30 dB capacity gains compared to conventional protocols such as TDMA and ALOHA. Numerical results validate the promising performance of the proposed scheme.

This work explores the channel estimation (CE) problem in uplink transmission for unsourced random access (URA) with a fluid antenna receiver. The additional spatial diversity in a fluid antenna system (FAS) addresses the needs of URA design in multiple-input and multiple-output (MIMO) systems. We present two CE strategies based on the activation of different FAS ports, namely alternate ports and partial ports CE. Both strategies facilitate the estimation of channel coefficients and angles of arrival (AoAs). Additionally, we discuss how to refine channel estimation by leveraging the sparsity of finite scatterers. Specifically, the proposed partial ports CE strategy is implemented using a regularized estimator, and we optimize the estimator's parameter to achieve the desired AoA precision and refinement. Extensive numerical results demonstrate the feasibility of the proposed strategies, and a comparison with a conventional receiver using half-wavelength antennas highlights the promising future of integrating URA and FAS.

Integrated optical wireless communication and sensing scheme for the hovering civil unmanned aerial vehicles (UAVs) will play an important role in the future wireless networks. In this paper, an integrated optical mobile communication and sensing system with user mobility for hovering UAVs is proposed, which employs the power division multiplexing method to share the same beamsteering device for both communication and sensing. To support mobility, we consider new challenges including the selection of the proper beam waist, the power allocation between sensing and communication, and the adjustment time of spatial light modulator (SLM). The ergodic achievable rate (EAR) is investigated, and the optimization problem to achieve the maximum EAR is solved. Besides, the EARs for different user speeds and different adjustment time of SLM are analyzed by numerical results. Simulation results show that the EAR of the proposed system is higher than conventional schemes with fixed power allocation, and close to the free-space optical communication scheme in the ideal case.

This paper discusses the unsourced random access (URA) problem in quasi-static fading multi-input and multi-output (MIMO) channel. A novel less-is-more (LIM) strategy is firstly proposed where only a small portion of data with strong energy-per-unit is transmitted into channels while others are implicitly embedded into the transmission. The LIM encoder generates extreme transmission sparsity and strong denoising capability. Correspondingly, the proposed decoder is elaborated with complexity analyses. Numerical results demonstrate that the proposed LIM-URA design shows a capacity performance close to the recently established MIMO achievable bound compared with other state-of-the-arts under identical spectral efficiency.

ABSTRACT The robustness against timing offsets of filter bank multi‐carrier (FBMC) is appealing for grant‐free massive access scenarios that mainly adopt asynchronous transmissions. In this work, we propose a compressed sensing based algorithm for joint active user detection as well as timing offset and channel estimation in uplink communication under the combination of FBMC and grant‐free massive access systems, which is critical for subsequent decoding or other processes at receiver. The channel estimation part is based on generalized approximate massage passing (GAMP). The active user detection and timing offset estimation are based on loopy belief propagation (LBP) rules, where the expressions of message passing and belief distributions are derived. Besides, since the receiver may have no prior knowledge about some parameters such as noise variance and activity probability, we introduce the expectation maximization (EM) approach into the proposed algorithm. Moreover, we develop a preamble design method to improve the detection and estimation performance. Simulation results show that the proposed EM‐LBP‐GAMP algorithm can achieve satisfying performance in terms of missed activity detection probability, timing offset estimation error and normalized mean square error of channel estimation.

The sixth-generation (6G) mobile network and the Industrial Internet of Things (IIoT) demand high-speed, low-latency communication in diverse environments. Hybrid free-space optical (FSO)/radio frequency (RF) systems offer a promising solution by combining the strengths of both technologies. However, maintaining robustness under dynamic atmospheric conditions remains a challenge. To tackle this issue, we propose a feedback-free hybrid FSO/RF system that analyzes atmospheric conditions from camera-captured images and utilizes a deep learning network for real-time power allocation. By dynamically optimizing link utilization, it mitigates obstacles and weather fluctuations while eliminating feedback overhead, enhancing efficiency. Simulations show that our feedback-free system maintains superior bit error rate (BER) and outage performance while offering greater stability and adaptability to dynamic environments. Compared to state-of-the-art methods, it achieves more reliable performance under varying conditions. By leveraging camera-based perception instead of feedback links, it optimally adjusts FSO/RF utilization, providing a practical, scalable framework for next-generation IoT applications and ensuring reliable communication under varying conditions.

Light-emitting diode (LED) based visible light positioning (VLP) has been widely studied to provide high-accuracy and low-cost localization services. Existing single-LED VLP systems suffer from severe energy attenuation when the receiver is far from the LED. Even though multiple LEDs could be deployed, the fusion of their positioning information remains an open problem. In this paper, we propose a multi-LED indoor VLP system with LED signal selection or combining. The receiver contains several differently-oriented photodiodes (PDs) working cooperatively as an angle-of-arrival estimator, and each LED individually provides the receiver with a localization result. With these single-LED positioning results, we select the results with the minimum localization error, or combine the results according to their distributions. Closed-form positioning error expressions are derived for the proposed methods. Moreover, increasing the LED number is shown to reduce the positioning error, but the reduction is negligible when the LED number is sufficiently large. Finally, an experimental testbench is built to verify the feasibility of the proposed methods, and a millimeter-level accuracy has been achieved, which is better than that of the classic trilateration method.

Due to the wide bandwidth and large observation area, spaceborne synthetic aperture radar (SAR) is easily interfered by other electromagnetic signals, namely radio frequency interference (RFI), which can severely degrade SAR image quality and submerge useful information. Classic parametric and non-parametric methods are used to suppress RFI as much as possible without considering the useful information. To protect the real reflected signals, semi-parametric methods, based on low-rank and sparse recovery, are proposed to mitigate RFI, but they suffer from the singular-value over-shrinking problem when RFI is not strictly low-rank, resulting in interference residues in the recovered scene. Hence, in this paper, a robust interference self-cancellation (RISC) method is proposed to protect raw ground scenes from polluted data with better extraction accuracy of RFI. The proposed model can adaptively fit in different scenes and backgrounds by using adjacent homologous interference (HI) subregions instead of the low-rank constraints, thus better protecting SAR scenes and enhancing its robustness. Based on the alternating direction method of multipliers (ADMM), we design two different solvers for the proposed optimization model, and both are tested on four different scenes of Sentinel-1 measured data. All experiments demonstrate that the proposed method has excellent performance in RFI mitigation and SAR image recovery.

As an advanced remote sensing technology, synthetic aperture radar (SAR) generates high-resolution images by transmitting continuous electromagnetic waves toward the target area. SAR has played a pivotal role in both contemporary research and practical applications. This underscores the importance of maintaining imaging integrity. However, the performance of SAR systems is severely affected by the increasingly prevalent radio frequency interference (RFI). RFI not only degrades the quality of SAR images but also hinders the accurate interpretation of SAR data. The rapid development and widespread use of modern electromagnetic devices have led to a diversification of interference types, resulting in complex mixed-mode interference. Traditional interference mitigation techniques struggle to effectively alleviate these issues. Moreover, varying terrains add significant difficulty to mitigating interferences, often resulting in residual interference in processed images and the loss of substantial scene information. To tackle these challenges, this article proposes a novel interference mitigation method called the group-parametric method. Unlike previous semiparametric methods, the group-parametric method refines both the interference and target models and achieves more effective interference mitigation and scene preservation by applying distinct regularizations to the refined models. Based on the new model, we have designed a structured trifactorization (STF) algorithm across frequency and time domains, which achieves data recovery through regularizations of low-rank and sparsity applied to the interference. Experimental verification with Level-1 data from LuTan-1 (LT-1) and Sentinel-1 confirms the effectiveness and superiority of our proposed model and method.

Existing works mainly rely on the far-field planar-wave-based channel model to assess the performance of reconfigurable intelligent surface (RIS)-enabled wireless communication systems. However, when the transmitter and receiver are in near-field ranges, the investigation of the channel statistics based on the planar-wave-based model will result in relatively low computing accuracy. To tackle this challenge, we initially develop an analytical framework for sub-array partitioning. This framework divides the large-scale RIS array into multiple sub-arrays, effectively reducing modeling complexity while maintaining acceptable accuracy. Then, we develop a beam domain channel model based on the proposed sub-array partition framework for large-scale RIS-enabled unmanned aerial vehicle (UAV)-to-vehicle communication systems, which can be used to efficiently capture the sparse features of RIS-enabled UAV-to-vehicle channels in both near-field and far-field ranges. Furthermore, some important propagation characteristics of the proposed channel model, including the spatial cross-correlation functions (CCFs), temporal auto-correlation functions (ACFs), frequency correlation functions (FCFs), channel capacities, and path loss statistics with respect to the different physical features of the RIS array and non-stationary properties of the channel model are derived and analyzed. Finally, simulation results are provided to demonstrate that the proposed framework is helpful to achieve a good tradeoff between the modeling complexity and accuracy for investigating the channel propagation characteristics, and therefore providing highly-efficient communications in RIS-enabled air-ground wireless networks.

As a recently proposed reconfigurable intelligent surface (RIS) architecture, active RIS has drawn considerable interest. The important feature of the active RIS is its ability to strengthen the impinging signals to mitigate the multiplicative fading effect inherent in passive RIS-aided systems. Herein, we explore the performance of an active RIS-aided uplink multi-user massive multiple-input multiple-output (MIMO) system, considering the phase noise at the RIS. Furthermore, a two-timescale scheme is utilized, where the base station (BS) beamforming is designed based on the instantaneous aggregated channel state information (CSI), while the statistical CSI is used for designing the phase shifts of the active RIS. In addition, the linear minimum mean square error (LMMSE) estimator is adopted to estimate the aggregated channel, which combines both the cascaded and direct channels. According to the estimated channel, a closedform expression for the lower bound of achievable rate is derived. Based on the theoretical expressions, the power scaling laws for the considered system are also investigated. Specifically, when each user’s transmit power is proportionally reduced by the quantity of BS antennasMor RIS elementsN, we find that the amplified thermal noise causes the lower bound of the achievable rate to approach zero asMorNtends to infinity. Moreover, an optimization approach based on a genetic algorithm (GA) is introduced to obtain the optimal phase shifts for maximizing the achievable rate. Numerical results reveal that the active RIS can greatly enhance the performance of the massive MIMO system compared to its passive counterpart.

For integrated sensing and communication (ISAC) systems, channel information that is essential for communication and sensing tasks fluctuates at different timescales. Specifically, the composite channel state information (CSI) for wireless communication is static during channel coherence time. However, this concept is less appropriate for describing the wireless channel for sensing. To this end, in this paper, we first introduce a new timescale to study the real-time variations of the path state information (PSI) (e.g., delay, angle, and Doppler) of individual multi-path, termed path-invariant time, during which the PSI remains constant. As the goal of environment sensing for PSI essentially aligns with the channel estimation for the recently proposed delay-Doppler alignment modulation (DDAM) technique, we introduce a novel framework for a bi-static ISAC system, which refers to as DDAM-based ISAC. To acquire the PSI, in this paper, by capitalizing on the dual timescales of wireless channels, we propose a novel algorithm, termed as adaptive simultaneously orthogonal matching pursuit algorithm with support refinement (ASOMP-SR). The performance of DDAM with the imperfectly sensed PSI is analyzed, where the signal-to-interference-plus-noise ratio (SINR) and the achievable spectral efficiency are derived. Numerical results unveil that the proposed ASOMP-SR algorithm achieves better sensing performance than the conventional orthogonal matching pursuit (OMP) algorithm, in terms of the normalized mean squared error (NMSE) and the number of multi-paths resolved. In addition, DDAM-based ISAC can achieve superior spectral efficiency and a reduced peak-to-average power ratio (PAPR) compared to standard orthogonal frequency division multiplexing (OFDM).

In this paper, we propose an innovative camera-screen communication system designed for real-time communication between 60Hz commercial screens and smartphone cameras. Firstly, we employ Color Modulation to encode information into a video, achieving flicker-free transmission imperceptible to human eyes. Subsequently, we utilize a consecutive characteristics of this modulation method and employ the Random Sample Consensus algorithm for the detection of Regions of Interest without screen detection.In addition, we apply least-squares method to detect the refresh stripe imposed on image samples, which is a severe interference in camera-screen communications. Finally, inter-frame differencing is used for decoding. Our experimental results highlight the robustness and utility of the proposed system, even in complex environments. Specifically, the system achieves up to 80% accuracy at a range of 3 meters.

Synthetic aperture radar (SAR) is a kind of active microwave remote sensing imaging radar, which can obtain high-resolution two-dimensional SAR images. As a multi-parameter, multi-channel SAR, polarimetric SAR (PolSAR) provides rich scattering information for topographic mapping, ocean exploration, polar observation, target identification, and many other fields. Compared to single-polarization SAR, multi-polarization SAR greatly improves the potential information of the data by extending the one-dimensional information. However, the above tasks cannot be carried out without clean SAR echo signal. The radio frequency interference (RFI) signals, seriously affect the subsequent tasks of PolSAR, and there is a great deal of potential information between polarized data. Therefore, this paper proposes a framework for combining multiple polarization data to improve low-rank based methods’ performance. Based on the proposed framework, one experiment is conducted on real PolSAR data, the experiment uses the PCA method to verify the applicability of the proposed framework in interference suppression. At last, the result verifies the framework achieves better suppression of low-rank based method.

Higher positioning rate means safer automatic driving, more authentic virtual reality experience, more stable drones, and more efficient robotics in intelligent factories. To achieve this goal, low-complexity algorithm and low-latency hardware are indispensable. In this paper, we propose a novel 3D visible light positioning algorithm based on angle of arrival (AOA). We apply two light-emitting diodes as transmitters and a photodiode array as a receiver. The algorithm can express, in closed form, the 3D coordinates and the azimuth of the receiver with respect to the AOAs, enabling the algorithm to have the lowest complexity. Furthermore, We derive the asymptotic probability distribution functions of the estimated coordinates, making it possible to evaluate the error pattern at different positions. Finally, we design and implement the positioning system with a field programmable gate array and achieve an ultra high positioning speed of over $\mathbf{8 k}$ samples per second with the average positioning error less than $\mathbf{6 m}$.

This paper investigates the performance of simultaneous transmission and reflection reconfigurable intelligent surface (STAR-RIS) assisted massive multiple-input multiple-output (MIMO) systems with direct links. We apply the two-timescale scheme to design the base station (BS) beamforming and the phase shifts of the STAR-RIS. Specifically, we derive the closed-form expression of the average achievable rate. Based on the derived rate, we theoretically draw insights from comparing STAR-RIS and conventional RIS under the same condition. Then, we optimize the phase shifts of the STAR-RIS using accelerated gradient ascent algorithm. Finally, numerical results are provided to validate our theoretical insights that STAR-RIS outperforms the conventional RIS under some special cases.

This paper investigates the near-field localization performance based on the hybrid reconfigurable intelligent surface (RIS), which contains both passive and active elements and is capable of simultaneously reflecting and receiving signals. The asynchronous scenario, where the transmitter and the receiver are not perfectly synchronized, is considered. The position error bound (PEB) of the hybrid RIS-assisted localization in the asynchronous scenario is derived, and the PEB expressions in different scenarios are compared. Moreover, we show that turning only a small part of the elements into active ones can significantly improve the PEB performance. We then investigate the design of the phase shift coefficients for not only the passive reflection elements but also the phase shifters connected to the active elements. The proposed Focnear scheme can efficiently improve the PEB performance compared with the random phases, and it can provide a good initial point for further optimizing the phase profiles of the hybrid RIS. Besides, we also show that increasing the interval between adjacent active elements can generally improve the PEB performance.

In the massive multiple-input and multiple-output (Massive MIMO) systems, the maximum likelihood (ML) detection problem is NP-hard and becoming classically intricate with the number of transmitting antennas and symbols increasing. The quantum approximate optimization algorithm (QAOA), a leading candidate algorithm running in the noisy intermediate-scale quantum (NISQ) devices, can show quantum advantage for approximately solving combinatorial optimization problems. In this paper, we propose the QAOA based on the maximum likelihood detection solver of binary symbols. In the proposed scheme, we first conduct a universal and compact analytical expression for the expectation value of the 1-level QAOA. Second, a Bayesian optimization based parameters initialization is presented, which can speedup the convergence of the QAOA to a lower local minimum and improve the probability of measuring the exact solution. Compared to the state-of-the-art QAOA based ML detection algorithm, our scheme has the more universal and compact expectation value expression of the 1-level QAOA, and requires few quantum resources and has the higher probability to obtain the exact solution.

In this paper, we study filter bank multi-carrier (FBMC)-based uplink massive connectivity systems with asynchronous transmission and frequency-selective channels. We focus on the joint active device detection, delay and channel estimation (JADDCE) problem as they are critical for subsequent decoding or other processes. The problem can be put into the compressed sensing (CS) framework, where the structural sparsity is explored, and an efficient algorithm is proposed for JADDCE. Besides, since parameters such as noise variance and activity probability may not be perfectly known by the receiver, we introduce the expectation maximization (EM) method into the algorithm and derive the updating rules of the unknown parameters. Simulation results show satisfying performance of the proposed algorithm under frequency-selective channels.

Massive access is recognized as one of the main use cases of future wireless networks. The characteristic of sporadic transmission in massive access makes the processes of channel estimation (CE) and active user detection (AUD) essential prerequisites for successful data decoding. In this article, we study joint CE and AUD in massive access system with cell-free structure. Specifically, we first establish the expectation maximization approximate message passing (EM-AMP) framework tailored for cell-free structure as a benchmark. Then, we investigate three new methods that exploit coarse user location information in different ways, namely, the variance bounding method, the variance fusion method, and the proposed EM on location method, where the last two methods can also generate finer location estimation as byproduct to CE and AUD at the cost of higher complexity. For single user scenario, we theoretically prove the optimality of the proposed method in channel variance estimation, validating the foundation of the proposed method. For multiuser scenario, we conduct various simulations to compare the performance of different methods. Our findings illustrate that harnessing coarse user location information yields substantial enhancements in CE and AUD performance. Moreover, the proposed method exhibits superior localization accuracy compared to the variance fusion method, all while maintaining comparable complexity, making it a good candidate for applications with both communication and sensing requirements.

Cognitive radio (CR) is regarded as the key technology of the 6th-Generation (6G) wireless network. Because 6G CR networks are anticipated to offer worldwide coverage, increase cost efficiency, enhance spectrum utilization, and improve device intelligence and network safety. This article studies the secrecy communication in an energy-harvesting (EH)-enabled Cognitive Internet of Things (EH-CIoT) network with a cooperative jammer. The secondary transmitters (STs) and the jammer first harvest the energy from the received radio frequency (RF) signals in the EH phase. Then, in the subsequent wireless information transfer (WIT) phase, the STs transmit secrecy information to their intended receivers in the presence of eavesdroppers while the jammer sends the jamming signal to confuse the eavesdroppers. To evaluate the system secrecy performance, we derive the instantaneous secrecy rate and the closed-form expression of secrecy outage probability (SOP). Furthermore, we propose a deep reinforcement learning (DRL)-based framework for the joint EH time and transmission power allocation problems. Specifically, a pair of ST and jammer over each time block is modeled as an agent which is dynamically interacting with the environment by the state, action, and reward mechanisms. To better find the optimal solutions to the proposed problems, the long short-term memory (LSTM) network and the generative adversarial networks (GANs) are combined with the classical DRL algorithm. The simulation results show that our proposed method is highly effective in maximizing the secrecy rate while minimizing the SOP compared with other existing schemes.

This article studies the topic of probabilistic on-off division multiple access (ODMA) system design under multiple input-multiple output (MIMO) and massive MIMO with hybrid analog/digital architectures for unsourced random access (URA). Though probabilistic ODMA in Gaussian multiple access channel (GMAC) manifests desirable capacity, probabilistic ODMA extension towards MIMO and hybrid massive MIMO encounters problems have not been discussed. Specifically, a portion of data bits are utilized to select on-off patterns and not transmitted. These pattern-correlated bits and others are restored iteratively with pilot-free transmission in the so-called process of joint pattern and data detection. Discrepant to the state-of-arts, such as interleaving division multiple access (IDMA), where prior of patterns is an essential prerequest for the later detection, probabilistic ODMA detects pattern and data simultaneously with only linearly modulated signals. In this work, two probabilistic ODMA transceiver designs are proposed by different ranks of signal component matrix in MIMO and hybrid massive MIMO. Overall, our proposed designs retain the simplicity of the original ODMA and further exploits the sparsity of on-off patterns. For MIMO, on-off patterns are correlated with common codebook and a receiver is designed in the spirit of uncoupled URA. For hybrid massive MIMO, a novel probabilistic ODMA transceiver achieves probabilistic channel estimation via low-rank matrix completion aided by the sparsity of on-off patterns. Meanwhile, joint pattern and data detection is accomplished by iterative treating interference as noise (TIN) strategy. Extensive simulations are conducted under fair comparisons with state-of-the-arts to verify the validity of the proposed schemes.

Multipath causes interferences to the optical receivers, and reduces the accuracy of visible light positioning (VLP). The existing works mainly focused on the simulation analysis of the signal responses incurred by the scattering of the indoor surfaces, or the positioning error caused by the multipath in the VLP systems. However, such works can only provide insights for how the multipath propagation affects the VLP systems, but cannot separate and exploit the multipath components. In this paper, we design a mode-mixed photodiode (PD) array to receive the overlapping signals of two light sources. The light sources can be light-emitting diodes (LEDs) or reflectors. Based on the diversity of the PDs, we propose an algorithm to separate the signals from the two light sources, which can estimate the directions and the powers of the light sources. Based on the analytical and simulation results, we show that the two light sources can be separated at the receiver without consuming extra time or frequency resources. Moreover, we can distinguish the line-of-sight component even though the received signal has been distorted by interferences from another light source or a reflecting wall, and improve the localization accuracy of the PD array.

Visible light positioning (VLP) is endowed with high accuracy in indoor scenarios. However, the positioning algorithms require plenty of beacon light-emitting diode (LED) coordinates stored in databases, which are expensive to obtain by manual measurements. To circumvent such laborious efforts, we propose a two-step automatic scheme for simultaneous VLP and LED database construction. Specifically, in the first step, a receiver with a photodiode (PD) array samples the optical signals from few benchmark LEDs to locate itself. In the second step, the receiver estimates the unknown beacon LED coordinates through its own locations and the beacon LED signals. For the proposed two-step scheme, we derive closed-form error expressions for the beacon LED coordinates to evaluate the benchmark LEDs’ arrangement and the sampling places. Simulation results agree with the analytical error expressions and reveal that the proposed scheme can achieve centimeter-level accuracy with reasonable transmit powers. Experimental results from the hardware platform verify the feasibility of the scheme. The proposed scheme can circumvent laborious manual measurements and allow the LED database to “grow” while the receivers wander and more receivers enter.

Reconfigurable intelligent surface (RIS)-assisted spatial index modulation (SIM) is a promising candidate for future wireless communication systems due to its high energy and spectrum efficiencies. When only one transmit antenna and one receive antenna are active in the RIS-assisted SIM, we develop a phase-alignment algorithm based on the signature constellation, which utilizes the signature constellation technology to achieve a good bit error rate (BER) performance with a low complexity. Furthermore, in the general scenario, we propose a novel optimization algorithm for RIS reflection coefficients, called maximum Euclidean distance-based low BER (MED-LBER) algorithm. The proposed algorithm aims to maximize the Euclidean distance between different received signals to improve the system performance. The achievable rate, BER performances, and computational complexities of the proposed algorithms are analyzed in detail. The impacts of imperfect channels on the performance of the proposed algorithms are studied. Extensive simulations are carried out to compare the performance of the proposed algorithms with existing algorithms, and demonstrate their superior BER performance.

Light-emitting diodes (LEDs) are widely used in visible light communication (VLC) systems, but their bandwidth is generally incomparable to the laser diodes (LDs). Though LDs are exploited in free space optical systems to provide high data rate, such systems generally fail for mobile users due to the challenges in acquisition, tracking and pointing. Recently, various centimeter-level visible light positioning (VLP) systems have been realized, making it possible to realize VLP-assisted communication systems. In this work, we design a novel VLP-assisted indoor laser communication system, where an LED provides the positioning signal for the mobile user and a narrow-beam laser carries high-speed data stream. The estimated position is updated regularly to continuously orient the laser boresight to the user. The error of the VLP system and the outage probability of the VLP-assisted laser communication system are both derived in closed forms. According to the analytical and simulation results, the outage probability is related to the changes of the system parameters, such as the transmission power of the laser and the user position. Moreover, the outage probability can be minimized by optimizing the laser’s beam divergence angle. An experimental platform is built to verify the feasibility of our proposed system.

Synthetic Aperture Radar (SAR) is a type of active microwave remote sensing imaging radar that can generate two-dimensional high-resolution images. Its ability to operate in all weather conditions and at all times has led to its widespread use. As a multi-parameter and multi-channel extension of SAR, polarimetric SAR (PolSAR) provides a wealth of scattering information for various applications, including topographic mapping, ocean exploration, polar observation, and target identification. Compared with single-polarization SAR, multi-polarization SAR enhances the information potential of the data by expanding its one-dimensional information, however, this potential cannot be fully realized without a clean SAR echo signal. The electromagnetic environment is becoming increasingly congested with radio frequency interference (RFI) signals, presenting a significant challenge for the subsequent tasks of PolSAR. Although there have been many related studies based on polarization information to carry out the aforementioned applications, there is a lack of research on the joint suppression of interference by using multi-polarization information, and single-polarization data alone is insufficient in effectively mitigating interference. To address these challenges, this paper presents a framework combining multi-polarization data to improve performance of low-rank based methods. Based on the proposed framework, experiments are conducted on real PolSAR data to assess the feasibility of the proposed framework in interference suppression. The results demonstrate that the framework significantly enhances the suppression performance of various low-rank based methods with clearer scene details being recovered.

This paper investigates the performance of a two-timescale transmission design for uplink reconfigurable intelligent surface (RIS)-aided cell-free massive multiple-input multiple-output (CF-mMIMO) systems. We consider the Rician channel model and design the passive beamforming of RISs based on the long-time statistical channel state information (CSI), while the central processing unit (CPU) utilizes the maximum ratio combining (MRC) technology to perform fully centralized processing based on the instantaneous overall channel, which is the superposition of the direct and RIS-reflected channels. Firstly, we derive the closed-form approximate expression of the uplink achievable rate for arbitrary numbers of access point (AP) antennas and RIS reflecting elements, which can be used to obtain energy efficiency through the proposed total power model. Relying on the derived expressions, we theoretically analyze the impact of important system parameters on the rate and draw explicit insights into the benefits of RISs. Then, based on the rate expression under statistical CSI, we optimize the phase shifts of RISs by using the genetic algorithm (GA) to maximize the sum rate and minimum rate of users, respectively. Finally, the numerical results demonstrate the correctness of our expressions and the benefits of deploying large-size RISs into cell-free mMIMO systems. Also, we investigate the optimality and convergence behaviors of the GA to verify its effectiveness. To give a more beneficial analysis, we present numerical results to show the high energy efficiency of the system with the help of RISs. Besides, our results have revealed the benefits of distributed deployment of APs and RISs in the RIS-aided mMIMO system with cell-free networks.

The reconfigurable intelligent surface (RIS) has drawn considerable attention for its ability to enhance the performance of not only the wireless communication but also the indoor localization with low-cost. This paper investigates the performance limits of the RIS-based near-field localization in the asynchronous scenario, and analyzes the impact of each part of the cascaded channel on the localization performance. The Fisher information matrix (FIM) and the position error bound (PEB) are derived. Besides, we also derive the equivalent Fisher information (EFI) for the position-related intermediate parameters. Enabled by the derived EFI, we verify that both the ranging and bearing information of the user can be obtained when the near-field model is considered for the RIS-User equipment (UE) part of the channel, while only the direction of the UE can be inferred in the far-field scenario. This result is well known in the scenario that the curvature of arrival (COA) is directly sensed by the traditional active large-scale array, and we prove that it still holds when the COA is sensed passively by the large RIS. For the base station (BS)-RIS part of the channel, we reveal that this part of the channel determines the type of the gain provided by the BS antenna array. Besides, in the single-carrier, single snapshot case, it requires both the BS-RIS and the RIS-UE part of the channel works in the near-field scenario to localize the UE. We also show that the well-known focusing control scheme for RIS, which maximizes the received SNR, is not always a good choice and may degrade the localization performance in the asynchronous scenario. The simulation results validate the analytic work. The impact of the focusing control scheme on the PEB performances under synchronous and asynchronous conditions is also investigated.

Energy efficiency (EE) is currently one of the primary concerns in wireless communication systems. In future 6G systems, achieving high energy-efficient communication with T-bit transmission rate requirements will heavily depend on resource and power parameters. This paper proposes a novel energy efficiency model that incorporates wireless resources and power for a multi-user multiple-input-multiple-output (MIMO) wireless communication system. Firstly, we analyze the equivalency between frequency and spatial dimensions and introduce a two-dimensional resource domain. Additionally, we propose an energy efficiency model incorporating the wireless resource and power. Using the model, we analyze the interaction between the two parameters aiming at the maximize energy efficiency and present an energy-efficient algorithm. Furthermore, We propose two methods for judging high energy efficiency communication of each user based on the model. Finally, our numerical results illustrate the performance of energy efficiency for each user and the advantages and disadvantages of each measurement method in the multi-user system.

The robustness of filter bank multi-carrier (FBMC) against delays is appealing for asynchronous grant-free massive connectivity systems. In this paper, we study the joint activity detection, delay and channel estimation (JADDCE) problem for filter bank multi-carrier (FBMC)-based uplink massive connectivity with asynchronous transmission and frequency-selective fading (FSF) channels. We formulate JADDCE as a compressed sensing (CS) problem to fully exploit the sparsity structure and propose an efficient algorithm based on generalized approximate message passing (GAMP) to solve it. Besides, since parameters such as noise variance and activity probability may not be perfectly known by the receiver, we introduce the expectation maximization (EM) method into the algorithm and derive the updating rules of the unknown parameters. We also utilize the analysis framework based on average mutual information (AMI) to find theoretical upper-bound of the channel estimation performance. Simulation results show satisfying detection and estimation performance of the proposed algorithm under FSF channels. Besides, the channel estimation performance can approach the theoretical upper-bound.

Millimeter-wave (mmWave) communication using lens antenna array has emerged as a promising techique for the next generation wireless communication systems, which has the advantage of considerably reducing the number of required radio-frequency (RF) chains. However, the beamspace channel estimation is a challenging problem due to the fact that the number of RF chains is much smaller than that of transmit/receive antennas at the base station (BS). In this paper, motivated by the sparsity structure of lens-antenna based beamspace channels, we first model beamspace channel estimation problem as a sparse signal recovery problem, which can be solved by the compressed sensing (CS) algorithms. Then, we propose a simplified learned approximate message passing (SL-AMP) network by pre-learning the prior parameters of beamspace channel. Specifically, we use the Wasserstein generative adversarial net (GAN) to learn the underlying channel distribution and generate the corresponding samples to address the lack of training data problem. Next we choose the Bernoulli Gaussian mixture distribution to capture the channel prior and derive the corresponding shrinkage function. Finally, simulations results show that the proposed SL-AMP network can achieve relatively better performance with much lower training complexity compared with the existing LAMP network.

With the continuous improvement of the communication frequency band, optical wireless communication (OWC) has attracted extensive attention. Due to the short wavelength and high directivity of optical signals, OWC has high requirements for precise positioning and beam alignment, which additionally increases the positioning and computing burden of the base station. In order to solve this pain point problem, this paper proposes an optical integrated sensing and communication system based on combination of optical intelligent reflecting surface (OIRS) and photodiode (PD) array. This system enables the integration of sensing and communication functionalities with a single transmission, a single device, and ultimately a single network infrastructure, which saves a lot of resources and improves system performance. Based on the OWC channel model and the OIRS physical model, we deduce the probability distribution function (PDF) of channel fading and the closed-form expression of system performance, which shows the influence of various parameters on performance. Simulations have been performed to verify the accuracy of the derived results. Based on theoretical results and simulation results, we make suggestions for system design.

This work studies the unsourced random access (URA) problem via random dictionary learning over Rayleigh block-fading channels with multiple receive antennas. We propose a novel pilot-free URA equipped with a concatenated URA coding structure enabled by on-off patterns. The inherent collision problem in URA is mitigated with slotted encoding structure. Other problems of state-of-the-arts such as low code rate and inefficient transmission are well-tackled. Distinctively, activity detection is not only accomplished in the original pilot-free manner but also enhanced with softer verdict. Facing the potential false alarm (FA) error, a joint channel pruning and dictionary learning procedure maintains FA under a low level. The learning process updates the noise variance of pseudo noise with explicit models to obtain channel estimation. Subsequently, for the first time, this work realizes joint on-off pattern detection and data restoration for on-off division multiple access (ODMA) under multiple-input and multiple-output (MIMO). Also, a successive interference cancellation (SIC) structure is adopted as an iterative approach to produce desirable performance. Moreover, we analyze the approximation of error ceiling and the performance lower-bound determined by feasible deterministic parameters. Finally, numerical simulations with fair comparisons validate the viability of the proposed URA scheme.

Massive machine-type communications (mMTC) or massive access is a critical scenario in the fifth generation (5G) and the future cellular network. With the surging density of devices from millions to billions, unique pilot allocation becomes inapplicable in the user ID-incorporated grant-free random access protocol. Unsourced random access (URA) manifests itself by focusing only on unwrapping the received signals via a common codebook. In this paper, we propose a URA protocol for a massive access cellular system with multi-user single input multiple output (MIMO). The proposed scheme encompasses a codebook enabling construction of sparse transmission frame, a receiver equipped with dictionary learning and error-correcting codes and a collision resolution strategy for the collided codeword. Discrepant to the existing schemes with necessary overhead for preamble signals, no overhead or pre-defined pilot sequences are needed in the proposed scheme, which is favorable for energy-efficient transmission and latency reduction. Numerical results verify the viability of the proposed scheme in practical massive access scenario.

In this paper, a novel random scattering transceiver design for unsourced random access (URA) is investigated. Distinctive from the state-of-the-art such as coded compressed sensing (CCS) kind and interleaving division multiple access (IDMA) kind, the data is split into two portions and the back-and-forth interleaving/de-interleaving for soft information update is dismantled. A portion of the data is encoded by compressed sensing (CS) and the rest is encoded by convolutional code LDPC (CC-LDPC). Based on the principle of probabilistic data association (PDA), a receiver with a turbo PDA multi-user detector (MUD) is designed by the random scattering transmission. Meanwhile, the proposed turbo PDA detector can also cooperate with the CC-LDPC decoder. A sliding window decoding structure is embedded in the proposed receiver. Moreover, given that permeable collision in URA can undermine the system performance, this paper treats collision as interference and elaborates different slot-wise MUD signal models combining the feature of random scattering, based on which a collision resolution procedure is proposed. Empirically, the proposed scheme shows faster system performance convergence and more accurate MUD performance than the IDMA kind. Numerical results with fair comparisons validate the viability of the proposed scheme.

The coordinates of beacon light-emitting diodes (LEDs) are assumed as known prior information in most visible light positioning (VLP) systems. However, such an assumption may be unpractical because the structures of the buildings change unpredictably due to the material aging or the changes of temperature and humidity. Therefore, a periodical update of the LEDs’ coordinates is necessary for accurate VLP. When the number of beacon LEDs increases, the manual measurement becomes too time-consuming and laborious. In this paper, we propose a novel LED coordinates estimator exploiting arbitrary number of angle-of-arrival (AOA) estimators. Each AOA estimator contains several differently oriented photodiodes (PDs) to detect the incident light direction. Considering the PDs’ thermal noises, a closed-form error expression is derived for the proposed LED coordinates estimator. Moreover, AOA estimator selection methods are proposed to detect the AOA estimators of a low signal-to-noise ratio (SNR) and improve the positioning accuracy of the LED coordinates. Analytical and simulation results show centimeter-level positioning accuracy, and verify that the selection methods can reduce the positioning error.

Massive multiple input multiple output (MIMO) antennas can generate narrow and high-gain beams to overcome the severe propagation loss and accurately locate the users. However, even with MIMO, the space resources provided by the basestations (BSs) are still limited. This motivates the study of the collaboration of multiple BSs with massive MIMO antennas, which has the potential to efficiently exploit the space resources and reduce the inter-user interference. In this paper, we study the joint beam allocation of multiple BSs based on the positioning information. We assume that several users are randomly located in a circular area, and one or two BSs are available to serve the users. If two users are in the same beam of a BS, the signals from the BS to these users would interference with each other. To avoid the inter-user interference, we propose the beam allocation scheme for the users based on their position information. Further, we derive the closed-form probability of no inter-user interference for one or two BSs. Simulation and analytical results show that the joint beam allocation of two BSs can reduce the inter-user interferences compared with just exploiting one BS.

Active reconfigurable intelligent surfaces (RISs) have recently been proposed to compensate for the severe multiplicative fading effect of conventional passive RIS-aided systems. Each reflecting element of active RISs is assisted by an amplifier such that the incident signal can be reflected and amplified instead of only being reflected as in passive RIS-aided systems. This work addresses the practical challenge that, on the one hand, in active RIS-aided systems the perfect individual channel state information (CSI) of the RIS-aided channels cannot be acquired due to the lack of signal processing power at the active RISs, but, on the other hand, this CSI is required to calculate the expected system data rate and RIS transmit power needed for transceiver design. To address this issue, we first derive closed-form expressions for the average achievable rate and the average RIS transmit power based on partial CSI of the RIS-aided channels. Then, we formulate an average achievable rate maximization problem for jointly optimizing the active beamforming at both the base station (BS) and the RIS. This problem is then tackled using the majorization–minimization (MM) algorithm framework, and, in each iteration low-complexity solutions for the BS and RIS beamforming are found based on the Karush-Kuhn-Tucker (KKT) conditions. To ensure the quality of service (QoS) of each user, we further formulate a rate outage constrained beamforming problem, which is solved using the Bernstein-Type inequality (BTI) and semidefinite relaxation (SDR) techniques. Numerical results show that the proposed algorithms can efficiently overcome the challenges imposed by imperfect CSI in active RIS-aided wireless systems.

Visible light positioning is a promising indoor positioning technique for its high accuracy and immunity to radio-frequency interferences. However, it is expensive and time-consuming to manually measure the coordinates of the massive beacon light-emitting diodes (LEDs). We propose a two-step scheme to locate the LEDs automatically. Firstly, the mobile receiver estimates its own coordinates based on the signals of few LEDs with known coordinates; secondly, the receiver samples the signals of the other unknown LEDs, and estimates their coordinates. The signals are all sampled by a photodiode (PD) array, which outputs the angles of arrival of the LED signals. Explicit positioning error expression is derived as a function of the noise terms and the locations of sampling. Simulation results show that the positioning scheme for the LEDs has centimeter-level accuracy with practical transmit powers. The proposed method can construct the database of the massive LED coordinates based on few beacon LEDs, significantly reducing the cost of manual measurements. Both analytical and simulation results show that the accuracy can be improved by properly choosing the transmit powers and the sampling places.

Large-scale reconfigurable intelligent surface (RIS) can effectively enhance the performance of millimeter wave (mmWave) unmanned aerial vehicle (UAV) to ground communication link with obstructed line-of-sight (LoS) path by exploiting more reflecting units. However, the non-negligible array dimension of large-scale RIS and the mobile property of the terminals bring significant variations in propagation char-acteristics, making conventional channel models inapplicable. To address this issue, we propose a dynamic sub-array partition scheme to divide the large-scale RIS into sub-arrays by exploiting the Rayleigh distance criterion and the mobile property of the transceivers. Based on the proposed scheme, a novel non-stationary channel model for large-scale RIS auxiliary mmWave UAV-to-ground mobile networks is developed, which outperforms existing models with well balance between model complexity and accuracy. Numerical results are provided to verify our analysis.

In this paper, we consider the channel modeling of a heterogeneous vehicular integrated sensing and communication (ISAC) system, where a dual-functional multi-antenna base station (BS) intends to communicate with a multi-antenna vehicular receiver (MR) and sense the surrounding environments simultaneously. The time-varying complex channel impulse responses (CIRs) of the sensing and communication channels are derived, respectively, in which the sensing and communication channels are correlated with shared clusters. The proposed models show great generality for the capability in covering both monostatic and bistatic sensing scenarios, and as well for considering both static clusters/targets and mobile clusters/targets. Important channel statistical characteristics, including time-varying spatial cross-correlation function (CCF) and temporal auto-correlation function (ACF), are derived and analyzed. Numerically results are provided to show the propagation characteristics of the proposed ISAC channel model. Finally, the proposed model is validated via the agreement between theoretical and simulated as well as measurement results.

In this paper, we propose a grouped-adaptive-connected (GAC) hybrid precoding structure, where the antennas are divided into groups and the connections between each group of antennas and each RF chain are dynamically controlled by RF switches. Compared with the fully-adaptive-connected (FAC) structure, the proposed GAC structure requires fewer RF switches. The problem of maximizing energy efficiency (EE) is transformed into an adaptive spectral efficiency (SE) maximization (ASEM) problem and a combinatorial optimization problem. For the ASEM problem, we propose an equivalent baseband channel design based hybrid precoding (EBCD-HP) algorithm. Then, utilizing the solution of the ASEM problem, we propose a cross-entropy based grouped-adaptive hybrid precoding (CE-GAHP) algorithm. Simulation results reveal the advantages of the proposed hybrid precoding scheme.

Visible light positioning (VLP) systems provide higher accuracy and lower costs compared to their radio-frequency counterparts. Angle-of-arrival (AOA) based VLP systems are stable and accurate. However, existing AOA VLP systems with a photodiodes (PD) array usually require an unrotated receiver, densely placed LEDs, or auxiliary devices such as accelerometers and gyroscopes. In this paper, we develop an approach to exploit LED clusters and a PD array to locate the receiver. The new approach increases the angle-difference-of-arrival (ADOA) positioning accuracy and circumvents the rotation estimation in AOA positioning. More importantly, the method only requires the solution of linear equation sets, which is computationally efficient. Simulations show that the proposed positioning method can achieve an average mean error below 0.1 m within a 3 m × 3 m area.

Channel estimation is one of the main challenges for implementing reconfigurable intelligent surface (RIS)-aided communication system with a large number of antennas at the base station (BS) because RIS is equipped with many passive reflective elements without active transmitting/receiving and signal processing abilities. In this paper, we focus on the channel estimation in a general RIS-aided multi-user mmWave communication system. Specifically, a novel two-phase channel estimation scheme consisting of on-line and off-line phases is proposed to estimate the BS-RIS channel, RIS-user channel, and BS-user channel, respectively. Inspired by the characteristics of few scatterers in mmWave communication systems, the antenna domain channels are converted into the virtual angular domain channels by using the discrete Fourier transform (DFT) matrix. Thus, the estimation problem can be formulated as a compressed sensing (CS) problem, and solved by using efficient vector approximate message passing (VAMP) algorithm with expectation-maximization (EM) to learn unknown parameters and obtain the estimates simultaneously. Simulation results show that, the above algorithm with two choices of prior distributions in the proposed mmWave RIS-aided multi-user system has fast convergence speed and desirable estimation performance.

Reconfigurable intelligent surface (RIS) consisting of a large number of programmable near-passive units has been a hot topic in wireless communications due to its capability in providing smart radio environments to enhance the communication performance. However, the existing research are mainly based on simplistic channel models, which will, in principle, lead to inaccurate analysis of the system performance. In this paper, we propose a general three-dimensional (3D) wideband non-stationary end-to-end channel model for RIS assisted multiple-input multiple-output (MIMO) communications, which takes into account the physical properties of RIS, such as unit numbers, unit sizes, array orientations and array configurations. By modeling the RIS by a virtual cluster, we describe the end-to-end channel by a superposition of virtual line-of-sight (V-LoS), single-bounced non-LoS (SB-NLoS), and double-bounced NLoS (DB-NLoS) components. We also derive an equivalent cascaded channel model and show the equivalence between end-to-end and cascaded modeling of RIS channels. Then, a sub-optimal solution with low complexity is used to derive the RIS reflection phases. The impact of physical properties of RIS, such as unit numbers, unit sizes, array orientations, array configurations and array relative locations, on channel statistical characteristics has been investigated and analyzed, the results demonstrate that the proposed model is helpful for characterizing the RIS-assisted communication channels.

Unmanned aerial vehicle (UAV) communications exploiting millimeter wave (mmWave) can satisfy the increasing data rate demands for future wireless networks owing to the line-of-sight (LoS) dominated transmission and flexibility. In reality, the LoS link can be easily and severely blocked due to poor propagation environments such as tall buildings or trees. To this end, we introduce a reconfigurable intelligent surface (RIS), which passively reflects signals with programmable reflection coefficients, between the transceivers to enhance the communication quality. Specifically, in this paper we generalize a three-dimensional (3D) non-stationary wideband end-to-end channel model for RIS auxiliary UAV-to-ground mmWave multiple-input multiple-output (MIMO) communication systems. By modeling the RIS as a virtual cluster, we study thepower delivering capabilityof RIS as well as thefading characteristicof the proposed channel model. Important channel statistical properties are derived and thoroughly investigated, and the impact of RIS reflection phase configurations on these statistical properties is studied, which provides guidelines for the practical system design. The agreement between theoretical and simulated as well as measurement results validate the effectiveness of the proposed channel model.

Traditional visible light positioning (VLP) systems estimate receivers’ coordinates based on the assumption that light-emitting diode (LED) coordinates are known accurately, which is not always practical. Even with accurate laser range finders, the structural changes of the buildings due to temperature, humidity, and material aging might affect the LED coordinates unpredictably. Besides, when the number of LEDs increases, it is time-consuming to build a database of LED coordinates. In this paper, we propose a novel system exploiting two optical angle-of-arrival (AOA) estimators to localize the LEDs, and the AOA estimators have fixed relative positions. Each AOA estimator has four photodiodes (PDs) towards different orientations to estimate the incident light direction. Considering the additive noises imposed on the PDs, we derive a closed-form error expression of the LED coordinate estimator. Analytical and simulation results show that the two AOA estimators should be set symmetrically with respect to the room center to reduce the error of the LED coordinates database. Moreover, an experimental platform is built with two fixed AOA estimators and one moving LED in order to verify the approach. The measured average localization error is 4.46 cm and the time of one localization is about 23.4 ms.

Lognormal-Rician random variables (RV) can model large-scale and small-scale fading, line-of-sight and non-line-of-sight channels in wireless communications. However, the versatile stochastic tool is mathematically intractable for correlated cases. In this work, we derive closed-form expressions for the outage probabilities of diversity receptions over dual-branch lognormal-Rician fading channels with arbitrary correlation at high signal-to-noise ratio (SNR). The analytical results show that the lognormal RV and the Rician RV, respectively, contribute an exponential factor and a fractional factor to the outage probability expressions. Some important insights are revealed based on the elegant expressions. For example, negative correlation between the lognormal RVs can suppress the outage probability, and the phase of the complex correlation coefficient between the accompanying Gaussian RVs in the Rician channels can significantly influence the performance.

Abstract The fifth generation (5G) wireless communication networks are being deployed worldwide from 2020 and more capabilities are in the process of being standardized, such as mass connectivity, ultra-reliability, and guaranteed low latency. However, 5G will not meet all requirements of the future in 2030 and beyond, and sixth generation (6G) wireless communication networks are expected to provide global coverage, enhanced spectral/energy/cost efficiency, better intelligence level and security, etc. To meet these requirements, 6G networks will rely on new enabling technologies, i.e., air interface and transmission technologies and novel network architecture, such as waveform design, multiple access, channel coding schemes, multi-antenna technologies, network slicing, cell-free architecture, and cloud/fog/edge computing. Our vision on 6G is that it will have four new paradigm shifts. First, to satisfy the requirement of global coverage, 6G will not be limited to terrestrial communication networks, which will need to be complemented with non-terrestrial networks such as satellite and unmanned aerial vehicle (UAV) communication networks, thus achieving a space-air-ground-sea integrated communication network. Second, all spectra will be fully explored to further increase data rates and connection density, including the sub-6 GHz, millimeter wave (mmWave), terahertz (THz), and optical frequency bands. Third, facing the big datasets generated by the use of extremely heterogeneous networks, diverse communication scenarios, large numbers of antennas, wide bandwidths, and new service requirements, 6G networks will enable a new range of smart applications with the aid of artificial intelligence (AI) and big data technologies. Fourth, network security will have to be strengthened when developing 6G networks. This article provides a comprehensive survey of recent advances and future trends in these four aspects. Clearly, 6G with additional technical requirements beyond those of 5G will enable faster and further communications to the extent that the boundary between physical and cyber worlds disappears.

Abstract Intelligent reflecting surface (IRS) is a new concept originating from metamaterials, which can achieve beamforming through controllable passive reflecting. This device makes it possible to engineer the wireless communication environment, and has drawn increasing attention. However, the associated channel models in current literature are mainly borrowed from conventional wireless channel models directly, omitting the unique features of IRS. In this paper, a geometry‐based stochastic channel model for IRS‐assisted wireless communication system is employed. The model has certain accuracy and low computational complexity. In particular, it captures the correlations of subchannels associated with different IRS elements, which is typically not considered in current works. Based on this channel model and the derived channel spatial correlation functions (CFs), an iterative reflection coefficients configuration method is proposed exploiting statistical channel state information to maximise the ergodic channel capacity. The impacts of the IRS spatial positions as well as the number of the IRS elements on the ergodic channel capacity is investigated through simulations. It is found that to obtain a larger ergodic channel capacity, the IRS should be placed in the vicinity of either the transmitter side or the receiver side, which is a useful guideline for practical deployment.

Abstract Low earth orbit constellation satellite communication has the characteristics of low propagation delay, low path loss, low launch cost and wide range of applications. Due to the low orbital altitude and the short orbital period of low earth orbit satellites, the relative position between satellites and gateway stations changes fast. As a result, the links between gateway stations and satellites need to be switched continuously. Based on the minimum handover frequency algorithm, this paper considers the balance of satellite workloads, and proposes a load balanced satellite handover strategy. In the proposed handover strategy, a joint optimization algorithm is employed, and the power allocation of the satellite is optimized to improve the system capacity. In the multi‐satellite connection model, an adaptive power allocation algorithm is proposed to guarantee the service quality of the system. Simulation results demonstrate the efficiency of the proposed satellite handover strategy.

Belief propagation (BP) decoding has natural advantages in throughput for polar codes to meet high-speed and low-latency requirements. The soft outputs of BP decoding can be utilized further for joint detection and decoding in the baseband communication system. However, its error-correction performance is not comparable with the successive cancellation list (SCL) decoding. Belief propagation flip (BPF) decoding is recently proposed to improve the error-correction performance of BP decoding and indicates the potential to compete with SCL decoding. In this paper, we propose an advanced BPF (A-BPF) scheme that reduces the decoding latency with the help of one critical bit and improves the error-correction performance by the proposed joint detection criterion. To improve area efficiency in the hardware level, an optimized sorting network is proposed and applied for the A-BPF decoder. The decoder is implemented on 65 nm CMOS technology for length-1024 and rate-1/2 polar codes, and the results show that the proposed decoder can achieve a close frame error rate performance to the SCL decoder with four lists and deliver a throughput of 5.17 Gb/s at Eb/N0= 4.0 dB.

For massive multiple-input multiple-output (MIMO) systems, linear minimum mean-square error (MMSE) detection has been shown to achieve near-optimal performance but suffers from excessively high complexity due to the large-scale matrix inversion. Being matrix inversion free, detection algorithms based on theGauss–Seidel(GS) method have been proved more efficient than conventionalNeumannseries expansion-based ones. In this paper, an efficient GS-based soft-output data detector for massive MIMO and a corresponding VLSI architecture are proposed. To accelerate the convergence of the GS method, a new initial solution is proposed. Several optimizations on the VLSI architecture level are proposed to further reduce the processing latency and area. Our reference implementation results on a Xilinx Virtex-7 XC7VX690T FPGA for a 128 base-station antenna and eight user massive MIMO system show that our GS-based data detector achieves a throughput of 732 Mb/s with close-to-MMSE error-rate performance. Our implementation results demonstrate that the proposed solution has advantages over the existing designs in terms of complexity and efficiency, especially under challenging propagation conditions.

Filter bank multiple access (FBMA) without subbands orthogonality has been proposed as a new candidate waveform to better meet the requirements of future wireless communication systems and scenarios. It has the ability to process directly the complex symbols without any fancy preprocessing. Along with the usage of cyclic prefix (CP) and wide-banded subband design, CP-FBMA can further improve the peak-to-average power ratio and bit error rate performance while reducing the length of filters. However, the potential gain of removing the orthogonality constraint on the subband filters in the system has not been fully exploited from the perspective of waveform design, which inspires us to optimize the subband filters for CP-FBMA system to maximizing the achievable rate. Besides, we propose a joint optimization algorithm to optimize both the waveform and the covariance matrices iteratively. Furthermore, the joint optimization algorithm can meet the requirements of filter design in practical applications in which the available spectrum consists of several isolated bandwidth parts. Both general framework and detailed derivation of the algorithms are presented. Simulation results show that the algorithms converge after only a few iterations and can improve the sum rate dramatically while reducing the transmission delay of information symbols.

Spatial domain modulation employs spatial degree of freedom to convey information bits. Traditional spatial domain modulation schemes include spatial modulation, spatial shift keying (SSK), and variations of these two schemes. To fully make use of the spatial degree of freedom and improve the achievable data rate of traditional spatial domain modulation schemes, we propose a successive-coded SSK (SC-SSK) modulation scheme. The core of the proposed SC-SSK scheme is a multiple step coded modulation strategy, in which different information bits are mapped to the designed pattern in each step. The constellation of the transmit signals is designed. A QR decomposition based successive detection algorithm is proposed for this particular coded modulation strategy. The achievable rate, the error rate performance, and the computational complexity of the proposed SC-SSK scheme are analyzed in detail. Simulations are carried out to compare the proposed the SC-SSK scheme with the reference spatial domain modulation schemes, and reveal the advantages of the proposed SC-SSK scheme.

Intelligent reflecting surface (IRS), which is composed of a large number of low-cost passive elements, has the ability to reflect the incident signal independently with an adjustable phase and amplitude shifts, has been regarded as a key and emerging technology for achieving the cost-effectively spectrum and addressing energy issues in the next generation of wireless networks. In this paper, we propose a general wideband non-stationary channel model for IRS-assisted multiple-input multiple-output (MIMO) communication scenarios, which aims at capturing the underlying propagation characteristics of IRS-assisted communication systems. By properly adjusting the key system parameters, the proposed channel model can be used to describe various IRS-assisted communication scenarios. Furthermore, we separate the channel between the mobile transmitter (MT) and mobile receiver (MR) into the subchannel between the MT and IRS, the subchannel between the IRS and MR, and the subchannel between the MT and MR. The physical properties of each subchannel are investigated accordingly; then, we develop an equivalent channel model to study the key characteristics of the proposed IRS-assisted channel model, such as the time-varying spatial-temporal (ST) cross-correlation functions (CCFs), temporal auto-correlation functions (ACFs), and frequency CCFs. Finally, numerical results demonstrate that the proposed channel model is practical for describing the IRS-assisted MIMO wireless communication scenarios.

For efficiently investigating the statistical properties of wideband multiple-input multiple-output (MIMO) channels for vehicle-to-vehicle (V2V) communication scenarios, we propose a novel computationally efficient solution to estimate the parameters of the proposed channel model for different propagation delays in this paper. To be specific, we first introduce a Unitary transformation method to estimate the propagation delay of the proposed channel model for the first tap in the preliminary stage before the mobile transmitter (MT) and mobile receiver (MR) move. Then, we estimate the real-time angular parameters based on the estimated delay and moving time/directions/velocities of the MT and MR. Furthermore, we estimate the expressions of the real-time complex channel impulse responses (CIRs), which can be used to characterize the physical properties of the proposed channel model, by substituting the estimates of the time-varying AoD and AoA and model parameters into the complex CIRs. Numerical results of the channel characteristics fit the theory results very well, which validate that the proposed channel model is practical for characterizing the beyond fifth-generation (B5G) V2V communication systems.

Considering mobile terminals with time-varying velocities and randomly moving clusters, a novel multi-mobility non-stationary wideband multiple-input multiple-output (MIMO) channel model for future intelligent vehicle-to-everything (V2X) communications is proposed. To describe the non-stationarity of multi-mobility V2X channels, the proposed model employs a time-varying acceleration model and a random walk process to describe the motion of the communication terminals and that of the scattering clusters, respectively. The evolution of the model parameters over time and the stochastic characteristics of the phase shift caused by the time-varying Doppler frequency are derived. The proposed model is sufficiently general and suitable for characterizing various V2X communication scenarios. Under two-dimensional (2D) non-isotropic scattering scenarios, the important channel statistical properties of the proposed model are derived and thoroughly investigated. The impact of the random walk process of the clusters and the velocity variations of the communication terminals on these statistical properties is studied. The simulation results verify that the proposed model is useful for characterizing V2X channels.

Free space optical communication has been applied in many scenarios because of its security, low cost and high rates. In such scenarios, a tracking system is necessary to ensure an acceptable signal power. Free space optical links were considered unable to support fast moving nodes, such as unmanned aerial vehicles, cars or pedestrians, in optical mobile communication because existing tracking schemes fail to track the nodes accurately and rapidly. In this paper, we propose a novel tracking system exploiting multiple beacon lasers. Each beacon laser's power is measured to estimate the orientation of the target. Unlike existing schemes which drive servo motors multiple times based on consecutive measurements and feedback, our scheme can directly estimate the next optimal targeting shift for the servo motors based on a single measurement, allowing the tracking system to converge much faster. Closed-form outage probability expression is derived for the optical mobile communication system with ideal tracking, where pointing error and moving statistics are considered. To maintain sufficient average power and reduce the outage probability, the recommended beam divergence of the main laser is expressed in closed form as a function of the target's statistics of random shift, providing insights to the system design.

For the scenarios with high throughput requirements, the belief propagation (BP) decoding is one of the most promising decoding strategies for polar codes. By pruning the redundant variable nodes (VNs) and check nodes (CNs) in the original factor graph, the graph is condensed to a sparse bipartite graph which is similar to the graph for low-density parity-check (LDPC) codes. In this paper, we introduce the bit-flipping scheme into the LDPC-like BP (L-BP) decoding and propose two methods to identify the error-prone VNs. By additional decoding attempts, the L-BP flip (L-BPF) decoding improves the error-rate performance with a similar average complexity for high Eb=N0values. The simulation results show that the L-BPF decoding achieves 0:25 dB gain compared with the L-BP decoding.

With the growing demand for high accuracy indoor localization, the fifth generation (5G) wireless communication technology based localization attracts increasing attention. Aiming at the indoor localization with a single base station, this paper proposes a 5G signal based localization method by employing the estimates of the angle of arrival (AoA) and the time of flight (ToF). The proposed method takes advantage of the large bandwidth and subcarrier spacing of 5G signals to improve localization accuracy. First, the base station carries out frequency synchronization and channel estimation to obtain the channel state information (CSI) matrix. Second, the CSI matrix is utilized for joint estimation of the AoA and ToF for each path. Finally, the corresponding geometric localization method is applied. Simulation results show that this method can achieve a centimeter-level localization accuracy.

Traditional asymptotic analysis techniques fail for lognormal random variables (RV) because a lognormal probability density function cannot be quantified by Taylor series at the origin, and the moment-generating function of a lognormal RV does not yield a unified form. In this work, we derive two closed-form asymptotic expressions for the multivariate cumulative distribution function (CDF) of L correlated lognormal RVs. Among these two asymptotic expressions, one has an elegant form but low converging speed, while the other has a fast converging speed but involves Gaussian Q-functions. Furthermore, we derive asymptotically tight upper and lower bounds for the multivariate CDF to evaluate the error of the asymptotic approximation. The new analytical results are shown to be effective in evaluating the outage probability of selection combining (SC) over correlated lognormal fading channels when Monte Carlo simulation is expensive. More importantly, the elegant asymptotic expressions show that little outage performance improvement can be introduced to the SC system by adding more antennas for some cases.

Millimetre wave (mmWave) signal is promising for the challenge of bandwidth shortage and canmotivate the research on large‐scale antenna arrays. In this paper, the authorsinvestigate the hybrid precoding design that combines with a radio frequency(RF) precoder and a digital precoder for multi‐user multiple‐input single‐output(MU‐MISO) systems with large‐scale antenna arrays. In practical large‐scaleantenna array systems, the small antenna spacing and the properties of ascattering environment can create a correlation between channel coefficients forseparated receive units. Such correlation prejudices the performance ofmulti‐user systems. This study proposes two approaches to reduce the channelcorrelation by the hybrid precoding structure. The first approach selects somevectors with the smallest influence with each other from all array responsevectors to design an angular‐based RF precoder. The second approach optimisesthe singular values characteristic of the virtual channel matrix before digitalprocessing for the RF precoder. Then, the MU‐MISO baseband precoding isimplemented by reduced RF chains. Numerical results show that they havedifferent performance depending on the environment. The first can achieve betterperformance when the channel correlation is not too high, while the second ismore effective when the channel realisation is terribly ill‐conditioned, especially for higher signal‐noise ratio (SNR).

Physical unclonable function (PUF) is a technique to produce secret keys or complete authentication in integrated circuits (ICs) by exploiting the uncontrollable randomness due to manufacturing process variations. For better PUF applications, efficient analysis of different designs is important. In this article, a mathematical model to analyze the performance of typical strong PUF designs is proposed and applied to arbiter PUF, ring oscillator (RO) PUF, and duty cycle (DC) PUF. For better reliability, a new PUF design, DC multiplexer (DC MUX) PUF proposed in our previous work is analyzed. The proposed model indicates that DC MUX PUF achieves 2% higher reliability than arbiter PUF under environment influences. It also shows that DC PUF achieves 10% higher reliability than RO PUF. For verification, the aforementioned four PUF designs are testified using HSPICE. As our model analysis indicates, for reliability DC MUX PUF outperforms arbiter PUF, and DC PUF outperforms RO PUF. For randomness, DC MUX PUF and DC PUF outperform arbiter PUF and RO PUF, respectively. For security, LR attacks on DC MUX PUF and arbiter PUF are performed. The training time for DC MUX PUF is 40 000 times of arbiter PUF.

Convolutional neural networks (CNNs) have gained great success in various fields, such as computer vision and natural language processing. Besides, with the breakthrough in unsupervised learning, generative adversarial network (GAN) is recently utilized to generate virtual data from limited data sets. The generative model of GAN has impressive applications, such as style transfer and image super-resolution. However, the promising performance of CNN and GAN comes at the cost of prohibitive computation complexity. The convolution (CONV) in CNN and the transposed CONV (TCONV) in GAN are the two operations that dominant the overall complexity. The prior works exploit the fast algorithms, Winograd and fast Fourier transform (FFT), to reduce the complexity of spatial CONV. However, Winograd only supports fixed filter size while FFT has high transform overhead. Moreover, very few works apply fast algorithms to accelerate GAN models. In this article, a reconfigurable and low-complexity accelerator on ASIC for both CNN and GAN is proposed to address these problems. First, by exploiting Fermat number transform (FNT), we propose two FNT-based fast algorithms to reduce the complexity of CONV and TCONV computations, respectively. Then the architectures of the FNT-based accelerator are presented to implement the proposed fast algorithms. The methodology to determine the design parameters and optimize the dataflow is also described for obtaining maximum performance and optimal efficiency. Moreover, we implement the proposed accelerator on 65 nm 1P9M technology and evaluate it on various CNN and GAN models. The post-layout results show that our design achieves a throughput of 288.0 GOP/s on VGG-16 with 25.11 GOP/s/mm2area efficiency, which is superior to the state-of-the-art CNN accelerators. Furthermore, at least $1.7\times $ speedup over the existing accelerators is obtained on GAN. The resulting energy efficiency is $275.3\times $ and $12.5\times $ of CPU and GPU.

Deep learning (DL) based autoencoder is a promising architecture to implement end-to-end communication systems. One fundamental problem of such systems is how to increase the transmission rate. Two new schemes are proposed to address the limited data rate issue: adaptive transmission scheme and generalized data representation (GDR) scheme. In the first scheme, an adaptive transmission is designed to select the transmission vectors for maximizing the data rate under different channel conditions. The block error rate (BLER) of the first scheme is 80% lower than that of the conventional one-hot vector scheme. This implies that higher data rate can be achieved by the adaptive transmission scheme. In the second scheme, the GDR replaces the conventional one-hot representation. The GDR scheme can achieve higher data rate than the conventional one-hot vector scheme with comparable BLER performance. For example, when the vector size is eight, the proposed GDR scheme can double the date rate of the one-hot vector scheme. Besides, the joint scheme of the two proposed schemes can create further benefits. The effect of signal-to-noise ratio (SNR) is analyzed for these DL-based communication systems. Numerical results show that training the autoencoder using data set with various SNR values can attain robust BLER performance under different channel conditions.

In this article, we propose a three-dimensional (3D) multi-input multi-output (MIMO) channel model for air-to-ground (A2G) communications in unmanned aerial vehicles (UAV) environments, where the UAV transmitter and ground receiver are in motion in the air and on the ground, respectively. A novel angular estimation algorithm is proposed to estimate the real-time azimuth angle of departure (AAoD), elevation angle of departure (EAoD), azimuth angle of arrival (AAoA), and elevation angle of arrival (EAoA) based on the non-stationary nature of the channel model. In the model, we investigate the time-varying spatial cross-correlation functions (CCFs) and temporal auto-correlation functions (ACFs) with respect to the different moving directions and velocities of the UAV transmitter and ground receiver. Furthermore, we derive and study the Doppler power spectral densities (PSDs) and power delay profiles (PDPs) of the proposed channel model. Numerical results show that characteristics of the proposed channel model are very close to those of practical measurements, which provide a new and practical approach to evaluate the performance of next generation UAV-MIMO communication systems.

Channel estimation and hybrid precoding are considered for multi-user millimeter wave massive multi-input multi-output system. A deep learning compressed sensing (DLCS) channel estimation scheme is proposed. The channel estimation neural network for the DLCS scheme is trained offline using simulated environments to predict the beamspace channel amplitude. Then the channel is reconstructed based on the obtained indices of dominant beamspace channel entries. A deep learning quantized phase (DLQP) hybrid precoder design method is developed after channel estimation. The training hybrid precoding neural network for the DLQP method is obtained offline considering the approximate phase quantization. Then the deployment hybrid precoding neural network (DHPNN) is obtained by replacing the approximate phase quantization with ideal phase quantization and the output of the DHPNN is the analog precoding vector. Finally, the analog precoding matrix is obtained by stacking the analog precoding vectors and the digital precoding matrix is calculated by zero-forcing. Simulation results demonstrate that the DLCS channel estimation scheme outperforms the existing schemes in terms of the normalized mean-squared error and the spectral efficiency, while the DLQP hybrid precoder design method has better spectral efficiency performance than other methods with low phase shifter resolution.

Though belief propagation (BP) polar decoders can achieve higher throughput than successive-cancellation (SC)-based decoders, and how to efficiently generate different belief propagation decoders (BPDs) which can meet various design specifications remains challenging. To this end, an autogeneration, which can translate the generation formula of BPDs to efficient hardware implementations, has been proposed in this article. For different requirements, two BPD architectures have been given: 1) low-cost decoder (Type-I) and 2) high-throughput decoder (Type-II). The autogeneration of them can support different code rates, code lengths, and parallelisms. Synthesis results show that Type-I and Type-II provide higher throughput and hardware efficiency than the state-of-the-art (SOA) SC decoders. Moreover, compared to the SOA BPDs, both Type-I and Type-II achieve similar even better energy- and area-efficiency with a comparable throughput, for fully parallel configuration. With the autogeneration, we are able to obtain the design space regarding different design metrics, such as area efficiency, energy efficiency, and power density, within which the design optimization under given design constraints can be conducted.

In intensity-modulation and direct-detection (IM/DD) multiple-input and multiple-output (MIMO) visible light communication (VLC) systems, spatial subchannels are usually correlated, and spatial modulation is a good choice to achieve the advantages of MIMO technology. Peak-to-average power ratio (PAPR) is a key issue in VLCs due to the limited linear dynamic range of light emitting diodes (LEDs). Single-carrier communication systems have a lower PAPR than orthogonal frequency division multiplexing (OFDM) communication systems. However, it is challenging to design a single-carrier spatial modulation for high-rate transmissions because of the time domain intersymbol interference. This paper develops an optical spatial modulation (OSM) scheme based on bipolar pulse amplitude modulation (PAM) and spatial elements for high-rate indoor VLC systems. Multiple data streams can be transmitted simultaneously in the proposed scheme. Based on the transmit strategy, we develop a low-complexity receiver algorithm that achieves better bit-error rate performance than reference schemes, and the proposed OSM scheme has a much lower PAPR than OFDM based OSM schemes. When the spatial subchannels are highly correlated, a spatial area division strategy is applied, and the receiver algorithm is investigated. The symbol-error rate expression of the proposed OSM scheme is derived, and the computational complexity is analyzed.

Deep neural network (DNN) has a wide range of applications in various fields, including solving sparse inverse problems. In this paper, we propose a novel network called the Stein's unbiased risk estimate based-trainable iterative thresholding algorithm (SURE-TISTA) for sparse signal recovery problems. Without prior information, SURE-TISTA outperforms TISTA, an algorithm based on the minimum mean squared error (MMSE) estimator. SURE-TISTA also shows a great robustness in many cases including large-scale and large-variance problems. Meanwhile, SURE-TISTA uses fewer learnable variables to achieve similar performance as learned approximate message passing (LAMP), which has more learnable parameters. Without any error measure estimator, SURE-TISTA achieves a near MMSE-based performance. Our numerical results indicate that SURE-TISTA is superior to TISTA and other traditional algorithms in many aspects, which can be promising in image denoising.

For massive multiple-input multiple-output (MIMO) detection, belief propagation (BP) based on graphical models has become a popular detection algorithm since it provides a good tradeoff between performance and complexity. To further lower the complexity of BP detection, an efficient BP detection based on channel hardening (BP-CH) is proposed. In this paper, the comparison in terms of both performance and complexity between proposed BP-CH and general BP is firstly investigated exhaustively. Simulation results have shown that the proposed BP-CH achieves similar performance behavior as general BP while keeping lower computational complexity. Additionally, an folded hardware architecture for proposed BPCH detector is designed to improve the implementation efficiency. Meanwhile, VLSI implementation results have verified the great advantage of BP-CH regarding hardware overhead, especially for scenarios with large system loading factor.

Deep learning (DL) based autoencoder is a potential architecture to implement end-to-end communication systems. In this paper, we first give a brief introduction to the autoencoder-represented communication system. Then, we propose a novel generalized data representation (GDR) to improve the data rate of DL-based communication systems. Finally, simulation results show that the proposed GDR scheme has lower training complexity, comparable block error rate performance and higher channel capacity than the conventional one-hot vector scheme. Furthermore, we investigate the effect of signal-to-noise ratio (SNR) in DL-based communication systems and show that training at high SNR can produce a good training convergence performance for the autoencoder.

Spatial modulation is a promising transmission scheme for massive multiple‐input multiple‐output (MIMO) systems to improve energy efficiency. In this study, considering the low‐complexity hybrid structure, the authors propose a joint spatial modulation and beamforming scheme for a hybrid massive MIMO system in both single user and multi‐user scenarios. Specifically, a design criterion of the channel control parameter is provided to reduce the high correlation among different channel coefficients in the hybrid massive MIMO system. In addition, with the statistical channel state information acquired at the base station, the optimum analogue beamforming vectors are designed with the goal of maximising the signal to leakage and noise ratio in the multi‐user scenario. Furthermore, the upper bound, the lower bound as well as the closed form approximation of achievable spectral efficiency of the proposed scheme are derived for both single user and multi‐user scenarios. Numerical simulation results demonstrate that the proposed scheme outperforms the conventional MIMO scheme.

As an improved version of successive cancellation (SC) polar decoder, an SC stack (SCS) decoder has been proposed for performance improvement. However, the existing SCS polar decoder suffers a lot from high time complexity at low signal-to-noise ratio (SNR) region and space complexity compared with the SC decoder. To this end, two improved decoders are proposed to reduce time and space complexity in both low and high SNR regions. The first one is the segmented cyclic redundancy check (CRC)-aided SCS (SCA-SCS) decoder, which is based on segmented parity checkers. The second one is the adaptive SCS (ASCS) decoder, which has the flexibility of stack depth and searching width. Furthermore, a channel condition estimator is proposed to select appropriate decision criteria for different SNR scenarios. Results have shown that for the polar code of length 1024 and rate 1/2, two improved SCS decoders can perform better than the traditional SCS decoder. The proposed SCA-SCS decoder and the ASCS decoder can achieve 10.8% and 11.42% time complexity reduction and 31.68% and 60.85% space complexity reduction on average over binary-input additive white Gaussian noise channels (BI-AWGNCs), respectively. Efficient parallel hardware architecture of the SCS polar decoder is first proposed and implemented with 90- and 65-nm technologies. Results have verified its advantages over the state of the art (SOA).

For polar codes, both successive cancellation list (SCL) decoding and list sphere decoding (LSD) aim to balance performance and complexity. The same list structure but different decoding schedules of SCL and LSD can lead to a combination of both schemes. In this paper, an efficient joint list decoder with SCL and LSD (JLSCD) is proposed to reduce time complexity. We apply SCL and LSD schemes simultaneously but independently, then merge them at the middle point of the decoding. Numerical results have demonstrated JLSCD scheme's advantage in complexity. FPGA implementation of JLSCD decoder is also given in this paper.

Polar code is increasing its popularity recently for its capacity-achieving property for B-DMCs. However, when designing decoders for polar code, it has always been an inevitable concern for us to balance the decoding performance and the hardware consumption. In this paper, we propose an approximate belief propagation (BP) decoder for polar code for the first time. By introducing the approximate computation schemes, we reduced the critical path delay (CPD) and the hardware consumption of the conventional BP decoders. Simulation results show that the proposed approximate BP decoder achieves nearly the same decoding performance as the conventional one. Advantages of the proposed decoder has been verified by FPGA implementation.

Recently, low-precision weight method has been considered as a promising scheme to efficiently implement inference of deep convolutional neural networks (DCNN). But it suffers from expensive retraining cost and accuracy degradation. In this paper, a low-bit and retraining-free quantization method, which enables DCNNs to deal inference with only shift and add operations, is proposed. The efficiency is demonstrated in terms of power consumption and chip area. Huffman coding is adopted for further compression. Then by exploring two-level systolic, an efficient hardware accelerator is introduced with respect to the given quantization strategy. Experiment results show that our method achieves higher accuracy than other low-precision networks without retraining process on ImageNet. 5× to 8× compression is obtained on popular models compared to full-precision counterparts. Furthermore, hardware implementation indicates good reduction of slices whereas maintaining throughput.

The design of measurement matrices is an important part in compressed sensing (CS). Random matrices superior to incoherence are considered to be optimal measurement matrices to achieve successful recovery. However, they are deficient in memory cost. Structure matrices like circulant matrices are preferred for low-memory cost. Nevertheless, their recovery performance is greatly damaged because of element coherence. In this paper, a new method called different-spaced selection & different-spaced flipping (DSS & DSF) is proposed to modify structure matrices. Based on circulant matrices, regular extraction and symbol flipping imposed on columns of measurement matrices can increase randomness to a large scale. As a result, not only near optimal recovery but also much less memory cost can be achieved. Compared with Gaussian random matrices, the memory cost can be reduced to 4% when measurement matrices based on circulant matrices are in 128 × 512 dimensions. An efficient hardware design and VLSI implementation are also presented at the end of this paper.

With low-density parity-check (LDPC) code and polar code selected as the standard codes for 5G eMBB scenario, one challenge is how to improve the hardware efficiency when both decoders are required by one system. Since LDPC and polar codes can be decoded with belief propagation (BP) algorithms, this similarity allows us to design a reconfigurable decoder, which can decode both codes at the cost of only one decoder. Numerical and implementation results are also given in this paper to show that the proposed decoder achieves higher hardware efficiency than stand-alone LDPC or polar decoder, without harming the error performance.

Sparse-code multiple-access (SCMA) is an effective non-orthogonal multiple-access (NOMA) technique. Existing detectors such as deterministic message passing algorithm (DMPA) are one-dimensional and require precise channel estimation. This paper proposes a blind detector from a two-dimensional perspective. Main work involves pattern construction and pre-filtering with different image techniques. In this paper, a 4 × 4 Sudoku template is applied for the pattern construction of one-dimensional SCMA signals. Total variation based on first order differential operator is adopted for global pre-filtering of DMPA. Image training is adopted in DMPA to further reduce the environment noise. The output signal of both pre-filtering methods are detect through DMPA with constant noise density N0. Numerical results show that two-dimensional blind detection can well compensate the performance when channel estimation of DMPA is not perfect. A general hardware architecture of the detecting method is also proposed in this paper.

A new multi-carrier multiple access scheme based on filter bank multi-carrier (FBMC) without sub-band orthogonality, referred to as imperfect reconstructed filter bank multiple access (iPR-FBMA), has been recently proposed. While it entitles substantial performance improvement on the peak-to-average power ratio and bit error rate compared to the conventional orthogonal frequency division multiplexing (OFDM) scheme, its achievable information rate, the ultimate performance limit of this non-orthogonal scheme, has not been fully unveiled. As a consequence, whether the introduction of non-orthogonality to FBMA is really worthy from information perspective remains an open issue. In this paper, we present a comparative study on the ergodic achievable rate and region of iPR-FBMA with that of two orthogonal multiple access schemes based on OFDM and OFDM with offset QAM (OFDM/OQAM), respectively, in uplink multiple access with flat fading channels. We prove that the achievable rate of iPR-FBMA is largely determined by the number of non-zero singular values of its transmission matrix, which is directly related to the means of subbands allocation, the oversampling factor and the numerical precision. Numerical results show that iPR-FBMA with critical sampling has even lower sum rate compared to OFDM and OFDM/OQAM although it is non-orthogonal. However, with oversampling, iPR-FBMA could get much larger rate region than the orthogonal schemes, especially for imbalanced power constraints and interleaved subband allocation, and it could achieve the entire channel capacity region if the numerical precision could be fully guaranteed.

This paper presents 3-D vehicle massive multiple-input multiple-output (MIMO) antenna array model for vehicle-to-vehicle (V2V) communication environments. A spherical wavefront is assumed in the proposed model instead of the plane wavefront assumption used in the conventional MIMO channel model. Using the proposed V2V channel model, we first derive the closed-form expressions for the joint and marginal probability density functions of the angle of departure at the transmitter and angle of arrival at the receiver in the azimuth and elevation planes. We additionally analyze the time and frequency cross-correlation functions for different propagation paths. In the proposed model, we derive the expression of the Doppler spectrum due to the relative motion between the mobile transmitter and mobile receiver. The results show that the proposed 3-D channel model is in close agreement with previously reported results, thereby validating the generalization of the proposed model.

Universal filtered multi-carrier (UFMC) is a potential waveform technology, which can efficiently combat different carrier frequency offsets (CFOs) of multiple users. First, adaptive modulation and power allocation are applied for each subcarrier to meet a preset bit error rate (BER) requirement by ignoring CFOs. Then, we prove that different CFOs will cause different interference variances to adjacent users, which results in performance degradation in UFMC systems. To reduce the interference caused by CFOs and improve achievable rate, a novel adaptive filter configuration algorithm is proposed to adaptively design the parameters of the finite impulse response filters. Specifically, the proposed algorithm is available for the UFMC systems, where the user equipments are allocated with different bandwidths. Finally, simulation results show that the proposed adaptive filter configuration algorithm can dramatically eliminate the interference caused by different CFOs, and achieve better BER performance and a higher achievable rate than the conventional scheme.

Correlation across transmit antennas in multiple- input single-output (MISO) broadcast channel has been studied to be detrimental generally. The current downlink beamforming schemes have not considered correlation enough such that they are not suitable for the broadcast systems with high correlation among transmit antennas. We propose two novel user grouping precoders to improve the sum rate performance for the case of ill-conditioned channel and the case of small transmit antennas (i.e., the number of transmit antennas is less than that of users). The first proposed precoder partitions all the users into small groups step by step based on the condition number. Downlink beamforming is then done in a small size for the optimal channel submatrix with smaller condition number. Inter-group interference from the subsequent allocated groups is nulled out in the null spaces of the previous allocated groups. The second precoder regularizes the number of users in the first group to improve the sum rate further. Simulation results verify that our schemes outperform the current beamforming schemes for the case of high transmit correlation. Moreover, the proposed precoders allow to be applied for the case of small antennas.

In this paper we propose a low-computational-complexity channel estimation algorithm for the downlink of massive MIMO systems. This algorithm employs the expectation propagation and expectation maximization techniques, assuming a sparsity promoting conditional Gaussian prior distribution on the channels. Its computational complexity scales only linearly with the number of transmit antennas, the number of pilots, and the channel length. Compared with existing state-of-the-art algorithms, it has a lower pilot overhead and is much more computationally efficient. The channel estimation mean-squared errors with the proposed algorithm and the existing block iterative support detection (block-ISD) and basic pursuit (BP) methods are simulated and compared. Results show that the performance of the proposed algorithm approaches the benchmark performance, and is significantly better than that of block-ISD and BP.

Imperfect reconstructed filter bank multiple access (FBMA) for uplink transmission has been proposed recently which gets rid of real orthogonality in current filter bank multicarrier schemes. While it eases the filter design task and achieves better bit error rate (BER) performance than orthogonal frequency division multiple access (OFDMA), it still suffers from high peak-to-average power ratio (PAPR) and high computational complexity. In this paper we propose an improved version of existing imperfect reconstructed FBMA system. The basic idea is to replace the group of narrow-banded subbands of one user by a wide-banded subband. We will show that this simple modification not only reduces the transceiver complexity, but also substantially improves the PAPR and BER performance without any fancy preprocessing. Another contribution of this work is to propose an enhanced symbol detection algorithm by exploiting the released design freedom from PR constraint through polyphase component selection. Simulations show that by using the proposed detection algorithm, the BER performance of our proposed CP-FBMA approaches to that of single carrier frequency division multiple access (SC-FDMA) and could be even better at high signal-to-noise ratio region. Those results suggest the proposed FBMA using wide-banded subands is a good candidate new waveform for future generation communication systems.

In this paper, we present a three-dimensional (3D) geometric channel model for vehicle-to-vehicle (V2V) communication environments. We adopt a two-cylinder model to describe moving vehicles as well as semi-ellipsoid model to depict stationary roadside scenarios, where the received signal is constructed as a sum of the line-of-sight (LoS), single-, and double-bounced rays. Accordingly, the proposed channel model is sufficient for depicting a variety of V2V scenarios, such as macro-, micro-, and picocells. From the reference model, we analyze the proposed channel statistics such as space correlation functions (CFs) and frequency CFs are studied for different channel parameters. Additionally, we perform the Doppler frequency due to the relative motion between the mobile transmitter (MT) and mobile receiver (MR). The results demonstrate that the proposed model can fit those of the previous channel models and the measurement results for V2V scenarios very well, which validate the accuracy of the proposed 3D channel model.

With the development of vehicle-to-vehicle (V2V) communications, the interference among different V2V communication groups will become the limitation for high-rate transmissions. Two intergroup interference coordination schemes are proposed for the V2V communication. The first proposed scheme is a Doppler-shift-based frequency-domain interference alignment scheme. Through pre- and postprocessing, we guarantee that one V2V group is free from interference, and the other V2V group is partially interfered. The second proposed scheme is a frequency-domain precoding scheme. Through frequency-domain precoding, the intergroup interference between the two V2V groups can be mitigated completely.

Filter bank multi-carrier (FBMC) entitles many advantages over orthogonal frequency division multiplexing (OFDM) and is considered to be a more competitive waveform in the future generation cellular communications. In current FBMC, the prototype filter is deliberately designed to meet the perfect reconstruction (PR) constraint to establish subband orthogonality in real domain, which may not be optimal from communication perspective. In this paper, we challenge the necessity of PR constraint by proposing a new FBMC framework, which directly accepts non-orthogonal transmission. The resulting imperfect reconstruction FBMC (iPR-FBMC) has several advantages over its PR FBMC counterpart: 1) the constraint on the prototype filter is relaxed; 2) more importantly, the prototype filter can now be optimized with new goal of improving the detection performance rather than having to meet the PR condition; and 3) it allows for more flexible subband management in multi-user scenario. We will show how those advantages can be exploited. Simulations show that with moderate increase in computational complexity, the proposed iPR-FBMC with optimized prototype filter has superior bit error rate (BER) performance to existing FBMC with PR constraint and even outperforms OFDM, especially in highly frequency selective channels. The findings may shed light into potential research on non-orthogonal FBMC without PR constraint.

In this study, the authors propose a cyclic prefix (CP) based imperfect reconstruction (IPR) filter bank multicarrier (FBMC) system in the uplink of multiuser networks, known as filter bank multiple access (FBMA). The prototype filter in the system is a common low‐pass filter without any perfect reconstruction (PR) or nearly perfect reconstruction (NPR) constraint. CP appended input signal format is applied to avoid inter‐symbol interference and facilitate the equalisation task. The received signal is jointly processed by multi‐subband equalisation. Besides, the input data symbols can be complex‐valued due to IPR design. Some properties and achievable data rate of the proposed system are discussed. Simulations illustrate that the proposed system with uniformly/non‐uniformly designed filter bank has a similar achievable data rate to orthogonal frequency division multiple access (OFDMA). Bit error rate performance of the proposed system is better than OFDMA and an error floor always exists when applying conventional PR FBMC to multi‐user networks. In addition, OFDMA is more sensitive to carrier frequency offset than FBMA because of full synchronisation requirement. Therefore, it suggests that the proposed CP based IPR FBMA system works well without PR/NPR conditions and more extensive development is feasible for the filter banks in the foreseeable future.

In indoor optical wireless communication systems with intensity modulation (IM) and direct detection (DD), diffuse links could be established through reflecting. For such a case, line-of-sight does not exist between the transmitter and receiver, and the channel delay spread will be large. The dispersion of the channel might cause inter-symbol interference. Also, the transmitted signal must be nonnegative. Thus most modulation schemes for radio communications cannot be used directly for such a system. This paper develops a new multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) IM/DD systems: real and imaginary coefficients separation (RIS) system. Two transmit data streams require only one inverse fast Fourier transform (IFFT) block, resulting in a low computational complexity. Compared with traditional OFDM based schemes, the RIS scheme achieves a better BER performance and requires fewer IFFT blocks at the transmitter. The transmitter architecture and receiver algorithm are analyzed in detail. BER performance and simulation results are provided.

Full-duplex transmission can be easily implemented on a visible light communication (VLC) link. In this paper, we investigate the performance of a VLC network with full-duplex optical links. We propose two contention protocols, named U-ALOHA and FD-CSMA, to utilize the full-duplex capability effectively. Their performances in terms of channel utilization and network throughput are analyzed and simulated and compared with a protocol with half-duplex links. The results show that the proposed protocols can effectively exploit the full-duplex capability. U-ALOHA achieves high channel utilization on the downlink channel while FD-CSMA has good performance on both downlink and uplink channels. Both of them outperform the protocol with half-duplex links when the traffic load is sufficiently high.

In visible light communication (VLC) systems with intensity modulation (IM) and direct detection (DD), the modulation bandwidth of light emitting diode (LED) is limited, and dispersion of the channel might cause inter-symbol interference. Also, the transmitted signal must be nonnegative. Thus most modulation schemes for radio communications cannot be used directly for such a system. This paper develops a multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) VLC system that overcomes the modulation bandwidth limitation of LED and satisfies the requirements of the transmitted signal for IM/DD. Also, two transmit data streams require only one inverse discrete Fourier transform (IDFT) block, resulting in a low computational complexity. The transmitter architecture, receive algorithm, bit error rate performance, channel capacity analysis, and simulation results are provided.

60 GHz technology is increasingly popular owing to its wide unlicensed spectrum resources and the support of numerous commercial giants. Orthogonal frequency division multiplexing (OFDM) is a promising technology for 60 GHz application. In this paper, we propose an improved pilot design based on the IEEE 802.11.ad standard and give the corresponding channel estimation method suitable for 60 GHz OFDM scheme. It is shown that the channel estimation method is particularly effective in slowly time-varying channel when signal to noise ratio (SNR) is high. The method is not sensitive to the change of frame length and can even work in a fast time-varying channel.

Visible light communication (VLC) utilizing white light emitting diode (LED) is considered as a strong candidate for the future lighting technology. The LEDs are distributed across the ceiling and have wide irradiance distribution for achieving adequate general lighting. But the bandwidth-limited LED channel and indoor multipath propagation delays cause severe inter-symbol interference (ISI). Bit error rate (BER) performance of the indoor visible light wireless system is degraded significantly when increasing the data rate. In this paper, we discuss about the equalization system using fractionally-spaced equalization (FSE) and evaluate its performance in a VLC system. As a result, the performance of FSE is superior to the equalizer with symbol interval sampling. Further more, this equalization method is suitable for a bandwidth-limited optical multipath channel.

This article studies adaptive modulation for QR decomposition and successive interference cancellation receivers in multiple‐input multiple‐output systems over slow‐ and fast‐fading channels. In slowly fading channels, adaptive modulation schemes with finite‐rate feedback under the constraints of a constant total transmit power, discrete‐rate and a target bit‐error‐rate (BER) are proposed. Specifically, the authors first develop adaptive modulation with modulation order feedback only. For power allocation and modulation order feedback, the authors establish an optimisation scheme that can be solved easily and analyse the effect of power‐level quantisation on the achievable data rates. For the fast fading channel case, the authors analyse the probability distribution of the ‘ R ’ matrix and derive a scheme that only requires feedback of the statistical information of the channel for adaptive modulation with optimal bit loading to guarantee that the BER target is met.

A generalized version of orthogonal frequency division multiplexing interleave division multiple access (OFDM-IDMA) referred to as grouped OFDM-IDMA (G-OFDM-IDMA) is introduced in this paper. By dividing users into groups and transmitting each group's data only on some (as opposed to all) subcarriers, G-OFDM-IDMA can have much lower decoding complexity compared with conventional OFDM-IDMA while preserving the bit error probability (BEP) performance and the bandwidth efficiency. The user grouping problem is formulated into an integer linear programming problem whose suboptimal solution is proposed and compared with the lower bound. The optimization complexity issue is also addressed. Simulations are carried out to test the performance of G-OFDM-IDMA under various system configurations. It is observed that up to 80% complexity could be saved when the conventional OFDM-IDMA is substituted by G-OFDM-IDMA configured according to the suboptimal grouping solution.

The bit error rate (BER) performance of interleave division multiple access (IDMA) based systems can be predicted by a semi-analytical method referred to as signal-to-noise ratio (SNR) evolution. SNR evolution tracks the average symbol SNR at each iteration and provides a faster solution than brute-force simulation. In this paper a revised SNR updating formula is proposed for orthogonal frequency division multiplexing interleave division multiple access (OFDM-IDMA) systems in Rayleigh fading channels. By alternating the order of expectations and division of random variables when updating the average symbol SNR, a more accurate approximation of the expected SNR is obtained compared with the existing formula. Hence improved BER prediction performance can be achieved, which is verified by simulations.

This paper presents a new joint frame synchronization and frequency offset estimation algorithm for orthogonal frequency division multiplexing (OFDM) systems as a modification to Zhang's method (Zhang et al., 2005). By designing a new training preamble weighted by PN sequence, the timing estimator is improved (at least 3dB with low SNR). By estimating time offset first, the computational load is greatly reduced with no loss in frequency offset estimation accuracy. The performance of the proposed method is evaluated by computer simulations in terms of timing error rate (TER) together with computational complexity.

Multicast is very efficient in distributing a large volume of data to multiple receivers over the Internet. Layered multicast helps solve the heterogeneity problem in multicast delivery. Extensive work has been done in the area of layered multicast, for both congestion control and error control. In this paper, we focus on network-supported protocols for streaming media. Most of the existing work solves the congestion control and error control problems separately and does not give an integrated efficient solution. In this paper, after reviewing related work, we introduce our proposed protocols, namely, router-assisted layered multicast (RALM) and router-assisted layered FEC (RALF). The former is a congestion control protocol, whereas the latter is an error control protocol. They work under the same framework and provide an integrated solution. We also extend RALM to RALM-II, which is compatible with transmission control protocol (TCP) traffic. We analyze the complexity of the proposed protocols in the network and investigate their performance through simulations. We show that our solution achieves significant performance gains with reasonable additional complexity.

The two well-known on-demand protocols for ad hoc networks - DSR and AODV show degraded performance with traffic load increase. In this paper, we propose a traffic-balanced scheme, called TB scheme, to solve this problem. The TB scheme adopts MAC layer information for routing decisions. It avoids involving heavy loaded nodes in a new route and thus balances network traffic. Simulation results show that the new scheme significantly improves DSR and AODV's performance under heavy load conditions.

In this paper, we propose RALF, a new FEC-based error control protocol for layered multicast video. RALF embodies two design principles: decoupling transport layer error control from upper layer mechanisms and decoupling error control and congestion control at the transport layer. RALF works with our previously proposed protocol RALM - a layered multicast congestion control protocol with router assistance. RALF provides tunable error control services for upper layers. It requires no additional complexities in the network beyond those for RALM. Its performance is evaluated through simulations in NS2.

Several layered multicast protocols have been proposed for congestion control in real-time multicast applications. Most of them are pure end-to-end protocols, thus having difficulty in coordinating receivers and coping with traffic variations. We propose RALM, a new receiver-driven router-assisted layered multicast protocol. RALM achieves much better performance at the expense of moderate additional complexity in the network. RALM is incrementally deployable. We evaluate RALM through simulations, and compare its performance with RLM, the well known layered multicast protocol.

Multicasting is a mechanism to send data to multiple receivers in an efficient way. We give a comprehensive survey on network and transport layer issues of Internet multicast. We begin with an introduction to the current Internet protocol multicast model-the "host group" model and the current Internet multicast architecture, then discuss in depth the following three research areas: (1) scalable multicast routing; (2) reliable multicast; and (3) multicast flow and congestion control. Our goal is to summarize the state of the art in Internet multicast and to stimulate further research in this area.