Unmanned aerial vehicles (UAVs) enable flexible network deployment but are vulnerable to detection due to the open-access nature of wireless signals. Visible light communication (VLC) emerges as a compelling technology for this context, offering inherent covertness through its reliance on line-of-sight links, while its wide spectrum supports high covert transmission rates. However, the high mobility of UAVs creates dynamic channel conditions that challenge traditional multiple access schemes. This motivates the utilization of rate-splitting multiple access (RSMA), a more robust and flexible framework for managing multi-user interference. Therefore, we investigate a cooperative, RSMA-based VLC system where a source UAV provides covert data transmission, assisted by a jamming UAV that creates power randomness to confuse wardens. By jointly optimizing the three-dimensional (3D) trajectory design and resource allocation of UAVs, we aim to maximize the minimum average covert transmission rate of users. To achieve this goal, the original problem is decomposed into three subproblems, namely horizontal coordinate arrangement, altitude adjustment, along with joint power assignment and rate-splitting, which are solved by the successive convex approximation method, geometric programming-based approach, and majorization-minimization algorithm, respectively. Numerical results validate the effectiveness of the proposed approach through extensive simulations under various parameters and comparative analyses against baselines, highlighting the potential of UAV-assisted visible light covert communications.

In this paper, we study a system in which a sensor forwards status updates to a receiver through an error-prone channel, while the receiver sends the transmission results back to the sensor via a reliable channel. Both channels are subject to random delays. To evaluate the timeliness of the status information at the receiver, we use the Age of Information (AoI) metric. The objective is to design a sampling policy that minimizes the expected time-average AoI, even when the channel statistics (e.g., delay distributions) are unknown. We first review the threshold structure of the optimal offline policy under known channel statistics and then reformulate the design of the online algorithm as a stochastic approximation problem. We propose a Robbins-Monro algorithm to solve this problem and demonstrate that the optimal threshold can be approximated almost surely. Moreover, we prove that the cumulative AoI regret of the online algorithm increases with rate$\mathcal {O}(\ln K)$, where$K$is the number of successful transmissions. In addition, our algorithm is shown to be minimax order optimal, in the sense that for any online learning algorithm, the cumulative AoI regret up to the$K$-th successful transmissions grows with the rate at least$\Omega (\ln K)$in the worst case delay distribution. Finally, we improve the stability of the proposed online learning algorithm through a momentum-based stochastic gradient descent algorithm. Simulation results validate the performance of our proposed algorithm.

Vehicular fog computing (VFC) offers a promising paradigm to alleviate vehicular computing burdens and ensure timely processing of computing tasks. In this paper, a heterogeneous VFC system leveraging hybrid visible light communication (VLC) and radio frequency (RF) communications is investigated, exploiting the interference resilience of VLC and the extended coverage of RF for efficient task offloading to multiple idle vehicles while harnessing their computing resources for parallel task computing. On this basis, an average task processing delay (TPD) minimization problem is formulated, thereby encompassing task offloading, computing and communication resource allocation for rapid processing of computing tasks. Then, the non-convex problem is decomposed into three subproblems and iteratively solved within a block coordinate descent (BCD) framework. Within this framework, the residual additive majorization-minimization algorithm is employed for task offloading and computing resource allocation, the transformed majorization-minimization method is applied for joint power adjustment of both VLC and RF, and the relaxed optimization and reconstruction approach is developed for subchannel assignment. Comprehensive simulations validate the superiority of the heterogeneous VFC system over local computing and VFC systems relying on VLC or RF, and demonstrate the convergence of the proposed BCD-based algorithm and its superiority over baselines. Additionally, the influence of key parameters on the average TPD is also characterized, providing essential design guidelines for practical implementations.

Covert communications play a crucial role in modern communication systems, ensuring privacy and security in urban environments. Visible light communication (VLC) emerges as a promising technology due to its potential for covert operations within established infrastructures. In this paper, the covert communication for non-orthogonal multiple access (NOMA)-based VLC systems is investigated, by leveraging the assistance of optical intelligent reflecting surface (OIRS).We develope a system model that allows covert user information to be hidden in the public user information, and to disrupt the warden by altering the channel gain through OIRS. Moreover, the problem is formulated to maximize the effective covert spectral efficiency (SE) of the visible light covert communication (VLCC) system with the detection error probability (DEP) and connection outage probability (COP) constraints, and then an optimization framework is proposed to solve the non-convex problem for both multiple-input single-output (MISO) and single-input single-output (SISO) scenarios. For MISO, a block coordinate descent-based optimization algorithm is developed, decomposing the problem into two sub-problems, named power allocation and OIRS configuration sub-problems. For SISO, closed-form solutions for the power ratio relative to OIRS configuration are derived, simplifying the optimization process. Moreover, we analyzed two distinct cases where Willie either possesses or lacks the knowledge of OIRS state, thereby revealing the impact of warden capabilities on covert communications. Furthermore, the effect of imperfect acquisition of Willie’s channel state information is also examined on the effective covert SE. Simulation results demonstrate that the proposed algorithm enhances the effective covert SE of VLCC systems, while exploring the impact of OIRS units number and other key factors. These findings highlight the potential of OIRS to improve the covert performance in future optical communications.

The growth of indoor terminals has put higher demands on wireless communications, which not only need to support high-rate communications, but are also required to provide power for energy-limited terminals. However, conventional wireless communication systems face challenges in meeting these demands due to the spectrum congestion and electromagnetic interference. Recently, visible light communication (VLC) has been emerged as a promising technology, which can take advantage of its ability to operate within the unlicensed spectra. Therefore, in this paper, VLC benefits from the facilitation of optical intelligent reflecting surface (OIRS) and non-orthogonal multiple access (NOMA) to support energy-constrained devices through simultaneous lightwave information and power transfer (SLIPT), which is well suited for future VLC systems. Moreover, an optimization framework is proposed for NOMA-based VLC systems with OIRS for SLIPT, and then the problem is formulated to minimize the total energy consumption with several key constraints. To solve this non-convex problem, we decompose it into four sub-problems, including the OIRS configuration, NOMA coefficient assignment, transmit power allocation, and direct current bias arrangement, which are solved iteratively by the block coordinate descent algorithm through relaxed iterative optimization, minorization-maximization algorithm, and successive convex approximation. Simulation results validate the convergence and effectiveness of the proposed algorithm, demonstrating the influence of OIRS unit number and other key factors on the system performance. These findings highlight the potential of OIRS to enhance the power transfer capabilities of future optical communications.

Nonlinear hybrid precoding can approach capacity, but it is computationally demanding. This paper investigates low-complexity nonlinear hybrid precoding algorithms designed to maximize the sum-rate of massive multiple-input multiple-output broadcast channels (MIMO-BC). We propose a closed-form solution for analog precoding in narrowband systems, transforming the constant modulus constraint into an equivalent unconstrained problem with phase variables, which can be efficiently solved using the Fletcher-Reeves conjugate gradient method. Additionally, we introduce a nonlinear hybrid precoding technique for wideband systems, specifically tailored to delay-phase networks, which leverages channel characteristics to reduce computational complexity without relying on optimization. To further reduce the complexity of subcarrier-level digital precoding, we develop a resource block (RB)-level nonlinear precoding algorithm, where signals within each RB are independently precoded using a common precoder. Simulation results show that the proposed algorithms achieve near-optimal sum-rate performance while maintaining computational efficiency in both narrowband and wideband scenarios.

To overcome the performance limitations and computational complexity induced by the unimodular constraint in conventional hybrid precoding designs, this paper proposes a novel optoelectronic base station (OE-BS) architecture, replacing the traditional phase shifter network with an optical network. The proposed OE-BS architecture facilitates simultaneous amplitude and phase control in the analog domain, effectively eliminating the unimodular constraint and significantly enhancing the design flexibility. Based on the proposed OE-BS architecture, a closed-form hybrid precoding solution is first derived for narrowband systems, thus avoiding iterative optimization procedures. For wideband systems, a user-selective hybrid precoding algorithm is developed, where the digital precoder is designed according to the zero-forcing criterion, and the analog precoder and power allocation are jointly optimized using an alternating optimization method. Simulation results demonstrate that the proposed OE-BS schemes outperform conventional methods in both narrowband and wideband scenarios, enhancing overall system performance while reducing computational complexity.

Low Earth orbit (LEO) satellite communications have received significant attention due to the global coverage and low-latency characteristics. Due to the limited size, weight, and power of LEO satellites, meeting the demand of satellite-terrestrial communications with minimal energy consumption while suppressing interference to co-existing systems remains a significant challenge. In this paper, a LEO satellite architecture with beam hopping (BH) is proposed, where the dynamic resource management and real-time load balancing are jointly implemented. Specifically, an optimization problem for the BH pattern, resource management, and load balancing with multi-satellite collaboration is formulated to achieve the long-term average energy minimization, while suppressing the interference to the co-existing system. Subsequently, by introducing the Lyapunov optimization method, the long-term energy minimization is transformed into a series of dynamic optimization problems for each time slot, thereby ensuring both the energy minimization and queue stability, where the block coordinate descent algorithm is employed to alternately address these issues. In particular, the BH pattern is determined by the multiplier penalty method combined with the majorization-minimization algorithm. Additionally, the resource management is addressed through the successive convex approximation technique, while real-time load balancing is achieved via quadratic programming. Overall, the proposed method, which can be effectively implemented within polynomial time, outperforms other benchmarks in simulations by achieving interference suppression in co-existing systems and reducing the energy consumption, while satisfying the communication demands.

The mega low-Earth-orbit (LEO) satellite constellation can achieve high throughput and low latency satellite communications, which is highlighted for next-generation communications. In this paper, the LEO satellite communications with coordinated beam hopping (BH) are investigated for throughput maximization and load balancing. Generally, the geographically non-uniform and time-varying packet arriving, along with the interference caused by dense beams, can present significant challenges. Hence, the proper BH pattern selection and dynamic power allocation are necessitated to enhance the throughput of satellite-terrestrial communications. Additionally, load balancing via inter-satellite links (ISLs) adjusts the queue length among satellites, thus indirectly altering the throughput by mitigating congestion. Therefore, an optimization problem is formulated to maximize long-term throughput by determining the BH pattern and dynamic power, while the problem for load balancing via ISLs is also proposed for stabilizing queues in a low congestion state to enhance the throughput. As both problems are inherently associated with each other through the queue length, they are solved in cycles. Specifically, the problem for long-term throughput is converted into decisions for each time slot with the Lyapunov drift-plus-penalty method, and then the BH pattern and power allocation are optimized alternately through successive convex approximation. Moreover, load balancing via ISLs is determined according to the queue length by minimizing the upper bound of Lyapunov drift, thereby decreasing the congestion of queues. Simulation results indicate that the proposed method can effectively improve the throughput and reduce the packet loss compared with the existing baselines, demonstrating the effectiveness of the Lyapunov optimization based method.

Beamforming and waveform design are key essentials for integrated sensing and communication (ISAC) in future wireless networks, which also inspires the emerging research of optical wireless ISAC (OW-ISAC). In this paper, we investigate the joint design of optical phased array (OPA) beamforming and orthogonal frequency division multiplexing (OFDM) waveform, thus extending the radio-frequency (RF)-ISAC paradigm to the optical band. First, the system model for the OPA-based OW-ISAC framework is introduced to facilitate simultaneous communication and sensing. Next, signal processing techniques are tailored for an optical OFDM waveform with precisely separated communication and sensing subcarriers. Then, the joint optimization problem of OPA beamforming and OFDM waveform design is formulated, decomposed, and resolved to reach a flexible compromise between communication and sensing performances. Moreover, numerical simulations validate the effectiveness of the proposed OW-ISAC scheme and reveal the trade-off between communication and sensing functionalities. Consequently, the capabilities of reliable communication and precise sensing will establish OW-ISAC as a powerful complement to RF-ISAC in the near future.

In this paper, we propose an innovative relay-aided physical layer security scheme leveraging cooperative transmission and jamming that operates effectively in time-division-multiple-access-based multi-user visible light communication (MuVLC) systems with obstructed communication links. First, to circumvent obstacles between the light source (LS) and legal receivers, we utilize a transmission relay (TR) to forward information signals from the LS to the receiver plane. Subsequently, to ensure communication security, we select a jamming relay to enlarge the channel capacity gap between eavesdroppers and legal receivers through cooperative jamming. Using stochastic geometry, we derive closed-form expressions for key performance metrics, including the cumulative distribution function of secrecy capacity, security outage probability, and their lower and upper bounds, both with and without relay cooperation. To further enhance system security, we introduce a disk-shaped security-protected zone around the TR. All analytical expressions are numerically verified through Monte Carlo simulations, serving as benchmarks to evaluate security performance. Numerical results confirm that our proposed scheme can ensure continuous signal reception and high-level security. Furthermore, while relay cooperation modestly enhances system security, the introduction of a security-protected zone around the TR yields substantial security improvement, which offers practical insights for designing secure time-division-multiple-access-based MuVLC where communication links are obstructed.

With the rising sophistication of cyber threats and the growing reliance on communication infrastructure, ensuring secure transmission has become a critical challenge. This work investigates the visible light covert communication for unmanned aerial vehicle (UAV) systems employing rate-splitting multiple access, aiming to conceal the very presence of transmission. Besides, the optimal detection threshold and detection error probability are derived to assess the covertness quantitatively. Through jointly optimizing the trajectory design and resource allocation, this work is devoted to maximizing the minimum average covert transmission rate (MACTR) among multiple users. Simulation results verify that the proposed algorithm substantially improves the MACTR compared to the baseline and conventional multiple access schemes, highlighting its effectiveness in enhancing the security for UAV systems.

Optical wireless integrated sensing and communication (OW-ISAC) is rapidly advancing as a compelling alternative to its radio-frequency counterpart. In this paper, an OW-ISAC framework utilizing optical phased array (OPA) is presented, which addresses the critical challenge of angle-domain sensing in OW-ISAC by incorporating OPA beamforming. First, a comprehensive system structure is proposed to enable simultaneous optical wireless communication and profile estimation for extended targets, where the interference among different angle grids arises from the omnidirectional detection of photodiode. Then, an interference-cancellation scheme is developed based on OPA beamforming, which is further transformed into the optimization for the observation matrix and subsequently solved by a perturbation-based method. Moreover, the capabilities of concurrent reliable communication and precise sensing are validated through numerical analysis. Furthermore, the trade-off between communication and sensing performance metrics is also unveiled by tuning the optimization threshold, which enhances the practical viability of OW-ISAC.

In this paper, we investigate the optimization of power allocation and optical intelligent reflecting surface (OIRS) configuration for simultaneous lightwave information and power transfer (SLIPT) in non-orthogonal multiple access (NOMA)based visible light communication (VLC) systems, while both the line-of-sight and OIRS-reflected channels are considered. The problem is formulated to minimize the total power consumption of the whole system, which is solved by the minorizationmaximization method and relaxed iterative algorithm under various key constraints. Moreover, simulation results not only show that the proposed algorithm effectively reduces the system energy consumption while satisfying the constraints, but also demonstrate the enhancement of the system by OIRS, which is especially significant at low signal-to-noise ratio conditions.

In this paper, a multi-satellite cooperative beam hopping (BH) scenario in a low Earth orbit (LEO) satellite constellation is studied for satellite-terrestrial communications. Generally, the geographically non-uniform and time-varying packet arrival process, as well as the interference caused by dense beams, presents significant challenges in throughput maximization. To address these issues, an optimization problem is formulated to maximize long-term throughput in a multi-satellite cooperation scenario by determining the BH pattern and dynamic power allocation, while satisfying both peak and average power constraints. Subsequently, the original long-term problem is converted into decisions for each time slot through the Lyapunov drift-plus-penalty method. Moreover, the non-convex problem is solved with the successive convex approximation algorithm. Simulation results indicate that the proposed method can effectively improve throughput compared with the existing baseline, which demonstrates the effectiveness of the proposed method.

In this paper, an optical intelligent reflecting surface (OIRS)-aided visible light positioning (VLP) system that adopt received signal strength (RSS) fingerprint method is proposed to enhance the positioning accuracy. We employ OIRS to improve the minimal RSS difference between neighboring positions in conventional RSS fingerprint-based VLP systems, involving a two-stage positioning scheme where the OIRS is strategically aligned to refine the positioning accuracy. Subsequently, the considered problem is formulated to maximize the minimum Euclidean distance by optimizing the OIRS configuration, which is then solved by an successive convex approximation (SCA)-based algorithm. Moreover, simulation results demonstrate that the proposed OIRS-assisted VLP system substantially outperforms conventional benchmarks, thereby achieving superior positioning accuracy.

This paper presents a novel integrated sensing and communication (ISAC) system based on multiple input multiple output (MIMO) and orthogonal frequency division multiplexing (OFDM) technologies with a non-orthogonal signal design that refines spectrum efficiency. Inspired by non-orthogonal multiple access (NOMA) scheme, we superimpose pseudo-random noise (PN) sequences and OFDM symbols in the power domain to enable simultaneous decimeter-level ranging and broadband communication over shared spectrum. A genetic algorithm optimizes power allocation to jointly achieve high-precision ranging and data throughput in the proposed frame. MIMO-based spatial processing can support high performance joint ranging and data detection. Experimental results demonstrate that the ranging precision of better than 1% of the actual distance and the data rate of over 1 Mbps can be achieved at a 6km distance under the signal-to-noise ratio (SNR) below -8dB, which confirm the system’s dual functionality and compatibility with legacy OFDM, offering a new direction for future integrated networks.

In this paper, a novel integrated control and communication (ICC) framework is proposed for vehicle platoons, ensuring platoon stability while optimizing energy consumption for communications. To address the high signaling overhead and long delays caused by multi-link competition in radio-frequency-based vehicle-to-vehicle communications, visible light communications (VLCs) are employed, allowing adjacent vehicles to establish collision-free links and simultaneously transmit motion status information (MSI) for platoon control. Besides, age of information (AoI) is utilized to quantify the heterogeneous timeliness of the MSI. Building upon this, a distributed linear control strategy is designed for vehicle platoons with a multi-predecessor-leader-multi-following information topology (IT) and heterogeneous AoIs. Afterward, through stability analysis, the maximum allowable AoI threshold is obtained to guarantee both internal stability and input-state string stability under external disturbances. Accordingly, taking the AoI threshold as a constraint, a long-term energy minimization problem is further formulated to optimize the energy consumed by multi-source, multi-hop, and multicast intra-platoon VLC while maintaining platoon stability. To solve this problem, an online distributed information scheduling policy based on Lyapunov optimization is proposed. Finally, simulation results reveal that the proposed ICC framework effectively maintains platoon stability across various ITs, while significantly reducing the energy consumption for communications to no more than 40% of that achieved by multiple baselines, thus highlighting its substantial potential in vehicle platoons.

In multicell downlink vehicular visible light communication (VLC) systems, vehicles at a cell edge experience lower achievable data rates compared with those at a cell center, primarily due to the weaker channel gain and intercell interference, leading to unfairness among vehicles. In this article, a cooperative transmission approach is proposed for a vehicular VLC system with optical intelligent reflecting surface (OIRS) to enhance the max-min fairness of the system by leveraging the additional OIRS-reflected channels and the interference-mitigating capabilities of cooperative transmission. To this end, the system model is established, followed by the formulation of a resource allocation problem aiming at enhancing max-min fairness, in which the minimum achievable data rate among vehicles is optimized. Then, an effective resource algorithm is proposed, transforming the original problem into an equivalent form and subsequently decomposing it into three subproblems, focusing on OIRS assignment, subchannel allocation, and power adjustment, respectively. By employing a block coordinate descent algorithm, the three subproblems are solved iteratively until convergence. In addition, simulation results confirm the improvement in max-min fairness brought by OIRS and the proposed cooperative transmission approach. Moreover, compared with various baselines, the proposed resource allocation algorithm significantly enhances in the max-min fairness for the vehicular VLC system, crucial for this application.

Uncrewed aerial vehicles (UAVs) have gained significant attention due to their low cost and efficient deployment. Specifically, UAV-based communications hold promise to supplement the coverage limitations of terrestrial networks and achieve superior connectivity for the Internet of Things (IoT). Moreover, UAVs with visible light communication (VLC) capability do not interfere with existing radio frequency (RF) communication infrastructure, thereby providing flexible, high-throughput on-demand wireless communications. In this article, considering the limited battery capacity of UAVs, we aim to maximize the energy efficiency (EE) of a multi-UAV downlink VLC system for IoT. To this end, the system model with co-channel interference is first established, followed by the formulation of the joint spatial deployment and resource allocation problem. Besides, the original problem is decomposed into three subproblems, focusing on spatial deployment, subchannel assignment, and power allocation. By employing the block coordinate descent (BCD) method, the three subproblems are solved alternately. Moreover, simulation results demonstrate that the proposed method achieves superior improvements in EE compared to other baselines. In addition, the convergence and computational complexity of the proposed algorithm, as well as the impacts of key parameters on the overall system performance are also investigated.

Optical intelligent reflecting surface (OIRS) is a promising technology in visible light communications (VLCs), which can help VLC overcome the shortcoming of being susceptible to occlusion. It is shown that OIRS has many advantages for nonorthogonal multiple access (NOMA)-based VLC due to its ability to reconfigure optical wireless channels. In this article, we propose an effective OIRS-aided physical layer security (PLS) scheme for NOMA-based VLC networks against multiple eavesdroppers (Eves). By exploiting artificial noise (AN) to jam Eves, the maximization problem of the minimum achievable secrecy data rate is investigated, subject to successive interference cancellation (SIC) decoding conditions and OIRS constraints. Specifically, the original problem is decomposed into the power allocation and OIRS configuration subproblems by a block coordinate descent (BCD) algorithm. Moreover, the semi-definite relaxation and successive convex approximation are employed to solve the subproblems, after which a stochastic probability assignment method is adopted for the integer OIRS constraint. Finally, simulation results demonstrate that AN can enhance the system performance in most scenarios, and the minimum achievable secrecy data rate can be significantly improved by the proposed algorithm, demonstrating the potential of OIRS for enhancing PLS in optical wireless communications.

As one of the six usage scenarios of the sixth-generation (6G) mobile communication system, integrated sensing and communication (ISAC) is regarded as a key enabler for the future Internet of Everything (IoE). While numerous studies have been conducted in radio-frequency (RF)-ISAC, free-space optical (FSO)-ISAC is also laying the foundation for the era of connection and intelligence. In this article, a direct-current (DC)-biased optical orthogonal frequency-division multiplexing (DCO-OFDM) scheme is proposed for FSO-ISAC. To derive the performance metrics for communication and sensing, we model the clipping noise of DCO-OFDM as additive colored Gaussian noise and establish an equivalent frequency-selective channel for FSO-ISAC. In addition, joint power allocation problems are formulated for both communication-centric and sensing-centric scenarios based on the derived performance metrics. Subsequently, these nonconvex joint optimization problems are decomposed into subproblems for DC bias and subcarriers, which can be solved by block coordinate descent algorithms. Furthermore, numerical simulations demonstrate the effectiveness of proposed methods and reveal the tradeoff between communication and sensing functionalities of FSO-ISAC based on DCO-OFDM.

Ultraviolet (UV) communication research has prioritized channel modeling for its critical role in system optimization. Current non-line-of-sight (NLoS) UV modeling mainly addresses obstacle-free scenarios and single-obstacle situations: the former manifests constrained applicability at small transceiver elevation angles with obstacle susceptibility, while the latter suffers from high modeling complexity and can only handle one-obstacle scenarios, which pose critical challenges for Internet of Things applications. To overcome these limitations, we propose a single-collision model for short-range NLoS UV channels incorporating both scattering and reflection effects. Initially, the impact of air scattering on the received pulse energy is presented for diverse obstacle situations, where an obstacle-boundary approximation method (OBAM) is developed to reduce the modeling complexity. Besides, the dimensions, coordinates, shapes, orientation angles, and number of obstacles are considered to emulate practical environments. Subsequently, the impact of obstacle reflection on the received pulse energy is investigated for single, double, and multiple obstacle situations. On this basis, we account for certain scenarios where obstacle surfaces comprise multiple sub-regions, each characterized by distinct reflection coefficients attributed to their varying material compositions. Moreover, we verify the proposed model by comparing it with the Monte-Carlo photon-tracing (MCPT) model and the obstacle-free integral model via simulations. These results demonstrate that the path loss curves obtained by the proposed model exhibit close alignment with those simulated by the MCPT model, while its calculation time is less than 10% that of the MCPT model. Additionally, when obstacle reflection is prominent, the assessment error of the proposed OBAM can be ignored in estimating the path loss of NLoS UV channels containing obstacles.

Non-orthogonal multiple access (NOMA)-based visible light communication (VLC) is considered a promising technique for next generation high-speed wireless communications. The emerging optical intelligent reflecting surface (OIRS) offers significant benefits to the NOMA-based VLC, since it can mitigate signal blockage issues of VLC and improve its performance by manipulating channel qualities of users. This paper investigates the enhancement of OIRS to the NOMA-based multi-cell VLC system and explores the resource allocation problem for the energy efficiency (EE) maximization. To this end, the channel gains of the system are discussed, considering both the line-of-sight and OIRS-reflected paths. Then, the system model of NOMA-based VLC is established and the optimization problem is formulated to maximize the overall EE of the system. Next, the original optimization problem is decomposed into three sub-problems, which are solved by the proposed algorithms iteratively. Moreover, the simulation results demonstrate the convergence of the proposed algorithm and the enhancement in EE achieved by OIRS, as well as the influence of key parameters on the system performance, which can offer insights on resource allocation for NOMA-based VLC systems with OIRS.

Optical intelligent reflecting surface (OIRS) has attracted increasing attention due to its capability of overcoming signal blockages in visible light communication (VLC), an emerging technology for the next-generation advanced transceivers. However, current works on OIRS predominantly assume known channel state information (CSI), while its estimation problem has not been studied yet. To bridge such a gap, this paper proposes a new and customized OIRS channel estimation protocol with joint space-time sampling under the alignment-based OIRS channel model. First, we unveil the spatial and temporal coherence characteristics and derive OIRS coherence distance and coherence time in closed form. Next, to achieve dynamic beam alignment for pilot transmission within the coherence time, we propose to tune the rotation angles of the OIRS reflecting elements following a geometric optics-based non-uniform codebook. Then, given the beam alignment within the considered coherence time, a sequential OIRS channel estimation method is proposed, where the OIRS is divided into multiple subarrays based on the coherence distance. The CSI for each subarray is estimated sequentially, followed by a space-time interpolation to retrieve full CSI for other non-aligned transceiver antennas. Numerical results validate our theoretical analyses and demonstrate the efficacy of the proposed OIRS channel estimation protocol as compared to benchmark schemes.

As transceiver elevation angles increase from small to large, existing ultraviolet (UV) non-line-of-sight (NLoS) models encounter two challenges: 1) cannot estimate the channel characteristics of UV NLoS communication scenarios when there exists an obstacle in the overlap volume between the transmitter beam and the receiver field-of-view (FoV), and 2) cannot evaluate the channel path loss for the wide beam and wide FoV scenarios with existing simplified single-scattering path loss models. To address these challenges, a UV NLoS scattering model incorporating an obstacle was investigated, where the obstacle’s orientation angle, coordinates, and geometric dimensions were taken into account to approach actual application environments. Then, a UV NLoS reflection model was developed combined with specific geometric diagrams. Further, a simplified single-scattering path loss model was proposed with a closed-form expression. Finally, the proposed models were validated by comparing them with the Monte-Carlo photon-tracing model, the exact single-scattering model, and the latest simplified single-scattering model. Numerical results show that the path loss curves obtained by the proposed models agree well with those attained by related NLoS models under identical parameter settings, and avoiding obstacles is not always a good option for UV NLoS communications. Moreover, the accuracy of the proposed simplified model is superior to that of the existing simplified model for all kinds of transceiver FoV angles.

In the double-sided near-field extremely large-scale multi-input multi-output (XL-MIMO) systems, due to the spherical-wavefront propagation, the line-of-sight (LoS) path exhibits multiple independent propagation components, leading to a channel rank greater than one. In contrast, the non-line-of-sight (NLoS) path is typically dominated by a single propagation component. Consequently, the unified modeling of mixed LoS and NLoS paths remains unresolved, particularly when with non-parallel and non-coplanar uniform linear arrays (ULAs) at the transceivers. Furthermore, there exist bottlenecks in the beamforming codeword design and low-overhead estimation in double-sided near-field communications. In this paper, we present a unified channel model to characterize both LoS and NLoS paths in extremely large-scale multiple-input multiple-output (XL-MIMO) systems. Apart from the transmitter (Tx)-side and receiver (Rx)-side separate response vectors, an additional Tx/Rx-coupled term with a Vandermonde windowing pattern is studied for the XL-MIMO LoS path. Two codebook-based beamfocusing schemes are proposed, which are termed as the beamspace-projection and diagonal-decomposition schemes. The achievable spectral efficiency and average power are theoretically analyzed in closed form. Under this framework, we further propose a low-overhead unified LoS/NLoS orthogonal matching pursuit (UOMP) algorithm for XL-MIMO channel estimation, which is then extended via 3-stage multiple-measurement-vector (3S-MMV) for complexity reduction. Last, simulation results demonstrate the superiority of the proposed strategies in both beamforming codeword selection and sparse channel estimation.

The underwater Internet of Things (UIoT) is crucial in developing marine resources. However, due to the low data rate of underwater channels, it is difficult to have a central server to process data from numerous devices as using terrestrial communications. Therefore, decentralized federated learning (DFL) with communication-efficient modifications is a promising alternative to empower UIoT with artificial intelligence and collaborative training. However, existing DFL strategies rely on a carefully designed small aggregation weight when aggregating parameters from neighbor nodes to mitigate the compression error, resulting in a slow convergence rate. In addition, the effect of data compression under time-varying topologies is not considered in current DFL algorithms. In response to these problems, this work studies a DFL framework with underwater acoustic channel and time-varying topology. Firstly, considering the low data rate and dynamics of the acoustic channel, we propose a practical scheme for adaptive compression and device connectivity. Moreover, we combine data compression and the error-compensation technique with time-varying topology and propose a DFL algorithm with aggregation weights decaying over time to achieve fast convergence under non-independent and identically distributed (non-IID) data. We derive a convergence bound for the proposed algorithm with respect to compression and time-varying topology and demonstrate that it achieves the same asymptotic convergence rate as centralized FL with perfect communication. Simulation results show that, compared with DFL algorithms without decaying aggregation weights and centralized FL schemes, the proposed algorithm exhibits higher accuracy and faster convergence rate in underwater environments.

Satellite-terrestrial communications based on mega low-Earth orbit (LEO) constellations enable extensive coverage and high data rates. However, the communication performance is significantly impacted by the non-uniform traffic distribution and the substantial interference caused by dense satellites. Therefore, in this paper, a multi-satellite cooperation architecture for satellite-terrestrial communications is proposed in LEO satellite constellations. Specifically, beam hopping (BH) and resource allocation enable a flexible solution for the non-uniform geographical distribution of communications, and are optimized to improve communication performance and avoid both intra- and inter-satellite interference in satellite-terrestrial communications. Moreover, load balancing via inter-satellite link (ISL) is implemented to further enhance the communication performance. Consequently, the overall problem for maximizing the network throughput and ensuring the latency metric is formulated, and then decomposed into three sub-problems: deep reinforcement learning (DRL) based BH scheduling, resource allocation with multi-satellite cooperation, and load balancing via ISLs. Specifically, DRL is utilized to determine the real-time BH pattern, and the resource allocation among beams is implemented by the majorization-minimization algorithm. Furthermore, for varying input traffic loads, different objectives for load balancing are established and solved by quadratic transformation and hybrid block successive approximation algorithm. Simulation results demonstrate that the proposed method outperforms other existing methods. Meanwhile, it obtains an 18.45% improvement in throughput compared with the benchmark without optimization, while the latency metric for satellite-terrestrial communications is also reduced by the proposed method.

Beyond 5G mobile networks are required to support the ultra-reliable data transmission with massive connectivity under high-mobility scenarios, where the delay-Doppler domain multiple access (DDMA) has been regarded as one of the potential candidates to avoid the severe performance degradation brought by double-dispersive channels. However, existing DDMA transceiver designs heavily rely on the shared codebooks or large guard space among user equipments (UEs), which restricts the flexibility, complexity, and spectral efficiency significantly. In this paper, we first propose the Zadoff-Chu training sequences-based frame structure and an element-wise iterative successive interference cancellation (SIC)-maximal ratio combining (MRC) detector for single-user transmission, which serves as the basis of flexible resource allocation in the delay-Doppler (DD) domain. The discussion is then extended to the MU downlink scenario, where rate splitting is adopted to effectively balance the noise and interference at the UE side to improve the bit error rate performance. For MU uplink cases, a non-orthogonal pilots-based iterative SIC-least square channel estimator is developed to promote the spectral efficiency while the iterative SIC-MRC detector is also provided. Simulation results demonstrate the excellent performance of the proposed DDMA scheme under typical DDMA patterns without guard space between UEs.

Integrated sensing and communication (ISAC) is regarded as one key enabler in the future sixth-generation (6G) mobile communication network. While considerable attention has been paid to radio-frequency (RF)-ISAC, optical wireless (OW)-ISAC is also rapidly developing as a powerful complement to its RF counterpart. In this paper, an enhanced asymmetrically clipped direct-current-biased optical orthogonal frequency division multiplexing (EADO-OFDM) scheme is proposed for flexible waveform design and resource allocation in OW-ISAC. A generalized OW-ISAC framework is first established to describe the working principles of both communication and sensing (C&S). Then, the signal model is introduced for EADO-OFDM, where the Price theorem is adopted to model the colored clipping noise and elicit a generalized frequency-selective channel. In addition, a joint optimization problem of resource allocation for EADO-OFDM is formulated to adaptively balance C&S performance metrics, and the solution to the problem is obtained by the block coordinate descent algorithm. Finally, extensive numerical simulations demonstrate the flexibility of the proposed EADO-OFDM scheme, while the C&S trade-off is also revealed during the resource allocation.

Optical wireless integrated sensing and communication (OW-ISAC) is emerging as a crucial technology to complement and augment its radio-frequency counterpart. In this paper, we propose an optical phased array (OPA)-based OW-ISAC framework to enable concurrent multi-user communication and environment imaging. The optical beamforming and atmospheric propagation are first elaborated to introduce the principles of OPA-based OW-ISAC. In addition, the investigation into the multi-beam property, direct detection scheme, and sensing task of imaging for OW-ISAC yields dedicated signal-to-interference-plus-noise ratio and contrast metrics for communication and sensing sub-systems, respectively. Moreover, the precoding matrices and photodiode orientations are jointly optimized to achieve optimal beamforming. Subsequently, numerical simulations illustrate the relationships between communication and sensing performance metrics. Furthermore, the proposed OW-ISAC scheme is substantiated in a realistic scenario with the optimized beamforming. The demonstrated high-precision sensing and reliable communication capabilities of OPA-based OW-ISAC can serve plentiful future applications in the era of connection and intelligence.

Optical intelligent reflecting surface (OIRS) has been considered a promising technology for visible light communication (VLC) by constructing visual line-of-sight propagation paths to address the signal blockage issue. However, the existing works on OIRSs are mostly based on perfect channel state information (CSI), whose acquisition appears to be challenging due to the passive nature of the OIRS. To tackle this challenge, this paper proposes a customized channel estimation algorithm for OIRSs. Specifically, we first unveil the OIRS spatial coherence characteristics and derive the coherence distance in closed form. Based on this property, a spatial sampling-based algorithm is proposed to estimate the OIRS-reflected channel, by dividing the OIRS into multiple subarrays based on the coherence distance and sequentially estimating their associated CSI, followed by an interpolation to retrieve the full CSI. Simulation results validate the derived OIRS spatial coherence and demonstrate the efficacy of the proposed OIRS channel estimation algorithm.

The Autonomous Underwater Vehicle (AUV)- assisted Underwater Internet of Things (UIoT) has received much attention due to its potential to develop marine resources with big data analysis. Given the low data rates and distributed data, decentralized federated learning (DFL) emerges as a promising avenue, enabling artificial intelligence integration and collaborative training within the underwater environment. In this paper, we combine DFL with the underwater scenario for the first time. A novel DFL algorithm with decaying aggregation weight is proposed to achieve fast convergence rate under non-independent and identically distributed (non-IID) data. In addition, the DFL algorithm is tailored to underwater acoustic channels, and we integrate adaptive compression, device connectivity, and time-varying topology considerations to enable practical deployment. We provide convergence analysis under convexity and connectivity assumptions. Simulation experiments validate the performance using the MNIST dataset, highlighting its effectiveness for practical UIoT applications.

Considering that optical intelligent reflecting surface (OIRS) can change the optical channel, the enhancement performance of OIRS on the energy efficiency (EE) of the non-orthogonal multiple access (NOMA)-based visible light communication (VLC) system is investigated. Specifically, a multi-cell VLC system is modelled with both line-of-sight (LoS) and OIRS-reflected paths, and then the problem is formulated to optimize the OIRS configuration and power allocation for the maximization of the overall EE while adhering to various constraints. Moreover, the original task is decomposed into two sub-problems, and is solved iteratively by the proposed algorithm. In addition, the convergence of the proposed algorithm and enhancement in EE achieved by OIRS are illustrated by simulation results, which offer beneficial insights on resource allocation for the NOMAbased VLC systems with OIRS.

Paving the way toward next-generation communications, reconfigurable intelligent surface (RIS) based free-space optical (FSO) links have emerged as a strong candidate in the unlicensed spectrum for enhanced capacity and signal quality. However, existing modulation schemes constrain the FSO links’ performance and are unsuitable for RIS-induced multipath fading. To address the issue, we firstly introduce the optical orthogonal time frequency space (O-OTFS) modulation scheme into such links, with orbital angular momentum (OAM) optical beams combined to enhance the performance. Moreover, we propose two combination systems with separate and integrated OAM-OTFS respectively, and further propose a novel quad-mode (QM)-OAM-OTFS scheme within the integrated system, demonstrating significant performance enhancement over counterparts. Numerical results indicate that the separate OAM-OTFS system enhances performance in terms of power and spectral efficiencies but reduces system robustness to turbulence with increasing OAM modes, the proposed QM-OAM-OTFS scheme effectively mitigates atmospheric turbulence effects and achieves efficient power and spectral utilization.

In this paper, a multi-satellite cooperation architecture for satellite-terrestrial communications is studied with the dense constellation of low Earth orbit (LEO) satellites. Moreover, beam hopping (BH) and frequency allocation enable a flexible solution for the non-uniform geographical distribution of communications, which are optimized to improve the performance and avoid interference in satellite-terrestrial communications. The total problem for maximizing the networking throughput and ensuring the delay fairness is decomposed into two sub-problems, in which the deep reinforcement learning is utilized to determine the BH pattern, and the frequency allocation among beams is implemented by the majorization-minimization algorithm. Simulation results demonstrate that the proposed method obtains a 13.0% improvement in throughput compared with the original benchmark, while the delay fairness can be ensured by the iterative solution of the two sub-problems.

Integrated sensing and communication (ISAC) is regarded as a key enabler in the upcoming era of connectivity and intelligence, where the optical spectrum emerges as a promising candidate for ISAC. This paper presents an optical wireless (OW)-ISAC scheme based on asymmetrically clipped direct-current-biased optical orthogonal frequency division multiplexing (ADO-OFDM). The Bussgang theorem is adopted to model the clipped OFDM signal and analyze the clipping noise. In addition, the adaptive resource allocation for ADO-OFDM is formulated as a joint optimization problem, which is decomposed into two sub-problems for DC-biased optical (DCO)asymmetrically clipped optical (ACO) power distribution and subcarrier power allocation. Then, the optimal resource allocation can be achieved by iteratively solving these sub-problems. Consequently, numerical results demonstrate the effectiveness of the proposed ADO-OFDM scheme and reveal the trade-off between communication and sensing functionalities in OW-ISAC.

Existing studies on ultraviolet (UV) non-line-of-sight (NLoS) channel modeling primarily focus on scenarios without any obstacle, which makes them unsuitable for small transceiver elevation angles in most cases. To address this issue, a UV NLoS channel model incorporating an obstacle was investigated in this paper, where the impacts of atmospheric scattering and obstacle reflection on UV signals were both taken into account. To validate the proposed model, we compared it to the related Monte-Carlo photon-tracing (MCPT) model that had been verified by outdoor experiments. Numerical results manifest that the path loss curves obtained by the proposed model agree well with those determined by the MCPT model, while its computation complexity is lower than that of the MCPT model. This work discloses that obstacle reflection can effectively reduce the channel path loss of UV NLoS communication systems.

Motivated by the ability of optical intelligent reflective surface (OIRS) to alter optical channels, the effectiveness of OIRS on aggregated visible light communication (VLC)-radio frequency (RF) systems is explored, and the system model is established in detail. With the aim of improving the energy efficiency (EE) of the aggregated system with OIRS, the EE maximization problem is formulated with diverse constraints, followed by the proposed block coordinate descent (BCD)-based resource allocation algorithm. Particularly, the EE maximization problem is partitioned into two subproblems, which are OIRS configuration and joint power allocation, and then are solved in the iteration process. Additionally, the substantial enhancement in EE brought by OIRS for the aggregated VLC-RF system, as well as the convergence and efficacy of the proposed BCD-based algorithm are demonstrated by comprehensive simulation results.

Optical intelligent reflecting surfaces (OIRS) can be deployed in visible light communication (VLC) systems to provide OIRS-reflected paths for multiple light-emitting diodes (LEDs) and users via adjustable reflective characteristics, thereby significantly mitigating the signal blockage challenges in VLC. To achieve this goal, the effectiveness of OIRS in the non-orthogonal multiple access (NOMA)-based multiple-input single-output (MISO) VLC system is explored, while the system model is established considering both the line-of-sight (LoS) and the OIRS-reflected paths. Subsequently, the problem is formulated with the objective of optimizing the OIRS configuration to maximize the achievable sum data rate with the diverse constraints. To address the highly intractable and non-convex problem, a relaxed iterative optimization algorithm based on the Taylor expansion is proposed in this paper. Moreover, numerical results are provided to show the improvement of the achievable sum data rate and the effects of the proposed relaxed iterative algorithm.

The scheduling of the laser inter-satellite links (LISLs) can effectively improve the network performance but is challenging in mega-constellations due to the large amount of satellites. In this paper, inter-OP LISL scheduling is realized by federated reinforcement learning, which reduces complexity by decomposing global scheduling into independent decisions for each satellite and solved by multi-agent reinforcement learning. Based on the constellations collaboration, federated learning (FL) with partial and global aggregations is utilized to reduce the overhead for the model update, where the geostationary orbit (GEO)-GEO collaboration for global aggregation can be performed infrequently since the indirect associations among the GEO satellites according to the motion of low Earth orbit (LEO) satellite. Meanwhile, simulation results indicate that the proposed method reduces the average hop count by over two hops and decreases the number of LISLs by over 25% compared to the fixed LISLs, and the model update overhead is reduced to 46.6% of that in the centralized method.

Integrated sensing and communication (ISAC) is one of the six usage scenarios of the sixth generation (6G) mobile communication system. Since an optical system can also provide communication and sensing abilities, optical ISAC has become a potential complement to radio-frequency (RF) ISAC. As orthogonal frequency division multiplexing (OFDM) has gained increasing interest in optical systems, we propose a direct-current-biased optical OFDM (DCO-OFDM) scheme for free space optical (FSO) ISAC in this paper. To derive the spectral efficiency for communication and the Fisher information for sensing, we model the clipping noise of DCO-OFDM with the Bussgang theorem to obtain the expression of the signal-to-noise ratio. In addition, based on the derived performance metrics, an optimization problem for power allocation is formulated, and an iterative algorithm is proposed to solve the problem efficiently. Meanwhile, numerical simulations demonstrate the effectiveness of the proposed algorithm and reveal the trade-off between communication and sensing functionalities of the OFDM-based FSO-ISAC system.

In response to the challenges presented by conventional material and texture recognition methods, our research introduces a system using visible light communication (VLC) technology. This approach provides a non-contact, non-destructive, dual-functional solution, overcoming the limitations of cost, safety, and environmental adaptability associated with traditional methods. Through a comprehensive design integrating hardware and software, our system utilizes VLC for precise and efficient recognition. Extensive testing confirms its effectiveness, achieving 97.7% accuracy in material identification and 93.8% in texture detection. This study highlights VLC's potential in enhancing automated recognition systems across various applications.

In this paper, we consider the scenario where the base station (BS) transmits time-sensitive information to various users via shared orthogonal sub-channels. The BS allocates sub-channels and power to different users to complete the data transmission task in each time slot. The metric Age of Information (AoI) is adopted to measure the timeliness of information. We jointly optimize the transmitting decisions, the sub-channel assignments, and the power allocation schemes to minimize the average AoI of the users subject to the average and peak power constraints. We propose a dynamic scheduling policy based on the Lyapunov optimization method and then prove that the proposed policy can satisfy the power constraints and achieve a near-optimal AoI performance. Numerical simulations indicate that the proposed policy is stable and satisfies the power constraints, which validates the theoretical analysis.

This work considers a wireless network transmitting status updates arriving randomly using the Hybrid Automatic Repeat Request (HARQ) protocol under average power con-straints. We measure the information freshness by the Age of Synchronization, which is defined as the time elapsed since the last synchronization. The updates transmitted unsuccessfully can be retransmitted with a higher success probability by the HARQ. However, there is a trade-off between retransmitting the old update with a high success probability and transmitting the new update with a low success probability. We model the time-average AoS minimization problem as a constrained Markov decision process (CMDP) and solve the problem by a relative value iteration algorithm. Numerical simulations show that our scheduling policy outperforms traditional ARQ and HARQ policies on the AoS performance and indicates the stability of our scheduling policy under different channel parameters.

The burgeoning technology of optical intelligent reflecting surfaces (OIRS) offers significant potential within the realm of visible light communication (VLC) systems. This is primarily attributed to the capability of OIRS to exploit reflected propagation paths, which in turn mitigates the inherent signal blockage challenges encountered in VLC. In this paper, a non-orthogonal multiple access (NOMA)-based multiple-input single-output (MISO) VLC system with the aid of OIRS is investigated, in which multiple light-emitting diodes (LEDs) serve multiple users equipped with a photodetector (PD) simultaneously. To this end, an analysis of the VLC channel gains is undertaken, encompassing the evaluation of both the line-of-sight (LoS) and the OIRS-reflected paths. Subsequently, the problem is formulated with the objective of jointly optimizing the active beamforming at LEDs and the passive beamforming at the OIRS to maximize the achievable sum data rate, while considering the constraints of OIRS and the successive interference cancellation (SIC) process. However, the formulated problem is non-convex. To address this challenge, a block coordinate descent (BCD) algorithm is introduced to decompose the original joint beamforming problem into two sub-problems. Specifically, relaxed iterative algorithms based on semi-positive definite relaxation and Taylor expansion are employed to solve these sub-problems. Additionally, a permutation-based genetic algorithm is proposed to tackle the decoding order problem. Meanwhile, the simulation results illustrate the improvement in the sum data rate achieved by the proposed algorithm, providing valuable insights for future research on OIRS-aided VLC systems.

Beam squint effect causes severe performance degradation for wideband beamforming inside Internet of thing (IoT) communication, and the true-time-delay (TTD) line has been regarded as a promising enabler to address this issue. However, beam training becomes a challenging puzzle in TTD-aided transceivers, where enormous beam directions bring about unacceptable training overhead, especially for the overhead-efficient IoT devices. In this paper, based on a joint delay-phase beamforming structure, we provide enhanced frequency-scanning-based training schemes for remarkable overhead reduction. Simultaneous beams pointing to different physical directions over a set of OFDM subcarriers can be generated to reduce overhead. The pointing directions can be flexibly controlled following two subcarrier-angular mapping policies: forward-pairing and backward-pairing. Besides, the power leakage problem is retrieved via the compressive phase retrieval (CPR) method to avoid beam mismatch. Furthermore, we adopt it into the multiuser scenario and propose a frequency-scanning-based simultaneous multi-user beam training (FS-MBT) scheme. The different subcarriers’ pencil beams illuminate a broad angular sector, while several sectors are merged into multi-finger wide beams for simultaneous multi-user training. Analytical and numerical results demonstrate the proposed schemes’ superiority over existing methods in both single-user and multi-user scenarios.

The promising visible light communication (VLC) technology, which performs superior for Internet of Things (IoT) networks, can alleviate the spectrum congestion of current radio frequency communications. To overcome the drawbacks of VLC such as blockages and high path loss, we propose a multiple-input single-output (MISO) VLC system equipped with optical intelligent reflecting surface (OIRS), to maximize the asymptotic capacity in the high signal-to-noise-ratio regime. Specifically, the characteristics of the OIRS-reflected channel are discussed for the developed OIRS-assisted MISO VLC system, based on which the OIRS optimization can be transformed into an association problem between the OIRS reflecting elements and the transmitter antennas. Next, considering different emission power on antennas, the capacity lower and upper bounds are derived for three different cases and the asymptotic capacities are obtained accordingly, thus giving rise to the objective function of the capacity maximization problem. To solve this problem, we propose a priori-assisted alternating optimization algorithm to jointly optimize the OIRS element alignment and transmitter emission power, which not only can achieve the globally optimal result but also has a low complexity since the solution to each subproblem is given in closed form. Finally, extensive numerical results are provided to show the performance of the proposed algorithm and offer beneficial insights for the design of the proposed OIRS-assisted MISO VLC.

Laser inter-satellite links (LISLs) have greatly extended communication distance between satellites, allowing for establishment of dynamic links to reduce communication delay. However, a closed-loop control is required for LISL, which causes high energy consumption. Proper scheduling of dynamic LISLs can effectively reduce energy consumption and communication delay. In this study, a satellite link mode with three fixed LISLs and one dynamic LISL is designed, and its feasibility is analyzed. The optimization problem is formulated and transformed into a Markov decision process (MDP) by modeling it as a sequential decision. By decomposing states, actions, and reward functions, the MDP is divided into the proposed multi-agent deep reinforcement learning (MADRL). Moreover, compressed sensing is utilized to cut down state information to reduce communication, storage, and computation overhead. Furthermore, network parameters and experience sharing, and prioritized experience replay have been adopted to improve stability and convergence speed of network training with a large number of agents. Experimental results show that under different routing strategies, the proposed MADRL can reduce energy consumption by over 15% and delay by approximately two hops compared to fixed LISLs scenario within several iterations.

Intelligent reflecting surface (IRS) alters the wireless channel by dynamically adjusting the propagation of wireless signals, thereby having the ability to enhance communication performance. Due to its low power consumption, IRS is expected to play an important role in improving energy efficiency (EE). In this paper, both optical IRS (OIRS) and radio frequency (RF) IRS are employed to assist aggregated visible light communication (VLC)-RF systems, and then a resource allocation algorithm is proposed to improve EE. To this end, a model for the IRS-assisted aggregated VLC-RF system is first established, followed by the formulated EE maximization problem. Furthermore, the EE maximization problem is decomposed into four subproblems, focusing on RF IRS configuration, optical subchannel assignment, OIRS arrangement, and joint power allocation. By employing block coordinate descent (BCD), the four subproblems are solved iteratively. Moreover, simulation results show the significant EE improvement brought by the IRS for aggregated VLC-RF systems. In addition, the convergence and effectiveness of the proposed BCD-based resource allocation algorithm, as well as the influence of key system parameters on EE are also demonstrated.

Extremely large-scale multiple-input multiple-output (XL-MIMO) promises to provide ultrahigh data rates in millimeter-wave (mmWave) and Terahertz (THz) spectrum. However, the spherical-wavefront wireless transmission caused by large aperture array presents huge challenges for channel state information (CSI) acquisition and beamforming. Two independent parameters (physical angles and transmission distance) should be simultaneously considered in XL-MIMO beamforming, which brings severe overhead consumption and beamforming degradation. To address this problem, we exploit the near-field channel characteristic and propose two low-overhead hierarchical beam training schemes for near-field XL-MIMO system. Firstly, we project near-field channel into spatial-angular domain and slope-intercept domain to capture detailed representations. Then we point out three critical criteria for XL-MIMO hierarchical beam training. Secondly, a novel spatial-chirp beam-aided codebook and corresponding hierarchical update policy are proposed. Thirdly, given the imperfect coverage and overlapping of spatial-chirp beams, we further design an enhanced hierarchical training codebook via manifold optimization and alternative minimization. Theoretical analyses and numerical simulations are also displayed to verify the superior performances on beamforming and training overhead.

Although the multi-antenna or so-called multiple-input multiple-output (MIMO) transmission is an enabling technology for past generations of wireless communication systems, its application to visible light communication (VLC) still faces a critical challenge due to the strong spatial correlation of VLC channels, which makes it difficult to achieve sufficient spatial multiplexing gain. This paper proposes to use optical intelligent reflecting surfaces (OIRS) to tackle this challenge. Firstly, we characterize the extremely near-field channel condition in the optical frequency range and reveal a peculiar “inter-element interference (IEI) free” property of the OIRS-reflected channel, where the OIRS reflecting elements can be individually configured to align with one pair of transmitter and receiver antennas without causing interference to each other. Next, we characterize the OIRS-assisted MIMO VLC capacities under different power constraints at the transmitter antennas, and then proceed to maximize them by jointly optimizing the OIRS element alignment and transmitter emission power. In particular, we propose two algorithms for the OIRS optimization, namely, location-aided interior-point algorithm and log-det-based alternating optimization algorithm, to balance the performance versus complexity trade-off; while the optimal transmitter emission power is derived in closed form. Numerical results are provided to validate the capacity improvement of OIRS-assisted MIMO VLC against the VLC without OIRS and demonstrate the superior performance of the proposed algorithms compared to baseline schemes.

The scheduling of the laser inter-satellite links (LISLs) can effectively decrease the energy consumption by deactivating idle LISLs and reduce the network latency by decreasing the average hop count through establishing appropriate dynamic LISLs, but is challenging in mega-constellations due to a large amount of satellites involved. In this paper, a federated multi-agent deep reinforcement learning (MADRL) method for LISL scheduling is proposed, where the global scheduling is decomposed into independent decisions for each satellite. To reduce the substantial communication overhead attributable to MADRL, asynchronous hierarchical federated learning with partial and global aggregations is utilized, transmitting the model parameters rather than status information among low Earth orbit (LEO) satellites and geostationary Earth orbit (GEO) satellites. The movement of LEO satellites maintains the consistency of local sample distribution and the indirect association among different GEO satellites. Therefore, the global aggregation with infrequent occurrence is asynchronous with the partial aggregation. Moreover, during the partial aggregation, the utility of each layer in the local model of the LEO satellite is also evaluated to minimize the uncertainty, allowing the adaptive upload to ensure efficiency and fairness within a limited power budget. The simulation results show that the proposed method reduces the average hop by about two hops and decreases the LISLs number by over 25%. Meanwhile, the communication overhead of LEO satellites is 46.6% of that in the centralized approach, with the training performance ensured.

The existing works on ultraviolet (UV) channel modeling primarily focus on non-line-of-sight (NLoS) communication scenarios, where the UV transceiver does not need to be aligned and can communicate around obstacles. However, NLoS scenarios also face problems such as long channel delay spread and severe path loss, and consequently, these phenomena will be exacerbated as the amount and dimension of obstacles increase. To tackle these problems, we investigate the channel models for UV diffused line-of-sight (LoS) communication scenarios comprehensively. First, a UV diffused-LoS channel model with an obstacle is put forward, where the radiation intensity distributions of UV light sources, the height difference between UV transceivers, as well as the obstacle dimension and orientation are incorporated to approach practical application scenarios. Besides, the channel modeling framework for diffused-LoS scenarios incorporating obstacles is investigated, where we take two obstacles as an example to illustrate the entire modeling process. Further, we validate the proposed models by comparing them with associated LoS and Monte-Carlo photon-tracing (MCPT) models via numerical calculations. The path loss results manifest that the proposed integration models agree well with the existing channel models, while their calculation time is much shorter than that of the MCPT model. Apart from that, the channel path loss and bit-error rate performance of diffused-LoS scenarios are superior to those of NLoS scenarios when obstacle reflection is apparent, and channel delay spreads of diffused-LoS scenarios are shorter than those of NLoS scenarios regardless of circumstances with one or two obstacles.

With the rapid development of the Internet of Things (IoT), a large amount of IoT devices are deployed to support various real-time applications, which put forward high requirements for data freshness. However, the limited battery capacity constraints the computation capabilities of terminal devices, thus decreases the timeliness of status updates. Mobile edge computing (MEC) has been considered as a solution to enhance the computing capacity of IoT devices and decrease their power consumption, thus extends their service life. In this paper, we aim to utilize MEC to decrease the power consumption of IoT devices while guaranteeing timeliness performance, which is measured by the metric age of information (AoI). We consider a multi-device scenario with time-varying channels and formulate the power-minimization problem with AoI constraints. Then we propose a dynamic scheduling policy based on Lyapunov optimization and analyze its performance theoretically. Numerical results validate the analysis and indicate that our policy outperforms the baselines.

Laser inter-satellite links (LISLs) enable longer-range communication and cross-satellite dynamic links that bypass intermediate satellites. However, the utilization of narrow laser beams necessitates closed-loop control for alignment and consumes substantial energy even during idle periods. Therefore, we propose a dynamic LISL scheduling algorithm and a satellite link pattern with one dynamic LISL and three fixed LISLs to optimize energy consumption and reduce communication delay. To simplify the computation complexity of the optimization problem, a Markov decision process (MDP) is constructed, and the problem is divided into the independent decision process of each agent by breaking down the state space, action space, and reward function. Experimental results indicate that the proposed method reduces communication delay by approximately 2 hops and saves over 15% of energy consumption compared to fixed link patterns.

XL-MIMO promises to provide ultrahigh data rates in Terahertz (THz) spectrum. However, the spherical-wavefront wireless transmission caused by large aperture array presents huge challenges for channel state information (CSI) acquisition. Two independent parameters (physical angles and transmission distance) should be simultaneously considered in XL-MIMO beamforming, which brings severe overhead consumption and beamforming degradation. To address this problem, we exploit the near-field channel characteristic and propose one low-overhead hierarchical beam training scheme for near-field XL-MIMO system. Firstly, we project near-field channel into spatial-angular domain and slope-intercept domain to capture detailed representations. Secondly, a novel spatial-chirp beam-aided codebook and corresponding hierarchical update policy are proposed. Theoretical analyses and numerical simulations are also displayed to verify the superior performances on beamforming and training overhead.

The non-orthogonal multiple access (NOMA) method is seen as one of the advantageous strategies for radio access that promises improved spectral efficiency in the fifth generation mobile networks (5G). This paper proposes an integrated broadcast and communication networks architecture based on NOMA, in which the physical channels of the cellular base stations are divided into two layers: one layer brings reinforced broadcast signals to the weakest areas of the broadcast tower and forms a single-frequency network transmission with it; the other layer provides diverse unicast services to users. In order to evaluate the spectral efficiency and coverage performance of the above system, an optimization problem based on the integrated spectrum revenue of the system is proposed. To maximize the objective function, the power allocation factors of the cellular base stations are optimized using the genetic algorithm. The simulation results validated that the system architecture is capable of integrating broadcast towers and cellular base stations to provide more reliable coverage of broadcast service and improve spectrum efficiency.

In this work, we study a system with a sensor forwarding status update to the receiver through an error-prone channel, and the receiver sends the transmission results to the sensor via a reliable link. We assume both transmission links suffer from random delays. We use Age of Information (AoI) to measure the freshness of the status information at the receiver. Our goal is to design a sampling policy that minimizes the expected time average AoI when the channel statistics are unknown. The problem is reformulated into a renewal-reward process optimization, and an online algorithm based on the Robbins-Monro algorithm is proposed. We prove that when the forward and backward transmission delays are bounded, the AoI difference between the online algorithm and the optimal policy decays with rate$\mathcal{O}(\ln K/K)$, where$K$is the number of successful transmissions. Simulation results validate the performance of our proposed algorithm.

Due to the wide and license-free bandwidth, visible light communication (VLC) functions as a potential technology to meet the exponentially expanding traffic demands in wireless communications. However, the sensitivity to obstacles and high-path loss are the key issues that practical VLC systems must carefully deal with. In this article, the utilization of optical intelligent reflecting surface (OIRS) array in VLC is considered to create additional light propagation paths, thereby achieving a remarkable performance gain. In the OIRS-assisted VLC system, though the power of the signal at receiver can be increased, the resource allocation is relatively complex. Besides, the OIRS also causes time delays among signals received via various propagation paths, which is usually overlooked in existing works. To overcome these issues, the OIRS-assisted VLC system is interpreted as an autoencoder (AE), named OIRS-AE, whose architecture is enhanced according to both the model-driven and data-driven perspectives. By this way, the processing modules at the transmitter, OIRS, and receiver, including the corresponding encoding, resource management, and decoding schemes, can be simultaneously optimized, which is expected to achieve more reliable communication. Moreover, the impact of the OIRS-induced time delay spread on system performance is explored under various situations. The simulation results show that the proposed OIRS-AE can outperform the traditional OIRS-assisted VLC systems in terms of bit error rate performance.

Although Direction-of-Arrival (DoA)-estimation has been well-researched in the recent decades, some specific scenarios with certain requirements are not yet delved in depth. In this letter, we introduce an innovative application for gridless DoA-estimation, called Orthonormal Atomic Norm Minimization (OANM). By applying OANM in case of 1-dimensional DoA-estimation with prior information aided, atomic separation is shown to enjoy a higher degree of freedom by outcoming with a lower DoA-estimation error compared to other methods that are also compressive sensing-based. OANM shows its power by utilizing a single snapshot of the received signal only and estimates all the DoAs of each mobile user in a noisy environment with relatively low error but also low computational complexity when atoms are close in separation.

With the capability of reconfiguring the wireless electromagnetic environment, intelligent reflecting surface (IRS) becomes a new paradigm for designing future wireless communication systems. In this paper, we consider optical IRS for improving the performance of visible light communication (VLC) under a multiple-input and multiple-output (MIMO) setting, where the mean square error (MSE) of the IRS-aided MIMO VLC is minimized by jointly designing the IRS and transceiver signal processing. To this end, the MIMO channel gains of the IRS-aided VLC are first derived under the point source assumption, based on which the MSE minimization problem is formulated subject to the emission power constraints and the IRS configuration constraints. Next, we propose an alternating optimization algorithm, which decomposes the original problem into three subproblems, to iteratively optimize the IRS configuration, the precoding and detection matrices for minimizing the MSE. Moreover, theoretical analysis on the performance of the proposed algorithm in high and low signal-to-noise ratio (SNR) regimes is investigated, revealing that the joint optimization process can be simplified in such special cases, and the algorithm’s convergence property and computational complexity are also discussed. Finally, numerical results show that IRS-aided schemes significantly reduce the MSE as compared to their counterparts without IRS, and the proposed algorithm outperforms other baseline schemes.

Extensive work has demonstrated the excellent performance of orthogonal time frequency space (OTFS) modulation in high-mobility scenarios. Time-variant wideband channel estimation serves as one of the key compositions of OTFS receivers since the data detection requires accurate channel state information (CSI). In practical wideband OTFS systems, the Doppler shift brought by the high mobility is frequency-dependent, which is referred to as the Doppler Squint Effect (DSE). Unfortunately, DSE was ignored in overall prior estimation schemes employed in OTFS systems, which leads to severe performance loss in channel estimation and the consequent data detection. In this paper, we investigate DSE of wideband time-variant channel in delay-Doppler domain and concentrate on the characterization of OTFS channel coefficients considering DSE. The formulation and evaluation of OTFS input-output relationship are provided for both ideal and rectangular waveforms considering DSE. The channel estimation is therefore formulated as a sparse signal recovery problem and an orthogonal matching pursuit (OMP)-based scheme is adopted to solve it. Simulation results confirm the significance of DSE and the performance superiority compared with traditional channel estimation approaches ignoring DSE.

With the explosive growth of the data traffic and the access number of intelligent devices, the wireless communication systems based on the radio frequency (RF) is suffering from the pressure of spectrum shortage and access congestion constantly, which can be alleviated with the help of visible light communication (VLC). However, due to the limitations of VLC such as finite coverage and inconvenient uplinks, it is imperative to form the hybrid systems and realize the complementary advantages of VLC and RF. In this paper, the hybrid VLC-RF system with multi-users is investigated, where the sum achievable rate and energy efficiency (EE) are maximized via the alternate optimization of transceiver association and power allocation. In addition, based on various types of multiple access points, EE is optimized under different QoS requirements for green communications. With the assistance of proper approximations, the overall problem is decomposed into several convex sub-problems, which can be solved analytically and avoid traversal permutation. The numerical simulations show the effectiveness and scalability of the proposed algorithm in the hybrid VLC-RF system, which can achieve superior performances than the conventional methods and single systems in multifarious scenarios.

Compared with underwater acoustic communication, underwater wireless optical communication (UWOC) has the advantages of wide communication bandwidth, high transmission speed and strong directional transmission, which make it have broad application prospects. However, the absorption and scattering effects in underwater environments cause a UWOC system to suffer from severe channel fading. To reduce the system complexity and power loss, the on- -off-keying (OOK) modulation scheme and one-bit analog-to-digital converter are considered in this work. Besides, to deal with the complex underwater optical channels and the nonlinearity of the one-bit quantization, a novel deep learning based architecture integrating the autoencoder (AE) and generative adversarial network (GAN), named hybrid AE-GAN, is developed. In the proposed hybrid AE-GAN, the generator in GAN is responsible for a generalized channel equalization, which aims to equalize both the impairments from underwater optical channels and the distortion from one-bit quantization. While the decoder in AE and the discriminator in GAN share the same network to distinguish true and fake as well as reconstruct the sent messages simultaneously. Meanwhile, the one-bit quantized signal is regarded as conditional information for GAN to assist in targeted channel equalization for various signals. Moreover, a specialized loss function and training strategy for the hybrid AE-GAN are also presented, which can optimize AE and GAN at the same time, and deal with the problem that the binarization of OOK stymies the gradient backpropagation. The simulation results indicate that the proposed hybrid AE-GAN can achieve superior performance.

In this work, we study a status update system with a source node sending timely information to the destination through a channel with random delay. We measure the timeliness of the information stored at the receiver via the Age of Information (AoI), the time elapsed since the freshest sample stored at the receiver is generated. The goal is to design a sampling strategy that minimizes the total cost of the expected time average AoI and sampling cost in the absence of transmission delay statistics. We reformulate the total cost minimization problem as the optimization of a renewal-reward process, and propose an online sampling strategy based on the Robbins-Monro algorithm. Denote K to be the number of samples we have taken. We show that, when the transmission delay is bounded, the expected time average total cost obtained by the proposed online algorithm converges to the minimum cost when K goes to infinity, and the optimality gap decays with rate ${\mathcal{O}}$(ln K/K). Simulation results validate the performance of our proposed algorithm.

As the linearly superposed category of multiuser superposition transmission (MUST), power-domain non-orthogonal multiple access (PD-NOMA) has been widely studied for visible light communication (VLC) systems. However, the utilization of successive interference cancellation (SIC) entails high complexity and latency. Error propagation (EP) caused by imperfect SIC would also degrade the system performance. To address this problem, a MUST-based SIC-free NOMA VLC system is studied in this work. At the transmitter, coded bits from users with diverse channel conditions are jointly mapped to a composite constellation by linear or non-linear superposition coding. At the receivers, independent demapping is employed directly to extract the soft information of related coded bits. Constellation constrained capacity regions for different MUST categories with SIC-free reception are derived and analyzed comprehensively. Considering user fairness, we restrict the comparison to the working points that satisfy the direct ratio policy (DRP). It is observed that the non-linear categories of MUST are superior to the linear one due to their Gray labeled composite constellation and show considerable throughput gain over orthogonal multiple access (OMA) although SIC is not utilized. Additionally, the throughput loss to conventional PD-NOMA is also very small.

In this paper, we consider a multi-user multi-channel wireless network with a base station sending random fresh updates. To measure the data freshness of the network, the metric age of synchronization (AoS) is adopted. Our goal is to minimize the expected average AoS of the network. We first obtain a policy-independent lower bound via convex optimization and stochastic analysis under the perfect channel case, where all channels are available for users. Then we propose the perfect matching and maximum weight matching policies to approach the optimal performance. Under certain conditions, we prove theoretically that the gap between the lower bound and the expected average AoS under proposed policies vanishes as the number of channels goes to infinity. Numerical results validate the theoretical analysis and indicate that our policies can reach near-optimal performance.

In this paper, we consider a multi-user interference network where a central controller allocates power resources to multiple base stations for maximizing the entire network utility under a long-term convex power budget constraint. The optimal power allocation strategy depends on the accurate and instant channel state information (CSI). However, due to users’ mobility and the existence of channel fading and interference, timely channel estimation is unavailable. To overcome the difficulty of unknown channel states, we resort to the Lyapunov drift analysis framework and design an online power allocation algorithm based on historical CSI. The algorithm can be proven to achieve sub-linear performance for both cumulative regret and power budget violation. The sub-linear regret indicates the proposed algorithm can asymptotically achieve the optimal static power allocation performance in hindsight. Simulation results are provided to validate the asymptotic optimal performance of the proposed algorithm, as well as its robustness in the presence of adversarial interference.

Visible light communication (VLC) is one of the candidates to realize data rates of up to tera-bps in indoor scenarios, and is a popular topic in Internet of Things (IoT) applications. However, the receiving performance of the VLC channel is affected by relative locations of transceivers directly, while the uniform illumination is also required when the IoT devices can appear anywhere with strong mobility. Consequently, both the illumination uniformity and the average achievable rate are optimized by joint lamp arrangement and power allocation in this article. Moreover, the achievable rate maximization under diverse uniformity constraints is also obtained based on different room sizes and various numbers of lamps. The feasibility and superiority of the proposed algorithms are demonstrated by simulation validations, while the lower and upper bounds of uniformity constraints are explored. Numerical results also show the changing tendency of average achievable rates in multifarious situations, and the proposed schemes realize the tradeoff between uniform illumination and communication performance.

Massive multiple-input multiple-output (MIMO) is one of the key techniques to achieve better spectrum and energy efficiency in 5G system. The channel state information (CSI) needs to be fed back from the user equipment to the base station in frequency division duplexing (FDD) mode. However, the overhead of the direct feedback is unacceptable due to the large antenna array in massive MIMO system. Recently, deep learning is widely adopted to the compressed CSI feedback task and proved to be effective. In this paper, a novel network named aggregated channel reconstruction network (ACRNet) is designed to boost the feedback performance with network aggregation and parametric rectified linear unit (PReLU) activation. The practical deployment of the feedback network in the communication system is also considered. Specifically, the elastic feedback scheme is proposed to flexibly adapt the network to meet different resource limitations. Besides, the network binarization technique is combined with the feature quantization for lightweight and practical deployment. Experiments show that the proposed ACRNet outperforms loads of previous state-of-the-art networks, providing a neat feedback solution with high performance, low cost and impressive flexibility.

Reconfigurable intelligent surface (RIS) has been recognized as a potential technology for 5G beyond and attracted tremendous research attention. However, channel estimation for RIS-aided systems is still a critical challenge due to the excessive amount of parameters in the cascaded channel. The existing compressive sensing (CS)-based RIS estimation schemes only adopt incomplete sparsity, which induces redundant pilot consumption. In this paper, we analyze and exploit the specific triple-structured sparsity of the cascaded channel, i.e., the common column sparsity, structured row sparsity after offset compensation and the common offsets among all users. Furthermore, a novel on-grid Multi-user Triple-Structured-Compressive-Sensing simultaneous orthogonal matching pursuit (MTSCS-SOMP) algorithm along with an enhanced super-resolution (gridless) generalized iterative reweighted (MTSCS-IR) scheme are successively proposed. The former is practical and can be easily employed with low computational complexity, and the latter is further proposed to handle the severe power leakage problem encountered in mmWave channel estimations. Besides, we extend the sparsity property and algorithms from uniform linear array (ULA) configuration to uniform planar array (UPA), by transforming cascaded channel from matrix to tensor. Simulation results show that our approaches can significantly reduce pilot overhead over 50% and achieve enhanced performance on estimation accuracy.

Though visible light communication (VLC) is a significant supplement to current communication technologies, disadvantages such as the sensitivity to obstacles limit its development and commercialization. As a revolutionizing technology, intelligent reflecting surface (IRS) offers an ability to reconfigure the wireless environment dynamically and passively, which is considered beneficial to improve the performance of VLC. This paper devotes to investigating the effect of VLC IRS and putting forward a joint resource management method for an instantaneous IRS-aided VLC system. To this end, the line-of-sight (LoS) and non-LoS channel gains are first discussed under the point source assumption, after which the system model is established and the optimization problem is formulated. Then, the frozen variable algorithm and minorization-maximization algorithm are proposed to iteratively maximize the overall spectral efficiency (SE), and detailed discussions on the weak/severe interference cases and computational complexity analysis are carried out. Moreover, numerical results are provided to show the improvement of SE and the effects of the proposed algorithms, which offers beneficial insights on joint resource management of IRS-aided VLC.

As a high-speed, large-capacity underwater communication technology, underwater wireless optical communication (UWOC) has broad development prospects. However, accurately characterizing the underwater optical channel for various underwater conditions is still a challenging task due to light absorption and scattering, which may limit further research on UWOC. To overcome these issues, this paper proposes a novel framework of the underwater optical channel impulse response (CIR) generator, named the UCIRG framework, based on the generative adversarial network. Unlike the classical Monte Carlo approach, which is a numerical simulation method taking a long time to calculate, the UCIRG framework can generate a large number of CIRs extremely close to the actual underwater optical CIRs in a short time based on limited underwater optical CIRs as training data. Besides, traditional approximation methods for underwater optical channels usually have limitations on underwater conditions, lacking generality and flexibility, hence a generalized algorithm for UCIRG is also investigated, which is valid for various underwater conditions and can further reduce training time. Moreover, the multi-carrier autoencoder based UWOC system is adopted to evaluate the performance of the trained UCIRG, and the simulation results prove that the CIRs generated by the UCIRG are extremely similar to the actual CIRs.

Reconfigurable intelligent surface (RIS) has been recognized as a potential technology for 5G beyond and attracted tremendous research attention. However, channel estimation in RIS-aided system is still a critical challenge due to the excessive amount of parameters in cascaded channel. The existing compressive sensing (CS)-based RIS estimation schemes only adopt incomplete sparsity, which induces redundant pilot consumption. In this paper, we exploit the specific triple-structured sparsity of the cascaded channel, i.e., the common column sparsity, structured row sparsity after offset compensation and the common offsets among all users. Then a novel multi-user joint estimation algorithm is proposed. Simulation results show that our approach can significantly reduce pilot overhead in both ULA and UPA scenarios.

In this work, we consider the problem of transmission rate selection for a discrete time point-to-point block fading wire-less communication link. The wireless channel remains constant within the channel coherence time but can change rapidly across blocks. The goal is to design a link rate selection strategy that can identify the best transmission rate quickly and adaptively in quasi-static channels. This problem can be cast into the stochastic bandit framework, and the unawareness of time-stamps where channel changes necessitates running change-point detection simultaneously with stochastic bandit algorithms to improve adaptivity. We present a joint channel change-point detection and link rate selection algorithm based on Thompson Sampling (CD-TS) and show it can achieve a sublinear regret with respect to the number of time steps$T$when the channel coherence time is larger than a threshold. We then improve the CD-TS algorithm by considering the fact that higher transmission rate has higher packet-loss probability. Finally, we validate the performance of the proposed algorithms through numerical simulations.

Facing the worldwide explosive growth of the wireless devices, the future wireless network is required to provide seamless access for the users with diverse Quality-of-Service (QoS) requirements. In this article, a novel multiple access scheme based on the principle of multidomain index modulation is proposed to address the downlink multiple access problem for users with diverse QoS requirements, including the Internet-of-Things (IoT) devices and the smart electronic devices. Unlike the conventional nonorthogonal multiple access (NOMA) schemes which utilize power-domain diversities to realize multiple access, in the proposed scheme, information for different users is conveyed by different domains of the transmitted signals, including the constellation domain, frequency domain, time domain, and spatial domain. As a result, the intracluster interference among users is alleviated and the successive interference cancellation is avoided to reduce the computational complexity of signal detection at the receiver side, which is well suited for the IoT devices. The closed form lower bounds and approximations of the achievable rate of the proposed scheme are further derived to provide accurate estimation of the achievable rate of the communication system. Simulation results validate the superior performance of the proposed scheme over the conventional orthogonal and NOMA schemes.

In-home health networks greatly rely on the massive connected monitoring devices. Compared to orthogonal multiple access (OMA), non-orthogonal multiple access (NOMA) can connect more monitoring devices and enhance the spectrum efficiency (SE) performance, which makes it an ideal solution to in-home health networks. However, conventional NOMA (C-NOMA) is mostly constrained to single-carrier scenario. The problem of multi-carrier NOMA lies in the inter-carrier interference (ICI) from neighboring carriers. In this article, we propose a sparse vector coding-based NOMA (SVC-NOMA) to suppress the ICI. We give closed-form expressions of capacity and symbol error rate (SER) performances for both C-NOMA and SVC-NOMA within the considered multi-carrier scenario. Simulation results demonstrate that compared to C-NOMA, SVC-NOMA has better capacity and SER performances. In addition, we find from our results that there is a trade-off between SVC-NOMA's ICI suppression ability and the system capacity performance.

In massive multiple-input multiple-output (MIMO) system, user equipment (UE) needs to send downlink channel state information (CSI) back to base station (BS). However, the feedback becomes expensive with the growing complexity of CSI in massive MIMO system. Recently, deep learning (DL) approaches are used to improve the reconstruction efficiency of CSI feedback. In this paper, a novel feedback network named CRNet is proposed to achieve better performance via extracting CSI features on multiple resolutions. An advanced training scheme that further boosts the network performance is also introduced. Simulation results show that the proposed CRNet outperforms the state-of-the-art CsiNet under the same computational complexity without any extra information. The open source codes are available at https://github.com/Kylin9511/CRNet.

Visible light communication (VLC) has gradually gained increasing attention in recent years, but new researches are still needed for conventional technologies in the VLC system, as the visible light signal must be real-valued and non-negative, and it is transmitted over the specific ergodic channel. In this paper, the definition of ergodic optical channel is firstly proposed, and the coded modulation average mutual information (CMAMI) of the VLC system is investigated under both fixed and ergodic optical channels. The gradient descent (GD) algorithm for constellation optimization of amplitude-phase modulation (APM) symbols is adopted, while the improved method for GD based on central symmetry is also proposed to accelerate the convergence for higher order constellations. The numerical simulation proves that the VLC system with optimized constellations can reach larger CM-AMI based on asymmetrically clipped optical orthogonal frequency division multiplexing (ACO-OFDM) compared with quadrature amplitude modulation (QAM) systems, and the influence of the ergodic optical channel is also evaluated.

This work is motivated by the need of collecting fresh data from power-constrained sensors in the industrial Internet of Things (IIoT) network. A recently proposed metric, the Age of Information (AoI) is adopted to measure data freshness from the perspective of the central controller in the IIoT network. We wonder what is the minimum average AoI the network can achieve and how to design scheduling algorithms to approach it. To answer these questions when the channel states of the network are time-varying and scheduling decisions are restricted to both bandwidth and power consumption constraint, we first decouple the multi-sensor scheduling problem into a single-sensor constrained Markov decision process (CMDP) by relaxing the hard bandwidth constraint. Next we exploit the threshold structure of the optimal policy for the decoupled single sensor CMDP and obtain the optimum solution through linear programming (LP). Finally, an asymptotically optimal truncated policy that can satisfy the hard bandwidth constraint is built upon the optimal solution to each of the decoupled single-sensor. Our investigation shows that to obtain a small average AoI over the network: (1) The scheduler exploits good channels to schedule sensors supported by limited power; (2) Sensors equipped with enough transmission power are updated in a timely manner such that the bandwidth constraint can be satisfied.

In this work, a wireless broadcast network with a base station (BS) sending random time-sensitive information updates to multiple users under bandwidth constraint is considered. To measure the effect of data desynchronization when the updates appear randomly because of external environment, the metric Age of Synchronization (AoS) is adopted in this work. It shows the amount of the time elapsed since freshest information at the receiver becomes desynchronized. The AoS minimization scheduling problem is formulated into a discrete time Markov decision process and the optimal solution is approximated through structural finite state policy iteration. An index based heuristic scheduling policy based on restless multi-arm bandit (RMAB) is provided to further reduce the computational complexity. Simulation results show that the proposed index policy achieves compatible performance with the MDP and is close to the AoS lower bound. Our work indicates that, to obtain a small AoS over the entire network, users with larger transmission success probability and smaller random update probability are more likely to be scheduled at smaller AoS.

In this paper, a novel index modulation aided non-orthogonal multiple access (NOMA) scheme is proposed for visible light communication (VLC). Unlike the conventional NOMA schemes, the proposed scheme conveys information for different users through both the index-domain and constellation-domain symbols with the aid of index modulation, which is able to cancel the intra-cluster interference completely and avoid the use of successive interference cancellation to decrease computational complexity. The achievable multi-user sum rate of the proposed scheme is analyzed and a closed-form expression of the lower bound is derived. Then, an approximation is further obtained to realize accurate prediction of the achievable multi-user sum rate. Simulation results validate the accuracy of the approximation of the achievable sum rate and the superior performance of the proposed scheme.

In this paper, an enhanced asymmetrically clipped DC biased optical OFDM (ADO-OFDM) with adaptive subcarrier allocation is proposed to accommodate the requirements of multi-users in downlink multiple access. The optimal optical power allocation is investigated to minimize the overall bit error rate (BER). Moreover, to meet the dimming demands for illumination, enhanced negative ADO-OFDM is investigated and combined with enhanced ADO-OFDM for multiplexing transmission. Furthermore, a dimming control scheme is proposed by changing the proportion of these two signals to a proper value, with full using of the entire dynamic range of light-emitting diodes. Simulation results show that the proposed scheme can achieve better BER performance at the same signal to noise ratio, while wide dimming range for illumination can be ensured compared to the conventional counterparts.

The study of frequency-selective hybrid precoding for broadband millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems has recently attracted major research interest. In this paper, we seek to provide an efficient solution to simultaneously conduct user scheduling and frequency-selective hybrid precoding for a broadband multiuser mmWave MIMO system. More specifically, we re-formulate the issue using the framework of stochastic-optimization. By utilizing the powerful tool of Lyapunov optimization, a dynamic weighted sum-rate maximization problem is formulated, towards which we develop an algorithm that simultaneously conducts i) user scheduling, ii) multi-carrier resource allocation, and iii) frequency-selective precoder design. It is also demonstrated that the proposed framework is capable of asymptotically optimizing a given stochastic utility (e.g. long-term sum rate). Simulation results are provided to demonstrate the superiority of the proposed algorithm.

Recently, the index modulation technique is introduced into the visible light communication (VLC) system to convey information bits in a more energy-efficient and spectral-efficient way. However, in practice, the index modulation aided VLC system should be carefully designed to be compatible with dimming control. In this paper, a novel hybrid dimming scheme is proposed for the index modulation aided VLC system, which combines the frequency-domain and time-domain dimming strategies to maximize the channel capacity and maintain the desired illumination level. Simulation results validate the superior performance of the proposed hybrid dimming scheme.

In this work, a wireless broadcast network with a base station (BS) sending random time-sensitive information updates to multiple users with interference constraints is considered. The Age of Synchronization (AoS), namely the amount of time elapsed since the information stored at the network user becomes desynchronized, is adopted to measure data freshness from the perspective of network users. Compared with the more widely used metric-the Age of Information (AoI), AoS accounts for the freshness of the randomly changing content. We formulate the scheduling problem into a discrete time Markov decision process and approximate the optimal solution through finite state policy iteration. An index based heuristic scheduling policy based on restless multi-arm bandit (RMAB) is provided to reduce computational complexity. Numerical results are presented to demonstrate the performance of the proposed policies.

Low‐density spreading multiple access (LDSMA) was proposed for supporting massive connectivity and low latency scenarios in 5th generation systems owing to its low implementation complexity. In this study, the authors analyse the performance loss of conventional LDSMA systems from two aspects: the one from low‐density spreading constellations, and the other from the mismatch between multi‐user detector and channel decoders. For the first aspect, they propose a new multi‐dimensional constellation designed via simple symbol remapping with the same constellation as in LDSMA in each dimension. And for the second aspect, newly designed Raptor‐like low‐density parity‐check codes are proposed to help retrieve the performance loss. Simulation results show that the performance of the proposed enhanced system is 1.4 dB and above superior to the conventional LDSMA system and less than 1.2 dB away from Shannon limit of Gaussian multiple access channel with symmetric users at sum rate of 1.5 and 2.25 bits per channel use.

As the bandwidth resource in TV white space (TVWS) is permitted to be used for the secondary users, multiple secondary networks coexisting in the same geographical area can provide multiple services without interfering with the primary service, such as the TV broadcasting. To satisfy the minimum rate requirements and maximise the sum rate of the multiple services, the problem of resource allocation can be modelled as a mixed integer non‐linear programming (MINLP). According to this model, the joint bandwidth and power allocation (JBPA) method is proposed to solve the resource allocation for multiple services. The JBPA method is sub‐optimal, which can approximate the performance of the optimal two‐step exhaustive search (TSES) method very well, especially when the number of services is larger than the number of idle channels. Compared with TSES, the JBPA method decreases the complexity from exponential level to polynomial level. Besides, compared with one channel occupied by single service, the JBPA can serve more services and provide stable performance when the number of services varies dynamically. Compared with the algorithm random bandwidth allocation, the JBPA can approximate the optimal performance provided by TSES better with the time complexity of the same order.

Deep learning has a wide application in the area of natural language processing and image processing due to its strong ability of generalization. In this paper, we propose a novel neural network structure for jointly optimizing the transmitter and receiver in communication physical layer under fading channels. We build up a convolutional autoencoder to simultaneously conduct the role of modulation, equalization, and demodulation. The proposed system is able to design different mapping scheme from input bit sequences of arbitrary length to constellation symbols according to different channel environments. The simulation results show that the performance of neural network-based system is superior to traditional modulation and equalization methods in terms of time complexity and bit error rate under fading channels. The proposed system can also be combined with other coding techniques to further improve the performance. Furthermore, the proposed system network is more robust to channel variation than traditional communication methods.

The inherent broadcast characteristics of the visible light communication (VLC) channel makes VLC downlinks susceptible to unauthorized terminals in many actual VLC scenarios, such as offices and shopping centers. This paper considers a multiple-input-single-output (MISO) VLC scenario with multiple light fixtures acting as the transmitter, a VLC receiver as the legitimate user, and an eavesdropper attempting to intercept the undisclosed information. To improve the confidentiality of VLC links, a physical-layer anti-eavesdropping framework is proposed to obscure the unauthorized eavesdroppers and diminishes their capability of inferring the information through smart beamforming over the MISO VLC wiretap channel. To cope with the intractable problem of finding the theoretically optimal solution of the secrecy rate and utility for the MISO VLC wiretapping channel, a reinforcement learning (RL)-based VLC beamforming control scheme is proposed to achieve the optimal beamforming policy against the eavesdropper. Furthermore, a deep RL-based VLC beamforming control scheme is proposed to handle the curse of dimensionality for both observation space and action space and avoid the quantization error of the RL-based algorithm. Simulation results show that the proposed learning-based VLC beamforming control schemes can significantly decrease the bit error rate of the legitimate receiver and increase the secrecy rate and utility of the anti-eavesdropping MISO VLC system, compared with the benchmark strategy.

Utilizing the illuminating light-emitting diode (LED) for signal transmission, the visible light communication (VLC) offloads the congested traffic in licensed spectrum while providing users with high quality of service. However, in shaded areas, the performance of VLC is affected. In this paper, we apply Device-to-Device (D2D) technology on the VLC network, where some mobile users are able to relay the data transmission to nearby end mobile users. Considering the autonomous behaviors of mobile users as relays (RUs), cellular service provider (CSP) and VLC service provider (VLCSP), we propose a hierarchical game framework and analyze the distributive strategies for each individual. In the game, the data packet size, the price of licensed spectrum and data rates in possible data transmission routes are sequentially determined with equilibrium solutions, and each VLC transmitter (VLCT) determines the optimal data transmission route with stable matching. Simulations depict the high performance when combining VLC and D2D with the proposed strategies.

The radio frequency spectrum crunch has triggered the harnessing of other sources of bandwidth, for which visible light is a promising candidate. Even though visible light communication (VLC) ensures high capacity, coverage is limited. This necessitates the integration of VLC and device-to-device (D2D) technologies into heterogeneous networks. In particular, mobile users which are accessible by the VLC transmitters can relay data to mobile users which are not, by means of D2D communication. However, due to the distributed behaviors of mobile users, determining optimal data transmission routes from VLC transmitters to end mobile devices is a major challenge. In this paper, we propose a reinforcement learning (RL)-based approach to determine multi-hop data transmission routes in an indoor VLC-D2D heterogeneous network. We obtain the rewards for the RL-based method dynamically, by formulating the interactions between the mobile users relaying the data as an equilibrium problem with equilibrium constraints and using alternating direction method of multipliers to solve it. The proposed technique can achieve optimal data transmission routes in a distributed manner. The simulation results demonstrate the effectiveness of the proposed approach, showing that transmission routes with low delays and high capacities can be achieved through the learning algorithm.

Visible light communication (VLC) is regarded as one of the promising candidate to complement the conventional radio frequency communication as it can potentially perform communication and illumination simultaneously. Recently, VLC is combined with generalized spatial modulation (GenSM) technology to improve spectral and energy efficiencies. In order to provide high speed communication and flexible dimming control simultaneously, in this paper, a spectral-efficient GenSM based hybrid dimming scheme, which combines the spatial-domain and time- domain dimming strategies, is proposed for VLC. The channel capacity of the proposed scheme is analyzed and a closed-form expression of the upper bound of the channel capacity is derived. According to the required illumination level, the spatial-domain and time-domain strategies are realized based on the maximization of the channel capacity. Simulation results substantiate the superior performance of the proposed dimming scheme over state-of-art dimming schemes.

As the underwater applications require reliable, fast and efficient communications, the underwater visible light communication (VLC) is attracting more and more attention. Due to the complicated channel condition of the underwater environment, accurate channel estimation is still challenging for underwater VLC systems. This paper investigates the underwater VLC channel estimation problem by following two main assumptions. Firstly, the number of the channel paths is assumed to be much smaller than the channel length, which will obtain the sparsity property in the distance domain. Secondly, the attenuation coefficient is assumed to be approximately linear with the frequency, which is true when the modulation bandwidth is much more narrow compared with the carrier frequency. The compressive sensing (CS) framework is formulated based on the two assumptions, whose sensing matrix has a large coherence. In this condition, the Bayesian CS algorithm is adopted instead of the conventional schemes, and the subspace scheme is also investigated for comparison. The simulation results demonstrate that the proposed scheme outperforms the conventional ones in terms of mean square error (MSE), hence better system performance can be expected.

Recently, mobile social networks (MSNs) has been playing an increasingly large proportion in people's daily life, and consequently attracted enormous amount of researches in this area, including network data collection, user behavior analysis and so on. Many of them are based on the community structure of the MSNs, the detection of which has attracted academic attention. In this paper, we propose an inter-community detection scheme under the framework of the emerging compressive sensing (CS) over graphs. Firstly, we extract the social structure among users by calculating the probability of two users encountering with each other. Then, the proposed scheme utilizes the encounter probability and the edge-clustering coefficient to define a novel additive property to make the CS algorithm applicable. Simulation results demonstrate that the proposed detection scheme can detect inter- community links more accurately than the conventional random walk based method.

To provide massive connectivity of a large number of low-power devices in massive machine-type communications (mMTC), advanced physical layer multiple access technologies are imperative for next generation of cellular networks. In this paper, a physical layer non-orthogonal multiple access (NOMA) scheme, called resource-pattern-aided bit-interleave coded multiple access, BICMA for short, is proposed for mMTC to support high and flexible user load energy-efficiently with low complexity. The rate-compatible length-scalable low-density parity check (LDPC) codes, recommended for 5G new radio in 3GPP work item, are firstly incorporated into NOMA schemes to provide great flexibility and compatibility with other services, like enhanced mobile broadband. In BICMA, instead of spreading, the constellation symbols of users are directly mapped to chips according to resource patterns, of which the density can be adjusted to reduce multi-user interference at each chip. Thanks to the rate-compatible length-scalable LDPC codes and flexible resource pattern, the parameter optimization of BICMA is of low complexity. Simulation results show that the energy-efficiency of BICMA is higher than that of orthogonal multiple access schemes, especially with relatively high sum spectral efficiency. Various user loads, as high as 18 users with spectrum efficiency of 0.25 bits/chip/user, can be supported by BICMA, and the required signal-to-noise ratio of each user only increases slightly while much more users could access the same channel resources simultaneously.

Sparse code multiple access (SCMA) is a newly proposed non-orthogonal multiple access technique for the 5th generation (5G) of wireless communication systems. It improves low-density signature multiple access (LDSMA) by replacing the inherent repetition code with multi-dimensional constellations. In this paper, the Gaussian-input channel capacities as well as the constellation constrained (CC) capacities of SCMA and LDSMA are analyzed and compared for the cases of Gaussian multiple access channel (GMAC) with random phase rotation and GMAC with fast fading, which disclose the performance potential of SCMA over LDSMA.

Hybrid precoding is introduced to millimeter wave (mmWave) multiple-input multiple-output (MIMO) System in order to reduce the number of radio frequency (RF) chains. In traditional hybrid precoding scheme, the maximum number of independent data streams is restricted by the number of RF-chains, which limits the achievable spectral efficiency (SE) rate. To further improve the SE rate, the generalized spatial modulation (GenSM) structure is considered. However, current gradient ascent iteration (GAI) algorithm of GenSM aided hybrid precoding is too complicated when the antenna array is large. In this paper we propose a novel low complexity algorithm for the analog and digital precoder design under GenSM hybrid precoding structure. Our approach gives additional unitary constraint to the digital precoder, formulating its design as a masked orthonormal procrustes problem. With this simplification, we accomplish the whole hybrid precoder design via turbo optimization (TO). Finally, numerical simulations are demonstrated to compare the achievable SE performance between our proposed low complexity scheme and some traditional hybrid precoding algorithms including the GAI algorithm.

Recently, the technique of multiple input multiple output (MIMO) is becoming more and more widespread in the communication systems in order to significantly improve the system capacity. The channel estimation is still one of the most tricky problems because of the large amount of coefficients to be estimated for one receive antenna. This paper focuses on designing and optimizing the time-domain training sequences for MIMO systems and proposes a novel sparse channel estimation scheme taking advantage of the spatial correlation among both the transmit antennas and the receive antennas. The structured compressive sensing (SCS) framework is set up, which is the main approach to recover the sparse channel for different antennas. It is illustrated in the simulation results that the proposed scheme has a higher probability to recovery the nonzero support of the channel than that of the traditional schemes, while the mean square error is also smaller, which means our scheme is expected to have a superior performance.

In this paper, we analyze asymptotic outage behavior of the uplink non-orthogonal multiple access (NOMA) over blockfading Gaussian multiple access channel (BF-GMAC) without CSIT when the number of active users tends to infinity. A closedform lower bound of the asymptotic outage probability is derived, and the accurate outage probability is numerically calculated and discussed for BF-GMAC with Nakagami-m fading. The numeral results disclose threshold effects for NOMA over such fading channels.

This paper deals with joint optimization of rate and power for NOMA-based layer-aware multicasting systems. We encode the source information into multiple layers with successive enhancement, and fix the ratios between the rates of any two layers. Superposition coding (SC) and successive interference cancellation (SIC) are applied to approach the capacity of broadcast channels. Based on channel statistics known at the transmitter, we adapt the integral transmission rate and the power allocation in order to maximize the expected user satisfaction defined by a utility function. We solve the problem by splitting it into several typical cases, each of which eliminates a different number of upper layers. We show the strong duality of each case and propose a two-dimensional bisection search algorithm to solve the dual problem. Simulation and discussion are under both i.i.d. and non- i.i.d. scenarios.

As a combination of spatial modulation (SM) system and massive multiple‐input multiple‐output (MIMO) system, massive SM aided MIMO (SM‐MIMO) system is recently proposed. In broadband scenarios, single‐carrier (SC) modulation is introduced to massive SM‐MIMO system, thus massive SC‐SM MIMO system is proposed for uplink multi‐user transmission over frequency‐selective fading channels. In this study, the uplink spectral efficiency (SE) of multi‐cell massive SC‐SM MIMO system is analysed, meanwhile the pilot contamination effect is taken into consideration. A tight SE lower bound of multi‐cell massive SC‐SM MIMO system is proposed with maximum ratio (MR) combining, which also takes into account the imperfect channel estimation, transmit antenna correlation and path loss. The tightness of the authors' proposed closed‐form SE lower bound is shown via simulation results. The optimal pilot reuse factor can be determined with different system configurations by simulations, and the pilot reuse factor that is larger than one is more suitable for less TAs and user equipments.

As a cluster between Internet of Things (IoT) and mobile social networks (MSNs), social IoT allows close interaction between humans and things. Many applications of IoT and MSN are based on the community structure to realize efficient services, and thus the detection of the community structure has become a key problem and attracted much academic attention. In this paper, a compressive sensing (CS) over graphs-based intercommunity detection scheme is proposed for the social IoT. By exploiting the probability of two nodes encountering each other, the encounter probability and the edge-clustering coefficient are utilized to define a novel metric for each connection and construct the measurement matrix. Then a CS-based detection algorithm is proposed to detect the intercommunity links. Moreover, the edge-clustering coefficient is exploited as the prior information to further improve accuracy and reduce complexity. Simulations show that the proposed detection scheme outperforms the conventional random walk-based scheme in the social IoT.

The application of hybrid precoding in millimeter wave (mm-wave) multiple-input multiple-output (MIMO) systems has been proven effective for reducing the number of radio frequency (RF) chains. However, the maximum number of independent data streams is conventionally restricted by the number of RF chains, which limits the spatial multiplexing gain. To further improve the achievable spectral efficiency (SE), in this paper we propose a generalized spatial modulation aided mmwave MIMO system to convey an extra data stream via the index of the active antennas group, while no extra RF chain is required. Moreover, we propose a hybrid analog and digital precoding scheme for SE maximization. Based on a narrowband mmwave-MIMO channel assumption, a closed-form lower bound is derived to quantify the achievable SE of the proposed system. By utilizing this lower bound as the cost function, a two-step algorithm is proposed to optimize the hybrid precoder. The proposed algorithm not only utilizes the concavity of the cost function over the digital power allocation vector but also invokes the convex 1∞relaxation to handle the non-convex constraint imposed by analog precoding. Finally, the proposed scheme is shown via simulations to outperform state-of-the-art mm-wave MIMO schemes in terms of achievable SE.

Multiple-input multiple-output (MIMO) techniques have constituted a key approach to facilitate the rapid data traffic growth in future 5G communication networks. However, the high cost and low efficiency nature of radio frequency (RF) resource has unfortunately reduced the benefit of MIMO. While spatial modulation (SM) technique has recently been proposed to reduce the RF resource for conventional small-scale MIMO systems, it is unclear if future 5G communication network, where conventional MIMOs encounter much different challenges, still embraces the benefits of SM. In order to address this issue, in this paper the application of SM in 5G communications is discussed, where we propose to incorporate SM into i) massive MIMO systems, and ii) millimeter wave (mmWave) MIMO systems, to strike a better tradeoff between energy efficiency and spectral efficiency for future wireless communications. It is revealed in this paper that SM is capable of improving the transmission data rates for 5G communication networks while maintaining an equal scale of RF resource occupancy, which therefore motivates the fruitful application of SM in 5G communications.

In this paper, a novel generalized spatial modulation (GenSM)-aided millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) system is proposed for indoor transmission over line-of-sight (LoS) channels. Unlike the conventional GenSM-based mmWave systems, the application of digital beamforming (DBF) and the impact of spatial-domain codebooks are explored in our work. More specifically, we first propose a closed-form lower bound for quantifying the achievable spectral efficiency (SE) of our system. Then we seek to optimize the spatial-domain codebook and DBF design with respect to SE maximization. Lastly, numerical simulations are provided to substantiate the performance gain achieved by the proposed system compared against the conventional spatial multiplexing (SMX) MIMO schemes with waterfilling precoding.

Due to the high cost and low energy efficiency of the dedicated radio frequency (RF) chains, the number of RF chains in a millimeter wave (mmWave) multiple-input multiple-output (MIMO) system is usually limited from a practical point of view. In this case, the maximum number of independent data streams is also restricted by the number of RF chains, which consequently leads to limiting the potentially attainable spatial multiplexing gain. In order to address this issue, in this paper, a novel generalized spatial modulation (GenSM) aided mmWave MIMO system is proposed, which enables the transmission of an extra data stream via the index of the active antennas group and requires no extra RF chain. Moreover, a two-step algorithm is also proposed to optimize the hybrid precoder design with respect to spectral efficiency (SE) maximization. Finally, numerical simulation results demonstrate the superior SE performance achieved by the proposed scheme.

Flat-fading channel models are usually invoked for analyzing the performance of massive spatial modulation multiple-input multiple-output (SM-MIMO) systems. However, in the context of broadband SM transmission, the severe inter-symbol-interference (ISI) caused by the frequency-selective fading channels can not be ignored, which leads to very detrimental effects on the achievable system performance, especially for single-carrier SM (SC-SM) transmission schemes. To the best of the author's knowledge, none of the previous researchers have been able to provide a thorough analysis on the achievable spectral efficiency (SE) of the massive SC-SM MIMO uplink transmission. In this context, the uplink SE of single-cell massive SC-SM MIMO system is analyzed, and a tight closed-form lower bound is proposed to quantify the SE when the base station (BS) uses maximum ratio (MR) combining for multiuser detection. The impacts of imperfect channel estimation and transmit antenna (TA) correlations are all considered. Monte Carlo simulations are performed to verify the tightness of our proposed SE lower bound. Both the theoretical analysis and simulation results show that the SE of uplink single-cell massive SC-SM MIMO system has the potential to outperform the uplink SE achieved by single-antenna UEs.

In generalised spatial modulation (GenSM) systems, the total number of active antenna groups is always not a power of two, which leaves room for designing an active antenna group (AAG) selection algorithm to achieve the constellation shaping gain and to improve the symbol error rate (SER). In this paper, based on uniform-cost search, a novel iterative AAG selection algorithm for GenSM systems is proposed with channel state information at the transmitter (CSIT). Based on the principle of minimum Euclidean distance maximization, the proposed algorithm reduces the computational complexity while maintaining the global optimality in improving SER comparing with exhaustive search algorithm. Monte Carlo simulations are performed to verify the global optimality in SER performance and favorable reduction in computational complexity striked by the proposed AAG selection algorithm.

Channel estimation for underwater acoustic (UWA) channels in orthogonal frequency division multiplexing (OFDM) systems is one of the most difficult problems in UWA communications. Because UWA channels are both frequency-selective and time-selective, the total number of channel coefficients to be estimated will significantly increase. Besides, the fast time-varying characteristics and strong Doppler effects will deteriorate the orthogonality between the OFDM subcarriers and cause inter-carrier interference (ICI). To handle these problems, in this paper, we firstly transform the discrete doubly selective UWA channel model into an expansion of complex exponential basis. Based on the basis expansion model (BEM) of UWA channels, we then equivalently transform the channel estimation problem into the recovery of sparse BEM coefficients. Finally, a modified sparse signal recovery scheme based on block sparse Bayesian learning is proposed to estimate the doubly selective UWA channel state information (CSI). Simulation results confirm its performance merits.

Uplink grant-free non-orthogonal multiple access (NOMA) is promising for machine-to-machine (M2M) communications to meet the requirement of sporadic short packets transmission of massive devices. In this paper, successive interference cancellation (SIC) and joint decoding (JD), as two well-known decoding techniques for NOMA, are analyzed for M2M random access. Due to the sporadic small packet transmission, a block-fading multiple access channel (BF-MAC) is considered, and the frequency-selectivity is also introduced. The achievable individual outage rate regions of SIC and JD, with deterministic active users, are first obtained based on individual outage events, then the analysis is extended to random access and the achievable individual outage rates and throughputs are derived. Numerical results show that SIC can perform as well as JD with relative low user load and transmission powers over frequency-flat BF-MAC. But with higher transmission powers or frequency-selectivity, the outage-limited maximum throughputs of JD are significantly higher than that of SIC.

In spatial modulation (SM) systems, the distribution of amplitude-phase modulation (APM) symbol causes the tradeoff of detection performance between APM symbol and active antenna, which infers the overall capacity of SM system. In this paper, the APM constellation and symbol labeling of SM system is investigated. The optimized APM constellation is designed to maximize the system coded modulation average mutual information (CM-AMI). The symbol labeling of SM system is optimized with binary switching algorithm (BSA) using the symbol detection pairwise error probability (PEP) criteria. Moreover, the proposed APM constellation and symbol labeling are applied in a coded spatial modulation (SM) system with the bit-interleaved coded modulation and iterative demapping (BICM-ID). Simulation result shows the BICM-ID SM system with proposed constellation and symbol labeling has a signal-to-noise ratio (SNR) gain compared to conventional constellation system and independent demapping system.

This paper proposes a novel doubly selective channel estimation scheme for MIMO-OFDM systems with both time- and frequency-domain training (TFDT) based on structured compressive sensing (SCS). The characteristics of the doubly selective channel is considered in three dimensions, i.e., sample-domain, tap-domain, and antenna-domain. The tap-domain channel impulse responses (CIRs) are jointly sparse, while the sample-domain CIRs can be expanded by specific bases. We utilize the received pseudo-random noise (PN) sequence to acquire the common support of the channel. After a cyclic reconstruction process, the channel estimation can be performed under an SCS model by exploiting the proposed pilot pattern and the spatial correlation among antennas. Simulation results demonstrate that the proposed scheme has a superior performance than that of conventional counterparts.

The non-orthogonal multiple access (NOMA) technology has been proposed and regarded as one of the potential promising technologies for the future 5G network. The extension of generalized spatial modulation multiple-input multiple-output (MIMO) to NOMA system improves both the spectral and energy efficiencies of the system, while maintaining the massive connectivity and low latency advantages, but it puts forward challenges for the multi-user and signal detection as well. In this paper, we propose a joint user activity and signal detection scheme based on the block-sparse compressive sensing (BS-CS) method in the uplink NOMA system, in which the generalized spatial modulation MIMO technology is used. By exploiting the structure and sparsity of the multi-user generalized spatial modulation signals, we formulate the detection problem into a block-sparse recovery problem. Then a BS-CS based detection algorithm, enhanced structured block-sparse compressive sampling matching pursuit (ESB-CoSaMP), is proposed to detect the active users and transmitted data efficiently. Moreover, the information of active antennas at each user is exploited in ESB-CoSaMP to further improve the accuracy. Simulations show that the proposed detection scheme outperforms the conventional CS and BS-CS based schemes.

In this paper, a spectral efficient channel estimation scheme is proposed for spatial-correlated sparse massive multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems with superimposed time-frequency training sequences (TSs) based on basis expansion model (BEM). This scheme first relies on the identical time-domain TS to acquire the partial channel common support, based on which the frequency-domain TSs inserted into the OFDM block are then utilized for accurate channel state information (CSI) acquisition. Besides, the superimposed TSs are adopted to reduce the TS overhead. Meanwhile, by exploiting the spatial correlations of massive MIMO channels, BEM can be utilized to reduce the number of the frequency-domain TSs and improve the spectral efficiency drastically from 22% to 95%. Simulation results show that the proposed scheme with 200 frequency-domain TSs can achieve almost the same performance as the conventional approach with 3200 frequency-domain TSs in massive MIMO systems with 256 transmit antennas.

In this paper, we investigate the impact of channel coding on two kinds of non-orthogonal multiple access (NOMA) schemes, either with a successive interference cancellation (SIC) receiver or with a joint decoding (JD) receiver. Firstly, the information-theoretic individual achievable signal-to-noise ratio (SNR) regions of SIC and JD are analyzed. Then single-user-oriented (SU) code and multi-users-oriented (MU) code are considered for SIC and JD, and the practical individual achievable SNR regions of SIC and JD with these two kinds of channel codes are obtained, which provides an intuitive way to evaluate system performance under different channel conditions and transmission rates. Simulation results show that, the practical boundaries of individual achievable SNR region of SIC generally lie inside the boundaries of JD, as predicted by the theoretical analysis. Moreover, SU code, which is capacity-approaching over point-to-point channel, performs better than MU code for SIC, while the choice of channel codes for JD depends on transmission rates and channel conditions.

The integration of radar and communication has attracted general interest with its advantage of equipment miniaturizing and high efficiency of spectrum, especially in intelligent transportation systems because of the need for transmitting and detecting simultaneously and the lack of room for various devices. Among various integration schemes, the integrated waveform which realizes transmitting and detection at the same time is a real integration with high efficiency and no interference. In this paper, we proposed a modified three-section integrated waveform based on linear frequency modulation and continuous phase modulation (LFM-CPM). Short Time Fourier Transform (STFT) is used for time-frequency analysis and the modification of the mapping codebook of the communication symbols restrains the spectrum of the integrated waveform within the original bandwidth of the radar system. Simulation results show that the modified waveform holds good bit error rate (BER) performance even when there is strong interference beside the original bandwidth of the radar system, while the waveform without modification can hardly complete the transmission with high BER.

LDS-OFDM and SCMA are two examples of non orthogonal multiple access (NOMA), which is considered to be a promising technology for next generation wireless communications due to its larger capacity region compared with orthogonal multiple access (OMA). Iterative receiver structure is used in these two systems to get better performance than the noniterative receiver, but a direct implementation of iterative receiver with conventional message passing algorithm has a prohibitive computational complexity, especially when the system uses high order modulation. In this paper, Gaussian approximation is used to solve this problem, and can reduce the complexity of detector effectively. Simulation results demonstrate that the proposed Gaussian approximation based iterative receiver offers significant performance gains over the traditional non-iterative receiver structure. Even compared with the computationally intractable message passing based iterative receiver, the performance does not degrade much. This means that the proposed method offers a tradeoff between performance and complexity, and is a good candidate for future use in LDS-OFDM and SCMA systems.

Compared with Wireless Local Area Networks (WLANs) in the ISM band, smaller path loss of the spectrum in TV White Space (TVWS) provides larger coverage region with the same power budget. More nodes derived from the larger coverage region increase the collision probability, which damages the throughput efficiency of carrier sense multiple access#x002F;collision avoidance (CSMA#x002F;CA) protocol in 802.11af. The scheme of the Channel Access based on Frequency Contention (CAFC) exploits single sub-carrier of Filtered Bank Multi-Carrier (FBMC) to realize frequency contention. CAFC improves the system throughput efficiency by decreasing the collision probability and removing channel idle time. The set of possible frequency contention outcomes can be enlarged to further decrease the collision probability with little cost appended to the system complexity. Compared with conventional CSMA#x002F;CA in 802.11af, the simulation indicates that the CAFC scheme enhances the saturated throughput efficiency significantly. Besides, the CAFC scheme is designed to be compatible with 802.11af, which reduces the application cost in practice.

The joint radar-communication system is a good way to meet the intelligent transportation systems' need of detecting the cars and barriers around and exchanging information from each other at the same time. Among various schemes, the joint system with integrated waveform attracts general interest because the interference between the radar and communication subsystems will be fundamentally avoided. For all kinds of integrated waveforms, the spectrum must be limited within B0, the original working band of the radar, in order to avoid the performance loss brought by the attenuation of the power amplifier (PA) and the interference from other devices out of band, which is inevitable in actual devices and environment. In this paper, the time-frequency characteristics of an integrated waveform based on Linear Frequency Modulation (LFM) and Continuous Phase Modulation (CPM) is studied in detail with the Short Time Fourier Transform (STFT). Based on the theoretical analysis, a modified three-section waveform with a limit of the mapping codebook of communication symbols is proposed. Simulation results show that the bandwidth of the modified waveform is limited within B0and the bit error rate (BER) performance with a band-limited PA and out-band interference is almost the same as the BER with ideal PA and no interference.

As the shortage of spectrum is becoming a serious problem to the wireless communication, visible light communication (VLC) is attracting widespread attention. However, the access to the backbone network is likely to completely modify the communication network, thus leading to a great cost. Fortunately, the technology of power line communication (PLC) is a perfect match to VLC, which provides power supply and connection for VLC to the backbone network. In this study, the authors take an integration of PLC and VLC into consideration and mainly focus on a communication framework with multi‐service. In the case of not making a big modification to the origin network, a direct re‐transmission concept is considered and three implementation schemes which differ in frequency domain, time domain, and bit division multiplexing (BDM) are proposed. The demonstration platform and the simulation results suggest that the proposed schemes have the ability to support multi‐service transmission with no big modification of current power line network. Moreover, the BDM scheme can accommodate different requirements of quality of service with better performance than conventional multi‐service schemes.

In this study, a spectral efficient channel recovery scheme is proposed for spatial‐temporal correlated sparse massive multiple‐input multiple‐output (MIMO) orthogonal frequency division multiplexing systems. By exploiting the spatial‐temporal correlations of massive MIMO channels, superimposed time‐frequency training sequences (TSs) are jointly utilised for accurate channel state information (CSI) acquisition. Moreover, a novel time‐frequency TS pattern design scheme is proposed to improve the recovery performance based on structured compressive sensing theory. Furthermore, the discrete prolate spheroidal basis expansion model of massive MIMO channels in the space‐domain is utilised to reduce the frequency‐domain pilots overhead and improve the spectral efficiency. Simulation results show that the proposed scheme could achieve accurate CSI with great improvement in spectral efficiency and computational complexity.

In the next generation of cellular networks, millimeter wave (mmWave) communications will play an important role. With the utilization of mmWave communications, the massive multiple-input multiple-output (MIMO) technique can be effectively employed, which will significantly improve system capacity. However, an effective channel estimation scheme is the prerequisite of system stability and in great need for improvement in massive MIMO systems. In this paper, a channel estimation scheme based on training sequence (TS) design and optimization with high accuracy and spectral efficiency is investigated in the framework of structured compressive sensing. As a new perspective to optimize the block coherence of the sensing matrix, the auto-coherence and cross-coherence of the blocks are proposed and specified as two kinds of key merit factors. In order to optimize the two factors, specific TS is designed and obtained from the inverse discrete Fourier transform of a frequency domain binary training sequence, and a genetic algorithm is adopted afterwards to optimize the merit factors of the TS. It is demonstrated by the simulation results that the block coherence of the sensing matrix can be significantly reduced by the proposed TS design and optimization method. Moreover, by using the proposed optimized TS's, the channel estimation outperforms the conventional TS design obtained by the brute force search in terms of the correct recovery probability, mean square error, and bit error rate, and can also approach the Cramer-Rao lower bound.

Generalized spatial modulation (GSM) is an extended architecture to the spatial modulation multiple antenna system. The GSM system uses multiple activated transmit antennas to further exploit the system transmission capacity. In this paper, we consider the GSM systems with channel diagonalization (CD-) transmitter pre-coder (TPC), channel inversion TPC, and without TPC. For each TPC scheme, the pairwise activated antenna detection discriminant and closed-form antenna detection pairwise error probability (PEP) of the GSM system are derived. We use the antenna detection PEP to calculate the analytical antenna detection symbol error rate union bound and the mutual information lower bound. We use asymptotic methods to analyze antenna detection diversity and coding gains of GSM system with or without TPC when the signal-to-noise ratio is high. Together with complexity analysis on the pre-coders and antenna detectors, we discover the CD-TPC trades the antenna detection performance for complexity reduction, while the CD-TPC scheme achieves a balance between the performance and complexity. This paper introduces methods to the performance evaluation of the GSM system, and provides a guideline for the GSM system design such as antenna configuration and TPC schemes selection.

Narrowband Internet-of-Things (NB-IoT) is one of the emerging 5G technologies, but might introduce narrowband interference (NBI) to existing broadband systems, such as long-term evolution advanced (LTE-A) systems. Thus, the mitigation of the NB-IoT interference to LTE-A is an important issue for the harmonic coexistence and compatibility between 4G and 5G. In this paper, a newly emerged sparse approximation technique, block sparse Bayesian learning (BSBL), is utilized to estimate the NB-IoT interference in LTE-A systems. The block sparse representation of the NBI is constituted through the proposed temporal differential measuring approach, and the BSBL theory is utilized to recover the practical block sparse NBI. A BSBL-based method, partition estimated BSBL, is proposed. With the aid of the estimated block partition beforehand, the Bayesian parameters are obtained to yield the NBI estimation. The intra-block correlation (IBC) is considered to facilitate the recovery. Moreover, exploiting the inherent structure of the identical IBC matrix, another method of informative BSBL is proposed to further improve the accuracy, which does not require prior estimation of the block partition. Reported simulation results demonstrate that the proposed methods are effective in canceling the NB-IoT interference in LTE-A systems, and significantly outperform other conventional methods.

The concept of generalized spatial modulation (GenSM) aided millimeter wave (mm-wave) multiple-input multiple-output (MIMO) has recently attracted substantial research interest, as it benefits from the large bandwidth of mm-wave MIMO, while maintaining a reduced number of radio frequency (RF) chains. However, due to the lack of precoding design, the preceding GenSM-aided mm-wave MIMO schemes suffered from severe performance loss. Inspired by the reduced-RF-chain structure making implementation cost low while maintaining the benefit of GenSM, in this paper, we incorporate the technique of hybrid precoding into GenSM-aided mm-wave MIMO, where a hybrid digital and analog precoding regime is proposed to enhance the system's achievable rate. Moreover, a closed-form expression is proposed to provide an accurate approximation to the spectral efficiency (SE) achieved by the proposed scheme. The proposed expression is further simplified in the region of high signal-to-noise ratio (SNR). By exploiting the proposed SE expressions as low-complexity cost functions, two algorithms, i.e. the gradient ascent algorithm and the high-SNR approximation algorithm, are exploited to optimize the hybrid precoders in terms of SE maximization. Finally, numerical simulations are provided to substantiate the superior SE performance achieved by the proposed scheme over other GenSM-aided mm-wave MIMO schemes as well as the state-of-the-art mm-wave MIMO systems.

Massive spatial modulation aided multiple-input multiple-output (SM-MIMO) systems have recently been proposed as a novel combination of SM and of conventional massive MIMO, where the base station (BS) is equipped with a large number of antennas and simultaneously serves multiple user equipment (TIE) that employ SM for their uplink transmission. Since the massive SM-MIMO concept combines the benefits of both the SM and massive MIMO techniques, it has recently attracted substantial research interest. In this paper, we study the achievable uplink spectral efficiency (SE) of a multi-cell massive SM-MIMO system, and derive closed-form expressions to asymptotically lower-bound the SE yielded by two linear BS combining schemes, including maximum ratio combining and zero forcing combining, when a sufficiently large number of BS antennas are equipped. The derivation takes into account the impact of transmitter spatial correlations, imperfect channel estimations, user-specific power controls, and different pilot reuse factors. The proposed asymptotic bounds are shown to be tight, even when the scale of BS antennas is limited. The new SE results facilitate a system-level investigation of the optimal number of uplink transmit antennas (TAs) N with respect to SE maximization. Explicitly, we provide theoretical insights on the SE of massive SM-MIMO systems. Furthermore, we demonstrate that massive SM-MIMO systems are capable of outperforming the SE of conventional massive MIMOs relying on single-TA TIEs.

Single frequency network (SFN) is a promising technology for broadcasting systems with the advantages of better coverage and higher spectral efficiency. However, transmitting local services with unequal contents in SFN remains to be a problem. Bit division multiplexing (BDM) enables local service insertion (LSI) by dividing the channel resources into two sub-channels at bit level, which can approach the theoretical bound of total capacity than traditional time division multiplexing (TDM) method. With BDM, the transmission of global services enjoys all SFN advantages, at the same time each local content uses the same frequency layer. The coverage ratio of global service area (GSA) and local service areas (LSAs) are limited due to interference and reception thresholds. This paper deals with the option of BDM strategy and the adaption of transmitter parameters to optimize the overall coverage performance. A metaheuristic optimization approach using genetic algorithm (GA) is proposed. The simulation and discussion are based on a realistic scenario.

The idea of combining spatial modulation (SM) with massive multiple-input multiple-output (MIMO) system, i.e. massive SM-MIMO, has recently attracted plenty of academic interest, as it could simultaneously harness the advantages of SM and massive MIMO. However, the spectral efficiency (SE) analysis for massive SM-MIMO is still lack of exploring. To this end, a novel SE expression is derived in this paper to provide a tight lower bound for massive SM-MIMO systems, in which the impacts of imperfect channel estimation and random user locations are all accounted for. The new SE expression enables an efficient system-level analysis, via which we can investigate the optimal number of user antennas for SE maximization. Interestingly, we found that massive SM-MIMO maintains a higher SE than the conventional massive MIMO with single-antenna users when a limited number of users are scheduled, while conventional massive MIMO is more suitable when a large number of users are served.

In this paper, a novel impulsive noise (IN) cancel- lation scheme based on structured compressed sensing (SCS) for multiple input multiple output orthogonal frequency division multiplexing (MIMO-OFDM) power line communication systems is proposed. The SCS theory is introduced to IN recovery for the first time in this paper to the best of the authors' knowledge, and the gap of lack of research on the IN mitigation for MIMO PLC systems is filled. First, the measurements matrix of the IN is obtained, and the SCS optimization framework is formulated through the proposed spatially multiple measuring method, by fully exploiting the spatial correlation of the IN signals at different receive antennas. To efficiently reconstruct the IN signal, an enhanced SCS-based greedy algorithm, structured a priori aided sparsity adaptive matching pursuit (SPA-SAMP), is proposed, which significantly improves the accuracy and robustness compared with the state-of-art methods. Theoretical analysis and computer simulations validate that the proposed scheme outperforms conventional methods in the typical MIMO PLC system.

Spectrum trading not only improves spectrum utilization but also benefits both secondary users (SUs) with more accessing opportunities and primary users (PUs) with monetary gains. Although existing centralized designs consider the special features of spectrum trading (e.g., frequency reuse, interference mitigation, multi-radio multi- channel transmissions, etc.), they have to deploy new infrastructure, deal with extra control overhead, have scalability issues, and may miss many instantaneous opportunities. To address those issues, in this paper, we propose a novel dynamic matching based distributed spectrum trading (DMDST) scheme in multi-radio multi- channel cognitive radio (CR) networks. We employ conflict graph to characterize interference relationship among SUs with multiple CR radios, and formulate the centralized PUs' revenue maximization problem under multiple constrains. In view of the NP- hardness of solving the problem and no existence of centralized entity, we develop the DMDST algorithms based on conflict graph observed by PUs, solve the problem via dynamic matching with evolving preferences, and prove its stability. Through extensive simulations, we show that the results of proposed DMDST algorithm is close to the optimal one and outperforms other distributed algorithms without considering spectrum reuse.

The visible light communication (VLC) technology is able to provide indoor users with high quality of service and low cost. However, the performance of the VLC is limited in shaded or strong sun- light areas. In this paper, we combine the VLC network with Device-to-Device (D2D) technology. We consider that some mobile users accessible to the VLC network is able to relay the transmitted data to the nearby mobile users. We suppose all the mobile users as relays (MUaRs), cellular service provider (CSP) and VLC service provider (VLCSP) are autonomous individuals, and propose a hierarchical game to analyze the optimal strategies for each of them. In the game, the VLCSP determines the data transmission route and data packet size first. Based on the behaviors of the VLCSP, the CSP determines the prices of wireless licensed spectrum. According to the behaviors of the VLCSP, the CSP and the MUaRs in the neighborhood, all MUaRs determines their transmission data rates for optimal utility. Therefore, there is a graphical game among all MUaRs. When all MUaRs consider the corresponding reactions of other MUaRs in the neighborhood and achieve Nash Equilibrium, the Stackelberg Equilibriums can be achieved in the Stackelberg games between the VLCSP and the CSP, between the CSP and all MUaRs and between the VLCSP and all MUaRs. Simulation results show the correctness of the analysis and the VLC network with multi-hop D2D relay is able to bring high profits for the CSP.

Generalized spatial modulation (GSM) is an extended architecture to the spatial modulation (SM) multiple antenna system. Multiple activated transmit antennas (TAs) are applied in the GSM system to further exploit the system transmission capacity. In this paper, we consider the transmitter pre-coding (TPC) GSM systems with channel diagonalization (CD) and channel inversion (CI) pre-coding schemes. The pairwise activated TA detection discriminant and closed-form pairwise error probability (PEP) function are derived. We use the TA detection PEP to calculate the analytical TA detection symbol error rate (SER) union bound and the mutual information lower bound. The analytical results are verified by numerical simulation over Rayleigh fading channels. We investigate the system with different number of antennas and TPC schemes to evaluate the error probability and mutual information performance of the GSM system. The results show that the CD-TPC GSM system with equal power allocation has the most TA detection mutual information, and the CD-TPC system with water-filling power allocation has the most joint APM domain and space domain mutual information.

A block sparse Bayesian learning (BSBL) based approach of narrowband interference (NBI) cancellation for cyclic prefixed orthogonal frequency division multiplexing based smart grid communications is proposed in this paper. The BSBL theory is firstly introduced to recover the practical block sparse NBI with a frequency offset compared with the sub-carriers. The block sparse representation of the NBI is constituted through the proposed temporal differential measuring approach. A BSBL based method, estimated partitioned BSBL, is proposed for NBI recovery. The intra-block correlation is firstly considered to facilitate the recovery of block sparse NBI. Reported simulation results demonstrate that the proposed methods are effective and significantly outperform conventional counterparts.

Visible light communication (VLC) using the light emitting diode (LED) will become an appealing alternative to the radio frequency communication technology for indoor wireless broadband access. However, VLC needs a ubiquitous network as its backbone to avoid becoming an information isolated island. Power line communication (PLC) systems could easily solve the informative problem of VLC while powering the LED lamps at the same time, which is considered as a good partner of VLC for the cost-effective implementation. In this paper, a novel and cost-effective framework of ubiquitous indoor broadband access based on deeply integrated VLC and PLC technology with only low-cost modification to the current infrastructure is therefore proposed. The broadband access network supports duplex transmission through each LED using the decode-and-forward (DF) working mode. This paper will present our recent research progress in this area, including a prototyping of duplex voice communications network based on hybrid PLC and VLC in our lab. Our research and development plan in this area for the near future will also be covered.

In conventional generalised spatial modulation (GSM) systems, the antenna combination selection is based on the distribution of channels, without channel state information (CSI). In this paper, an A-Star based heuristic method and a sequential forward selection method for antenna combination selection in GSM systems are proposed to provide a superior symbol error rate (SER) performance with CSI. The A-Star based heuristic method is designed to minimize the union bound of SER rather than to maximize the minimum Euclidean distance between symbols. And the sequential forward antenna combination selection method is designed to reduce the computational complexity compared to the A-Star based heuristic method, but with slight loss in SER performance. Monte Carlo simulations are implemented to verify the performance of these proposed methods.

In this paper, we propose a new method to realize bidirectional visible light communication (VLC). We implement the bi-directional VLC system based on digital television terrestrial multimedia broadcasting-advanced (DTMB-A) and apply time-domain synchronous OFDM (TDS-OFDM) in the physical layer. A new type of APSK constellation with Gray mapping is used to provide a considerable shaping gain in both the independent demapping and the iterative demapping. Our system realizes the goal of bi- directional transmission between the station and the train, by which we could download driving data conveniently instead of getting on the vehicle. We also measure the output spectrum of photodetector and present our analysis. Through the experiment testing, the download rate of this bi-directional VLC system can achieve 63.67 Mb/s and the physical layer rate can reach 180Mb/s when two computers are connected through our system within the distance of 0.1m. With transmission distance exceeding 0.1m, the signal using 256APSK constellation mapping and 1/2 code rate can still be normally demodulated.

In practical multiple input multiple output (MIMO) systems, accurate knowledge of the channel state information (CSI) is a prerequisite to guarantee the system performance. The conventional CSI acquisition methods for MIMO system usually rely on the orthogonal (either time‐ or frequency‐domain) training sequences (TSs) to estimate the channel associated with each transmit–receive antenna pair, which is not spectrally efficient. This study proposes a non‐orthogonal time‐domain training‐based CSI acquisition approach for MIMO systems under the framework of structured compressive sensing. By exploiting the spatial–temporal correlations of the sparse MIMO channels, a spatially–temporally spARsity‐adaptivE‐simultaneous orthogonal matching pursuit algorithm is proposed, which could use the inter‐block interference free region of very small dimension within the received TS to recover the multiple channels. Furthermore, the proposed algorithm could utilise the priori channel partial common support to improve the recovery probability and reduce the complexity. Simulation results show that the proposed scheme has better performance and higher spectral efficiency than the conventional MIMO schemes, which might be an appealing solution for the future wireless communications.

Power line communication (PLC) techniques present a no extra wire solution for the communication purpose in a smart grid due to the ubiquity and low cost. Moreover, the through-the-grid property of PLC has naturally extended its possible applications, including but not limited to the automatic meter reading, line quality monitoring, online diagnostics, and network tomography. To guarantee the performance of communications as well as other applications in PLC systems, accurate channel state information (CSI) acquisition should be performed regularly. However, the conventional pilot-based CSI acquisition approaches in PLC systems have not made full use of the channel characteristics and hence suffer from a low spectral efficiency. In this paper, by exploiting the parametric sparsity and discretizing the electrical length in the well-known PLC channel model, we formulate the non-sparse (either time domain or frequency domain) PLC channel into a compressive sensing (CS) applicable problem. Furthermore, we propose a spectrally efficient CSI acquisition scheme under the framework of Bayesian CS and extend it to the multiple-input multiple-output PLC by investigating the channel spatial correlation. Compared with the existing sparse CSI acquisition schemes for PLC, such as the annihilating filter-based and the estimating signal parameters via rotational invariance technique-based ones, the proposed scheme has better mean square error performance and noise robustness.

Spectrum trading not only improves spectrum utilization but also benefits both secondary users (SUs) with more accessing opportunities and primary users (PUs) with monetary gains. Although the existing centralized designs consider the special features of spectrum trading (e.g., frequency reuse, interference mitigation, multi-radio multi-channel transmissions, and so on), they still have to face many practical but challenging issues, such as the new infrastructure deployment, the extra control overhead, and the scalability issues. To address those issues, in this paper, we propose a novel matching-based multi-radio multi-channel spectrum trading (M3-STEP) scheme in cognitive radio (CR) networks. We employ conflict graph to characterize the interference relationship among SUs with multiple CR radios, and formulate the centralized PUs' revenue maximization problem under multiple constrains. In view of the NP-hardness of solving the problem and no existence of centralized entity, we develop the M3-STEP algorithms based on conflict graph observed by PUs, solve the problem via dynamic matching with evolving preferences, and prove its pairwise stability. Simulation results show that the proposed M3-STEP algorithm achieves close to optimal performance and outperforms other distributed algorithms without considering spectrum reuse.

In this paper, we study the design of frequency domain turbo equalization (FDTE) in single carrier (SC) multiple-input multiple-output (MIMO) systems, and prove the equivalence of two typical FDTE schemes under constant-modulus modulation. In the design of FDTE schemes, received signals and soft decision symbols obtained at the previous iteration are utilized to recover the transmitted symbols under the minimum mean squared error (MMSE) criterion. Frequency domain equalization with frequency domain decision feedback (FDE- FDDF), and frequency domain equalization with parallel interference cancellation (FDE-PIC) are presented with details. Under constant-modulus modulation, the FDE-FDDF scheme and the FDE-PIC scheme are proved to be equivalent mathematically. The equivalence of the two FDTE schemes under constant-modulus modulation has also been verified through numerical simulations.

Generalised spatial modulation (GSM) is extended from the concept of spatial modulation (SM). Due to its superior energy efficiency to classical multiple-input multiple-output (MIMO) techniques, GSM has attracted plenty of interest under the study of large-scale MIMO systems. In this paper, we propose a low-complexity symbol detector for GSM based on the framework of sparse Bayesian learning (SBL), which effectively recovers the GSM symbols by exploiting the inherent sparsity property of GSM. Compared to other sparsity-based symbol detectors, e.g., detectors based on the basis pursuit (BP) method and the orthogonal matching pursuit (OMP), the SBL-based detector is capable of achieving a superior reconstruction accuracy using less receive antennas. Numerical simulations are performed to substantiate the performance of the proposed detector.

Power line communication (PLC) systems present a “no new wires” solution for the telecommunication access with the additional advantages of ubiquitous availability, easy installation and cost effectiveness. In this paper, by exploiting the parametric sparsity of the PLC channels, we propose a robust sparse channel state information (CSI) acquisition scheme under the framework of Bayesian compressive sensing (CS), which could significantly reduce the pilot overhead and improve the spectral efficiency. Compared to the current sparse CSI acquisition schemes for PLC, including those based on annihilating filter and estimating signal parameters via rotational invariance techniques (ESPRIT) algorithm, the proposed scheme is numerically demonstrated to have better mean squared error (MSE) performance. Furthermore, the proposed method is an appealing solution for practical PLC system, since the performance degradation due to the parameter discretization is not significant.

Spatial modulation (SM) is a modulation scheme that exploits the spatial dimension of multiple-input multiple-output (MIMO) channels to encode the transmitted information. It is shown in this paper that, by properly rotating the phase of the transmitted symbols, the minimum Euclidean distance (MED) of the constellation of the received symbols can be enlarged and hence the error performance can be improved. Moreover, we propose an algorithm that incorporates an iteration process to design the phase-only pre-scaling factors, which only rotate the phases of different transmitted symbols according to the instantaneous channel realization. Simulation results demonstrate that the proposed method is capable of outperforming the conventional SM scheme and other pre-scaling SM schemes.

In 5th generation (5G) wireless communication networks, non-orthogonal multiple access (NOMA) is a key technique for supporting massive connectivity and high spectral efficiency. In NOMA systems, the signals sent by multiple users are superimposed at the receiver, which is a multiple access channel (MAC) in information-theoretic perspective. In this paper, we propose near-capacity multi-user bit-interleaved coded modulation with iterative demapping (MU-BICM-ID) scheme for two-user Gaussian multiple-access channel (GMAC), especially for high spectral efficiency scenarios. To retrieve the shaping loss, we suggest to use amplitude phase shift keying (APSK) constellation instead of conventional quadrature amplitude modulation (QAM) constellation. Furthermore, we disclose the fact that the low-density parity-check (LDPC) code optimized for point-to-point is not suitable for GMAC, and therefore we propose and optimize Raptor-like LDPC code for GMAC. Via extrinsic information transfer (EXIT) chart analysis as well as bit-error rate (BER) simulation, we illustrate the near-capacity performance of the proposed scheme.

Digital television terrestrial multimedia broadcasting (DTMB) is the digital television terrestrial broadcasting (DTTB) standard apllied in the People's Republic of China. The performance degradation of each transmission step in DTMB system is analyzed thoroughly in this paper. The overall degradation includes two main parts: the time-frequency bandwidth loss and imperfect coded modulation scheme. This analysis provides the guideline for the potential improvement in each step. Aiming to retrieve degradations in each process, we propose some alternative and effective proposals which could be adopted in future DTTB system.

In this paper, a novel impulsive noise (IN) cancellation scheme based on priori aided compressive sensing (CS) for OFDM-based communications systems is proposed. The IN is reconstructed from the frequency-domain measurements at the null sub-carriers based on the CS theory using greedy algorithms. With the aid of the a priori partial support obtained from the proposed time-domain thresholding method, we propose the enhanced greedy algorithm of priori aided sparsity adaptive matching pursuit (PA-SAMP) to improve the accuracy and robustness of the IN recovery. Theoretical analysis and computer simulations validate that the proposed method outperforms conventional CS-based and other classical IN mitigation methods for OFDM-based communications systems.

As the shortage of spectrum is becoming a serious problem to the wireless communication, visible light communication (VLC) is attracting widespread attention. However, the accession to the backbone network is likely to completely modify the communication network, thus leading to a great cost. Fortunately, the technology of power line communication (PLC) is a perfect match to VLC, which provides power supply and connection for VLC to the backbone network. In this paper, we take an integration of VLC and PLC into consideration and mainly focus on a communication framework with multifunction. In the case of not making a big modification to the origin network, a direct re-transmission concept is considered and two implementation schemes which differ in frequency and time domain are proposed. A demonstration platform is built which suggests that both schemes have the ability to support positioning and multi-service transmission in a high data rate with no big modification of current power line network.

A multi-user access scheme is proposed by combining bit-interleaved coded modulation with iterative demapping and decoding (BICM-ID) and multi-user detection (MUD), which is called MU-BICM-ID in this paper. In the proposed scheme, amplitude-phase shift keying (APSK) constellation and serial-concatenated convolutional code (SCCC) are adopted as coded modulation scheme. Unlike traditional orthogonal multiple access (OMA) or successive interference cancellation (SIC) based superposition coding, as a practical implementation of simultaneous decoding, MU-BICM-ID can approach any point on the upper boundary of the capacity region for multiple access channel (MAC) at relative high spectrum efficiency. The extrinsic information transfer (EXIT) analysis results show that, for the case of two users both with 1/2-rate and 16-APSK over Gaussian MAC (GMAC), the reception signal-to-noise ratio (SNR) thresholds of two users are close to the theoretical limit, which is also verified by BER simulation results.

In this paper, we propose a low-complexity single- carrier frequency-domain iterative receiver for triply-selective underwater acoustic (UWA) fading channels, which combines frequency domain decision feedback equalization (FD-DFE) with iterative channel estimation. Due to long multipath channels, frequency domain turbo equalization has to use large block size to achieve low computational complexity and high data efficiency, but UWA channel coherence time is often much shorter than block length. We utilize pilots or soft decision symbols obtained at the previous iteration to re-estimate channel at each turbo iteration, thus achieving satisfactory performance. Although having slightly inferior bit error performance to time-domain turbo equalizers, the proposed FDE scheme reduces the complexity by three orders of magnitude, making it affordable for real-time implementation. The proposed iterative receiver has been verified through both numerical simulations and undersea experiment data collected in the Surface Processes and Acoustic Communications Experiment 2008 (SPACE08).

Two-user Single-Input Single-Output degraded Gaussian broadcast channel (GBC) with finite input constellations is addressed in this paper. Multiple downlink multiple access schemes including orthogonal multiple access (OMA) schemes and non-orthogonal multiple access (NOMA) schemes are compared from several aspects. For OMA schemes, power control is not considered and constant power for each symbol is assumed. For NOMA schemes, two typical schemes are considered which are traditional linear superposition coding (SC) and recently proposed bit division multiplexing (BDM). BDM is essentially an instantiation of non-linear SC. Capacity region analysis and simulation results are performed to give an insight into the downlink multiple access schemes. The results show that BDM can increase the system throughput compared with OMA schemes and has lower implementation complexity as well as better practical performance compared with traditional linear SC. Given typical transmission rate requirements of two users, the gaps to the union theoretical SNR thresholds for two users by BDM are both less than 2.5dB with independent demapping employed at receiver, which are far better than those achieved by OMA schemes and traditional linear SC.

The dual pseudo noise padded (DPNP) time‐domain synchronous‐orthogonal frequency division multiplexing (TDS‐OFDM) which utilises the second pseudo noise (PN) sequence for channel estimation is able to reduce the complexity and improve the accuracy of channel estimation compared to the classical TDS‐OFDM. However, because of the duplicate PN sequence, DPNP TDS‐OFDM will suffer from severer out‐of‐band emission, which is a headache issue for the conventional TDS‐OFDM. In this study, a novel out‐of‐band suppression approach based on a new PN design criterion as well as a windowing operation is proposed for the DPNP TDS‐OFDM systems. The frame structure and system model are also modified to accommodate to the approach, while the overlap‐and‐add approach is adopted to guarantee the spectral efficiency. Both simulation and experimental results show that this approach could achieve satisfactory performance with much less complexity and no spectral efficiency loss compared to the classical DPNP TDS‐OFDM system. In addition, an iterative channel estimation method is also proposed for the modified frame structure to combat against the long channel delay spread.

Spatial modulation (SM) is a recently proposed multiple-input multiple-output (MIMO) architecture that exploits the space domain for information transmission. In this paper, SM systems with continuous amplitude-phase modulation (APM) and the corresponding maximum likelihood (ML) active transmit antenna detector are considered. We introduced a method to compute the theoretical transition probability matrix and the mutual information of the ML antenna detector. For SM multiple-input single-output (MISO) system, we derive the error probability and the mutual information of the antenna detector in closed-form. For multiple receive antenna SM system, the union bound of error probability and the lower bound of antenna detection mutual information are proposed. The analytical results are verified by numerical simulation over Rayleigh fading channels. We investigate systems with different number of antennas and APM symbol input distributions to evaluate the performance of SM. The joint APM and antenna detection mutual information of SM systems is compared to the capacity of vertical Bell Labs layered space-time (V-BLAST) spatial multiplexing MIMO scheme. Our research gives achievable transmission rates of SM-MISO/MIMO systems and can be a guideline for suitable application scenarios.

An error transfer chart is introduced to trace the iterative process of the decision feedback equalizer for the single-carrier modulation. In the investigated iterative MF-DFE structure, the forward filter is set to the matched filter to maximize the signal power, while the feedback filter is used to cancel the inter-symbol interference. MMSE detection is applied to minimize the decision error power. The trajectory of iterations is shown in the error transfer chart, in which MF-DFE maps a decision error power to an SINR and MMSE detection further converts the SINR to a new decision error power. The final state of iterations is shown to be the intersection of the MMSE curve and error power transfer curve corresponding to the MF-DFE algorithm. It is shown by simulation that the error transfer chart is a useful tool to analyze the convergence of non-linear iterative equalizers.

In this paper, we study the optimal broadcast radius in cellular network from the perspective of energy efficiency. Firstly, we consider the scenario where the base stations have already been deployed. The relationship between the power-saving gain and the possible energy efficient broadcast radius is derived. Based on it, we investigate the optimal number of tiers of cells in the broadcast range. Next, we turn to the scenario with the base stations to be deployed. The power consumption per effective coverage area is defined to measure the energy efficiency of different broadcast radius, based on which we analyze the optimal broadcast radius under different channel conditions. Simulation results confirm that the optimal energy efficient broadcast radius is positively correlated with the static power of the broadcast channel, negatively correlated with the minimal required broadcast service rate in the two scenarios, and negatively correlated with the original cell radius in the first scenario.

Conventional time domain synchronous orthogonal frequency division multiplexing (TDS-OFDM) systems have the difficulty to support 256QAM or higher-order modulations and suffer from performance loss, especially under the severe fading channels with long delays. In this letter, a simultaneous time-frequency channel estimation method based on compressive sensing (CS) is proposed to solve this problem. First, the auxiliary channel information is obtained by exploiting the signal structure of TDS-OFDM. Then, a very few frequency-domain pilots in the OFDM block are used to acquire the accurate channel impulse response under the framework of CS. Besides, the auxiliary channel information is utilized to further reduce the complexity of the classical CS algorithm. Simulation results show that the proposed scheme can well support 256QAM under fading channels with long delays and have better performance than the conventional schemes.

A novel compressive sensing (CS) based narrowband interference (NBI) cancellation approach for power line communication (PLC) systems is proposed in this paper. The repeated training sequences of the preamble in PLC systems are utilized for differential measuring to acquire the measuring vector. Under the CS framework, the sparse high-dimensional NBI signal can be reconstructed from the measuring vector of much smaller size obtained through the proposed CS-based differential measuring (CSDM) method. It is verified by theoretical analysis and simulations that the proposed method outperforms conventional anti-NBI methods under the PLC channel.

In this paper, an optimized time-frequency interleaving scheme is proposed to combat against the narrowband interference (NBI) and time-domain impulsive noise (TIN) for orthogonal frequency division multiplexing (OFDM) systems in power line communications (PLC). To improve both the anti-NBI and anti-TIN performance, we propose two criteria based on which the optimized interleaving scheme is designed: (i) to increase the number of different OFDM blocks for one forward error correction (FEC) codeword, which is aimed at TIN mitigation; and (ii) to increase the number of different sub-carriers mapped to the data cells in one FEC codeword, which is aimed at NBI mitigation. Simulations show that the proposed interleaving scheme can effectively reduce the impairments caused by NBI and/or TIN in OFDM systems under PLC environments, and hence achieve better performance than the conventional block interleaving scheme.

In this paper, the framework of amplitude phase shift keying (APSK) constellation labeling design for satellite transmission is proposed. It consists of two steps, the constellation design and the joint phase-labeling optimization. Both iterative and non-iterative demapping scenarios are considered in the design framework. The proposed APSK constellation labeling exhibits a low peak-to-average power ratio and achieves a considerable performance gain compared with its counterpart approved by the consultative committee for space data systems, as verified by both the mutual information analysis and bit error rate simulation.

Extended spatial modulation (ESM) is proposed as a novel spatial modulation scheme in this paper. Extended spatial modulation is a spatial modulation design where the number of active antennas can be various. The data rate could be improved as more symbols are available for transmitting and thus more information bits can be modulated during each time slot. The minimum distance of the transmitted symbols can also be further maximized as more symbols are available for optimizing selection which further improves the symbol error performance. Moreover, a near-optimal detection scheme with low complexity is also proposed in this paper. An approximated analytical expression for the symbol error probability (SEP) of the proposed detection scheme is derived along with the complexity analysis. Numerical simulations are proposed to demonstrate the performance of the proposed detection scheme and to validate the theoretical analysis.

Simultaneous broadcasting of multiple services in multiple-input and multiple-output (MIMO) broadcasting system is highly desirable. In this paper, the recently proposed technique, bit division multiplexing (BDM), is applied to MIMO broadcasting system, which significantly improves the spectral efficiency of multi-service broadcasting compared with conventional time division multiplexing (TDM) approach. Both average mutual information (AMI) analysis and bit error rate (BER) simulation results demonstrate that BDM can achieve higher overall transmission rate or lower minimum SNR requirement of multiple services. Furthermore BDM in MIMO system may decrease the complexity of MIMO detector accordingly.

In this paper, we propose a time-domain oversampled receiver for orthogonal frequency division multiplexing (OFDM) in underwater acoustic communication. Without changing the structure of the transmitter, significant gains are acquired by time- domain oversampling at the receiver. Using time- domain oversampling, the energy of transmitted symbols is concentrated on the first N subcarriers, while additive Gaussian noises are distributed in all the subcarriers. Because of the sparsity of underwater acoustic channels, most energy of the channel impulse responds is concentrated on a few paths, and other positions are submerged by noises. Channel estimation based on the discrete Fourier transform is employed to eliminate noises. After estimating the energy of additive Gaussian noises, the truncation of channel impulse responds eliminates the majority of noises. Simulations are implemented in multipath channels and fading channels. Without changing the structure of the transmitter, the proposed oversampled receiver outperforms the conventional receiver in bit error rate (BER) performances significantly, with a little increased computational complexity.

Time‐domain synchronous orthogonal frequency division multiplexing (TDS‐OFDM) enjoys the higher spectrum efficiency and faster synchronisation than the classical cyclic prefix OFDM (CP‐OFDM). However, TDS‐OFDM suffers from performance degradation especially under severely fading channels with long delays. To solve these problems, the authors propose an energy‐efficient OFDM scheme called time–frequency‐training orthogonal frequency division multiplexing (TFT‐OFDM) based on the time–frequency joint channel estimation under the framework of compressive sensing (CS). The power of the guard interval (GI) in the proposed scheme can be reduced to achieve higher energy efficiency, which is infeasible for CP‐OFDM. This method first utilises the time‐domain pseudo noise sequence to acquire partial support information of the channel, and then some frequency‐domain pilots are used for the exact channel estimation. Simulation results show that TFT‐OFDM with CS can achieve much higher energy efficiency than the classical CP‐OFDM, and outperforms the conventional OFDM schemes in both static and mobile environments. Moreover, for the channel with long delay spread, the TFT‐OFDM scheme with CS can demonstrate robustness and much better performance than the conventional OFDM schemes. In this way, the TFT‐OFDM scheme can use the same GI length for larger broadcasting coverage and hence further achieve higher energy efficiency.

This paper proposes a novel coded-modulation scheme using amplitude phase shift keying (APSK) constellations with Gray mapping for the broadband power line communication systems for the first time. In this scheme, three key techniques including the Gray-APSK constellation design, the bit mapping technique, and the method of simplified soft APSK demapper are jointly employed to enhance the error-control performance while ensuring low complexity. Simulation results over AWGN and power line channels show that the proposed scheme provides a remarkable receiving performance gain, compared to its counterpart specified in the G.hn standard.

In this paper, the spectrum notch technique which combines the time-domain windowing and training sequence (TS) redesigning for the time domain synchronous orthogonal frequency division multiplexing (TDS-OFDM) system is proposed. An improved frame structure with overlapping between adjacent frames for the dual pseudo noise padded (DPNP) TDS-OFDM system is put forward to make the spectral efficiency undiminished. The simulation results show that this method can deepen the notches in the power spectrum density (PSD) compared to the conventional TDS-OFDM system by about 7 to 8 dB without any spectrum efficiency cost, which could be in accordance with the -30dB spectrum notch requirements in the spectral mask defined in HomePlugAV 1.1.

In this paper, we focus on designing energy-efficient opportunistic multicast scheduling method and user sleeping mechanism for scalable video transmission in a wireless network. The goal is to minimize the total energy consumption for receiving video data with bandwidth constraint and users' minimum QoS requirements. To utilize opportunistic multicast scheduling, unequal erasure protection (UXP) scheme is used for scalable video at the application layer. Considering the encoding characteristics and unequal protection scheme of scalable video, the UXP frame-level QoS is defined. And a low-complexity scheduling algorithm is proposed by transforming frame-level QoS requirement into slot-level QoS requirement at each time slot dynamically. Simulation results shows that our proposed algorithm can achieve better tradeoff between users' energy consumption and QoS requirements than conventional multicast scheduling schemes. Besides, multicast users' energy efficiency can be improved further if larger decoding delay is allowed at the receiver.

The amplitude phase shift keying (APSK) constellation with Gray labeling (Gray-APSK) is able to achieve considerable shaping gains in comparison with the quadrature amplitude modulation (QAM) counterpart under both iterative and non-iterative scenarios. Nevertheless, the Gray-APSK constellation with the fixed radius cannot provide the optimal performance for every code rate of practical usage. In this paper, we propose a method based on the genetic algorithm to optimize the parameters of Gray-APSK constellations by maximizing the mutual information for the targeted code rate. Certain performance gains can be achieved via optimizing the radius of the Gray-APSK, as verified by both the average mutual information (AMI) analysis and the error performance evaluation.

Frequency domain equalization (FDE), as a linear equalizing method, is widely adopted in many applications with either OFDM or single carrier (SC) modulation. This paper analyzes the the noise transfer effect, a case for noise colorization, after FDE. This process is investigated for CP-OFDM, ZP-OFDM and SC systems where SC is treated as a multi-carrier modulation with the number of subcarriers equal to 1. Based on this analysis and by assuming the de-mapping independence among subcarriers, the average channel capacity for the systems is derived. We have proved that the capacity of the CP-OFDM system is at least equal to that of ZP-OFDM in both low and high SNR scenarios and that the capacity of the SC system is no greater than that of either ZP-OFDM or CP-OFDM systems. In a multi-path channel, if the IDFT size of the OFDM symbol is not equal to the DFT size of the equalizer, the leakage of the enhanced noise in a deeply faded sub-channel will cause the loss of capacity in adjacent sub-channels and the overall average capacity is thus reduced.

In this paper, a signaling-embedded preamble design is proposed for the orthogonal frequency division multiplexing (OFDM) system with transmit diversity. Utilizing the transmit diversity, two identical training sequences (TSs) are embedded in two OFDM symbols separately in discrete Fourier transform (DFT) domain and transmitted by two different antennas simultaneously. The relative distance between the two TSs in DFT domain could be varied in order to indicate several bits of signaling information for the receiver to acquire the transmission parameters signaling (TPS) quickly. Furthermore, the preamble can also be used for frame synchronization, carrier frequency offset (CFO) estimation, and coarse channel estimation. The computational simulations were carried out under both additive white Gaussian noise (AWGN) and multipath channels, and the results demonstrated the design works accurately on the preamble detection, CFO estimation and signaling demodulation under both static and dynamic channels.

Due to the unequal error protection (UEP) property of both the irregular low-density parity-check (LDPC) code and the higher-order modulation, bit mapping is proposed as a joint optimization of the LDPC coded-modulation scheme. In this paper, we discuss the bit mapping optimization performed by employing an additional DEMUX following the given bit interleaver, and then apply it to the second generation digital video broadcasting system for cable (DVB-C2). The extrinsic information transfer (EXIT) chart technique is adopted to analyze the influence of bit mapping on the iterative decoding process. Based on the EXIT-chart analysis, an optimization method is proposed to design the DEMUX for performance improvement. As an example, we re-design the DEMUX for the DVB-C2 system with reduced DEMUX width. The superior performance of the optimized DEMUX is verified by the bit error rate (BER) simulation.

In this paper, we analyze the conventional unequal erasure protection (UXP) scheme for scalable video transmission, and propose a dynamic hybrid UXP/ARQ transmission framework to improve the performance of the conventional UXP method for bandwidth-constrained scalable video transmission. This framework applies automatic retransmission request (ARQ) to the conventional UXP scheme for scalable video transmission, and dynamically adjusts the transmission time budget of each group of picture (GOP) according to the feedback about the transmission results of the current and previous GOPs from the receiver. Besides, target video quality is introduced and optimized to adapt to the channel condition in pursuit of more efficient dynamic time allocation. Simulation results show that compared with the conventional UXP scheme, the average peak signal to noise ratio (PSNR) of the reconstructed video can be improved significantly in a wide range of packet loss rate.

A generalised spatial modulation (SM) scheme with multiple active transmit antennas, named as multiple active-spatial modulation (MA-SM), is proposed in this paper. By allowing multiple transmitting antennas in the SM system to transmit different symbols at the same time instant, MA-SM takes advantages of the low complexity of SM and high multiplexing gain of Vertical-Bell Lab Layered Space-Time (V-BLAST) system. In the MA-SM system, the transmitted symbols are mapped into a high dimensional constellation space including the spatial dimension. The general principle for designing the efficient MA-SM for arbitrary number of transmit antennas and modulation scheme is presented. Moreover, a near-optimal detection scheme with low complexity for MA-SM is also proposed and analyzed. A closed form bound for the bit error probability (BEP) of the proposed detection scheme is also derived in this paper. Numerical results with the comparison among the existing multiple-input multiple-output (MIMO) systems such as space time block code (STBC) and V-BLAST demonstrate the efficiency of MA-SM.

Bit-interleaved coded modulation with iterative demapping (BICM-ID) is investigated for multiple-input multiple-output (MIMO) channels. Based on the average mutual information (AMI) analysis, vertical Bell-lab layered space-time (V-BLAST) rather than space-time block code (STBC) is chosen to be combined with BICM-ID. Based on the extrinsic information transfer (EXIT) chart analysis, a novel algorithm in searching for a labeling to well match the outer code is proposed for MIMO channels, which facilitates a near-capacity V-BLAST-BICM-ID scheme. Computer simulation confirmed the near-capacity performance.

To revive the lack of spectral resources due to the expansion of service requirements and implementation of flexible service transmission, next generation digital television broadcasting system should define frame structures to support efficient and compatible multi-service transmission for different applications, especially terrestrial high definition television (HDTV) and mobile TV services. The approach to support compatible OFDM symbols with different sub-carrier intervals and improve the spectral efficiency is discussed. In this paper, two novel TDS-OFDM composite frame structures are proposed with the concept of physical sub-channel (PSC) to support services with different quality of service (QoS) requirements. Simulation results show that the proposed frame structures can provide multi-service transmission with diverse requirements and high spectral efficiency as well.

In this paper, a new channel coding scheme for the narrowband powerline communication (PLC) system employing quasi-cyclic (QC) low-density parity-check (LDPC) codes of multiple code rate and multiple code length is proposed. Combined with proper constellation mappings, 4 transmission modes can be supported, which provides different error correction capabilities. A comparison with the coding scheme in the G3 specification is performed, confirming that the proposed scheme is superior to G3 in terms of both flexibility and reliability while the efficiency is higher than that of G3. A study on the implementation complexity on FPGA is also conducted to prove the feasibility of using the proposed scheme in future practical narrowband PLC system.

Signal space diversity (SSD) is a well-known power- and bandwidth-efficient diversity technique with constellation rotation and coordinate interleaving. This paper investigates issues of combining SSD with coded modulation systems (CMSs), e.g., bit-interleaved coded modulation (BICM) and its iterative version, BICM-ID. It demonstrates that the average mutual information (AMI) between the signal after rotated constellation mapping and the signal before or after the soft demapper varies with the rotation angle. A new criterion for determining the optimal rotation angle by maximizing such AMI is therefore proposed. Specific considerations of combining SSD with BICM (BICM-SSD) and BICM-ID (BICM-ID-SSD) are addressed. The demapper's extrinsic information transfer (EXIT) curve in traditional BICM-ID systems exhibits different slopes under different channels, which consequently prevents traditional BICM-ID systems from having simultaneously excellent performance under different channels. It is shown that such a different-slope problem can be simply mitigated by a proper use of SSD. Analysis and simulation show that the proposed BICM-ID-SSD systems hold a near-capacity performance under both additive white Gaussian noise (AWGN) and Rayleigh fading channels simultaneously.

Through the investigation on frame header (FH) structures of DTMB system, i.e. the digital terrestrial television broadcasting standard of China, spectrum sensing methods based on PN cross-correlation (PNCC) are proposed for all FH options. Miss-detection probability, decision threshold and sensing time requirement of the proposed methods are analyzed in detail. Comparisons on spectrum sensing performance, robustness to time dispersive channel and carrier frequency offset are made between the proposed methods and the existing methods based on PN autocorrelation (PNAC). Both theoretical analyses and simulation results demonstrate the considerable advantage of the proposed PNCC methods over the existing PNAC methods.

This paper proposes a bit loading algorithm for orthogonal frequency division multiplexing (OFDM) systems. With the restrictions of available transmit power and required bit-error-rate (BER) performance, the algorithm aims at achieving the maximal data rate. Although the proposed algorithm is based on the Hughes-hartogs algorithm, the computational complexity is only $O(k \times M)$,where k denotes the number of iterations required to meet the constraints and M denotes the number of subcarriers. Simulation results show that $15$ iterations are sufficient to meet the constrains, and the degradation of information bits loaded per OFDM symbol is less than one percent over the entire available transmit power. The proposed algorithm is appropriate for practical OFDM systems with either uniform or different SNR gaps in wireless channels for its low computational complexity and good performance.

Transmit diversity is an attractive technology in broadcasting environment. When it is applied in Chinese Digital Terrestrial Television Broadcasting (DTTB) system, the training sequences (TS) from the different transmit antennas for channel estimation and synchronization will interfere with each other. In order to overcome the problem, two methods are proposed in which the orthogonal TS is designed based on pseudo noise (PN) sequences. One method holds good compatibility with the existing network and the other one obtains good performance under time selective channel. The corresponding channel estimation method is also presented and analyzed in this paper. Simulations show that the proposed schemes can effectively estimate the channel state information and achieve great diversity gain, even more than 3.5dB under fast fading channels. Moreover they can both work well in some cases where the system with single antenna can't work.

To efficiently suppress the noise through a time-domain threshold value after the least square (LS) estimation, the discrete Fourier transform (DFT) based channel estimation (CE) is usually carried out in practical orthogonal frequency division multiplexing (OFDM) systems for its inherent noise immunity. In this paper, an improved DFT based CE method for OFDM systems is proposed. Taking advantage of the wavelet decomposition, the proposed method obtains an optimal threshold value according to the minimum mean-square error (MMSE) optimality criterion. Both theoretical analysis and computer simulation show that, the proposed method not only reduces the mean-square error (MSE) without MSE floor compared with the conventional methods, but also improves the symbol error rate (SER) performance by being more close to that of the ideal known-channel case over both static and dynamic multipath channels.

The unavoidable Doppler frequency shift reduces the correlation peak for code acquisition in global positioning system (GPS). In contrast to conventional methods where the code phase and Doppler frequency shift are separately treated, this paper proposes a novel three-step scheme for joint code acquisition and Doppler estimation, whereby not only the Doppler effect on the correlation peak is removed, but also the reduced detection probability due to noise enhancement in low signal-to-noise ratio (SNR) environments is avoided. The theoretical analysis shows that the proposed method has low complexity and fast acquisition speed. Computer simulations demonstrate that code acquisition with high detection probability and Doppler frequency shift estimation with high accuracy can be simultaneously achieved.

This paper summarizes some key features for future Chinese digital television terrestrial broadcasting (DTTB) systems including improved frame structure, transmit diversity, advanced channel coding and modulation, multi-service support, broadcasting return channel solution and etc.

Time-domain synchronous orthogonal frequency division multiplexing (TDS-OFDM) outperforms cyclic prefixed-OFDM (CP-OFDM) in spectrum efficiency at the expense of higher complexity. Up to now, TDS-OFDM has only been used in unidirectional transmission, such as broadcasting applications. The overwhelming complexity of removing the implicitly superposed interferences between the PN sequences and IDFT information blocks, caused by multiple users, hinders the application of time-domain synchronous orthogonal frequency division multiple access (TDS-OFDMA) in multi-user communication scenarios. To solve this problem, this paper proposes a novel TDS-OFDMA scheme, including joint cyclicity reconstruction and joint channel estimation, utilizing the new "time-space two-dimensional frame structure". Theoretical analysis and computer simulation show that the proposed scheme not only can achieve the purpose of multiple access but also has an even better system performance in mobile environment than the conventional single-user TDS-OFDM system.

Bit-interleaved coded modulation (BICM) is a sub- optimal scheme from the average mutual information (AMI) point of view due to independent demapping. However, this AMI loss can be neglected for signal constellations with Gray mapping at high coding rates. AMI of amplitude-phase shift keying (APSK) constrained additive white Gaussian noise (AWGN) channel is provided in this paper, which shows that the BICM scheme has considerable loss with APSK constellations since no Gray mapping exists. Bit-interleaved low-density parity-check (LDPC) coded modulation (BILCM) is an excellent scheme and has been employed in many broadcasting and communication systems, however, such scheme also suffers from the independent demapping loss. Therefore, BILCM with iterative demapping and decoding (BILCM-ID) is proposed in this paper to overcome such loss. Simulation results show that the BILCM-ID scheme outweighs BILCM scheme by about 0.5 and 0.3 dB over AWGN channel for 32-APSK constellation at coding rates 2/3 and 4/5, respectively.

In order to improve the system performance over wireless fading channel, transmit diversity technique has been proven as an attractive and effective solution especially for broadcasting systems. In this paper, a PN sequence design scheme based on both cyclic-shift m-sequences and space-time block coded (STBC) m-sequences for time domain synchronous orthogonal frequency-division multiplexing (TDS-OFDM) system with transmit diversity is proposed. Based on this novel design, flexible channel estimation which provides a trade-off between maximum delay spread and maximum Doppler spread over double-selective channel is investigated and a radix-2 fast Fourier transform (FFT) approach is adopted for implementation from the viewpoint of computational complexity. Simulations show that the performance of the proposed transmit diversity scheme is better than conventional one and perform well under different propagation conditions.

In order to improve the system performance over both frequency- and time-selective fading channels, orthogonal frequency division multiplexing (OFDM) system with transmit diversity technique is an effective and attractive solution. However, the received signal is the superposition of the signals from different transmit antennas. Hence, to obtain accurate channel state information (CSI) is difficult but very important to recover transmitted OFDM symbols. Conventional channel estimation methods use subcarrier pilots or training sequences as the channel sounding signal to obtain the estimation of channel frequency response (CFR). Actually, decision-directed channel estimation (DDCE) method which takes advantage of the fact that m-QAM constellation subcarriers in OFDM symbol also contain much valuable hidden-information and can be used to perform the channel estimation as well as tracking of time-varying channel. In this paper, a simple and effective partial hard decision method for m-QAM is investigated. Furthermore, a simplified DDCE method based on an accurate initial CFR and partial decision results of OFDM subcarriers for transmit diversity system is proposed. Simulation results demonstrate that the proposed algorithm works well over the slow-varying frequency-selective fading channel.

Pseudo-noise (PN) sequence is widely used in wireless transmission systems for synchronization, which is usually performed via cross-correlation, i.e. PN correlator, between the received signal and a locally generated PN sequence. However, the hardware implementation of such a correlator becomes the inhibiting bottleneck of integration due to the long tapped-delay line, particularly in the case of oversampling systems. Furthermore, few attempts have been made to improve the structure until now attributing to the random but simple coefficients of the PN correlator. This paper proposes a novel grouping and segmentation structure for filters with simple coefficients, such as a PN correlator. Remarkable hardware reduction has been achieved by multiplexing tapped-delay line and further operations skillfully. The arithmetic complexities of the conventional direct form structure and the proposed structure are compared based on a digital terrestrial multimedia broadcasting (DTMB) system. The advantages of the proposed structure are demonstrated by the FPGA implementation results.

Accurate channel estimation is essential for coherent signal transmission. This paper presents a novel channel estimation algorithm for time-domain synchronous orthogonal frequency division multiplexing (TDS-OFDM) systems with identical pseudo-noise (PN) sequence padding. Simple cyclic reconstruction and tail cancellation methods are proposed to estimate the channel impulse response (CIR). Phase equalization and partial hard decision on the received data symbols are used to improve the estimation performance. Simulation results show that the proposed channel estimation algorithm brings good BER performance (approaching the ideal case) with fast convergence (no iteration required) over frequency-selective slowly fading channels.

Multiple amplitude shaped offset quadrature phase-shift keying (MA-SOQPSK) modulation based on multiple nonlinear power amplifiers (NLPAs) provides both high power-added efficiency and spectrum efficiency. However, the phase misalignment between the different branches of MA-SOQPSK signal in the process of the modulation can significantly degrade the bit-error-rate (BER) performance. A novel scheme of correcting this phase error is proposed in this paper, which contains a phase lock loop (PLL) in the modulation terminal. Theoretical estimation and simulation on the variance of the phase error are also given to validate our method. The BER simulation result shows that our scheme improves the performance more than 3 dB at 10"3, and the improvement becomes more distinct as the SNR.

With the introduction of the Modified Matched Filter method, we first present a novel blind maximum-likelihood timing recovery approach for the all-digital receivers suffering from carrier frequency offset. Then a suboptimum version, equivalent to the Gardner timing recovery loop employing the matched filter instead and independent of the frequency offset estimation, is derived to reduce the computational complexity. Moreover, a Mismatch criterion is introduced for the suboptimum simplification to provide the carrier-frequency-offset range in which it suffers negligible steady-state performance degradation and facilitate the system design. The effectiveness of these two methods as well as the validity of the Mismatch criterion are demonstrated through simulations.