Ultraviolet (UV) localization has attracted significant attention for its ability to operate without line-of-sight. Existing UV localization techniques usually require anchor nodes to be placed in specific geometric configurations, including linear alignment or co-location. However, this requirement is often impractical in real-world scenarios, where obstacles such as walls and equipment prevent anchor nodes from being placed in ideal positions. In this paper, we consider general scenarios without constraints on anchor deployment and propose a UV localization technique, referred to as EIUL (Evolutionary and Invex approximation-based Ultraviolet Localization), to address non-line-of-sight scenarios. We adopt the single-scattering model and the geometric relationship between the transmitting beam and the receiver’s field of view to develop a least-squares-based estimator for jointly estimating the target node’s location, along with the transmitting beam’s azimuth angle and elevation angle. We first apply differential evolution to obtain an initial estimate, which is then used to facilitate invex approximation, thereby formulating an invexified objective function to further refine the location estimate. We derive the Lipschitz constant for the gradient of the invexified objective function and employ the Nesterov’s accelerated gradient descent to achieve the global minimum. Additionally, we present a concise form of the Cramer-Rao lower bound for UV localization based on the single-scattering model, which is straightforward to compute numerically. Extensive simulations demonstrate that EIUL provides more accurate parameter estimation with lower computational complexity compared to existing techniques.

Semantic communication is an emerging technology to improve the communication efficiency in future networks. In this paper, we propose a multi-AP multi-user adaptive semantic and bit communication framework for image transmission, where each user can communicate with the access points via either semantic communication (SemCom) or bit communication mode. While the peak mean square error (PMSE) is a key parameter to characterize the difference between the original and corresponding recovered images, this metric has no closed form. We propose a data regression approach to approximate the PMSE. Then, the cost functions for the two types of communication modes are designed, where the delay and energy consumption for image transmission and the PMSE for the recovered image are considered simultaneously. Moreover, the computation delay and energy consumption for semantic feature extraction and recovery are also integrated into the SemCom cost function design. Then, an overall user cost minimization problem is formulated to jointly optimize the communication mode decision, user association, channel selection, power control, and computation resource allocation. To solve the formulated problem, we propose an improved particle swarm optimization based semi-cooperative matching (IPSO-SCM) algorithm, where a semi-cooperative matching (SCM) game is established to determine the communication mode decision, user association, and channel selection and the improved particle swarm optimization algorithm is designed to jointly optimize the power control and computation resource allocation in each step of the constructed SCM game. We further prove the effectiveness, convergence, stability, and extensibility of the proposed IPSO-SCM algorithm. Simulation results are provided to demonstrate the superiority of the proposed scheme.

This paper studies the problem of long-term, high-quality data collection in dynamic industrial environments using autonomous mobile robots (AMRs). We formulate a max–min data-rate optimization problem that jointly designs AMR trajectories, receive beamforming, sensor association, and decoding policy under mobility, communication, and battery constraints. The problem is challenging because motion decisions directly influence link quality, which affects decoding and beamforming efficiency, while all of these factors are tightly coupled with battery dynamics, resulting in a mixed-integer, time-varying, and non-convex design. To handle this strong coupling and the need for scalable long-horizon decision-making, we propose a semi-distributed reinforcement learning framework that combines cloud-level global coordination with fog-level local adaptability. In this framework, a cloud-layer deep Q-network determines AMR-sensor associations, and a fog-layer federated actor-critic algorithm jointly designs continuous trajectories and beamforming. A Fubini–Study distance-assisted k-means clustering method enhances multi-antenna directional gains, and next-generation multiple access (NGMA) improves spectral efficiency and interference suppression. Theoretical analysis confirms the convergence and computational efficiency of the proposed algorithm. Simulations show that the proposed approach achieves faster and more stable convergence, sustains large-scale coverage through periodic recharging, and significantly improves the minimum data rate compared with orthogonal multiple access-based baselines.

In traditional fog-radio access networks (F-RANs), deploying many enhanced remote radio heads (eRRHs) faces challenges due to site limitations and fronthaul link costs. To tackle this issue, we introduced smart helpers (SHs) in our earlier study. SHs mainly listen to communications between eRRHs and users, smartly cache popular content, and serve users without having a fronthaul link to the macro base station (MBS). In this paper, we explore using an unmanned aerial vehicle (UAV) as the SH to assess the potential benefits of integrating UAVSHs into F-RANs. Because UAVs typically have limited battery life and cover smaller areas, we leverage a reconfigurable intelligent surface (RIS) to extend the service area of the UAVSH. To assess the performance of the considered UAVSH-aided F-RAN, we formulate the problem of average delivery delay minimization. To tackle the problem, we develop an algorithm to optimize user and RIS scheduling and caching decisions using multiagent reinforcement learning (MARL). The simulation results numerically prove that integrating a UAVSH into a RIS-assisted F-RAN leads to significant improvements in delivery delay, fronthaul load, and cache hit rate metrics.

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.

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.

The multi-access edge computing (MEC)-enabled low Earth orbit (LEO) satellite network is a promising approach to meet the growing ubiquitous diverse computation demands around the world. In this paper, a joint task offloading and resource allocation strategy is proposed for hybrid MEC-enabled LEO satellite networks, where two types of MEC tasks, namely delay-sensitive edgy-cloud task and data-and computation-intensive cloudy-edge task, are considered simultaneously. Specifically, we first design the cost functions for the two types of tasks, which take the delay-sensitive feature of edgy-cloud task and data-and computation-intensive characteristics of cloudy-edge task into consideration. Then, an overall terminal cost minimization problem is formulated for task offloading and resource allocation under the communication and computation capability constraints and the service delay requirements. In practice, terminals usually only care about their own costs, but satellites pursue the overall cost minimization of all the served terminals. Thus, considering the individual and collective rationality simultaneously, a two-level hierarchical game is constructed to solve the formulated problem. In the upper level, a hedonic coalition formation game is established, which enables each terminal to make the coalition selection and task offloading decision based on the designed coalition switch rule. In the lower level, the joint channel and power allocation in each coalition is first formulated as a noncooperative game to represent the individual rationality of each terminal. Then, each satellite performs the optimal computation resource allocation to maximize the coalition value with collective rationality. We prove that the Nash equilibrium (NE) for the noncooperative game exists and the coalition partition converges to a Nash stable state. Simulation results are provided to demonstrate the superiority of the proposed strategy.

In traditional fog-radio access networks (F-RANs), enhanced remote radio heads (eRRHs) are connected to a macro base station (MBS) through fronthaul links. Deploying a massive number of eRRHs is not always feasible due to site constraints and the cost of fronthaul links. This paper introduces an innovative concept of using smart helpers (SHs) in F-RANs. These SHs do not require fronthaul links and listen to the within-coverage eRRHs’ communications. Then, they smartly select and cache popular content. This capability enables SHs to serve users with frequent on-demand service requests potentially. As such, network operators have the flexibility to easily deploy SHs in various scenarios, such as dense urban areas and temporary public events, to expand their F-RANs and improve the quality of service (QoS). To study the performance of the proposed SH-aided F-RAN, we formulate an optimization problem of minimizing the average transmission delay that jointly optimizes cache resources and user scheduling. To tackle the formulated problem, we develop an innovative multi-stage algorithm that uses a reinforcement learning (RL) framework. Various performance measures, e.g., the average transmission delay, fronthaul load, and cache hit rate of the proposed SH-aided F-RAN are evaluated numerically and compared with those of traditional F-RANs.

Millimeter-wave (mmWave) grant-free (GF) access is a promising approach for massive machine-type communication (mMTC) networks to improve the access efficiency and alleviate the shortage of spectrum resources. Due to the lack of authentication, mmWave GF-mMTC networks are vulnerable to the pilot contamination attack (PCA), which can cause severe performance degradation of the channel estimation (CE) and user activity identification (UAI). However, the existing PCA resistance schemes for mmWave GF-mMTC networks perform the CE, UAI, and PCA detection through two separated phases, which will limit the system performance. To solve the problem, we establish a three-dimensional (3-D) transmission model with time-correlated two-dimensional sparsity for mmWave GF-mMTC networks under PCA, where the user activity sparsity, the virtual angular channel sparsity, and the temporal correlation of legitimate user (LU) status are jointly considered. Based on the established transmission model, we develop a 3-D compressive sensing based joint CE, UAI, and PCA detection (3D-CS-JCUPD) scheme. In this scheme, a parallel expectation-maximization vector approximate message passing with multiple measurement vector (Parallel EM-VAMP-MMV) algorithm is proposed to estimate the channel virtual representation (CVR) and the LU status is identified with the aid of different temporal correlation features between LUs and attackers. Moreover, we also develop a location information aided joint CE, UAI, and PCA detection (LIA-JCUPD) scheme to address the situation when attackers and LUs exhibit similar temporal correlations, where the BS utilizes the recorded LU location information to distinguish the LU status. Simulation results show that the developed schemes can achieve substantial performance gains over several reference schemes.

Channel estimation poses significant challenges in millimeter-wave massive multiple-input multiple-output systems, especially when the base station has fewer radio-frequency chains than antennas. To address this challenge, one promising solution exploits the beamspace channel sparsity to reconstruct full-dimensional channels from incomplete measurements. This paper presents a model-based deep learning method to reconstruct sparse, as well as approximately sparse, vectors fast and accurately. To implement this method, we propose a trimmed-ridge regression that transforms the sparse-reconstruction problem into a least-squares problem regularized by a nonconvex penalty term, and then derive an iterative solution. We then unfold the iterations into a deep network that can be implemented in online applications to realize real-time computations. To this end, an unfolded trimmed-ridge regression model is constructed using a structural configuration to reduce computational complexity and a model ensemble strategy to improve accuracy. Compared with other state-of-the-art deep learning models, the proposed learning scheme achieves better accuracy and supports higher downlink sum rates.

For the scenario of large-scale users in millimeter wave (mmWave) multi-user multiple-input multiple-output (MU-MIMO)-orthogonal frequency division multiplexing (OFDM) systems, power allocation on each subcarrier (SC), association between SCs and users (SC-UE association) and precoder design are mutually dependent due to the hybrid MIMO-OFDM structure. This interdependence suggests that these factors should be jointly considered and optimized to make full use of both spatial and frequency diversity. In this paper, an iterative optimization framework is proposed for optimizing both the sum rate and the minimum user rate considering the beam squint effect. The optimization framework first performs a coarse SC-UE association based on an SC-level channel clustering, and then alternatively optimizes the hybrid analog and digital (HAD) precoder and SC-UE association in an iterative manner. The SC-UE association problems are solved by the greedy algorithm and heuristic method for sum rate and minimum rate optimization problems, respectively. The computational complexities of the proposed methods are evaluated and compared with several existing benchmarks. Numerical simulations show that the proposed methods outperform the corresponding benchmarks, while hold comparable running times by contrast due to its fast convergence rate.

Ultraviolet (UV) communication is widely known for its local confidentiality due to rapid signal attenuation over distance. However, the inherent low background interference in UV communication environments can make signal transmission more easily detectable by an eavesdropper. In this study, we introduce covert communication metrics based on overall photon counting and peak power to assess analytically the covert performance of UV non-line-of-sight (NLOS) communications on photon counting channels. By integrating the covert metrics with the UV omni-directional NLOS channel model, we demonstrate standoff boundaries for the first time. Our simulation results confirm the effectiveness of UV NLOS covert communication and offer vital insights into the design and operational strategies of UV NLOS covert communication systems.

Marine mammals and their ecosystem face significant threats from, for example, military active sonar and marine transportation. To mitigate this harm, early detection and classification of marine mammals are essential. While recent efforts have utilized spectrogram analysis and machine learning techniques, there remain challenges in their efficiency. Therefore, we propose a novel knowledge distillation framework, named XCFSMN, for this problem. We construct a teacher model that fuses the features extracted from an X-vector extractor, a DenseNet and Cross-Covariance attended compact Feed-Forward Sequential Memory Network (cFSMN). The teacher model transfers knowledge to a simpler cFSMN model through a temperature-cooling strategy for efficient learning. Compared to multiple convolutional neural network backbones and transformers, the proposed framework achieves state-of-the-art efficiency and performance. The improved model size is approximately 20 times smaller and the inference time can be 10 times shorter without affecting the model’s accuracy.

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.

Due to the lack of authentication, millimeter-wave (mmWave) grant-free massive machine-type communication (GFmMTC) networks are vulnerable to the pilot contamination attack (PCA), which will cause serious performance degradation of channel estimation (CE) and active user detection (AUD). However, the existing works towards the PCA detection in the mmWave GFmMTC networks perform the CE, AUD, and PCA detection through two separated phases, which will limit the system performance. To solve the problem, in this paper, we establish a three-dimensional transmission model with time-correlated two-dimensional sparsity for both legitimate users (LUs) and attackers in mmWave GFmMTC networks, where the LU and attacker activity sparsity, the virtual angular channel sparsity, and the temporal correlation of L U activity are jointly considered. Based on the established transmission model, a three-dimensional multiple measurement vector-compressive sensing (MMV-CS) based joint CE and AUD scheme against PCA is proposed. Specifically, we first formulate the joint CE and AUD under PCA as a three-dimensional MMV-CS problem. Then, by utilizing the sparsity of user activity and angular virtual channel, we develop a parallel expectation-maximization vector approximate message passing with MMV (Parallel EM- VAMP-MMV) algorithm to efficiently solve the formulated problem. Simulation results show that the proposed scheme can achieve a substantial performance gain over comparison methods.

Ultraviolet (UV) communication is a promising technology for civilian and military secure communication systems due to nonline-of-sight transmission, low background noise, and high local security. The full-duplex relay-assisted UV communications can achieve longer communication distances and higher efficiency of time–frequency utilization compared with direct UV communications. However, due to the strong scattering effect, serious interrelay-interference (IRI) is inevitably introduced in multihop full-duplex relay links. To mitigate the impacts of IRI, this article proposes an alternate iterative-Newton method (AINM) to optimize jointly the relay placement and transmit powers of each relay. We further propose a space-division coupled full-duplex relay configuration to reduce the influence of IRI. Numerical results show that the proposed AINM can significantly decrease the bit-error rate (BER); and the proposed space-division scheme can further decrease the BER but sacrifices about half achievable information rate. Besides, we demonstrate that, when the communication links are strong, the distance between adjacent relays should gradually decrease and the transmit power increase from the source node to the destination node. However, when the communication links are weak, each relay should adopt its maximum transmit power to achieve the minimum BER.

Positioning and sensing have long been an important area of research. Recently, this field has attracted more attention due to the rapid deployment of emerging applications and next-generation communication networks. On the one hand, emerging applications like extended reality (XR) and autonomous vehicle systems need to precisely “see” the physical world, thus greatly increasing the demands on positioning and sensing technologies. Moreover, these applications also require data rate communication links, and thus technologies like cellular networks and WiFi are excellent for supporting these applications. On the other hand, with the evolution of wireless networks, positioning, and sensing have also been considered important functions of future wireless networks that can further enhance communication performance. Although existing wireless communication has achieved significant success in the past several decades, achieving satisfying positioning and sensing performance for these emerging applications remains a challenge due to the complexity of the wireless environment and the stringent performance requirements.

Positioning has recently received considerable attention as a key enabler in emerging applications such as extended reality, unmanned aerial vehicles, and smart environments. These applications require both data communication and high-precision positioning, and thus they are particularly well-suited to be offered in wireless networks (WNs). The purpose of this paper is to provide a comprehensive overview of existing works and new trends in the field of positioning techniques from both academic and standard perspectives. The paper provides a comprehensive overview of indoor positioning in WNs, covering the background, applications, measurements, state-of-the-art technologies, and future challenges. The paper outlines the applications of positioning from the perspectives of public facilities, enterprises, and individual users. We investigate the key performance indicators and measurements of positioning systems, followed by the review of the key enabler techniques such as artificial intelligence/large models and adaptive systems. Next, we discuss a number of typical wireless positioning technologies. We extend our overview beyond the academic progress, to include the standardization efforts, and finally, we provide insight into the challenges that remain. The comprehensive overview of existing efforts and new trends in the field of indoor positioning from both academic and standardization perspectives would be a useful reference to researchers in the field.

This is Part II of the double-part Special Issue (SI) on Positioning and Sensing Over Wireless Networks. The two-part SI aims to bring cutting-edge and novel contributions on positioning and sensing over wireless networks for future and emerging applications. The accepted 51 papers are arranged into eight groups: 1) fundamental performance analysis and optimization; 2) positioning and sensing with cellular networks; 3) positioning and sensing with WiFi networks; 4) positioning and sensing with emerging communication technologies; 5) positioning and sensing applications; 6) cooperative positioning and sensing; 7) reconfigurable intelligent surfaces (RIS)-assisted positioning and sensing; and 8) privacy and security. The contributions made by the papers in Part II are summarized as follows, which correspond to the last four paper groups.

Continuous variable quantum key distribution (CV-QKD) protocol has drawn much attention due to its compatibility with existing optical communication systems. In this paper, we propose a quaternary modulated CV-QKD protocol using displacement receivers and adopt the post-selection scheme to overcome the ‘3dB limit’. We first establish the model of displacement receiver for discriminating quaternary modulated coherent signals in a realistic situation. The performance of non-adaptive displacement receiver and multi-stage feedforward receiver are both investigated under different noises and device imperfections. To improve the receiver performance, we numerically optimize the displacement operation and check the quantum advantage of the displacement receivers over the classical homodyne detection. Then we analyze the security of the proposed CV-QKD protocol. The secret key rate is derived for both types of displacement receivers under the collective beam splitting attack. We also optimize the transmitted signal photons for different channel transmission efficiencies under practical system constraints. Numerical results shed light on the practical application of displacement receivers in CV-QKD protocols. This includes evaluating the necessity of optimizing the displacement and incorporating the feedforward structure in a displacement receiver according to different practical system limitations. Moreover, under higher channel transmission efficiency and increased receiver noise level, a larger coherent amplitude is required for transmitting signals to attain the maximum secret key rate.

Cell-free integrated sensing and communication (ISAC) offers simultaneous high-quality communication and robust target sensing, boasting superior spectral efficiency. This approach has emerged as a crucial technique in meeting the demanding requirements of 6G systems. For such a system, we investigate Voronoi-cluster multi-resolution assemblages, which involves the creation of a multi-resolution hierarchical codebook designed to support robust channel estimation and accurate latency estimation. Compared with the classical channel estimation method, the proposed scheme achieves a balance between search efficiency and detection accuracy. Leveraging on the benefits of the cell-free structure, we also propose a joint correlation multiple targets sensing algorithm to acquire the round-trip latencies of multiple targets. Moreover, we evaluate the performances of normalized mean square error, detection rate and latency estimation rate. Simulation results demonstrate that the proposed cell-free ISAC architecture provides superior detection performances than the classical centralized ISAC counterpart.

This paper investigates how to suppress the Age of Information (AoI) in an opportunistic channel access system, which allows multiple mobile devices to access a base station without central coordination while being aware of instant channel quality. An optimization problem is formulated to minimize the average AoI by optimizing each mobile device’s probability of contending for channel access opportunity and the threshold of offload rate. We derive the exact expression of the average AoI and generate a reformulated optimization problem. Although being non-convex, the reformulated problem is tackled by the following operations. First, we leverage the Dinkelbach method and the block coordinate descent method to convert the reformulated problem into an iterative solving procedure of two non-convex sub-problems, which optimize the contending probability and the threshold of offload rate respectively. Second, for each non-convex sub-problem, we explore the piecewise differential monotonicity for the cost function, and achieve the associated optimal solution by transforming them into standard monotonic optimization problems. Numerical results can verify the effectiveness of the proposed method through the comparison with benchmark methods.

The fifth-generation (5G) and beyond networks are expected to accommodate both the original human-to-human (H2H) communication and the emerging massive machine-type communication (mMTC). To enable a harmonious coexistence between the two different types of services, we propose a resource-efficient mMTC/H2H coexistence scheme by jointly considering the random access (RA) and data transmission, where the entire uplink resources are divided for the proposed RA and data transmission procedures. Based on the proposed scheme, we derive the average achievable throughput of the bursty mMTC service and develop a time-nonhomogeneous Markov chain model to characterize the joint state transition of H2H user equipments (HUEs). To tackle the cumbersome Markov model, we approximately decompose the constructed time-nonhomogeneous Markov model into multiple independent Markov chains, where each decomposed Markov chain characterizes one single HUE’s state transition. Then, the decomposed Markov model is transformed into a semi-Markov process and the corresponding steady-state condition is obtained based on the queueing network analysis for H2H service. By approximating the evolution of number of HUEs in different states as M/M/1 queues, we derive the stationary probabilities for the embedded Markov chain of the semi-Markov process and obtain the data transmission success probability of each HUE. Based on the abovementioned analytical framework, we formulate a constrained nonlinear integer programming (NLIP) problem to maximize the mMTC throughput under the constraints of H2H quality-of-service (QoS) stabilization and resource allocation. By adopting the modified particle swarm optimization (PSO) algorithm, we solve the formulated problem and obtain the efficient resource allocation strategy for the mMTC/H2H coexistence. Simulation results demonstrate that the developed analytical framework and modified PSO algorithm achieve close to the optimal mMTC/H2H coexisting performance and can be adapted to various network settings.

In this paper, we study an uplink reconfigurable intelligent surface (RIS) aided orthogonal frequency division multiplexing (OFDM) system, where the RIS simultaneously acts as a passive reflector for a user’s quadrature phase-shift keying (QPSK) OFDM signal and an information source which sends information through spatial modulation of the reflected signal. We present a maximum-likelihood (ML) detector for the detection of RIS signals and analyze the error probability theoretically. We derive closed-form expressions of the error probability for the detection of RIS signals for the special case of flat-fading channels. Then we analyze the error probability of the detection of user’s signals. Analysis indicates that faulty detection of RIS signal results in channel estimation errors, consequently compromising the precision of OFDM signal detection. High correlation between code words limits the error propagation to the user but decreases accuracy for RIS signal’s detection. Addressing this, an index modulation with beamforming (IM-BF) code is proposed to enhance the transmission for the user, and a quadrature reflection modulation with beamforming (QRM-BF) code is proposed for a performance trade-off between the user and the RIS. Simulation results demonstrate the theoretical results, and show that the QRM-BF code has better performance for the RIS transmission, and both schemes outperform benchmark schemes from the literature for the user’s transmission. Simulation results also show that the IM-BF scheme significantly improves the user transmission performance compared to the state of the art.

This paper studies the joint secure transceiver design for the full-duplex integrated sensing and communication (ISAC) system, in which the base station performs the target tracking and communicates with the downlink and the uplink users by reusing the resources. Here, the target, referred to as Eve, is a potential eavesdropper with the intention of intercepting both the downlink and the uplink information. The security problem of the full-duplex ISAC system has not been studied well, where the sensing and communication signals suffer from the serious interference. In this paper, we jointly design the information beamformer, the radar waveform, the uplink communication receive filter, and the radar receive filter to achieve the downlink and uplink communication security and the target tracking. Specifically, both the Eve’s signal-to-interference-plus-noise ratio minimization and the secrecy rate maximization problems, subject to the sensing and the communication constraints, are formulated for the ISAC system from the perspectives of the quality of service and the secrecy rate. An iterative algorithm is proposed for solving the formulated problems. We prove that, under appropriate conditions, the proposed iterative algorithm converges to the Karush-Kuhn-Tucker optimal point of the original problem without the rank 1 constraint. We further extend the joint design to the case of the imperfect wiretap channel and the angle uncertainty. Numerical results show that our scheme remarkably outperforms the benchmark approaches. The performance is close to that of designing the secure communication and the sensing separately.

With the rapidly increasing number of satellites, the handover scheme design is critically important for the low Earth orbit (LEO) satellite networks, especially for the mega-constellations that include massive number of LEO satellites. However, the existing handover schemes for LEO satellite networks are designed based on the static propagation conditions, which cannot satisfy the dynamic feature of communication environment caused by the mobility of LEO satellites and users. To address this issue, a centralized adaptive intelligent handover scheme for mega-constellations is proposed, where the dynamics of the propagation conditions and limited LEO satellite capacity are taken into considerations. Specifically, we first use a three-state Markov model to characterize the dynamically varying propagation conditions between satellites and users. Then, the Loo model is employed to describe the dynamic land mobile satellite channels. By considering the user transmission rate requirement and the load-balancing demand of satellites, we design the user utility function and formulate an optimization problem that aims to maximize the overall long-term utility of the network. To reduce the handover decision-making complexity, a multi-agent successive hysteretic deep Q-learning algorithm is developed and it can efficiently solve the formulated problem by reducing the state and action space. To reduce the signaling overhead and the computation complexity of the proposed centralized handover scheme brought to the control center, a distributed intelligent handover scheme is further developed, where each user is enabled to independently make the handover decision only based on the local information. Simulation results show that both the proposed centralized and distributed approaches can efficiently improve the network performance over the existing schemes.

Internet of Robotic Things (IoRT) emphasizes the integrated robotic, artificial intelligence computing, and communication technologies, enabling more sophisticated operations and decision-making. As a crucial element of IoRT, mission-critical applications, such as industrial manufacturing and emergency services, impose stringent requirements on ultra-reliable and low-latency communication (URLLC). The paper focuses on addressing URLLC challenges in the context of IoRT, particularly when autonomous mobile robots (AMRs) coexist with static sensors. We prioritize safe and efficient AMRs’ travel through trajectory design and communication resource allocation in IoRT systems without the need of any prior knowledge. To enhance network connectivity and exploit diversity gains, we introduce the flexible decoding and free clustering as the next-generation multiple access technologies in spectrum-limited downlink IoRT system. Then, aiming at minimizing the decoding error probability and travel time, we formulate a long-term multi-objective optimization problem by jointly designing AMRs’ trajectory and communication resource. To accommodate the inherent dynamics and unpredictability in the IoRT system, we introduce a multi-agent actor-critic deep reinforcement learning (DRL) framework, offering four distinct implementations, each accompanied by comprehensive complexity analyses. Simulation results reveal the following insights: 1) in terms of DRL implementations, off-policy algorithms with deterministic policies outperform their on-policy counterparts, achieving approximately a 67% increase in rewards; 2) In terms of communication schemes, our proposed flexible decoding and free clustering strategies under designed trajectories can effectively reduce decoding errors; and 3) In terms of algorithm optimality, our DRL framework shows superior flexibility and adaptability in communication environments compared to traditional A* search and heuristic methods.

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.

Coupled with identification, 3D positioning can significantly enrich location-based services. Resonant beam (RB) is emerging as a promising solution to indoor positioning due to its self-aligning and energy-focused transmission. We propose a system for simultaneous localization and identification using RB as the individual medium. The base station (BS) employs a single-source RB, and each mobile target (MT) is equipped with a signal reflection module. For 3D localization, the BS estimates direction by analyzing RB’s spatial distribution and determines the distance from its frequency components. For identification, the passive MT captures the RB for power and reflects its identity (ID) to BS as spot flicker signals. To demonstrate the working principles, we have developed models for location estimation and ID recognition, as well as the power flow within the RB channel. In implementation, we incorporate a threshold regulation scheme for accurate image signal retrieval, along with an input power distribution model tailored for multi-access scenarios. Through simulating the entire process, we verify the system’s feasibility, including confirming the viability of ID recognition. We also evaluate localization performance, averaging ~ 1 cm at heights of 1.5 m ~ 2.5 m, showing promise for a wide range of potential applications.

Military active sonar and marine transportation are detrimental to the livelihood of marine mammals and the ecosystem. Early detection and classification of marine mammals using machine learning can help humans to mitigate the harm to marine mammals. This paper proposes a cross-covariance attended compact Feed-Forward Sequential Memory Network (CC-FSMN). The proposed framework shows improved efficiency over multiple convolutional neural network (CNN) backbones. It also maintains a relatively decent performance.

Ultrasonic intra-body communication (IBC) is an emerging technique to enable revolutionary healthcare applications. This paper proposes an ultrasonic index modulation with spread spectrum (UsIM-SS) scheme for IBCs. The information bits consist of modulation bits and index bits. The core idea of UsIM-SS is that the modulation bits are spread by spreading codes and the index bits are leveraged to select the time-hoping sequence to transmit the spreading bits. We also find that the existing ultrasonic wideband (UsWB) and UsIM can be regarded as special cases of the proposed UsIM-SS. Furthermore, the soft decision-based maximum likelihood (S-ML) receiver and hard decision-based ML (H-ML) receiver are proposed for UsIM-SS, providing a trade-off between bit-error rate (BER) and complexity. Theoretical spectrum efficiency (SE) and BER expressions for UsIM-SS using S-ML and H-ML are also derived and verified by extensive Monte Carlo simulations. Both theoretical and simulation results show that UsIM-SS can achieve lower BER and higher SE than existing UsWB and Us 1M techniques.

This paper proposes a downlink transmission scheme for intelligent reflecting surface (IRS) enhanced cognitive-satellite-aerial-network to support massive access of Internet-of-Things devices (IoTDs). By sharing the same frequency band with satellite network, the aerial network offers services for IoTDs having line-of-sight links through space division multiple access, and for IoTDs locating in blocked area via IRS-enhanced non-orthogonal multiple access. Assuming that only the imperfect channel state information is available, we formulate a transmit power minimization problem subject to the probabilistic constraints of the quality-of-service requirements for IoTDs, the co-channel interference power limitation, and unit-modulus requirement for IRS. To tackle this mathematically intractable problem, we propose a generalized zero-forcing based low-complexity robust transmission algorithm, integrating the second-order Taylor expansion and Bernstein-type inequality, to obtain a satisfactory performance while reducing the computational load. Finally, simulation results validate the effectiveness and superiority of the proposed robust algorithms compared to existing algorithms.

Cognitive satellite and terrestrial network (CSTN) is considered as a promising technology to provide ubiquitous connectivity for various users within wide-coverage. This paper proposes a robust downlink transmission scheme for multiple intelligent reflecting surfaces (IRSs) assisted CSTN. Here, the satellite network adopts multigroup multicast transmission scheme to serve many earth stations, while the terrestrial network exploits space division multiple access and multi-IRS-enhanced non-orthogonal multiple access technology to communicate with many terrestrial users. By assuming that these two networks share the same frequency band having only the angular information based imperfect channel state information of each user, we formulate an optimization problem to minimize the total transmit power subject to the constraints of quality-of-service requirement for each user, per-antenna transmit power budgets of satellite and BS, and unit-modulus requirement for each reflecting element. To tackle this mathematically intractable problem, we then employ angular discretization together with the successive convex approximation method to obtain the active beamforming (BF) vectors of satellite and BS, the passive BF vector of IRS, and the power allocation coefficients. Moreover, we propose a generalized zero forcing BF and alternative optimization to obtain the suboptimal solutions of the optimization problem with low computational complexity. Finally, simulation results are given to demonstrate the effectiveness and superiority of the proposed two schemes over the benchmarks.

In practical cell-free (CF) massive multiple-input multiple-output (MIMO) networks with distributed and low-cost access points, the asynchronous arrival of signals at the user equipments increases multi-user interference that degrades the system performance. Meanwhile, rate-splitting (RS), exploiting the transmission of both common and private messages, has demonstrated to offer considerable spectral efficiency (SE) improvements and its robustness against channel state information (CSI) imperfection. The signal performance of a CF massive MIMO system is first analyzed for asynchronous reception capturing the joint effects of propagation delays and oscillator phases of transceivers. Taking into account the imperfect CSI caused by asynchronous phases and pilot contamination, we derive novel and closed-form downlink SE expressions for characterizing the performance of both the RS-assisted and conventional non-RS-based systems adopting coherent and non-coherent data transmission schemes, respectively. Moreover, we formulate the design of robust precoding for the common messages as an optimization problem that maximizes the minimum individual SE of the common message. To address the non-convexity of the design problem, a bisection method is proposed to solve the problem optimally. Simulation results show that asynchronous reception indeed destroys both the orthogonality of the pilots and the coherent data transmission resulting in poor system performance. Besides, thanks to the uniform coverage properties of CF massive MIMO systems, RS with a simple low-complexity precoding for the common message obtained by the equal ratio sum of the private precoding is able to achieve substantial downlink sum SE gains, while the application of robust precoding to the common message is shown to be useful in some extreme cases, e.g., serious oscillator mismatch and unknown delay phase.

A dense satellite-terrestrial integrated mobile-edge computing network (SATIMECN) architecture is developed to meet the computing demands for next generation networks. We formulate an average weighted sum energy consumption minimization problem by jointly considering task ratio allocation of computing or offloading at local and the gateway (GW), ground user terminal (GUT)-satellite association relation, GUT multiple-input and multiple-output (MIMO) precoding, and computation resource allocation at local and the GW. Due to the stochastic property of the optimization problem, we adopt Lyapunov optimization theory to transform it into a deterministic one. Then, we decompose the optimization problem into four subproblems and solve each one iteratively. Specifically, task ratio allocation of computing or offloading at local and the GW is obtained in a closed-form expression using the delay constraint. Then, the binary GUT-satellite association subproblem is solved by the weighted minimum mean-squared error and quadratic transform based fractional programming (QTFP) methods. Moreover, the MIMO precoding subproblem is solved by QTFP and interior point methods. Finally, the computation resource allocation subproblem for local and edge computing is derived in closed-form expressions. Simulation results demonstrate that the tradeoff between the average weighted sum energy consumption and the average queue length can be realized by adjusting the Lyapunov control parameter. Moreover, the proposed MIMO communication and frequency reuse schemes for dense satellite network can realize efficient computation offloading with relative low cost.

Rapid and efficient sensor data acquisition plays a critical role in the decision-making process of each robot in a multi-robot smart factory. This paper investigates the trajectory design of autonomous mobile robots (AMRs) and communication resource allocation problems in industrial Internet of Things. Specifically, by exploiting both power and spatial domains, we adopt non-orthogonal multiple access to improve network connectivity in a spectrum-efficient manner, while the multi-antenna technique is employed to enhance diversity gain. The average sum rate is maximized by jointly optimizing the transmit power of sensors and the trajectory of AMRs. To deal with prior knowledge and dynamic channel conditions, we reformulate the long-term maximization problem as a Markov decision process, and further develop a provably efficient multi-agent reinforcement learning algorithm with a near-optimal regret bound. Our theoretical analysis reveals that both the decentralized execution and the experience exchange method are beneficial to accelerate convergence. Simulation results show that our proposed algorithm can reduce at least 80% convergence time compared to the centralized baseline, and can gain better rewards than the conventional$\epsilon $-greedy exploration.

To satisfy the highly heterogeneous requirements of Internet of Things applications for the sixth-generation (6G) networks, the machine-type communication (MTC) aims to support multiple types of services having diverse traffic demands, which will cause significant challenges in the grant-free based channel estimation (CE) and multi-user detection (MUD) scheme design for massive MTC (mMTC) networks. To address this challenge, we develop a multi-state Markov chain based transmission model to characterize the diverse time-varying traffic demands for MTC users, where the temporal correlation of user activity and the data length diversity are jointly exploited. Based on the developed transmission model, a multi-service oriented joint CE-MUD scheme is proposed to realize the efficient CE, user activity identification and data detection. Specifically, we first construct the joint block sparse structure for the transmitted pilot and data signals to fully explore the structured sparsity of the pilot and data symbols. Then, we convert the joint CE-MUD into a maximum a posteriori probability (MAP) problem such that the block sparsity of the transmitted signals and the diverse traffic demands provided by the established transmission model can be efficiently exploited. Moreover, we further develop an adjustable prior probability aided Bayesian sparsity adaptive matching pursuit (APP-BSAMP) algorithm to efficiently solve the formulated MAP problem. In the proposed algorithm, we first adjust the prior user activation probabilities through the approximate message passing (AMP) based detector to reduce the impact of the misestimation of user transmission status. Then, we jointly reconstruct the transmitted pilot and data signals under the Bayesian pursuit framework, where the active user set is obtained by maximizing the posterior probabilities. Simulation results show that the proposed scheme can achieve a substantial performance gain over existing methods.

Densely deployed light emitting diodes (LEDs) typically lead to high spatial correlation in multiple-input multiple-output (MIMO) visible light communication (VLC) systems. Although precoding can effectively alleviate the spatial correlation issue, most existing precoding algorithms require a dedicated baseband chain for each LED, leading to high energy consumption and hardware complexity when a large number of LEDs are used. In this paper, a successive interference cancellation (SIC)-based precoding scheme with sub-connected architecture (SIC-SA) is proposed. In the considered model, each baseband chain is connected to an LED sub-array containing multiple LEDs to reduce the complexity. Since, in this case, SIC-based precoding can only determine the signal of each baseband chain for an LED sub-array, while its target is to mitigate the spatial correlation between individual LEDs, the electrical/optical power of each LED must also be jointly optimized to accomplish the target. This joint SIC-based precoding, power allocation, and direct current offset design problem is formulated as an achievable sum rate maximization problem under dimming control and electrical power constraints. A two-step iterative algorithm is proposed to solve this problem. In the first step, the SIC-based precoding is designed to alleviate the multi-user interference. In the second step, the power allocation of LEDs is optimized by matrix decomposition and convex optimization, and a closed-form solution of DC offset is derived. Furthermore, considering the dynamic scenarios, a SIC-based precoding scheme with dynamic sub-connected architecture (SIC-DSA) is proposed, in which a switch network is used to adaptively adjust the LED sub-array structure based on the channel state information. Simulation results show that the proposed SIC-SA and SIC-DSA respectively achieve 0.1340 bps/Hz/W and 0.1305 bps/Hz/W energy efficiency gains over the zero-forcing precoding scheme with SA, when the signal-to-noise ratio is 30 dB.

In this paper, the performance of optimally displaced threshold detection (ODTD) for discriminating ternary coherent signals is theoretically investigated. We first establish the receiver model for ternary phase shift keying optical quantum communication system using Kennedy receiver with ODTD in a realistic situation. Then we analytically study the error probability for ODTD and formulate a joint optimization problem to minimize this error probability of detection, which is a challenging mixed-integer programming (MIP) problem due to the discreteness of the detection threshold. We then propose an efficient algorithm to tackle this MIP problem and design the displacement and the threshold on both detection branches. We also discuss the receiving scheme using ODTD in the case of unequal prior probabilities and optimize the signal power distribution between the two branches. Numerical results show that for the discrimination of ternary coherent signals using Kennedy-type receiver, ODTD can mitigate the influence of thermal noise and dark count noise and is robust to imperfect quantum efficiency. Besides, we find that the error probability performance can be improved by optimizing the decoding order of the ternary signals and the reflectance of the beam splitter.

There is an increasing interest in direction-of-arrival (DOA) estimation using nested arrays composed of vector sensors. Considering acoustic vector sensors (AVSs) commonly used in underwater applications, this paper proposes a novel algorithm, called augmented nested quaternion-MUSIC (ANQ-MUSIC), to perform DOA estimation. By judiciously arranging the multi-component outputs of AVSs, we model the received signals from the entire nested AVS array as a quaternion observation vector in a compact way to reduce the computational complexity. Next, we formulate a quaternion-based difference co-array (QDCA) model via vectorizing the quaternion covariance matrix (QCM). Based on the obtained insights from the QDCA model, we derive a suitable QCM, which is constructed by applying the spatial smoothing technique. Finally, classical quaternion-MUSIC is logically introduced to estimate the DOA parameters. In simulations, we take into account non-uniform received noise and inter-component correlated noise, which may occur in practical underwater environments. The results demonstrate that the proposed method shows superiority in angular resolution and achieves a desirable trade-off between estimate accuracy and computational burden, besides showing robust performance in the above test scenarios.

In physical-layer security, secrecy capacity is an important performance metric. This work aims to determine the secrecy capacity for an indoor visible light communication system consisting of a transmitter, a legitimate receiver and an eavesdropping receiver. In such a system, both signal-independent noise and signal-dependent noise are considered. Under nonnegativity and average optical intensity constraints, lower and upper bounds on secrecy capacity are derived by the variational method, the dual expression of the secrecy capacity, and the concept of “the optimal input distribution that escapes to infinity”. By an asymptotic analysis at large optical intensity, there is a small gap between the asymptotic upper and lower bounds. Then, by adding a peak optical intensity constraint, we further analyze the exact and asymptotic secrecy-capacity bounds. For practical considerations, the effects of imperfect channel state information, multi-photodiode eavesdropper, and artificial noise on secrecy performance are also discussed. Finally, the derived secrecy-capacity bounds are verified by numerical results.

The diverse time-varying transmission demands cause significant challenges in the grant-free based multi-user detection (MUD) scheme design for massive machine-type communications (mMTC) networks. In this paper, we develop a multistate Markov model to characterize the diverse time-varying traffic demands, where the temporal correlation of the user activity and the data length diversity are considered simultaneously. Based on the developed Markov model, a diverse traffic demands oriented MUD scheme is proposed to realize the efficient joint user activity and data detection. Specifically, we first construct the block sparse structure for the transmitted signal to fully exploit the structured sparsity of the data matrix. Then, we convert the MUD into a maximum a posteriori probability (MAP) problem such that the block sparsity of the transmitted signal and the temporal correlation and data length diversity provided by the established Markov model can be efficiently exploited. Moreover, we further develop an intra-block pruning aided Bayesian block orthogonal matching pursuit (IBPA-BBOMP) algorithm such that the formulated MAP problem is efficiently solved. Simulation results show that the proposed scheme can achieve a substantial performance gain over existing methods.

Marine science and Internet of Underwater Things applications rely significantly on collecting data from underwater sensors. Data collection using long-distance underwater acoustic communications consumes a lot of energy in underwater sensor nodes, which are powered by batteries. To achieve low-energy consumption, we can use the autonomous underwater vehicle (AUV) to move close to sensor nodes and exploit the short-range and high-rate communications. Most of the existing AUV-based data collection schemes consider the scenarios having the knowledge of node positions, where the cruising trajectory can be computed before the AUV’s departure. These schemes cannot apply to some scenarios such as turtle tracking for a certain sea area having no position information. To this end, we first propose a planning-while-detecting approach to dynamically detect the sensors on turtles and adjust the AUV cruising direction to collect data. To further improve data efficiency under the energy limit of the AUV, we group the sensors that can share the same trajectory using their detected directions. A grouping-based dynamic trajectory planning (GDTP) is then proposed to determine the next cruising direction that can visit the group of sensors having the largest amount of data and demanding the least cruising energy at the risk of detection errors. Simulation results show that GDTP achieves significantly higher data collection efficiency than the existing trajectory planning algorithms in dynamic scenarios, and as the communication range increases, it can even outperform the existing algorithms with node locations.

We propose a partial cooperative zero-forcing (PCZF) decoding scheme for the uplink cell-free massive MIMO system, wherein the neighboring access points (APs) around each user equipment (UE) share the channel state information (CSI) and jointly suppress the interference using the zero-forcing technique. Using asymptotic analysis, we derive a closed-form asymptotic expression for a lower bound on the achievable rates. Considering the unique and complex form of the achievable rates, we propose power control schemes according to two criteria. The first criterion is to maximize the minimum achievable rate. For this criterion, we propose a target-SINR-tracking (TST)-based bisection algorithm. Since the power control update functions are standard interference functions, the TST-based bisection method always converges to the optimal solution. The second criterion is to maximize the sum rate, for which we propose two power control algorithms: 1) randomization and scaling algorithm (RSA) and 2) fractional programming algorithm (FPA). In each iteration of the RAS algorithm, we first exploit the randomization technique to transform the sum-rate maximization problem into a series of power minimization problems, and then improve the sum rate by scaling. In the FP algorithm, we derive a lower bound on the sum rate, and then propose an iterative approach based on the Lagrangian dual transform and fractional programming to maximize the sum-rate lower bound. Numerical results validate the theoretical analysis and verify the efficiency of the proposed power control algorithms.

The noises embedded in signals will degrade the signal processing quality. Traditional denoising algorithms might not work in practical systems since the statistical characteristics of noises might not be learned. To address this issue, we propose an efficient residual shrinkage convolutional neural network (RSCNN) aided denoiser based on the principle of the domain transformation, shrinking and inverse transforming operations conducted by the traditional denoiser. The proposed RSCNN is composed by the batch normalization layer, domain transformation layers, the shrinkage module and inverse transformation layers, wherein transformation layers consist of convolutional layers and the nonlinear activation function. Moreover, we propose a thresholds learning subnetwork to automatically determine the thresholds, so as to enhance noise suppressing performances. Furthermore, we compose the data set by preprocessing the received signals, and design the loss function according to different denoising requirements. To validate the efficiency and universality of the RSCNN aided denoiser, we apply the proposed RSCNN denoiser to three different application scenarios, including the modulation recognition, detection and decoding. After the offline training, at the online deployment stage, we utilize the RSCNN denoiser to reduce the noise power and improve the signal to noise ratios. Simulation results demonstrate that the proposed intelligent denoiser can efficiently improve the signal processing capabilities to achieve higher modulation recognition accuracy, better detection and decoding performances with lower complexity than benchmark schemes.

Mobile edge computing (MEC) is a key technology that enables the deployment of applications (or services) at the proximity of mobile users. However, the performance of mobile edge computing is sensitive to the quality and availability of underlying connection links. It is still unclear to what extent intermittent connectivity affects the performance of mobile edge computing. In this paper, we make the first attempt to quantify the influence of intermittent connectivity on mobile edge computing from a theoretical perspective. Specifically, we propose an analytical framework based on discrete-time Markov chain and derive a closed-form expression of the task processing time under different network conditions. Our model can be further extended to account for the case with group task arrivals. We also conduct extensive simulations to examine the accuracy of our proposed analytical models with both synthetic and real-world user mobility traces. The results show that our model can well capture the influence of intermittent connectivity on MEC. Our model sheds important insights into the impact of intermittent connectivity on task processing in MEC, which we believe should be taken into account when designing future MEC systems.

We investigate multiuser detection under uplink asynchronous scenario, where the triangular successive interference cancellation (T-SIC) detection scheme is exploited. As the existing analysis was conducted under some ideal assumptions for simplicity, the asynchronous scenario is analyzed under rigorous assumptions in this paper so that important insights are revealed for practical systems. Specifically, the average symbol error rate (SER) formulas are derived for a two-user system with arbitrary$M$-ary quadrature amplitude modulation ($M$-QAM) over Rayleigh fading channels. These solutions are shown to have higher accuracies compared to the existing solutions. Furthermore, a novel iterative detection algorithm is proposed, i.e., the parallel T-SIC method, which is more efficient and effective compared to the iterative T-SIC method. Insightful discussion is conducted in terms of the potentials of the concerned iterative detection method. Simulation results show that the most significant improvement of the iterative detection reflects on the SER for the strongest user, and the reduction is always within 50% of the SER compared to the primary detection. The gains obtained by the iterative detection is negligible for higher order$M$-QAM cases.

Satellite based spectrum sensing is studied for a system consisting of multiple satellites and a gateway (GW), where these satellites perform spectrum sensing and send mass spectrum-sensing data to the GW. To address the challenges of mass spectrum-sensing data and limited transmission capacity of the links from spectrum-sensing satellites to the GW, we propose a method called joint anomalous data repairing and deep convolutional neural network based spectrum reconstruction (ADRD-SR), which can reconstruct the original spectrum-sensing data from the incomplete data. Specifically, the GW preprocesses the incomplete data using the anomalous data repairing algorithm. A deep convolutional neural network is constructed and well trained, then it is activated to reconstruct the preprocessed spectrum data. Additionally, to sustain good reconstruction performance by tracing the dynamical spectrum-sensing data, we design a real-time evaluation oriented spectrum reconstruction framework, through seeking the events when the mean absolute error (MAE) becomes larger than a predefined threshold. Furthermore, the ADRD-SR method can reduce the MAE by more than 68% over the conventional reconstruction methods. Moreover, the reconstructed spectrum data can be used to assist spectrum sensing, and the corresponding probability of correct detection is only degraded by 5% even when 75% of the data is discarded.

To reduce the computational complexity and channel estimation overhead for multi-intelligent reflecting surface (IRS)-assisted millimeter wave (mmWave) multiple-input multiple-output (MIMO) communication, we consider a joint design of the hybrid precoders at the base station and the passive precoders at the IRSs to maximize the ergodic spectral efficiency by exploiting the twin-timescale channel state information (CSI). Specifically, the digital precoder is designed according to the instantaneous CSI of a reduced-dimensional assist channel matrix, while the IRS passive reflection coefficient matrices and the analog precoder are optimized using the statistical CSI of all links. However, such a design problem is challenging to solve due to the non-convexity and the twin timescale. This work proposes efficient algorithms to jointly design the precoders, where the update of the IRS reflection coefficient matrices is independent of the hybrid precoders and the design of the analog precoder is independent of the digital precoder. Simulation results demonstrate the effectiveness of the proposed algorithms and provide the application scenes of the fully-connected and subarray-connected architectures. The results also show that the ergodic spectral efficiency for the fully-connected architecture using the twin-timescale CSI can approach that using the existing CSI schemes with less channel estimation overhead and computational complexity.

A key challenge for millimeter wave (mmWave) multiple-input multiple-output (MIMO) communication is that the signals at mmWave band are highly susceptible to blockage. To address this challenge, we introduce intelligent reflecting surface (IRS) to increase coverage area and improve communication performance. This paper considers a joint design of hybrid precoders at the base station and the passive precoder at the IRS to maximize the average spectral efficiency in an IRS-assisted mmWave MIMO system by exploiting the mixed timescale channel state information (CSI). Specifically, the hybrid precoders are designed according to the instantaneous CSI of the overall channel, while the IRS reflection coefficient matrix is optimized using the statistical CSI of all links. However, such a design problem is challenging to solve due to the non-convexity and the mixed timescale. This work proposes efficient algorithms to design jointly the hybrid precoders and the IRS reflection coefficient matrix where the update of the IRS reflection coefficient matrix is independent of the hybrid precoders. Simulation results demonstrate the effectiveness of the proposed algorithms. More interestingly, the results also show that adding low-cost reflector elements at the IRS can reduce the number of required high-cost radio frequency chains.

The communication systems for Poisson channels require long pilot for channel estimation and may result in large percentage of overhead due to the signal-dependent noise of Poisson-distributed signal, i.e., both signal part and noise part experience random processes. In this paper, both blind and semi-blind channel estimation methods are studied to shorten the overhead and increase the transmission efficiency for Poisson channels. First, fractionally spaced equalizers are proposed based on modified constant modulus algorithm (CMA) and subspace (SS). Second, a data-aided iterative channel estimation (ICE) is designed and analyzed in terms of its asymptotic unbiasedness and convergence. The proposed methods are evaluated based on both a constant channel scenario and a varying channel scenario. Numerical simulation results show that the modified CMA has the worst bit-error rate performance but requires the lowest computational complexity. Besides, both SS based channel estimation and ICE have negligible overhead and comparable bit-error rate performances with respect to the conventional periodic pilot based channel estimation having 50% overhead.

For massive multiple-input multiple-output systems operating in frequency-division duplex mode, downlink channel state information (CSI) acquisition will incur large overhead. This overhead is substantially reduced when sparse channel estimation techniques are employed, owing to the channel sparsity in the angular domain. When a sparse channel estimation method is implemented, the measurement matrix, which is related to the pilot matrix, is essential to the channel estimation performance. Existing sparse channel estimation schemes widely adopt random measurement matrices, which have been criticized for their suboptimal reconstruction performance. This paper proposes novel data-driven solutions to design the measurement matrix. Model-based autoencoders are customized to optimize the measurement matrix by unfolding the classical basis pursuit algorithm. The obtained data-driven measurement matrices are applied to existing sparse reconstruction algorithms, leading to flexible hybrid data-driven implementations for sparse channel estimation. Numerical results show that the proposed data-driven measurement matrices can achieve more accurate reconstructions and use fewer measurements than the existing random matrices, thereby leading to a higher achievable rate for CSI acquisition. Moreover, compared with existing pure deep learning-based sparse reconstruction methods, the proposed hybrid data-driven scheme, which uses the novel data-driven measurement matrices with conventional sparse reconstruction algorithms, can achieve higher reconstruction accuracy.

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

The generalized Kennedy receiver can be used to improve the secret key rate of continuous variable quantum key distribution protocol and thus attracts much attention recently. In this letter, we analytically study the performance of the post-selection based generalized Kennedy receiver in the presence of thermal noise. To improve the performance of the generalized Kennedy receiver, we optimize the displacement operation for different post-selection parameter and different thermal noises. Numerical results show that the thermal noise can greatly degrade the performance of the post-selection based generalized Kennedy receiver. Besides, the resistance of the generalized Kennedy receiver to the thermal noise can be improved by choosing an appropriate post-selection parameter.

To satisfy the explosive access demands of Internet-of-Things (IoT) devices, various kinds of multiple access techniques have received much attention. In this article, we investigate the multicast communication of a satellite and aerial-integrated network (SAIN) with rate-splitting multiple access (RSMA), where both satellite and unmanned aerial vehicle (UAV) components are controlled by network management center and operate in the same frequency band. Considering a content delivery scenario, the UAV subnetwork adopts the RSMA to support massive access of IoT devices (IoTDs) and achieve desired performances of interference suppression, spectral efficiency, and hardware complexity. We first formulate an optimization problem to maximize the sum rate of the considered system subject to the signal-interference-plus-noise-ratio requirements of IoTDs and per-antenna power constraints at the UAV and satellite. To solve this nonconvex optimization problem, we exploit the sequential convex approximation and the first-order Taylor expansion to convert the original optimization problem into a solvable one with the rank-one constraint, and then propose an iterative penalty function-based algorithm to solve it. Finally, simulation results verify that the proposed method can effectively suppress the mutual interference and improve the system sum rate compared to the benchmark schemes.

Relay-assisted free-space optical (FSO) communication systems are exploited as a means to mitigate the limiting effects of the turbulence induced atmospheric scintillation. However, conventional ground relays are stationary, and their optimal placement is not always feasible. Due to their mobility and flexibility, unmanned aerial vehicles (UAVs) provide new opportunities for FSO relaying systems. In this paper, a hovering UAV-based serial FSO decode-and-forward relaying system is investigated. In the channel modelling for such a system, four types of impairments (i.e., atmospheric loss, atmospheric turbulence, pointing error, and link interruption due to angle-of-arrival fluctuation) are considered. Based on the proposed channel model, a tractable expression for the probability density function of the total channel gain is obtained. Closed-form expressions of the link outage probability and end-to-end outage probability are derived. Asymptotic outage performance bounds for each link and the overall system are also presented to reveal insights into the impacts of different impairments. To improve system performance, we optimize the beam width, field-of-view and UAVs' locations. Numerical results show that the derived theoretical expressions are accurate to evaluate the outage performance of the system. Moreover, the proposed optimization schemes are efficient and can improve performance significantly.

Surveillance performance is studied for a wireless eavesdropping system, where a full-duplex legitimate monitor eavesdrops a suspicious user's link with artificial noise (AN) assistance. Different from the existing works, the suspicious receiver is assumed to be capable of detecting the presence of AN. Once such receiver detects the AN, the suspicious user will stop transmission, which can therefore degrade the surveillance performance. Hence, to improve the surveillance performance, AN should be transmitted covertly with a low detection probability. Under these assumptions, an optimization problem is formulated to maximize the surveillance performance under a covert constraint. Then, based on the detection ability at the suspicious receiver, a novel scheme is proposed to solve the optimization problem using an iterative search. Moreover, we investigate the impact of both the suspicious-transmitter-to-suspicious-receiver and the monitor-to-suspicious-receiver links uncertainties on the covert surveillance performance. Simulations are performed to verify the analyses. We show that the uncertainty in the suspicious user's link can enhance the surveillance performance, while the uncertainty in the monitor-to-suspicious-receiver link can degrade the surveillance performance.

Reconfigurable Intelligent Surface (RIS) can create favorable multipath to establish strong links that are useful in millimeter wave (mmWave) communications. While previous works assumed Rayleigh or Rician fading, we use the fluctuating two-ray (FTR) distribution to model the small-scale fading in mmWave frequency. First, we obtain the statistical characterizations of the product of independent FTR random variables (RVs) and the sum of product of FTR RVs. For the RIS-aided and amplify-and-forward (AF) relay systems, we derive exact end-to-end signal-to-noise ratio (SNR) expressions. To maximize the end-to-end SNR, we propose a novel and simple way to obtain the optimal phase shifts at the RIS elements. The optimal power allocation scheme for the AF relay system is also proposed. Furthermore, we evaluate important performance metrics including the outage probability and the average bit-error probability. To validate the accuracy of our analytical results, Monte-Carlo simulations are subsequently conducted to provide interesting insights. It is found that the RIS-aided system can attain the same performance as the AF relay system with low transmit power. More interestingly, as the channel conditions improve, the RIS-aided system can outperform the AF relay system using a smaller number of reflecting elements.

This paper investigates the massive access for a satellite-aerial-terrestrial network (SATN), where a high-altitude platform (HAP) is deployed as a relay to assist the uplink transmission from terrestrial user equipment (UE) to satellite. Unlike previous works, we adopt radio frequency (RF) and free space optical for the aerial-terrestrial and satellite-aerial links, respectively. Specifically, by assuming that imperfect angular information (IAI) of each UE is known at the HAP, we develop a space division multiple access (SDMA) scheme to maximize the ergodic sum rate (ESR). To this end, we first exploit the IAI to calculate the analytical expression of channel correlation matrix. Then, by considering the limitation of array freedom, we propose a subspace-based UE grouping and scheduling scheme to cluster all UEs into groups. Next, we present a computationally effective beamforming (BF) scheme for each UE at HAP to efficiently implement SDMA in the RF link. Furthermore, a closed-form expression for the ESR of the SATN is derived to validate the proposed BF and SDMA schemes. Finally, simulation results corroborate the derived theoretical formulas and reveal the impacts of array size, angular estimation error, the number of UEs and scheduling threshold on the system performance.

Underwater wireless optical communication (UWOC) has been recently proposed for high-rate data services. In underwater scenarios, the channel conditions are complicated due to various effects of the water waves, such as absorption, scattering and turbulence, which exhibit different statistical properties with diverse water types. Such capricious phenomena make channel estimation (CE) and signal detection (SD) become challenging issues for high-rate and reliable transmissions. To address these challenges, we devise a deep learning (DL) based joint channel classification (CC), CE and SD scheme for UWOC systems. Different from existing systems, where CE is typically an independent and fundamental module, we combine CE with SD by utilizing a new deep neural network (DNN). Furthermore, the proposed scheme exploits the channel characteristics extracted through an offline training powered by a new robust DNN channel classifier (CC), which can identify and classify the water types online to produce optimized estimated combinational weights (ECW) for improving the CE/SD performances under time-varying UWOC channels. Simulation and experimental results demonstrate the superiority of the proposed system in terms of link performances in various UWOC channel environments.

Covert transmission is studied for an intelligent reflecting surface (IRS) aided communication system, where Alice aims to transmit messages to Bob without being detected by the warden Willie. Specifically, an IRS is used to increase the data rate at Bob under a covert constraint. For the considered model, when Alice is equipped with a single antenna, the transmission power at Alice and phase shift at the IRS are jointly optimized to maximize the covert transmission rate with either instantaneous or partial channel state information (CSI) of Willie's link. In addition, when multiple antennas are deployed at Alice, we formulate a joint transmit beamforming and IRS phase shift optimization problem to maximize the covert transmission rate. One local optimal algorithm and two low-complexity suboptimal algorithms are proposed to solve the problem. Furthermore, for the case of imperfect CSI of Willie's link, the optimization problem is reformulated by using the triangle and Cauchy-Schwarz inequalities. The reformulated optimization problems are solved using an alternative algorithm, semidefinite relaxation (SDR) and Gaussian randomization techniques. Finally, simulations are performed to verify our analysis. The numerical results show that an IRS can degrade the covert transmission rate when Willie is closer to the IRS than Bob.

Ultrasonic intra-body communication (IBC) is a promising enabling technology for future healthcare applications, due to low attenuation and medical safety of ultrasonic waves for the human body. A splitting receiver, referred to as the splitting-detection separate-decision (SDSD) receiver, is introduced for ultrasonic pulse-based IBCs, and SDSD can significantly improve bit-error rate (BER) performance over the traditional coherent-detection (CD) and energy detection (ED) receivers. To overcome the high complexity and improve the BER performance of SDSD, a splitting-detection joint-decision (SDJD) receiver is proposed. The core idea of SDJD is to split the received signal into two streams that can be separately processed by CD and ED, and then summed up as joint decision variables to achieve diversity combining. The theoretical channel capacity and BER of the SDSD and SDJD are derived for M-ary pulse position modulation ( M-PPM) and PPM with spreading codes. The derivation takes into account the channel noise, intra-body channel fading, and channel estimation error. Simulation results verify the theoretical analysis and show that both SDSD and SDJD can achieve higher channel capacity and lower BER than the CD and ED receivers with perfect channel estimation, while SDJD can achieve the lowest BER with imperfect channel estimation.

Intra-body communication (IBC) plays a significant role in future health care systems. This article proposes a pulse-based ultrasonic index modulation (UsIM) to enable high data rate and low power IBCs. Two grouping-based UsIM strategies, namely ultrasonic grouped-chip indexed modulation (UsCIM) and ultrasonic chip-group indexed modulation (UsGIM), are proposed to implement multiple access for intra-body networks (IBNs). UsCIM uses the chip indices in a group to convey additional information bits for a biosensor node, while UsGIM uses the group indices and each node occupies one chip in a group. Three receivers, including the maximum likelihood (ML), the maximum ratio combination (MRC), and the energy detection (ED) receivers, are then proposed and studied. The theoretical bit-error rate (BER) upper bound for the ML receiver, and theoretical BER expressions for the MRC and ED receivers are derived. The system performance metrics in terms of complexity, throughput and energy efficiency are analyzed. The analytical and simulation results reveal that UsGIM achieves a lower BER than UsCIM under channel fading and multipath effect. Both UsCIM and UsGIM achieve higher throughput and energy efficiency than the existing IBC systems, and both strategies are important for energy-constrained implanted IBNs.

Massive machine-type communications (mMTC) is one of the three generic services for the fifth-generation (5G) wireless communications system. To utilize fully the high rate transmission feature of the 5G system to support massive MTC devices (MTCDs), we propose a group-based random access and data transmission scheme, where the data packets of MTCDs are first aggregated by the MTC gateways (MTCGs) and then forwarded to the base station. The access process of the MTC network is divided into two phases, namely the intra-group transmission phase and MTCG forwarding phase. The entire resources are also partitioned for the two phases. We employ the discrete-time nonhomogenous Markov model to characterize the joint queue-length evolution of multiple MTCGs, which cannot be analyzed directly due to the exponential complexity and time-nonhomogeneity. To facilitate the analysis, we approximately decompose the joint nonhomogenous queue-length evolution process into multiple independent nonhomogenous queue-length evolution processes with the same state transition probabilities. Then, we establish an equivalent single queue-length evolution based homogenous Markov chain by constructing a virtual queue and determine the corresponding stationary distribution by using the Gauss-Jordan elimination method. An optimization problem is formulated to maximize the average network throughput subject to the constraints on the resource partition for the two phases and the MTCG forwarding threshold. By developing a modified differential evolution algorithm, we provide an efficient solution to the formulated problem, which can be arbitrarily close to the optimal solution. Simulation results show that the proposed scheme can efficiently improve the network performance over the existing schemes.

Novel sparse reconstruction algorithms are proposed for beamspace channel estimation in massive multiple-input multiple-output systems. The proposed algorithms minimize a least-squares objective having a nonconvex regularizer. This regularizer removes the penalties on a few large-magnitude elements from the conventional$\ell _{1}$-norm regularizer, and thus it only forces penalties on the remaining elements that are expected to be zeros. Accurate and fast reconstructions can be achieved by performing gradient projection updates within the framework of difference of convex functions (DC) programming. A double-loop algorithm and a single-loop algorithm are proposed via different DC decompositions, and these two algorithms have distinct computational complexities and convergence rates. An extension algorithm is further proposed by designing new step sizes for the single-loop algorithm. The extension algorithm has a faster convergence rate and can achieve approximately the same level of accuracy as the proposed double-loop algorithm. Numerical results show significant advantages of the proposed algorithms over existing reconstruction algorithms in terms of reconstruction accuracies and runtimes. Compared with the benchmark channel estimation approaches, the proposed algorithms can achieve smaller channel reconstruction error and higher achievable spectral efficiency.

A novel dimming control scheme, termed as generalized dimming control (GDC), is proposed for visible light communication (VLC) systems. The basic idea of GDC is to exploit adaptively time, spatial and signal domain resources for enhancing communication performance under practical illumination constraints. In particular, to satisfy the illumination constraints, an incremental algorithm for index mapping is first proposed to achieve target optical power and uniform illumination. Next, GDC having the optimal activation pattern is investigated to improve the bit-error rate (BER) performance of VLC. In particular, the BER performance of GDC is analyzed using the union bound technique. Based on the analytical BER bound, the optimal activation pattern of GDC scheme having the minimum BER criterion (GDC-MBER) is obtained by exhaustively searching all conditional pairwise error probabilities. However, since GDC-MBER requires high search complexity, two low-complexity GDC schemes having the maximum free distance criterion (GDC-MFD) are proposed. The first low-complexity GDC-MFD scheme, termed as GDC-MFD1, is derived by a lower bound of the free distance using the Rayleigh-Ritz theorem. The second low-complexity GDC-MFD scheme, termed as GDC-MFD2, is proposed to reduce further the computation by exploiting the time-invariance characteristics of the VLC channel. Simulation and numerical results show that GDC-MBER, GDC-MFD1 and GDC-MFD2 have similar BER performance, and they can achieve more than 3 dB SNR gains compared with conventional dimming control schemes for a BER of 10-4and a dimming level of 50%.

Because of the potential applications in quantum information processing tasks, discrimination of binary coherent states using generalized Kennedy receiver with maximum a posteriori probability (MAP) detection has attracted increasing attentions in recent years. In this paper, we analytically study the performance of the generalized Kennedy receiver having optimally displaced threshold detection (ODTD) in a realistic situation with noises and imperfect devices. We first prove that the MAP detection for a generalized Kennedy receiver is equivalent to a threshold detection in this realistic situation. Then we analyze the properties of the optimum threshold and the optimum displacement for ODTD, and propose a heuristic greedy search algorithm to design these parameters. We prove that the ODTD asymptotically approaches the Kennedy receiver with threshold detection when the signal power is large, and we also clarify the connection between the generalized Kennedy receiver with threshold detection and the one-port homodyne detection. Numerical results show that the proposed heuristic greedy search algorithm can obtain a lower and smoother error probability than the existing methods.

In maritime wireless communications, unmanned surface vehicles (USVs) can improve coverage and transmission performance due to their agile maneuverability and flexible deployment. This paper considers a USV-enabled maritime wireless network, where a USV is employed to assist the communication between the terrestrial base station and ships. Considering the maritime environment characteristics and earth curvature, we establish the systematic USV kinetics and information transmission models. To guarantee fairness, we aim to maximize the minimum expected throughput overall ships by jointly optimizing the trajectory and communication resource allocation, subject to the constraints of the USV kinetics, safe sailing, breakpoint distances, line-of-sight links, resource allocation, and information-causality. Due to the complexity of the maritime two-ray signal propagation model, we propose a channel approximation method to find an upper bound of the throughput for the original problem. By the problem decomposition, two sub-problems are derived and solved iteratively using successive convex approximation and interior-point methods. Simulation results confirm the effectiveness of the proposed method and show that USV can significantly improve transmission performance in maritime wireless networks.

A free-space optical communication system using non-mode-selective photonic lantern (PL) based coherent receiver is studied. Based on the simulation of photon distribution, the power distribution at the single-mode fiber end of the PL is quantitatively described as a truncated Gaussian distribution over a simplex. The signal-to-noise ratios (SNRs), bit-error rate (BER), and outage probability of PL based receiver using selection combining (SC), equal-gain combining (EGC), and maximal-ratio combining (MRC) over Gamma-Gamma turbulence channels are analyzed and compared with the single-mode fiber (SMF) receiver and multimode fiber (MMF) receiver. We demonstrate that EGC is the most suitable combining for PL based receiver because the PL power distribution has limited influence on the BER and outage probability when EGC is used and can greatly affect the BER and outage probability when SC is used. Numerical results show that PL based receiver with EGC requires 6 dB less SNR than the SMF receiver and 5.5 dB less SNR than the MMF receiver at BER of 10−6in moderate turbulence.

A high-coverage algorithm termed enhanced camera assisted received signal strength ratio (eCA-RSSR) positioning algorithm is proposed for visible light positioning (VLP) systems. The basic idea of eCA-RSSR is to utilize visual information captured by the camera to estimate first the incidence angles of visible lights. Based on the incidence angles, eCA-RSSR utilizes the received signal strength ratio (RSSR) calculated by the photodiode (PD) to estimate the ratios of the distances between the LEDs and the receiver. Based on an Euclidean plane geometry theorem, eCA-RSSR transforms the ratios of the distances into the absolute values. In this way, eCA-RSSR only requires three LEDs for both orientation-free 2D and 3D positioning, implying that eCA-RSSR can achieve high coverage. Based on the absolute values of the distances, the linear least square method is employed to estimate the position of the receiver. Therefore, for the receiver having a small distance between the PD and the camera, the accuracy of eCA-RSSR does not depend on the starting values of the non-linear least square method and the complexity of eCA-RSSR is low. Furthermore, since the distance between the PD and camera can significantly affect the performance of eCA-RSSR, we further propose a compensation algorithm for eCA-RSSR based on the single-view geometry. Experiment results show that positioning errors of less than five centimeters is achievable for eCA-RSSR. Simulation results show that eCA-RSSR can achieve 80th percentile accuracy of about four centimeters and can improve the coverage ratio at low cost.

Multi-objective resource allocation is studied for edge-caching enabled fog-radio access network. Notably, joint maximization of the energy-efficiency (EE) and spectrum-efficiency (SE) and interference management are investigated for distributing contents from the cache-enabled fog access points (F-APs) and cloud base station (CBS) to the user devices (UDs). In our envisioned system, the UDs are grouped into multiple non-overlapping device-clusters based on their locations. A rate-splitting with common message decoding based transmission strategy is applied to enable UDs of each device-cluster to receive data from a suitably selected F-AP and CBS over the same radio resource blocks. To maximize system EE and SE jointly, a multi-objective optimization problem (MOOP) is formulated and it is solved in three stages. At first, by employing the$\epsilon $-constraint method, the MOOP is converted to an EE-SE trade-off optimization problem. Then, by leveraging iterative function evaluation based power control and generalized 3D-resource matching, the EE-SE trade-off optimization problem is solved and a novel resource allocation algorithm is proposed to obtain near-optimal Pareto-front for the proposed MOOP. To reduce the complexity of obtaining near-optimal Pareto-front, a sub-optimal resource allocation algorithm is proposed as well. Finally, a low-complexity algorithm is devised to select a suitable operating EE-SE pair from the obtained Pareto-front. The conducted simulations demonstrate that the proposed resource allocation schemes achieve substantial improvement of system EE and SE over the benchmark schemes.

Channel capacity bounds are derived for a point-to-point indoor visible light communications (VLC) system with signal-dependent Gaussian noise. Considering both illumination and communication, the non-negative input of VLC is constrained by peak and average optical intensity constraints. Two scenarios are taken into account: one scenario has both average and peak optical intensity constraints, and the other scenario has only average optical intensity constraint. For both two scenarios, we derive closed-from expressions of capacity lower and upper bounds. Specifically, the capacity lower bound is derived by using the variational method and the property that the output entropy is invariably larger than the input entropy. The capacity upper bound is obtained by utilizing the dual expression of capacity and the principle of “capacity-achieving source distributions that escape to infinity”. Moreover, the asymptotic analysis shows that the asymptotic performance gap between the capacity lower and upper bounds approaches zero. Finally, all derived capacity bounds are confirmed using numerical results.

Coordinated direct and relay transmission (CDRT) is a promising strategy to extend cell coverage. However, higher spectral efficiency (SE) cannot be guaranteed in CDRT due to the cooperative process. Thus, non-orthogonal multiple access (NOMA) based CDRT is investigated, where a relaying user assists a base station (BS) in communicating with a cell-edge user (CEU) while the BS directly communicates with a cell-center user (CCU). Specifically, a novel joint downlink-uplink adaptive scheme is proposed for NOMA-based CDRT to improve the SE further. The proposed scheme jointly designs uplink and downlink via NOMA and adaptive transmission to implement bidirectional communication. First, we study the performance of the proposed scheme under both perfect and imperfect successive interference cancellation (SIC), and closed-form expressions are derived for the outage probability, diversity order, outage throughput (OT), ergodic sum rate (ESR), and energy efficiency (EE). Then, an optimal power allocation algorithm using the segment optimization and a modified particle swarm optimization algorithm are proposed to maximize the ESR under perfect and imperfect SIC, respectively. Numerical results verify the superiority of the proposed scheme in terms of the ESR, OT, EE, and the outage performance of CEU. The effectiveness of the proposed power allocation algorithms is also validated.

Underwater wireless optical communication (UWOC) has huge potential for its high speed, low latency and reliable security. However, besides the channel impairments such as absorption and scattering, dynamic ocean environments can also degrade the system performance and even incur link interruptions. In this paper, we propose a 4 × 4 fixed-scale multiple-input multiple-output UWOC system to remove the impediment of link misalignment. The system employs two collimated lens to perform a mapping from each source to associated photodetector. The bit-error rate expression is also derived to evaluate the system performance. Numerical simulations illustrate that the absorption and scattering induced path loss can be alleviated by this system when the scattering angles are relatively small and the communication range is moderate. Furthermore, the system diminishes the sensitivity of underwater vehicles to dynamic ocean environments. By taking the window truncation effect into consideration, we quantify the enhancement of system robustness to link misalignment.

In this paper, we consider the impact of turbulence on performance of UWOC systems and investigate capacity and bit-error rate (BER) of underwater wireless optical links under weak and strong turbulence by deriving the expressions of average capacity and BER. Numerical results suggest that turbulence degrades both capacity and BER performance as expected. This work provides a theoretical analysis tool for system design and performance evaluation of UWOC systems.

This paper investigates the uplink space division multiple access in a satellite-aerial-terrestrial network, where multiple users access to satellite with the aid of a unmanned aerial vehicle, and the two hops adopt radio frequency (RF) and free space optical (FSO), respectively. We first formulate an optimization problem to maximize the ergodic sum rate (ESR) of the considered system. Then a statistical channel state information (CSI)-based beamforming (BF) scheme is proposed to solve the non-convex ESR maximization problem. The main advantage of the proposed method is that only statistical CSI is used to obtain BF vectors so that a low implementation complexity can be achieved. Then, by assuming that the RF and FSO links undergo Nakagami-m and Gamma-Gamma fading, respectively, closed-form expression for ESR of the considered system is derived. Simulation results validate the derived expression and confirm the effectiveness of the proposed BF scheme.

Ultrasonic intra-body communication (IBC) is a promising enabling technology for many future healthcare applications, due to the low attenuation and medical safety of ultrasonic waves for human body. This paper proposes a splitting-detection joint-decision (SDJD) receiver structure for ultrasonic pulse-based IBCs. The core idea is to split the received signal into two steams for the coherent-detection (CD) and energy detection (ED), and then the two detection outputs are summed up as joint decision variables to estimate the received signals. The theoretical channel capacity and bit-error rate (BER) expressions are derived, considering receive noise, tissue attenuation, and channel fading in intra-body channels. Both simulation and theoretical results show that the SDJD receiver outperforms the pure CD, ED, and splitting-detection separate-decision receivers in terms of BER performance. In addition, the proposed receiver has a low complexity requirement on the hardware and decision algorithm. Therefore, SDJD is an important structure to receive signals correctly and efficiently for ultrasonic IBCs.

Sparse channel estimation for massive multiple-input multiple-output systems has drawn much attention in recent years. The required pilots are substantially reduced when the sparse channel state vectors can be reconstructed from a few numbers of measurements. A popular approach for sparse reconstruction is to solve the least-squares problem with a convex regularization. However, the convex regularizer is either too loose to force sparsity or lead to biased estimation. In this paper, the sparse channel reconstruction is solved by minimizing the least-squares objective with a nonconvex regularizer, which can exactly express the sparsity constraint and avoid introducing serious bias in the solution. A novel algorithm is proposed for solving the resulting nonconvex optimization via the difference of convex functions programming and the gradient projection descent. Simulation results show that the proposed algorithm is fast and accurate, and it outperforms the existing sparse recovery algorithms in terms of reconstruction errors.

It has been shown that long pilots should be employed in a Poisson channel with inter-symbol interference to obtain accurate channel estimate, which may reduce the transmission efficiency. In this paper, an iterative data-aided channel estimation and signal detection algorithm is proposed with significant shortened overhead but can approach the bit-error rate performance under perfect channel state information. In this algorithm, periodic pilots are not required and only one short pilot sequence is utilized to obtain a rough initial channel estimate, which will be strengthened and updated by iteratively employing the blockwise data symbols as additional pilots. It is proved that treating the detected data symbols as pilots is a biased estimation, where the bias is proportional to the bit-error rate. Numerical results show that the proposed approach employing only 20 initial pilots can approach the optimal bound. The same bound needs to be achieved by assigning at least 500 pilots using periodic pilot based channel estimation.

Intelligent reflecting surface (IRS) is a new technique to improve the ergodic capacity in wireless networks. IRS consists of a large number of passive elements which digitally manipulate electromagnetic waves, and thus can act as a passive precoder in the communication. This paper introduces the IRS to millimeter wave (mmWave) multiple-input-multiple-output (MIMO) systems. Specifically, we consider a joint design of hybrid precoders at the base station (BS) and the passive precoder at the IRS to maximize the ergodic capacity of the system. In particular, since the instantaneous channel state information (CSI) of the BS-IRS link and the IRS-user link is challenging to obtain in practice, the statistical CSI is exploited for the joint hybrid and passive precoder design. However, such a design problem is challenging to solve due to the non-convexity. Thus, the block-coordinate-descent based algorithms are proposed to solve the problem efficiently. Simulation results demonstrate that, compared with the traditional systems without IRSs, the joint design of hybrid and passive precoding improves the ergodic capacity significantly. The results also show that adding some low-cost reflector elements at the IRS can help reduce the number of high-cost RF chains in the BS of the IRS-assisted mmWave MIMO systems.

Hybrid architecture is a promising candidate precoding scheme to balance the achievable spectral efficiency and power consumption in millimeter wave (mmWave) multiple input multiple output (MIMO) systems. A practical partially dynamic subarray-connected architecture is developed to improve the transmission performance. In this proposed architecture, the set of antennas in each subarray is fixed, but the subarrays connected to each radio frequency chain are dynamic. Moreover, we study how to optimize jointly the partially dynamic subarray structure and the hybrid precoders under the constraints of total transmit power and hardware limitation. This joint optimization problem is divided into two sub-problems. For the first sub-problem, a low complexity algorithm is proposed to determine the partition of subarrays using the long-term spatial channel covariance. Then, the penalty decomposition method is adopted to design the hybrid precoders. Numerical results verify that the partially dynamic subarray design algorithm offers one or two orders of computation time saving compared with the existing algorithms. Moreover, the proposed structure achieves spectral efficiency gain using less hardware, compared with the fully dynamic subarray structure adopted in the existing algorithms.

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

To provide high data rate for high-speed trains (HSTs), it is required to use emerging wireless communication systems, such as the fifth generation (5G). An asymmetric 5G mobile relay system is investigated for HSTs, where the mobile relay is deployed at the HST to avoid high penetration loss of the direct link between the base station (BS) and the users (TIE) inside carriages. The sub-6GHz frequency is utilized for the BS-relay link while the relay-TIE link adopts the millimeter wave frequency. Therefore, the BS-relay link experiences κ-μ fading and the relay-TIE link experiences static fluctuating two-ray fading. Moreover, the channel aging effect is considered due to the mobility of HST. For the considered system, we first study the exact statistical characterizations of the end-to-end signalto-noise ratios. Then, we derive exact closed-form expressions for key performance metrics, such as outage probability, average bit-error rate, and average achievable rate per unit bandwidth. The significant effects of channel aging, system and channel parameters on the mobile relay system are revealed from theoretical analysis and are further illustrated by simulation results. Our investigation reveals that the mobile relay system is a promising network architecture for HST communications and can provide steady and high-speed data provisioning to HST passengers against the significant bottleneck of channel aging.

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

User scheduling, beamforming and energy coordination are investigated in smart-grid powered cellular networks (SGPCNs), where the base stations are powered by a smart grid and natural renewable energy sources. Heterogeneous energy coordination is considered in SGPCNs, namely energy merchandizing with the smart grid and energy exchanging among the base stations. A long-term grid-energy expenditure minimization problem with proportional-rate constraints is formulated for SGPCNs. Since user scheduling is coupled with the beamforming vectors, the formulated problem is challenging to handle via standard convex optimization methods. In practice, the beamforming vectors need to be updated over each slot according to the channel variations. User scheduling needs to be updated over several slots (frame) since the frequent scheduling of user equipment can cause reliability issues. Therefore, the Lyapunov optimization method is used to decouple the problem. A practical two-scale algorithm is proposed to schedule users at each frame, and obtain the beamforming vectors and amount of exchanged natural renewable energy at each slot. We prove that the proposed two-scale algorithm can asymptotically achieve the optimal solutions via tuning a control parameter. Numerical results verify the performance of the proposed two-scale algorithm.

Strong correlation of link gains over multiple wavelengths has been demonstrated in non-line-of-sight optical wireless scattering communication. In this paper, we further demonstrate the strong correlation of the channel impulse responses for different wavelengths. A novel channel estimation method is proposed based on such correlation, where only certain reference wavelengths are assigned with pilot signals. On each channel assigned by the pilot signal, we adopt an improved least-squares method to estimate the channel tap coefficients and the tap length. The channel tap coefficients for other wavelengths can be estimated based on the inter-wavelength correlation. Under the proposed channel estimation, the channel estimation accuracy can be improved as shown by the simulations. We further propose an optimization method for the correlation-based channel estimation pilot pattern based on the receiver channel state feedback. Simulation results show that optimized pilot pattern can achieve significant performance gain over the fixed pilot pattern. Moreover, the channel estimation performance is insensitive to the channel state feedback frequency.

Non-orthogonal multiple access (NOMA) and cognitive radio (CR) are envisioned as promising solutions to achieve high spectral efficiency for future wireless networks. This work investigates the outage performance of an overlay cognitive NOMA system with imperfect successive interference cancellation (SIC). The outage probability of primary user and secondary user are derived in closed forms. To obtain further insights, asymptotic expressions of outage probability and system throughput are evaluated when the transmit signal-to-noise ratio approaches infinity. An optimal power allocation coefficient is provided to maximize the system throughput. Moreover, the overlay cognitive NOMA system is compared with the underlay CR system and the overlay cognitive orthogonal multiple access (OMA) system in terms of outage probability. Finally, the performance analysis is validated by simulations. The simulation results demonstrate that the outage performance and system throughput of overlay cognitive NOMA system are superior to those of overlay cognitive OMA system and underlay CR system when the imperfect SIC satisfies certain conditions.

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

Transmit antenna selection (TAS) is an emerging technology in physical layer security. To provide new insights into the achievable secrecy performance of TAS in practical communication systems, this paper investigates the average secrecy rate (ASR) and secrecy outage probability (SOP) under practical modulation schemes in TAS aided multiple-input multiple-output multiple-antenna eavesdropper (MIMOME) wiretap channels over Rayleigh fading. Particularly, this research concentrates more on the square M-ary quadrature amplitude modulation (M-QAM). Furthermore, in the considered MIMOME channel, a single antenna is selected to transmit the secret message, and selection combining (SC) or maximal-ratio combining (MRC) is utilized at the legitimate receiver and the eavesdropper. Based on this system model, novel expressions for the ASR and SOP are formulated to characterize the secrecy performance of the finite-alphabet driven MIMOME channel. Besides exact analysis, an asymptotic analysis is performed using the considered performance metrics in high signal-to-noise ratio (SNR) regime. Theoretical analyses suggest that the asymptotic ASR and SOP converge to finite constants in high SNR regime due to the discrete constellation constraint, and we find that the asymptotic behaviour of discrete inputs differs from that of Gaussian inputs. Furthermore, we derive concise expressions to characterize the rate of convergence (ROC) of the ASR and SOP, respectively. To unveil more system design insights, we discuss the relationship between the ROC and several important system parameters such as the antenna number and the modulation order.

Secrecy transmission is investigated for a cooperative jamming scheme, where a multi-antenna jammer generates artificial noise (AN) to confuse eavesdroppers. Two kinds of eavesdroppers are considered: passive eavesdroppers who only overhear the legitimate information, and active eavesdroppers who not only overhear the legitimate information but also jam the legitimate signal. Existing works only treat the passive and active eavesdroppers separately. Different from the existing works, we investigate the achievable secrecy rate in presence of both active and passive eavesdroppers. For the considered system model, we assume that the instantaneous channel state information (CSI) of the active eavesdroppers is available at the jammer, while only partial CSI of the passive eavesdroppers is available at the jammer. A new zero-forcing beamforming scheme is proposed in the presence of both active and passive eavesdroppers. For both the perfect and imperfect CSI cases, the total transmission power allocation between the information and AN signals is optimized to maximize the achievable secrecy rate. Numerical results show that imperfect CSI between the jammer and the legitimate receiver will do more harm to the achievable secrecy rate than imperfect CSI between the jammer and the active eavesdropper.

Machine-type communications (MTC) technology, which enables direct communications among devices, plays an important role in realizing Internet-of-Things. However, a large number of MTC devices can cause severe collisions. As a result, the network throughput is decreased and the access delay is increased. To address this issue, a throughput-oriented non-orthogonal random access (NORA) scheme is proposed for massive machine-type communications (mMTC) networks. Specifically, by employing the technique of tagged preambles (PAs), multiple MTC devices (MTCDs) choosing the same PA can be distinguished and regarded as a non-orthogonal multiple access (NOMA) group, which enables multiple MTCDs to share the same physical uplink shared channel for transmissions by multiplexing in the power domain. The Sukhatme's classic theory and the characteristic function approach are adopted to formulate an optimization problem. The aim is to maximize the throughput subject to the constraints on the power back-off factor, the number of MTCDs included in a NOMA group, and the successful transmission probability. Based on the particle swarm optimization (PSO) algorithm, the formulated optimization problem is efficiently solved. The derived solution can be used to adjust the access class barring factor such that more MTCDs can obtain the access opportunities. Moreover, a low-complexity suboptimal solution is also developed, which can achieve near-PSO performance under high data rate requirement. Simulation results show that the proposed scheme can efficiently improve the network performance and comparison is made with the existing schemes.

Hybrid architecture is a promising candidate precoding scheme to balance the achievable spectral efficiency and power consumption in millimeter wave multiple input multiple output systems. A practical partially dynamic subarray-connected architecture is developed to improve the transmission performance. In this proposed architecture, the set of antennas in each subarray is fixed, but the subarrays connected to each radio frequency chain are dynamic. Moreover, we study how to optimize jointly the partially dynamic subarray structure and the hybrid precoders under the constraints of total transmit power and hardware limitation. This joint optimization problem is divided into two sub-problems. For the first sub-problem, a low-complexity algorithm is proposed to determine the partition of subarrays using the long-term spatial channel covariance. Then, the penalty decomposition method is adopted to design the hybrid precoders. Numerical results verify that the partially dynamic subarray design algorithm offers one or two orders of computation time saving compared with the existing algorithms, and the hybrid precoding algorithm outperforms the existing algorithms in terms of spectral efficiency. Moreover, compared with the fully dynamic subarray structure adopted in the existing algorithms, the proposed structure achieves spectral efficiency gain and energy efficiency gain using less hardware.

An optical quantum communication system with receiver diversity is proposed for free-space communication using the optical combining technique and a generalized Kennedy receiver. The quantum channel model of the proposed system is established using the P -representation in the presence of both turbulence and thermal noise. A conditional dynamics based Kennedy receiver with threshold detection is proposed to mitigate the influence of both turbulence and thermal noise. The error probabilities for different types of generalized Kennedy receiver, i.e., the Kennedy receiver, the Type-II receiver, and the conditional dynamics based Kennedy receiver, are analytically studied and the lower bounds for these error probabilities are obtained. Numerical results show that both the Kennedy receiver and the Type-II receiver fail in the presence of either turbulence or thermal noise. The performance of the proposed conditional dynamics based Kennedy receiver with threshold detection can surpass the standard quantum limit given by the homodyne receiver in either weak turbulence or small thermal noise.

Monte-Carlo models are analyzed for multiple scattering channels in optical wireless communications. It is demonstrated that the system impulse response function can be obtained by Monte-Carlo integration model. The convergence performance for the Monte-Carlo integration model is analyzed and improved by introducing different sampling methods. The simulation results show that the gamma function model for channel impulse response function can only be applied to the cases where the common volume between the transmitted light beam and the receiving field-of-view is open. Numerical simulation suggests that for a three-order scattering case, the computation efficiency of the Monte-Carlo integration model based on partial importance sampling is about 12 times of the original Monte-Carlo integration model based on uniform sampling, and 5.6 times of the widely used Monte-Carlo simulation model. The numerical results also show that the Monte-Carlo integration model based on partial importance sampling has higher computation efficiency than the Monte-Carlo simulation model in a higher-order scattering communication scenario.

Heterogeneous networks provide flexible deployments for operators to improve spectrum efficiency and increase coverage. However, driven by new generation wireless devices, the exponential increase of data traffic has triggered new challenges of wireless networks to meet the green communications requirement. Therefore, energy-efficient design has emerged as a promising technique in heterogeneous networks. In this paper, we investigate the energy efficiency maximization problem for downlink transmissions by jointly considering user association and power allocation in a two-tier heterogeneous network with multiple small cells. We first consider a system model without the co-channel interference between small cells. The energy efficiency maximization problem is formulated under certain prescribed quality-of-service requirement and maximum power limit constraint. The original optimization problem is non-convex and NP-hard, and it involves integer programming. We first relax the formulate problem into a continuous one and decouple it into user association and power allocation subproblems. A gradient-based algorithm is used to solve the power allocation problem. Then, an iterative joint user association and power allocation algorithm is proposed to achieve the maximum energy efficiency. Moreover, we consider a more sophisticated system with the limited bandwidth resource, in which multiple small base stations require to share the same frequency band to serve users, and the co-channel interference is introduced. Inspired by the original Dinkelbach method, we use a lower bound approximation and the Lagrangian approach to derive a closed-form expression of power allocation, which reduces the computational complexity. Simulation results show that the proposed algorithms have improved energy efficiency when compared with other the existing schemes.

We investigate adaptive transmission schemes over a hybrid radio frequency (RF)/free space optical (FSO) backhaul network for connecting macro-cell base station with small-cell base station through multiple parallel buffer-aided relay nodes. The proposed adaptive transmission schemes improve delay-throughput trade-off of backhaul network by exploiting the degrees-of-freedom over both RF and FSO links and capturing quality-of-service (QoS) requirements. We study two different system configurations, namely, single-carrier and multi-carrier hybrid RF/FSO systems. Our goal is to maximize the constant data arrival rate to the network such that the total queue occupancy in the network is bounded with certain acceptable queue-length bound violation probability. Towards this goal, we formulate novel optimization problems in order to maximize the end-to-end effective capacity over RF and FSO links of both system configurations. Efficient solutions are derived by employing the Lagrangian optimization technique. Some interesting insights are also revealed by considering special cases on link conditions and QoS requirements. Simulation results provide the following two observations: (i) the proposed adaptive transmission scheme outperforms the conventional switch-over hybrid RF/FSO transmission, especially in clear to moderately harsh weather conditions; and (ii) compared to several non-buffer-aided and buffer-aided relaying with non-adaptive transmission schemes, the proposed adaptive transmission scheme substantially improves the supportable data arrival for the hybrid RF/FSO backhaul network.

The model-based power allocation has been investigated for decades, but this approach requires mathematical models to be analytically tractable and it has high computational complexity. Recently, the data-driven model-free approaches have been rapidly developed to achieve near-optimal performance with affordable computational complexity, and deep reinforcement learning (DRL) is regarded as one such approach having great potential for future intelligent networks. In this paper, a dynamic downlink power control problem is considered for maximizing the sum-rate in a multi-user wireless cellular network. Using cross-cell coordinations, the proposed multi-agent DRL framework includes off-line and on-line centralized training and distributed execution, and a mathematical analysis is presented for the top-level design of the near-static problem. Policy-based REINFORCE, value-based deep Q-learning (DQL), actor-critic deep deterministic policy gradient (DDPG) algorithms are proposed for this sum-rate problem. Simulation results show that the data-driven approaches outperform the state-of-art model-based methods on sum-rate performance. Furthermore, the DDPG outperforms the REINFORCE and DQL in terms of both sum-rate performance and robustness.

In the small-cell networks with multiple-input-single-output broadcasting (MISO-BC) backhauls, the joint dirty-paper coding and power control are investigated for the {MISO-BC} backhauls and access links in order to minimize the system transmit power. Considering the proportional rates of MISO-BC backhauls and flow-conservation constraints, the formulated optimization problem is {non-convex}. Moreover, the formulated problem couples the precoding vectors with the power-control variables. In order to handle the {non-convex} optimization problem and decouple the backhaul and access links, the structure of the formulated problem is investigated such that the optimal precoding vectors and optimal power-control variables are independently obtained. Moreover, the optimal precoding vectors are obtained in closed-form expressions. Simulation results are used to show the performance improvement over the benchmark scheme.

In the small-cell networks (SCNs) with multiple small-cell base stations (ScBSs), the joint design of beamforming vectors, user scheduling and ScBS sleeping is investigated with the constraints on proportional rate. A long-term grid-energy expenditure minimization problem is formulated for the considered SCNs, which are powered by the smart grid and natural renewable energy. Since the scheduled user indicators are coupled with the beamforming vectors, the formulated problem is challenging to handle. In order to decouple the beamforming vectors from the scheduled user indicators, the Lyapunov optimization technique is used. As a result, a practical two-scale algorithm is proposed to allocate the user scheduling indicators and ScBS sleeping variables at the coarse-grained granularity (frame) as well as obtain the beamforming vectors at the fine-grained granularity (slot). Numerical results are used to verify the performance of the proposed two-scale algorithm.

Strong correlation of link gains over multiple wavelengths has been demonstrated in non-line-of-sight optical wireless scattering communication. In this paper, we further demonstrate that the channel impulse responses for different wavelengths are also strongly correlated. Thus, a novel channel estimation method based on such correlation over multiple wavelengths is proposed, where only certain reference wavelengths are assigned pilot signal. On each channel with assigned pilot signal, we adopt an improved least-squares channel estimation method to estimate the tap parameters and the tap length (or the number of taps). The channel tap coefficients on other wavelengths can be estimated based on the correlation. Under the proposed channel estimation, the channel estimation accuracy can be improved as shown by the simulations. Moreover, under the same total channel estimation overhead, the accuracy of the proposed channel estimation first increases and then decreases with the number of reference wavelengths.

In this paper, when transmit antenna selection (TAS) and maximal ratio combining (MRC) schemes are deployed for secrecy enhancement, the impact of channel correlation on secrecy performance is investigated over Rayleigh fading channels. Specially, both the exact and asymptotic secrecy outage probability (SOP) for TAS/MRC are derived based on a novel correlation model, where the correlated legitimate channels and eavesdropper channels are modeled as a set of conditional independent channel gains. Moreover, the lower bound of SOP is obtained in high signal-to-noise ratio (SNR) region. Finally, simulation results verify our analysis, and show that the channel correlation between the legitimate receiver and the eavesdropper is beneficial to the SOP at high SNR.

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

The joint design of beamforming vector and artificial noise covariance matrix is investigated for the multiple-input-single-output-multiple-eavesdropper simultaneous wireless information and power transferring (MISOME-SWIPT) systems. In the MISOME-SWIPT system, the base station delivers information signals to the legitimate user equipment and broadcasts jamming signals to the eavesdroppers. A secrecy energy efficiency (SEE) maximization problem is formulated for the considered MISOME-SWIPT system with imperfect channel state information, where the SEE is defined as the ratio of sum secrecy rate over total power consumption. Since the formulated SEE maximization problem is non-convex, it is first recast into a series of convex problems in order to obtain the optimal solution with a reasonable computational complexity. Two suboptimal solutions are also proposed based on the heuristic beamforming techniques that trade performance for computational complexity. In addition, the analysis of computational complexity is performed for the optimal and suboptimal solutions. Numerical results are used to verify the performance of proposed algorithms and to reveal practical insights.

We investigate resource optimization for the downlink cloud small cell network, where the baseband unit pool communicates with the buffer-aided small remote radio heads (SRRHs) through free space optical fronthaul, and SRRHs transmit to the user equipments (UEs) by using time division multiplexing-based millimeter wave access links. Our objective is to maximize the supportable aggregate data arrival rate in the network by exploiting the inter-dependence of fronthaul and access links. Toward this objective, we consider maximum acceptable end-to-end queue-length bound violation probability constraints, load-balancing constraints in the access link, fronthaul link selection constraints, and transmit power budget constraints of fronthaul and access links. Since the joint fronthaul and access link optimization is a non-convex and combinatorial problem, we develop an iterative solution by decomposing the original optimization problem into two sub-problems. The first sub-problem optimally obtains fronthaul and access link power allocation and fronthaul link selection by using Lagrangian dual decomposition and canonical one-to-one matching techniques. By employing the Lagrangian dual decomposition and alternating optimization techniques, the second sub-problem obtains near optimal data arrival rate for each UE, UE-SRRH associations, fronthaul rate allocation among the transmitted data for the UEs, and the transmission duration scheduling in millimeter wave access link. An algorithm of polynomial complexity is developed in order to determine the supportable aggregate data arrival rate by considering the statistical quality-of-service requirements, and its convergence is proved. The simulation results depict that the proposed scheme significantly improves the aggregate data arrival rate over several benchmark schemes.

The problem of full-duplex primary user (PU) signal space extraction is studied for cognitive radios, and polarization Jones vector distance (PJVD) under generalized likelihood ratio test (GLRT) paradigm is used as the extraction metric. Based on the orientation and magnitude difference of the signal samples from different PUs, the GLRT-based PJVD (GLRT-PJVD) method can efficiently detect each PU's signal space by calculating the PJVD between any two samples. It is found that the samples from the same PU experience smaller PJVD in comparison with those from different PUs. Due to the Gaussian distributed noise, the resulting PJVD is non-central chi-squared distributed. Based on the distribution, the extraction metric is constructed as the likelihood ratio of PJVD. A closed-form expression of the optimal threshold is derived under the large argument assumption for amplitude modulated primary signals. Theoretical analysis shows that the proposed GLRT-PJVD method outperforms the polarization similarity-based method in terms of both error probability and computational complexity. It is revealed that the performance gain is proportional to the SUs' antenna number, the PUs' polarization angle, and the signal amplitude difference. The analytical results are verified by the Monte Carlo simulation, and the performance of GLRT-PJVD is validated.

Power allocation for optical wireless scattering communication over broad spectra is studied based on link gain correlation. We formulate two types of power allocation problems that aim to maximize the transmission rate and to minimize the linear minimum mean square error of detection. Two scenarios of power allocation problems are considered where the parallel channels are aggregated for single-user transmission and separated for different users. We solve the power optimization problems and show that the power allocation remains fixed if the link gain ratios are uniform over the broad spectra. Furthermore investigations show that for single-user transmission optimization, under imperfectly uniform link gain ratios, the optimal power allocation for one link gain realization performs close to optimality for other link gain realizations. Numerical results show that for the single-user transmission with parallel channels, fixed power allocation incurs negligible losses in terms of transmission rate, and signal distortion. Such a finding can significantly reduce the amount of channel feedback for single-user transmission optimization. However, for the multi-user transmission, these performance losses are noticeable.

Simultaneous wireless information and power transfer (SWIPT) is a promising technique to address the energy scarcity problem in heterogeneous cellular networks (HCNs). Since information security is a critical issue in the SWIPT HCNs, we consider the secrecy beamforming design for a multiple-input single-output HCN with one macrocell base station (BS) and multiple femtocell BSs. In the considered system, all BSs send confidential messages to information receivers and energy signals to energy receivers (ERs). The ERs have the potential to wiretap the confidential messages. Consequently, by taking fairness into account, we propose a sum logarithmic secrecy rates maximization beamforming design problem under the energy harvesting constraints for ERs. The formulated design problem is nontrivial to solve due to the nonconvexity in the objective and the constraints. To tackle the design problem, a semidefinite relaxation and successive convex approximation-based centralized beamforming design algorithm is proposed. Our analysis reveals that using single-stream beamforming to transmit confidential messages does not cause any loss of optimality. Moreover, an alternating direction method of multipliers-based distributed beamforming design is also proposed. Simulation results demonstrate the effectiveness of the proposed algorithms.

In this paper, considering both the antenna correlation and the channel correlation between the legitimate receiver and the eavesdropper, we comprehensively investigate the secrecy performance for transmit antenna selection and maximal ratio combining (TAS/MRC) scheme in a multi-input multi-output multi-antenna eavesdropper (MIMOME) system. Different from the existing works, the correlated legitimate channels and correlated eavesdropper channels experience Nakagmai-${m}$fading with distinct fading parameters, and are modeled as a combination of conditionally independent channel gains and independent channel gains. By this novel correlation representation, the combined effect of antenna correlation and channel correlation between the legitimate receiver and the eavesdropper on secrecy performance is quantified via the secrecy outage probability (SOP) under a more general fading circumstance. In addition, two special cases of correlated channels are addressed in detail. Finally, simulations are performed to verify our analysis. The analysis and simulation results show that at high signal-to-noise ratio (SNR), antenna correlation at the legitimate receiver can improve the SOP performance for the correlated MIMOME wiretap channels, and this finding is contrary to the conventional wisdom that antenna correlation degrades the SOP performance.

Uplink transmission is an issue for visible light communications due to the unpleasant irradiance from the source light when placed close to the users. To overcome this problem, this paper proposes the use of relays to lower the required source optical power. A popular multi-carrier modulation scheme, termed direct-current biased optical orthogonal frequency division multiplexing, is employed to achieve high spectral efficiency. In addition, both amplitude-and-forward (AF) and decode-and-forward (DF) protocols are used. The theoretical models of AF and DF protocols are also obtained and verified by simulations. To minimize the source optical power while satisfying reliable communications, this paper formulates the associated optimization problems for AF and DF protocols. Exhaustive search is first used to obtain the optimal configuration for the system. As exhaustive search requires high computation efforts and can be time-consuming, two low-complexity suboptimal designs for AF and DF protocols are then proposed, and the proposed suboptimal designs can approximate the performance of exhaustive search in high signal-to-noise ratio regions. Numerical and experimental results indicate that when compared with the counterpart without a relay, the proposed relay-assisted system requires much lower source optical power under the constraints of reliable transmissions.

It is challenging to quantify the left tail behavior of the cumulative distribution function of a sum of lognormal random variables, especially when they are correlated and non-identically distributed. The mathematical intractability hampers the analysis on diversity receptions over lognormal fading channels. In this paper, a novel asymptotic technique is exploited to study non-identically distributed dual-branch lognormal fading channels with correlation. Closed-form asymptotic outage probability expressions are derived for maximum-ratio combining (MRC), equal-gain combining (EGC), and selection combining (SC). Three counterintuitive facts are revealed: 1) one of the two channels contributes no asymptotic performance gain to the diversity reception systems under certain conditions, implying that a link can be discarded without causing asymptotic performance loss; 2) SC outperforms EGC under certain conditions; and 3) the outage probability of SC is arbitrarily close to that of MRC under certain conditions. The new result reveals insights into the long-standing problem of asymptotic analysis for correlated lognormal fading channels, paving the way for analysis on more general channel models. This paper also provides an efficient approach to evaluate the non-identically distributed lognormal fading channels at a high signal-to-noise ratio. Practically, the analytical results prevent some redundant units that contribute no performance gain.

This paper presents a detailed secrecy performance analysis of correlated multi-antenna wiretap channels with three popular diversity combining schemes, namely maximal ratio combining, selection combining, and equal gain combining at the legitimate receiver. For single-input multiple-output wiretap channels, both exact and asymptotic expressions are derived for the secrecy outage probability (SOP) of these systems. The findings suggest that, compared with the scenario where all the channels are independent, correlation of the main channels alone increases the SOP by a factor of 1/ det(U), where det(U) is the determinant of correlation matrix U. In contrast, when the average SNR at the legitimate receiver is much larger than that at the eavesdropper, correlation between the main channels and the eavesdropper channels has a positive effect on SOP. Moreover, in the case of multiple-input and multiple-output wiretap channels, two transmit antenna selection schemes with or without the eavesdropper's channel state information are evaluated by the SOP and the average secrecy capacity for the considered correlated channel model, respectively. Finally, numerical simulations are conducted to corroborate the analytical results.

The joint design of beamforming vector and artificial noise covariance matrix is investigated for multiple-input-single-output-multiple-eavesdropper simultaneous wireless information and power transferring (MISOME-SWIPT) systems. A secrecy energy efficiency (SEE) minimization problem is formulated in the MISOME-SWIPT system with imperfect channel state information and proportional secrecy rate constraints. Since the formulated SEE minimization problem is non-convex, it is first recast into a series of convex problems in order to obtain the optimal solution with a reasonable computational complexity. Numerical results are used to verify the performance of the proposed algorithm and to reveal practical insights.

Security is a critical issue in full duplex (FD) communication systems due to the broadcast nature of wireless channels. In this paper, joint design of information and artificial noise beamforming vectors is proposed for the FD simultaneous wireless information and power transferring (FD-SWIPT) systems with loopback self-interference cancellation. To guarantee high security and energy harvesting performance of the FD-SWIPT system, the proposed design is formulated as a secrecy rate maximization problem under energy transfer rate constraints. Although the secrecy rate maximization problem is non-convex, we solve it via semidefinite relaxation and a two-dimensional search. We prove the optimality of our proposed algorithm and demonstrate its performance via simulations.

In traditional cognitive radio networks, the spectrum sharing schemes are spectrum-efficient in specific circumstances. In order to improve the robustness of spectrum sharing schemes in a dynamic environment from the perspective of energy efficiency, an adaptive spectrum sharing scheme is proposed for full duplex cognitive radios in this work. The proposed schemes allow secondary users (SUs) to adapt their way of accessing the licensed spectrum according to the behaviors of primary users (PUs). The SUs adaptively decide to access the licensed spectrum in the transmit phase, provided that PUs' traffic arrives at a low rate and energy efficiency gains exist. Based on the intermittent nature of the arrival and departure processes of PUs' traffic, the closed-form expressions of energy efficiency for the traditional and the proposed schemes are investigated. By defining energy efficiency gain and energy efficiency loss, the impacts of the spectrum sensing error and channel estimation error are analyzed to evaluate the energy efficiency of the proposed scheme. It shows that the proposed adaptive spectrum sharing scheme improves the overall energy efficiency. The numerical results validate the energy efficiency improvements and the robustness of the proposed scheme.

The problem of full-duplex primary users (PUs) signal extraction is studied for cognitive radio networks, and the polarization Jones vector distance (PJVD) is used as the extraction metric. Based on the orientation and magnitude difference of the signal samples from different PUs, the PJVD based signal extraction (PJVD-SE) can efficiently detect each PU's signal polarization by calculating the PJVD between any two samples. Due to the Gaussian distributed noise, the resultant PJVD for primary samples is noncentral chi-squared distributed. Based on the distribution, the extraction error probability is investigated. Theoretical analysis shows that the proposed PJVD-SE technique outperforms the polarization similarity based technique in terms of both error probability and computation complexity. It is found that the performance gain is proportional to the SU's antenna number, PUs' polarization angle and PUs' signal amplitude difference. In addition, the presented analytical studies are verified through extensive simulation results and the performance of PJVD-SE is validated.

Secrecy performance is investigated for relay networks with multiple antennas Nt at the transmitter. As K relays are available, we propose an incremental threshold relaying scheme with low complexity, where both transmit antenna selection (TAS) and relay selection are deployed without eavesdropper channel state information (CSI). Both the exact and approximate secrecy outage probability (SOP) of proposed scheme are derived with outdated CSI under the cases of maximal ratio combining (MRC) scheme at the legitimate receiver. In the high signalto-noise ratio (SNR) regime, due to outdated CSI, the secrecy diversity order of MRC decreases to Nt+1 for relay selection and K + 1 for antenna selection. Finally, simulation results validate our analysis, and show that the proposed scheme alleviates the effect of the transmitter and relay links on SOP.

Absorption, scattering and turbulence are the three main characteristics for underwater optical wireless communications (UOWC). Among these three factors, absorption and scattering, respectively, characterize the energy loss and direction change when photons propagate through underwater wireless channels interacting with water molecules or suspended particles. In recent years, several analytical methods originated from free space optical communications are used to model various underwater channels, while only statistical models are employed to consider the effects of absorption and scattering containing all the scattering components. To facilitate in-depth theoretical analysis, we propose an expression to model the spatial probability density function of optical intensity for all scattering components in UOWC links in this paper. After that, we model the photodiode receiving process and give a clear explanation on the related parameters. Numerical results indicate that the bit-error rate performance deteriorates as the turbulence gets stronger, and larger multiple-input multiple-output array can alleviate the negative effect caused by fading. Besides, the effect of turbulence on BER is more important than that of the link geometry, and the increase in transmitted power will weaken the diversity gain from MIMO configuration.

Absorption and scattering are traditionally considered as the most important factors to affect the performance of underwater optical wireless communications (UOWC). Recently, the theoretical models from free space optical (FSO) communications are applied to model the underwater turbulence, and the turbulence-induced fading may introduce fluctuations to the light intensity. However, the effect of turbulence on UOWC channels might be different from FSO channels due to the interference from absorption and scattering. In this work, we first introduce the log-normal distribution to represent the weak turbulence. After that, we deduce the average capacity of turbulent buoy- based multiple-input multiple-output (MIMO) systems. Numerical results demonstrate that turbulence will boost the average capacity under low transmitted signal-to-noise ratio (SNR) and reduce the average capacity when the transmitted SNR which is defined as the transmitted power divided by the noise at the receiver is sufficiently high enough. Besides, stronger turbulence exerts more influence on the capacity, and the increasing attenuation length will eliminate the effect of turbulence. Moreover, MIMO could offset the impact of turbulence-induced fading, which indicates that it cannot improve the capacity performance under low SNR but could bring positive effects when SNR becomes high.

Exact outage probability analysis for equal-gain combining (EGC) over lognormal fading channels results in multi-fold integrals, and traditional asymptotic analysis techniques cannot work for lognormal fading channels due to the channels' infinite diversity order. In this work, we derive a closed-form asymptotic outage probability expression for dual-branch EGC over correlated lognormal fading channels with different statistics. The new result reveals insights into the long-standing problem of asymptotic analysis for correlated lognormal fading channels, paving the way for analysis on more general channel models. The result also provides an efficient way to evaluate the performance of dual-branch EGC over correlated and nonidentically distributed lognormal channels at high signal-to-noise ratio.

Visible light communication (VLC) is considered as a promising candidate to improve the performance of indoor communication as the complement of wireless radio frequency (RF) communications due to the scarcity of RF resources. Combining the VLC with software-defined small-cell networks will substantially improve the user data rates in indoor heterogeneous networks. In this paper, we introduce the software-defined philosophy into orthogonal frequency-division multiple access-based heterogeneous software-defined and twinned VLC and RF small-cell networks. The pivotal issues of energy efficient (EE) subchannel and power allocation are investigated in the context of software-defined VLC and RF small-cell networks. We formulate the EE resource allocation problem as a non-convex optimization problem, and then, transform it into a convex one using Dinkelbach's method. In addition, distributed subchannel and power allocation algorithms for both VLC and RF are proposed for solving the problem based on the powerful alternative direction method of multipliers. Simulation results verify the effectiveness of resource allocation algorithms conceived for the heterogeneous software-defined twinned VLC and RF small-cell networks in terms of its good convergence and overall performance.

Existing indoor positioning methods for visible-light communication systems require large database, powerful signal processing units, additional sensors, such as gyroscopes, or the receiver placed toward a certain angle. These assumptions limit the applications of the indoor positioning systems in low-cost scenarios. In this paper, we propose a novel positioning framework based on the angle differences of arrival (ADOA) in 3-D coordinate systems, which can be used in receivers with image sensors or photodiodes. The proposed ADOA positioning does not require the receiver to be placed toward a certain angle and no additional sensor is required. Two positioning algorithms are proposed: one is based on the method of exhaustion (MEX), and the other is based on the least squares method (LSM). The MEX algorithm is analytically proved to be the optimal, while the LSM algorithm has much lower complexity. Both the upper and lower bounds are derived for the average discrepancy between the exact position and the estimated position. These performance bounds can facilitate the design of a light-emitting diode array. Experimental results show that the MEX algorithm can achieve an average error of 3.20 cm with a time cost of 0.36 s, and the LSM algorithm can achieve an average error of 14.66 cm with a time cost of 0.001 s.

The double-scattering fading models have found applications in many transmission scenarios. In this paper, approximate probability density functions (PDFs) are proposed for double-Nakagami fading and Gamma–Gamma fading, based on Hankel’s expansion of the modified Bessel function of the second kind. The proposed PDF is not only consistent with the exact PDF when the difference between the two shape parameters is equal to an integer plus one half, but also accurately approximates the distribution when the shape parameters are both greater than or equal to 2. The proposed approximate PDF has been compared with the power series expansion and the mixture Gamma model. It is found that the proposed PDF is in simple form and no parameters need to be tabulated. Moreover, the proposed PDF does not require an increasing number of terms to maintain precision with increasing diversity order of the reception scheme. Closed-form outage probability, ergodic capacity, and symbol error rate expressions have been derived using the proposed approximate PDFs for equal-gain combining, selection combining, and two-stage reception schemes. The proposed approximations are also leveraged for performance analysis of space-time block codes and free-space optical links. The precision of the proposed approximations is validated by comparing with the simulation results.

We propose a statistical delay quality-of-service (QoS) aware joint power allocation and relaying link selection scheme for uplink of a multichannel coherent free space optical communication-based fronthaul network. The proposed scheme assigns suitable relays and aggregation nodes to the remote radio heads and allocates transmit power among the orthogonal optical channels. Specifically, the proposed scheme maximizes total end-to-end effective capacity of a fronthaul network subject to certain transmit power budgets at both remote radio heads and relay nodes. The joint power allocation and relaying link selection are formulated as a mixed integer non-linear programing problem. We obtain near optimal solution to such an optimization problem by using Lagrangian dual decomposition and minimum weight matching techniques. Our analysis reveals that in order to maximize the aggregate end-to-end effective capacity, transmit power allocation and relaying link selection should consider both statistical delay-QoS requirements of the transmitted traffics and channel gains of the transmission links. Simulation results demonstrate that our proposed scheme improves statistical delay-QoS aware throughput in presence of atmospheric turbulence fading and pointing error.

In traditional cognitive radio networks (CRNs), the interweave, underlay, overlay, and hybrid spectrum sharing schemes are only spectrum-efficient in specific circumstances. In order to improve the robustness of spectrum sharing schemes in a dynamic traffic environment from the perspective of energy efficiency, adaptive spectrum sharing schemes are proposed in this paper for both half-duplex (HD) and full-duplex (FD) CRNs. The proposed schemes allow the secondary users to adaptively access the licensed spectrum, provided that PUs' traffic arrives at a low rate and energy efficiency gains exist. Based on the intermittent nature of the arrival and departure processes for PUs' traffic, closed-form expressions of energy efficiency for the traditional interweave, underlay, overlay, hybrid and the proposed schemes are investigated for both HD and FD modes. By defining energy efficiency loss, the impacts of the spectrum sensing error and channel estimation error are analyzed to evaluate the energy efficiency of the proposed schemes. Theoretical analysis shows that the proposed adaptive spectrum sharing schemes improve the minimum energy efficiency in HD mode and improve the overall energy efficiency in FD mode. The numerical results validate the energy efficiency improvements and the robustness of the proposed schemes.

Robust quality-of-service (QoS)-based and secrecy rate-based secure transmission designs are investigated for a multiple-input single-output system with multiple eavesdroppers and a cooperative jammer. Two scenarios are considered: (a) eavesdroppers' channel state information (ECSI) is available and (b) ECSI is unavailable. In scenario (a), a QoS-based design is considered to minimize the worst case signal-to-interference-and-noise ratio at the eavesdroppers and to guarantee the QoS of the legitimate receiver. A secrecy rate-based design is also studied where the worst case secrecy rate is maximized. In scenario (b), a QoS-based design is considered to maximize the power of jamming signals under the QoS constraint of the legitimate receiver, and the secrecy rate-based design is not applicable. In all these designs, we jointly optimize the transmit beamforming vector and the covariance matrix of jamming signals under individual power constraints. As the optimization problems are non-convex, we propose an algorithm for each problem through semidefinite relaxation. Our analysis and simulation results show that, even though the linear precoding scheme in our designs is transmit beamforming rather than the general rank transmit covariance, this does not cause any loss of optimality.

This paper investigates the physical-layer security for an indoor visible light communication network consisting of a transmitter, a legitimate receiver, and an eavesdropper. Both the main channel and the wiretapping channel have non-negative inputs, which are corrupted by additive white Gaussian noises. Considering the illumination requirement and the physical characteristics of lighting source, the input is also constrained in both its average and peak optical intensities. Two scenarios are investigated: one is only with an average optical intensity constraint and the other is with both average and peak optical intensity constraints. Based on the information theory, closed-form expressions of the upper and lower bounds on secrecy capacity for the two scenarios are derived. Numerical results show that the upper and lower bounds on secrecy capacity are tight, which validates the derived closed-form expressions. Moreover, the asymptotic behaviors in the high signal-to-noise ratio (SNR) regime are analyzed from the theoretical aspects. At high SNR, when only considering the average optical intensity constraint, a small performance gap exists between the asymptotic upper and lower bounds on secrecy capacity. When considering both average and peak optical intensity constraints, the asymptotic upper and lower bounds on secrecy capacity coincide with each other. These conclusions are also confirmed by numerical results.

We propose two cooperative secure transmission schemes to protect a two-hop buffer-aided network assisted by an energy harvesting relay. In the first scheme, we assume that the knowledge of the energy harvesting and fading channels states is known in a non-causal manner (offline). In the second scheme, we assume that this knowledge is known in a causal manner (online). For both schemes, we first design an effective link selection policy by taking into account of the transmission efficiency and information security requirements. We then optimally allocate the harvested power at the relay node. For the offline scheme, we maximize the average secrecy rate under the stability constraints of the data queue and the energy queue according to the proposed link selection policy, and design a two-stage iterative algorithm to select the transmission link and allocate relay’s transmit power. In the online scheme, we first model the average secrecy rate maximum problem as a Markov decision process, and then utilize the causal knowledge to select the best transmission link and optimally allocate relay’s transmit power. In addition, the exact and asymptotic closed-form expressions are derived for the ergodic secrecy rate. Numerical results are presented to validate our analysis and demonstrate that the proposed schemes outperform the other buffered-aided secure transmission schemes assisted by the energy harvesting relay in terms of average secrecy rate.

The widespread application of wireless services and dense devices access has triggered huge energy consumption. Because of the environmental and financial considerations, energy-efficient design in wireless networks has become an inevitable trend. To the best of our knowledge, energy-efficient orthogonal frequency division multiple access (OFDMA) heterogeneous small cell optimization comprehensively considering energy efficiency maximization, power allocation, wireless backhaul bandwidth allocation, and user quality of service is a novel approach and research direction, and it has not been investigated. In this paper, we study the energy-efficient power allocation and wireless backhaul bandwidth allocation in OFDMA heterogeneous small cell networks. Different from the existing resource allocation schemes that maximize the throughput, the studied scheme maximizes energy efficiency by allocating both transmit power of each small cell base station to users and bandwidth for backhauling, according to the channel state information and the circuit power consumption. The problem is first formulated as a non-convex nonlinear programming problem and then it is decomposed into two convex subproblems. A near optimal iterative resource allocation algorithm is designed to solve the resource allocation problem. A suboptimal low-complexity approach is also developed by exploring the inherent structure and property of the energy-efficient design. Simulation results demonstrate the effectiveness of the proposed algorithms by comparing with the existing schemes.

Prior asymptotic performance analyses are based on the series expansion of the moment-generating function (MGF) or the probability density function (PDF) of channel coefficients. However, these techniques fail for lognormal fading channels because the Taylor series of the PDF of a lognormal random variable is zero at the origin and the MGF does not have an explicit form. Although lognormal fading model has been widely applied in wireless communications and free-space optical communications, few analytical tools are available to provide elegant performance expressions for correlated lognormal channels. In this paper, we propose a novel framework to analyze the asymptotic outage probabilities of selection combining (SC), equal-gain combining (EGC), and maximum-ratio combining (MRC) over equally correlated lognormal fading channels. Based on these closed-form results, we show: 1) the outage probability of EGC or MRC becomes an infinitely small quantity compared to that of SC at high signal-to-noise ratio (SNR); 2) channel correlation can cause an infinite performance loss at high SNR; and 3) negatively correlated lognormal channels can outperform the independent lognormal channels. The analyses reveal insights into the long-standing problem of asymptotic performance analyses over correlated lognormal channels, and circumvent the time-consuming Monte Carlo simulation and numerical integration.

The Beckmann distribution has a wide range of applications in radio-frequency communications, free-space optical (FSO) communications, and underwater wireless optical communications (UWOC). However, the cumulative distribution function (cdf) of the Beckmann random variable (RV) does not have a closed-form expression, which makes it challenging to derive analytical solutions for the outage probability of systems involving Beckmann RVs. In this paper, we study the generalized Beckmann distribution, which includes the Beckmann, Rayleigh, Rician, Nakagami-m, Hoyt, κ-μ, η-μ, single-sided Gaussian, and the Beaulieu-Xie distributions as special cases. Three approaches are proposed to estimate the cdf of the generalized Beckmann distribution, including closed-form upper and lower cdf bounds, single-fold integration based on the closed-form characteristic function, and a left-tail cdf approximation. We compare the three approaches in terms of the ranges of applications and the computation time complexity. Based on the new cdf estimation techniques, one can efficiently evaluate the outage probabilities of pointing-error-limited FSO systems, UWOC systems, and maximum-ratio combining over arbitrarily correlated generalized Beckmann channels.

Data over cable service interface specification (DOCSIS) introduced the possibility of a variable bit-loading over the subcarriers within a channel in its release DOCSIS 3.1. This variable bit-loading will improve the data rates. However, to limit the encoding processing overhead, the concept of profiles was introduced. Each profile defines the modulation per subcarrier for a given channel while the number of allowed profiles is limited. Thus, an efficient profile assignment scheme, which determines the best set of profiles based on the users’ channel conditions, is needed. Although various profile assignment algorithms have been proposed in the literature, realistic evaluation of these schemes has been difficult, as channel quality measurements of real DOCSIS 3.1 systems has not previously been available. In this paper, we exploit DOCSIS 3.1 measurement data to evaluate performance of the proposed algorithms. We propose to employ principal component analysis to derive low-dimensional clustering variables in order to ensure efficient profile optimization. We show how this technique can be employed with different clustering algorithms to improve the spectrum efficiency of the profiles by extracting the most important information of the channels in low-dimensional vectors. This not only reduces the complexity of the clustering, but also ensures better throughput. Moreover, we adapt the clustering algorithms to tailor them to the profile optimization problem. Finally, we present an exhaustive simulation-based performance analysis to compare the different algorithms for various scenarios using extrapolation of the measurements data.

The joint design of beamforming and artificial noise vectors is proposed for the full-duplex simultaneous wireless information and power transferring (FD-SWIPT) systems when multiple eavesdroppers can wiretap information from FD base station (FD-BST) and FD user equipment (FD-UE). To guarantee high security and energy harvesting performance of the FD-SWIPT system, the proposed design is formulated as a sum-information-transmission-rate-maximization (SITRM) problem under information-leakage and energy constraints. Besides, we consider the fairness issue between uplink and downlink information-transmission rates by formulating a fairness-aware-SITRM (FA-SITRM) problem. Since the eavesdroppers can receive the information of FD-BST and FD-UE, the information-leakage region becomes non-convex and challenging to handle. Hence, we propose efficient algorithms to solve the SITRM and FA-SITRM problems optimally via semidefinite relaxation (SDR) and a 1-D search. In each search iteration, our proposed algorithms can recover the rank-one constraints when the application of SDR cannot obtain the optimal solutions. Moreover, we analyze the computational complexities and propose two suboptimal solutions to the formulated problems. For the SITRM problem, our numerical results show that the performance achieved by one of two suboptimal algorithms is close to the performance of optimal algorithm with increasing the maximum transmission power of FD-BST.

This paper investigates joint user association (UA) and user scheduling (US) for load balancing over the downlink of a wireless heterogeneous network by formulating a network-wide utility maximization problem. In order to efficiently solve the problem, we first approximate the nonconvex throughput achieved with US to a concave function, and demonstrate that the gap for such an approximation approaches zero when the number of users is sufficiently large. Then, by exploiting a distributed convex optimization technique known as alternating direction method of multipliers, a joint UA and US algorithm, which can be implemented on each user's side and base station (BS)'s side separately, is proposed to obtain the single-BS association and resource allocation solutions. A remarkable feature of the proposed algorithm is that apart from load balancing, multiuser diversity is exploited in the association process to further improve system performance. We also extend the algorithm design to multi-BS association, whereby a user is associated with multiple BSs. The simulation results show the superior performance of the proposed algorithms and underscore the significant benefits of jointly exploiting multiuser diversity and load balancing.

Heterogeneous small cell network with energy harvesting is a promising technique in the next generation mobile communications. However, the cross tier and co-tier co-channel interference can be severe due to the spectrum sharing in inter tier and intra tier of a heterogeneous small cell network. This paper investigates the problem of power allocation and subchannel assignment with the consideration of cross tier/co-tier interference mitigation, energy harvesting, and incomplete channel state information. The power allocation problem in heterogeneous small cell network is modeled as a non-cooperative game by introducing a time-varying cross tier/co-tier interference pricing with simultaneous wireless information and power transfer. Subchannel allocation is modeled as a non-cooperative potential game by minimizing the total interferences experienced by users on each subchannel. Iterative algorithms of power optimization and subchannel allocation are proposed to obtain the Nash equilibrium points. Simulation results are presented to verify the effectiveness of the proposed algorithms in the heterogeneous small cell network.

A robust quality-of-service (QoS) based secure transmission design is investigated for a cooperative jamming aided multiple-input single- output system with unknown eavesdroppers. By assuming legitimate receiver's channel state information (CSI) is imperfect and eavesdroppers' CSI is unavailable, our system design goal is to maximize the power of artificial jamming signals, and to guarantee a minimum QoS at the legitimate receiver. The transmit beamforming vector and covariance matrix of jamming signals are jointly optimized under individual power constraints. As this optimization problem is non-convex, we propose a solution method by using the semidefinite relaxation approach. Numerical results show that, our design, in which the precoding scheme of information transmission is transmit beamforming, outperforms its transmit covariance based counterpart.

We investigate the precoding, remote radio head (RRH) selection and signal splitting in the simultaneous wireless information and power transferring (SWIPT) cloud radio access networks (C-RANs). The objective is to minimize the power consumption of the SWIPT C-RAN. Different from the existing literature, we consider the nonlinear fronthaul power consumption and the multiple antenna RRHs. By switching off the unnecessary RRHs, the group sparsity of the precoding coefficients is introduced, which indicates that the precoding process and the RRH selection are coupled. In order to overcome these issues, a group sparse precoding and signal splitting algorithm is proposed based on the majorization-minimization framework, and the convergence behavior is established. Numerical results are used to verify our proposed studies.

Statistical delay quality-of-service (QoS) aware adaptive resource allocation scheme is proposed for a multi-carrier coherent free space optical (FSO) communications based fronthaul network. The proposed resource allocation assigns remote radio heads (RRHs) to the suitable aggregation nodes (ANs) and allocates the transmit power to the orthogonal optical carriers. Specifically, the proposed resource allocation provides delay-QoS at the link layer by maximizing the effective sum capacity of the all the RRHs subject to transmit power budgets at the RRHs and capacity constraint of the wired fronthaul links connecting the ANs with the baseband unit (BBU) pool. The considered resource allocation is formulated as a mixed-integer non-linear programing (MINLP) problem. We use two transmission link optimization techniques, namely, independent link optimization (ILO) and joint link optimization (JLO), in order to solve the proposed MINLP problem. Under both optimization techniques, the proposed MINLP problem is decomposed into two subproblems which are iteratively solved in order to obtain the optical transmit power allocation and assignments of RRHs to the ANs. Our analysis reveals that the optical transmit power allocation and RRH-AN assignments depend on both the atmospheric turbulence fading and delay-QoS requirements. Numerical results demonstrate that the JLO technique achieves significant higher effective capacity (EC) compared to the ILO technique in the strict statistical delay-QoS constraints. However, the EC performance gap between the JLO and ILO techniques is reduced in the loose statistical delay-QoS constraints.

Traditionally, primary users (PUs) in cognitive radio networks typically work in frequency division duplex or time division duplex mode, and secondary users (SUs) can protect PUs by blindly learning every PU's signal space and transmitting in the orthogonal space. However, the fifth-generation communication promotes the full-duplex technique and PUs are likely to work in FD mode in the future, i.e., two PUs transmit simultaneously at the same frequency. The emerged problem is that the sample of signal may be a compound signal from multiple PUs, and each PU's signal space cannot be directly derived, thus the available degree of freedom (DoF) for SUs' transmission may be limited and strong interference will be incurred to PUs. In this work, cognitive radio communications with full-duplex PUs is studied. Based on the distinctive polarization features of samples from different PUs, we propose a novel blind polarization adaption technique, which allows SUs to extract the signal space of each PU from the hybrid samples by studying polarization similarity of the sampled vectors. In order to reduce the polarization similarity inaccuracy induced by noise depolarization effect, a subspace based noise suppression method is also proposed. Numerical results show that the noise can be largely suppressed even in the low signal-to-noise-ratio region, which allows the proposed polarization similarity based polarization adaption technique to guarantee satisfactory interference level on PUs.

Uplink transmission design for visible light communication (VLC) is challenging due to the high optical power requirement for reliable links, which may result in unpleasant irradiance to humans. Current studies have proposed to use radio frequency (RF) communication for uplink while treating VLC as a downlink transmission technique. However, in typical scenarios such as hospitals or aircraft cabins, non-RF communications are preferred. Thus, it is beneficial to design a system which can utilize VLC for uplink transmission while minimizing its optical power. In this paper, we propose a amplify and forward based VLC uplink system which can significantly mitigate the optical power of the source light. The multi- carrier modulation scheme termed DC biased optical orthogonal frequency division multiplexing is employed, and the theoretical model of the proposed uplink system is studied. Based on this analytical model, the key system parameters are optimized. Numerical and simulation results verify that, when compared to an equal power allocation based relay system and a reference system without a relay, the proposed system can simultaneously achieve higher spectral efficiency and lower the source optical power.

In this paper, we investigate the dynamic subchannel and power allocation in licensed/unlicensed spectrum sharing heterogeneous small cell networks with incomplete channel state information (CSI). We explore the formulated problem using the Lyapunov optimization method by considering co-tier interference and cross- tier interference in both licensed and unlicensed spectrums. The constraints of the minimum user quality of service (QoS), the maximum transmit power limit and the unique of subchannel allocation are also considered to achieve the optimal power and subchannel allocation. Based on the framework of Lyapunov optimization, the problem of energy efficient (EE) optimization can be broken down into three subproblems. Two of which are linear and the rest can be solved by introducing Lagrangian function. The mathematical analysis and simulation results confirm that the proposed scheme can achieve a significant utility performance gain and the EE-delay tradeoff.

Prior visible-light communication (VLC) positioning systems require the receiver to be placed towards a certain angle, or extra sensors like gyroscopes must be equipped to estimate the angle of the receiver. In this work, we propose a positioning scheme using cameras based on angle difference of arrival (ADOA). ADOA-based positioning does not need the receiver to know its tilting angle, and the position estimation is modeled as a three-variate optimization problem in a finite search region, which is tractable in most smart devices. Our experimental and numerical results show that the ADOA-based VLC positioning scheme achieves accuracy of centimeters and the average cost time is as low as 0.2 seconds.

Non-orthogonal multiple access (NOMA) is considered as a promising technology for the fifth generation mobile communications. Energy-efficient resource allocation scheme is studied for a downlink NOMA wireless network, where multiple users can be multiplexed on the same subchannel by applying successive interference cancellation technique at the receivers. Most previous works focus on resource allocation for sum rate maximization with perfect channel state information (CSI) in NOMA systems. We formulate the energy-efficient resource allocation as a probabilistic mixed non-convex optimization problem by considering imperfect CSI. To solve this problem, we decouple it into user scheduling and power allocation sub-problems. We propose a low-complexity suboptimal user scheduling algorithm and a power allocation scheme to maximize the system energy efficiency under the maximum transmitted power limit, imperfect CSI and the outage probability constraints. Simulation results are provided to show that the proposed algorithms yield much improved energy efficiency performance over the conventional orthogonal frequency division multiple access scheme.

Prior results on performance analysis for cognitive relay networks mainly involve perfect channel state information (CSI), which is not readily available. Thus, the effects of outdated CSI on the performance of a multi-antenna multi-relay cognitive radio system are investigated in this work. To exploit the benefits of multiple antennas, collaborative zero-forcing beamforming at the secondary source is proposed to enhance the system performance under the outdated CSI. At the destination, the maximum ratio combining (MRC) diversity scheme is adopted. The Nth best relay selection strategy is applied before the secondary data transmission process. Closed-form expressions for the exact and asymptotic outage probabilities are derived for the secondary user over the Rayleigh fading in the first hop and Nakagami-m fading in the second hop with and without feedback delay. These analytical results can reveal the system diversity order and coding gain. Monte Carlo simulations are carried out to verify the correctness of our analysis. These new analytical results can reveal insights into the characteristics of the proposed system over the outdated fading channels, and can provide useful design criteria for relay-assisted spectrum sharing networks.

A new (Beaulieu-Xie) fading model was recently proposed to describe line-of-sight and non-line-of-sight components in wireless channels. In this work, we consider both outage probability and error rate performance of selective combining over this new fading channel model with arbitrary channel correlation. Closed-form expressions are obtained for asymptotically tight upper and lower bounds. The analytical results are verified by Monte Carlo simulations.

This paper introduces the design and implementation of an optical camera communication (OCC) system. Phase uncertainty and phase slipping caused by camera sampling are the two major challenges for OCC. In this paper, we propose a novel modulation scheme to overcome these problems. The undersampled differential phase shift on-off keying is capable of encoding binary data bits without exhibiting any flicker to human eyes. The phase difference between two consecutive samples conveys one-bit information which can be decoded by a low frame rate camera receiver. Error detection techniques are also introduced to enhance the reliability of the system. Furthermore, we present the hardware and software design of the proposed system. This low-cost communication system has been implemented with a Xilinx FPGA and a Logitech commercial camera. Experimental results demonstrate that a bit-error rate of 10-5can be achieved with 7.15 microwatts received signal power over a link distance of 15 centimeters.

In this paper, we propose a cooperative secure transmission for two-hop relay network with limited feedback. In the proposed scheme, the channel state information associated with cooperative users is quantized and limited bits are feedback for cooperative relay and jammer selection. By considering the quantization error brought by limited feedback, we investigate the performances of transmission outage probability and secrecy outage probability, and their closed-form expressions are derived. On this basis, we optimal design the target transmission rate and secrecy rate so that the average secrecy rate is maximized under the constraints of maximum permitted transmission outage probability and secrecy outage probability requirements. Simulation results are presented to verify the analytical results for the proposed scheme and prove its secrecy performance improvement in terms of the secrecy performance with light overhead.

A new strategy of iterative quantum receivers with photon-number-resolving detectors is proposed to discriminate coherent states. The iterative quantum receiver provides a bit error rate closer to the Helstrom limit when compared with a recently proposed non-iterative quantum receiver. Moreover, the proposed iterative receiver is robust against various imperfections, such as mode mismatch, non-ideal quantum efficiency and dark counts. The simulation results indicate that the iterative receiver is capable of achieving 2.16 dB improvement upon the standard quantum limit, leaving only 0.65 dB from the Helstrom limit. Besides, with Turbo encoder and decoder, the iterative receiver gives about 1.46 dB improvement over the conventional non-iterative quantum receiver and approaches the equivalent Helstrom limit.

Non-orthogonal multiple access (NOMA) exploits successive interference cancellation technique at the receivers to improve the spectral efficiency. By using this technique, multiple users can be multiplexed on the same subchannel to achieve high sum rate. Most previous research works on NOMA systems assume perfect channel state information (CSI). However, in this paper, we investigate energy efficiency improvement for a downlink NOMA single-cell network by considering imperfect CSI. The energy efficient resource scheduling problem is formulated as a non-convex optimization problem with the constraints of outage probability limit, the maximum power of the system, the minimum user data rate, and the maximum number of multiplexed users sharing the same subchannel. Different from previous works, the maximum number of multiplexed users can be greater than two, and the imperfect CSI is first studied for resource allocation in NOMA. To efficiently solve this problem, the probabilistic mixed problem is first transformed into a non-probabilistic problem. An iterative algorithm for user scheduling and power allocation is proposed to maximize the system energy efficiency. The optimal user scheduling based on exhaustive search serves as a system performance benchmark, but it has high computational complexity. To balance the system performance and the computational complexity, a new suboptimal user scheduling scheme is proposed to schedule users on different subchannels. Based on the user scheduling scheme, the optimal power allocation expression is derived by the Lagrange approach. By transforming the fractional-form problem into an equivalent subtractive-form optimization problem, an iterative power allocation algorithm is proposed to maximize the system energy efficiency. Simulation results demonstrate that the proposed user scheduling algorithm closely attains the optimal performance.

An exact closed-form average bit-error rate expression of a selection combining (SC) scheme is derived for a differential cooperative system employing an amplify-and-forward relay with simultaneous wireless information and power transfer capability. Monte Carlo simulations verify the analytical expressions. In particular, the SC scheme can achieve the same performance as that of a suboptimal maximal ratio combining scheme, and this is achieved with lower implementation complexity and energy consumption for SC.

Two weighted selection combining (WSC) schemes are proposed for a differential decode-and-forward relaying system in Rayleigh fading channels. Compared to the conventional SC scheme, the decision variable of the relay link is multiplied by a scale factor to combat the error propagation phenomenon. Average bit-error rate (ABER) expressions of the two proposed WSC schemes are derived in closed form and verified by simulation results. For the second WSC scheme, asymptotic ABER expression and diversity order are derived to gain more insight into this scheme. Moreover, it is demonstrated that both WSC schemes can overcome the extra noise amplification induced by the link adaptive relaying scheme. The first WSC scheme is slightly inferior to the second one, which has a higher complexity. Both proposed WSC schemes outperform the conventional SC scheme.

Due to the dynamic arrival of data packets and time-varying channel states, secure transmission opportunities will be wasted when there is no confidential message to transmit, and data packet transmission can be delayed while waiting for the next available secure transmission opportunity. In order to seize every precious secure transmission opportunity and reduce the data packet waiting time, we propose a secure transmission protocol in which the under-utilized secure transmission opportunities can be exploited to transmit key packets, and these key packets will encrypt the confidential messages in the subsequent transmissions. Following the above-mentioned principle, we first apply this protocol to a single-input single-output network, and optimally allocate the secure transmission opportunities for the key and data transmissions, such that the secrecy rate is maximized under the constraints of the minimum energy efficiency and queue stability requirements. In addition, the packet delay for the proposed protocol is investigated using a generating function approach and a closed-form expression of the packet delay is derived. Then, we extend our work to the multiple-input single-output network and utilize the key queue as well as the artificial noise to protect the confidential messages. Similarly, we also optimally allocate the transmission opportunities for the key and data transmissions to maximize the secrecy rate and study the data packet delay performance. Since all secrecy transmission opportunities are utilized in the proposed protocol, the full secrecy rate is achieved. Numerical results are demonstrated to verify the performance superiority of the proposed protocols when compared with the other key encrypted schemes in terms of the data packet delay and the secrecy rate.

The application of renewable energy is a promising solution for realizinggreen communications. However, if the cellular systems are solely powered by the renewable energy, the weather dependence of the renewable energy arrival makes the systems unstable. On the other hand, the proliferation of the smart grid facilitates the loads with two-way energy trading capability. Hence, a hybrid powered cellular system, which combines the smart grid with the base stations, can reduce the grid energy expenditure and improve the utilization efficiency of the renewable energy. In this paper, the long-term grid energy expenditure minimization problem is formulated as a stochastic optimization model. By leveraging the stochastic optimization theory, we reformulate the stochastic optimization problem as a per-frame grid energy plus weighted penalized packet rate minimization problem, which is NP-hard. As a result, two suboptimal algorithms, which jointly consider the effects of the channel quality and the packet reception failure, are proposed based on the successive approximation beamforming (SABF) technique and the zero-forcing beamforming (ZFBF) technique. The convergence properties of the proposed suboptimal algorithms are established, and the corresponding computational complexities are analyzed. Simulation results show that the proposed SABF algorithm outperforms the ZFBF algorithm in both grid energy expenditure and packet delay. By tuning a control parameter, the grid energy expenditure can be traded for the packet delay under the proposed stochastic optimization model.

Cognitive radio enabled small cell network is an emerging technology to address the exponential increase of mobile traffic demand in next generation mobile communications. Recently, many technological issues, such as resource allocation and interference mitigation pertaining to cognitive small cell network have been studied, but most studies focus on maximizing spectral efficiency. Different from the existing works, we investigate the power control and sensing time optimization problem in a cognitive small cell network, where the cross-tier interference mitigation, imperfect hybrid spectrum sensing, and energy efficiency are considered. The optimization of energy efficient sensing time and power allocation is formulated as a non-convex optimization problem. We solve the proposed problem in an asymptotically optimal manner. An iterative power control algorithm and a near optimal sensing time scheme are developed by considering imperfect hybrid spectrum sensing, cross-tier interference mitigation, minimum data rate requirement, and energy efficiency. Simulation results are presented to verify the effectiveness of the proposed algorithms for energy efficient resource allocation in the cognitive small cell network.

A multiple-input single-output cognitive radio downlink network is studied with simultaneous wireless information and power transfer. In this network, a secondary user coexists with multiple primary users and multiple energy harvesting receivers. In order to guarantee secure communication and energy harvesting, the problem of robust secure artificial noise-aided beamforming and power splitting design is investigated under imperfect channel state information (CSI). Specifically, the transmit power minimization problem and the max-min fairness energy harvesting problem are formulated for both the bounded CSI error model and the probabilistic CSI error model. These problems are non-convex and challenging to solve. A 1-D search algorithm is proposed to solve these problems based on S-Procedure under the bounded CSI error model and based on Bernstein-type inequalities under the probabilistic CSI error model. It is shown that the optimal robust secure beamforming can be achieved under the bounded CSI error model, whereas a suboptimal beamforming solution can be obtained under the probabilistic CSI error model. A tradeoff is elucidated between the secrecy rate of the secondary user receiver and the energy harvested by the energy harvesting receivers under a max-min fairness criterion.

This paper investigates the joint user association (UA) and user scheduling (US) for load balancing in a wireless downlink heterogeneous network by formulating a network-wide utility maximization problem. In order to efficiently solve the problem, we first approximate the original non-convex throughput function to a concave function, and demonstrate that the gap for such approximation approaches zero when the number of users is sufficiently large. Then, a distributed algorithm is further proposed to obtain the UA and US solutions by exploiting the convex optimization technique known as alternating direction method of multipliers. A remarkable feature of the proposed algorithm is that apart from load balancing, multiuser diversity is exploited in the association time to further improve system performance. The simulation results show the superior performance of the proposed algorithm and underscore the significant benefits of jointly exploiting multiuser diversity and load balancing.

In order to improve the delay quality-of-service aware throughput performance of the optical wireless communications, a joint power allocation and relay selection scheme is proposed for a relay assisted multichannel coherent optical wireless communication system. The proposed joint power allocation and relay selection scheme provides the delay aware quality-of-service guarantee by maximizing the end-to-end effective capacity under the average transmit power constraints at both source as well as relay nodes. The joint power allocation and relay selection problem is formulated as a non-convex and combinatorial optimization problem, and the solution of this optimization problem is obtained using a time-sharing relaxation technique. Subsequently, a fast convergent algorithm is proposed for the joint power allocation and relay selection by considering the delay aware quality-of-service requirements of the services transmitted over the optical wireless communication channels. Numerical results demonstrate that the proposed joint power allocation and relay selection scheme provides the strict statistical delay aware quality-of-service with an improved throughput without increasing the transmit power requirements.

Energy-efficient design has emerged as a promising technique in heterogeneous networks. We study the energy efficiency problem of joint user association and power allocation in a two-tier heterogeneous network with small cells. The energy efficiency is maximized under certain prescribed quality-of-service requirement and maximum power limit constraint. The original optimization problem is a nonconvex integer programming and is NP-hard. A continuous and convex relaxation method is employed to solve this problem. Then, an iterative joint user association and power allocation algorithm is proposed to maximize the energy efficiency. Simulation results show that the proposed algorithm has improved energy efficiency when compared with a reference scheme using fixed power allocation.

Heterogeneous cloud small cell network (HCSNet) with energy harvesting is a promising technique in the next generation mobile communications. However, the cross- tier and co-tier co-channel interference can be severe due to the spectrum sharing in inter-tier and intra- tier of HCSNet. This paper investigates the problem of power allocation and subchannel assignment with the consideration of cross-tier/co-tier interference mitigation, energy harvesting and incomplete channel state information. The power allocation problem in HCSNet is modeled as a non-cooperative game by introducing a time-varying cross-tier/co-tier interference pricing with simultaneous wireless information and power transfer. Subchannel allocation is modeled as a non-cooperative potential game by minimizing the the total interferences experienced by users on each subchannel. Iterative scheme of power optimization and subchannel allocation is proposed to obtain the Nash equilibrium points. Simulation results are presented to verify the effectiveness of the proposed algorithms in HCSNet.

A max-min fairness resource allocation is studied for wideband cognitive radio under sensing-based spectrum sharing with simultaneous wireless information and power transfer. Specifically, the throughput of the worse-case secondary user is maximized by jointly optimizing the sensing time, transmit power and subchannel allocation, subject to constraints on energy harvesting, interference power and transmit power. The formulated max-min fairness resource allocation problem is a mixed integer non-convex programming. An efficient one-dimensional search algorithm based on the proposed transmit power and subchannel allocation scheme is designed to solve the formulated problem. Several tradeoffs are found, such as a tradeoff between the sensing performance and the throughput of the secondary user under a max-min fairness criterion.

Non-orthogonal multiple access (NOMA) is a promising technique for the fifth generation mobile communication due to its high spectrum efficiency. By applying superposition coding and successive interference cancellation techniques, multiple users can be multiplexed on the same subchannel in NOMA systems. Previous works focus on subchannel and power allocation to maximize the sum rate; however, the energy-efficient resource allocation problem has not been studied for NOMA systems. In this paper, we aim to optimize subchannel assignment and power allocation to maximize the energy efficiency for the downlink NOMA network. Assuming perfect knowledge of the channel state information at base station, we propose low-complexity suboptimal algorithms which include subchannel assignment and power allocation for subchannel users. In the power allocation scheme, difference of convex functions programming approach is exploited to transform and approximate the original optimal problem into a convex optimization problem. Simulation results show that our proposed algorithms yield much better improvements than orthogonal frequency division multiple in terms of sum rate and energy efficiency.

In order to improve the energy efficiency of the optical wireless communications, a joint power allocation and relay selection scheme is proposed for a relay assisted non-line-of-sight optical wireless communication system employing coherent detection with a dual optical channel multiplexing technique. The proposed joint power allocation and relay selection scheme aims to improve energy efficiency of the optical wireless communication system by minimizing the total average transmit power, and provides the delay aware quality-of-service guarantee through maintaining a minimum required effective capacity over the atmospheric turbulence fading channels. The joint power allocation and relay selection problem is formulated as a non-convex and combinatorial optimization problem, and the solution of this optimization problem is obtained using a time-sharing relaxation technique. Subsequently, a fast-convergent algorithm is proposed for the joint power allocation and relay selection by considering the delay aware quality-of-service requirements of the services transmitted over the optical wireless communication channels. Numerical results demonstrate the advantages of the proposed joint power allocation and relay selection scheme in terms of the energy saving for different statistical delay constraints.

We investigate a quality-of-service-aware and energy-aware adaptive power allocation scheme for a point-to-point and line-of-sight optical wireless communication system employing the coherent detection and polarization multiplexing. The proposed power allocation scheme minimizes the average transmit power and provides the delay aware quality-of-service guarantee through maintaining a required effective capacity over the fading channels. The power allocation scheme is formulated as a convex optimization problem, and using the sub-gradient method, a fast convergent algorithm for the optimal power allocation is proposed. Numerical results demonstrate the advantages of the proposed power allocation algorithm in terms of the energy saving for the case of having strict statistical delay constraints.

The last decade has witnessed a booming era of wireless vehicular networks, supporting diverse road traffic services and applications. Information dissemination/sharing among vehicles is the fundamental goal of vehicular networks. Although diverse information dissemination/sharing mechanisms have been proposed in the existing literature, the physical layer outage performance of information sharing has not been analyzed. Against this background, in this paper, we study the outage probability of road traffic information sharing in underlay cognitive vehicular networks under both a general scenario and a specific highway scenario. The general scenario relies on the Nakagami-m channel, while the highway scenario is its special case associated with the Rayleigh fading channel. Moreover, we also invoke a real-world dataset containing the locations of Beijing taxis to conduct simulations, the results of which verify the accuracy of our theoretical analysis.

Energy-efficient power allocation and wireless backhaul bandwidth allocation are studied for orthogonal frequency division multiple access heterogeneous small cell networks. Different from the existing resource allocation schemes that maximize the throughput, the studied scheme maximizes energy efficiency by allocating both transmit power of each small cell to users and bandwidth for backhauling, according to the channel state information and the circuit power consumption. The problem is formulated as a non-convex nonlinear programming problem which is then decomposed into two convex subproblems. A near optimal iterative resource allocation algorithm is designed to solve the resource allocation problem. We also develop a suboptimal but low-complexity approach by fixing bandwidth allocation factor. Simulation results demonstrate the effectiveness of the proposed algorithm.

In order to increase throughput and improve system performance for free space optical communications, we consider a pilot-symbol assisted modulation (PSAM) subcarrier phase shift keying (PSK) system with maximum likelihood (ML) phase estimation. Using the phase error probability density function of ML estimation phase error, we study the error rate performance of the subcarrier PSK system with carrier phase error in lognormal turbulence channels. Results reveal the differences between the imperfectly synchronized system and the perfectly synchronized system, and how the performances are influenced by the pilot symbol length. An analytical expression of asymptotic signal-to-noise ratio penalty is derived and verified with numerical results.

Prior work made a comparison between the diversity orders of adaptive decode-and-forward (ADF) and improved adaptive decode-and-forward (IADF) over free- space optical (FSO) Gamma-Gamma fading channels. However, the diversity orders of FSO lognormal channels are infinite, and thus the prior method of comparison becomes invalid for FSO lognormal fading channels. In this work, we compare the performance of ADF and IADF over lognormal fading channels based on asymptotic relative diversity order. We analytically prove that the frame error rate of IADF is much smaller than that of ADF when the signal-to-noise ratio is sufficiently large.

Non-orthogonal multiple access (NOMA) is a promising technique for the fifth generation mobile communication due to its high spectral efficiency. By applying superposition coding and successive interference cancellation techniques at the receiver, multiple users can be multiplexed on the same subchannel in NOMA systems. Previous works focus on subchannel assignment and power allocation to achieve the maximization of sum rate; however, the energy-efficient resource allocation problem has not been well studied for NOMA systems. In this paper, we aim to optimize subchannel assignment and power allocation to maximize the energy efficiency for the downlink NOMA network. Assuming perfect knowledge of the channel state information at base station, we propose a low-complexity suboptimal algorithm, which includes energy-efficient subchannel assignment and power proportional factors determination for subchannel multiplexed users. We also propose a novel power allocation across subchannels to further maximize energy efficiency. Since both optimization problems are non-convex, difference of convex programming is used to transform and approximate the original non-convex problems to convex optimization problems. Solutions to the resulting optimization problems can be obtained by solving the convex sub-problems iteratively. Simulation results show that the NOMA system equipped with the proposed algorithms yields much better sum rate and energy efficiency performance than the conventional orthogonal frequency division multiple access scheme.

Prior results on exact error rates and outage probabilities of diversity receptions over arbitrarily correlated fading channels involve intractable nested integrals, which makes the performance analyses challenging to carry out. In this paper, we derive asymptotic expressions and asymptotically tight bounds for error rates and outage probabilities of diversity receptions (including maximal-ratio combining, selection combining, and equal-gain combining) over arbitrarily correlated Beckmann fading channels. These new asymptotic performance expressions reveal insights into the diversity systems over arbitrarily correlated Beckmann fading channels, and the accuracy of the asymptotic performance expressions is explicitly quantified by the performance bounds. Using these analytical tools, one can assess the performance of the diversity reception systems over arbitrarily correlated Beckmann fading channels without resorting to time-consuming Monte Carlo simulation or numerical multifold integration.

We consider secure resource allocations for orthogonal frequency division multiple access (OFDMA) two-way relay wireless sensor networks (WSNs). The joint problem of subcarrier (SC) assignment, SC pairing and power allocations, is formulated under scenarios of using and not using cooperative jamming (CJ) to maximize the secrecy sum rate subject to limited power budget at the relay station (RS) and orthogonal SC allocation policies. The optimization problems are shown to be mixed integer programming and nonconvex. For the scenario without CJ, we propose an asymptotically optimal algorithm based on the dual decomposition method and a suboptimal algorithm with lower complexity. For the scenario with CJ, the resulting optimization problem is nonconvex, and we propose a heuristic algorithm based on alternating optimization. Finally, the proposed schemes are evaluated by simulations and compared with the existing schemes.

Due to environmental concerns of rising energy consumption caused by explosive growth in the demands of wireless multimedia services, energy efficiency has become an important consideration in the design of future wireless communication systems. In this paper, we investigate and propose an energy-efficient scheme of joint antenna-subcarrier-power allocation for min-rate guaranteed services in the downlink multiuser orthogonal frequency division multiplexing distributed antenna systems (OFDM-DASs) with limited backhaul capacity. Our aim is to maximize the energy efficiency in an OFDM-DAS based on the constraints of users' Quality of Service, subcarrier reuse, backhaul capacity, and remote antenna units' transmit power. By exploring the properties of the complex nonconvex energy efficiency optimization problem, we transform the problem into an equivalent problem based on fractional programming, and then, use the alternating direction multiplier method to decompose the problem into a series of simpler subproblems, where their optimal or suboptimal solutions can be easily achieved. We propose the corresponding low-complexity methods to solve the subproblems and, then, the whole problem. The numerical results demonstrate the effectiveness of the proposed low-complexity energy-efficient scheme and illustrate the fundamental tradeoff among energy consumption, spectral efficiency, and energy efficiency.

Prior performance analyses of diversity receptions over arbitrarily correlated Nakagami-m fading channels use power correlation matrix to define correlation among branches. However, the power correlation matrix cannot uniquely determine the joint probability density function of the branch amplitudes as well as the system performance. In this paper, we derive asymptotically tight and closed-form upper and lower bounds for the error rates and the outage probabilities of maximum-ratio combining, equal-gain combining, and selection combining over arbitrarily correlated Nakagami-m fading channels. The correlation between the Nakagami-m random variables is defined through the correlation matrix of the accompanying Gaussian random variables, which can uniquely determine the joint probability density function of the Nakagami-m variables. Using the analytical results and Monte Carlo simulation, we show that the performance of diversity reception systems over Nakagami-m fading channels cannot be uniquely determined by the power correlation matrix of the branches. Furthermore, we also study the factors that determine the asymptotic performance of the diversity receptions.

In this paper, we consider an orthogonal frequency division multiple access based full duplex distributed antenna system (FDDAS) with energy recycling capability and develop an energy efficient resource allocation scheme for such system. In particular, in order to optimize the energy efficiency of FDDAS, we formulate the problem of joint subchannel allocation and power control as a mixed integer nonlinear programming problem. Since the formulated optimization problem is a NP-hard problem whose computation complexity will rise exponentially with larger network scale, we develop a low complexity subchannel allocation and power control algorithm. The convergence property of the proposed algorithm is established. Simulation results show that the proposed algorithm for FDDAS results in a higher system energy efficiency than the existing algorithm for distributed antenna systems without energy recycling capability.

Effective capacity is defined as the maximum constant traffic arrival rate that a communication channel can support in order to guarantee a certain statistical delay constraint. We investigate the effective capacity performance of the optical wireless communications employing the coherent detection and polarization multiplexing over the Gamma-Gamma turbulence fading channels for two different power adaptation techniques, namely, independent power adaptation and joint power adaptation. In a polarization multiplexing system, independent power adaptation allocates transmit power to a particular channel considering only the signal-to-noise ratio statistics of that particular channel whereas the joint power adaptation allocates transmit power to a particular channel considering the joint signal-to-noise ratio statistics of both channels. Our analysis reveals the superiority of the joint power adaptation technique in order to support the stringent statistical delay constraint services over the strong turbulence fading channels compared to an independent power adaptation technique. However, the numerical results demonstrate that the performance gap between the joint and independent power adaptations gets significantly reduced as the statistical delay constraints become loose and/or the turbulence fading gets weaker.

A unified performance analysis is presented for dual-hop amplify-and-forward (AF) relaying systems operating over non-identically distributed Rician fading channels. A general, two-parameter AF relaying model is used such as to cover various AF relaying schemes, namely, variable-gain AF relaying, fixed-gain AF relaying, and channel-assisted relaying, as well other unconventional schemes. Although, in some applications, systems operating over Rician fading links can be approximated and analyzed using equivalent systems operating over Nakagami- m fading links, we show that this is not sufficiently accurate in the case of AF relaying systems. Therefore, single-fold infinite-series expressions for the probability density function (PDF) and the cumulative distribution function (CDF) of the instantaneous end-to-end received signal-to-noise ratio (SNR) of such systems are derived, and Monte-Carlo simulations are used to verify the precision of the derived expressions. In addition, the effects of the relaying model parameters, as well as the channel fading parameters, on system performance are studied.

Cognitive radio enabled small cell network is an emerging technology to address the exponentially increasing mobile traffic demand of next generation mobile communications. Recently, many technological issues pertaining to cognitive small cell network have been studied, such as resource allocation, but most studies focus on spectral efficiency maximization. Different from the existing works, we investigate the power control and sensing time optimization problem in cognitive small cell, where imperfect hybrid spectrum sensing and energy efficiency are considered. The energy efficient sensing time and power allocation optimization is modeled as a non-convex optimization problem. We solve the problem in asymptotically optimal manner. An iterative power control algorithm and a near optimal sensing time scheme are developed with the consideration of imperfect hybrid spectrum sensing and energy efficiency. Simulation results are presented to verify the effectiveness of the proposed algorithms for energy efficient resource allocation in cognitive small cell network.

Exact error rate expressions for equal-gain combining over arbitrarily correlated Rician channels involve intractable integrals. Thus, asymptotic error rate expression was derived to provide accurate approximation in large signal-to-noise ratio region. However, the existing asymptotic analysis cannot evaluate the accuracy of the asymptotic error rate expression for equal-gain combining. In this work, we derive closed-form and asymptotically tight upper and lower bounds for the error rate of equal-gain combining over Rician fading channels with arbitrary correlation. These bounds can be used to evaluate the accuracy of the asymptotic error rate expression for equal-gain combining at high signal-to-noise ratio without using the time-consuming Monte Carlo simulation.

Bit-error rate (BER) performance of subcarrier M-ary phase-shift keying (MPSK) and M-ary differential phase-shift keying (MDPSK) is analyzed for optical wireless communications over the lognormal turbulence channels. Both exact BER and approximate BER expressions are presented. We demonstrate that the approximate BER, which is obtained by dividing the symbol error rate by the number of bits per symbol, can be used to estimate the BER performance with acceptable accuracy. Through our asymptotic analysis, we derive closed-form asymptotic BER performance loss expression for MDPSK with respect to MPSK in the lognormal turbulence channels.

Prior work on exact error rate of diversity receptions over arbitrarily correlated Rician channels involves intractable integrals. Although asymptotic error rates provide closed-form approximations which are accurate at high signal-to-noise ratio, it is still unknown at what signal-to-noise ratio the approximation becomes accurate. In this paper, asymptotically tight closed-form upper and lower bounds are derived for maximal-ratio combining and selection combining over arbitrarily correlated Rician channels. Using these bounds, we can show at what signal-to-noise ratio that the asymptotic error rates are accurate without resorting to time-consuming Monte Carlo simulation.

Optical wireless communication (OWC) systems provide many advantages over radio frequency (RF) wireless technologies in some scenarios, including significantly higher data rates and a large amount of available license-free frequency spectrum. Recently, OWC has also been proposed in fifth generation 5G standard as a tool to augment capacity due to RF spectrum crunching challenges. Various forms of OWC can be used to augment RF capacity at both access and backhaul/fronthaul levels. Despite the major advantages and various application areas, the widespread deployment of OWC is delayed by several challenges, such as the demand to maintain strict line-of-sight alignment between transmitter and receiver in long range outdoor applications; the need to combat attenuation due to adverse weather conditions such as fog, cloud, and turbulence; to modulate light emitting diodes at high frequencies without distortion at indoor visible light communication (VLC) applications and retaining power levels within the eye safety limits for laser transmitters and comfortable illumination levels for LED transmitters.

New data services and applications are emerging continuously and enhancing the mobile broadband experience. The ability to cope with these varied and sophisticated services and applications will be a key success factor for the highly demanding future network infrastructure. One such technology that could help address the problem would be optical wireless communications (OWC), which presents a growing research interest in the last few years for indoor and outdoor applications. This paper is an overview of the OWC systems focusing on visible light communications, free space optics, transcutaneous OWC, underwater OWC, and optical scattering communications.

A unified capacity analysis of a free-space optical (FSO) link that accounts for nonzero boresight pointing errors and both types of detection techniques (i.e. intensity modulation/direct detection as well as heterodyne detection) is addressed in this work. More specifically, an exact closed-form expression for the moments of the end-to-end signal-to-noise ratio (SNR) of a single link FSO transmission system is presented in terms of well-known elementary functions. Capitalizing on these new moments expressions, we present approximate and simple closed-form results for the ergodic capacity at high and low SNR regimes. All the presented results are verified via computer-based Monte-Carlo simulations.

Optimal relay placement is studied for free-space optical multihop communication with link obstacles and infeasible regions. An optimal relay placement scheme is proposed to achieve the lowest outage probability, enable the links to bypass obstacles of various geometric shapes, and place the relay nodes in specified available regions. When the number of relay nodes is large, the searching space can grow exponentially, and thus, a grouping optimization technique is proposed to reduce the searching time. We numerically demonstrate that the grouping optimization can provide suboptimal solutions close to the optimal solutions, but the average searching time linearly grows with the number of relay nodes. Two useful theorems are presented to reveal insights into the optimal relay locations. Simulation results show that our proposed optimization framework can effectively provide desirable solution to the problem of optimal relay nodes placement.

ABSTRACT Closed‐form asymptotic expressions for bit error rate and outage probability are derived for multi‐branch equal gain combining and selection combining receiver diversity over equally correlated Rician channels. Numerical results indicate that these analytical solutions can provide accurate estimation of bit error rate and outage probability in large signal‐to‐noise ratio regimes. The analytical results reveal some important insights into the performance characteristics of equal gain combining and selection combining diversity operating over equally correlated Rician fading channels. Copyright © 2013 John Wiley & Sons, Ltd.

Security issues of terrestrial free-space optical (FSO) communications over strong atmospheric turbulence are investigated. Based on a subcarrier intensity modulated FSO system model, we first study the scintillation reciprocity of the system from a coherence time perspective. A private secret key-based cryptosystem with key management is introduced to enhance FSO security, and a key agreement approach is proposed based on statistics of the random atmospheric turbulence-induced fading channel measurements. Secret key rate of the key agreement scheme is studied for the strong turbulence channel characterized by the K-distribution. Practical key agreement protocol is designed based on channel identification.

Ultra-wideband (UWB) technology can provide a ranging accuracy of tens of centimeters with fine time resolution. UWB radio occupies a large bandwidth, e.g. at least 500 MHz, which translates to high multipath resolution. Thus, UWB is a suitable technology for accurate positioning through estimating time of flight (TOF) of the transmitted signal. In this paper, we present a highly accurate positioning scheme by estimating the round trip time (RTT) of frequency-converted transponded signal. The proposed scheme avoids complicated beam-forming techniques and clock synchronization between the unknown terminal and the referenced stations. To avoid employing high rate sampling analog-to-digital converters, we propose a novel time estimation method based on sliding correlation and a fractional delay polyphase filter. We use low-rate sampling data with polyphase filter to estimate the RTT, and obtain an equivalent sampling rate as high as tens of gigahertz. The theoretical analysis and numerical experiments demonstrate that the proposed scheme can realize high accuracy positioning.

We study the performance of multi-branch hierarchical combining diversity systems. Closed-form expressions are derived for asymptotic error rate and asymptotic outage probability of hierarchical selection-combining maximal-ratio combining (SC-MRC) and selection-combining equal-gain combining over Rician channels with arbitrary correlation. Comparison is made between hybrid-selection maximal-ratio combining (HS/MRC) and hierarchical SC-MRC, and it is shown that SC-MRC suffers at most 0.6 dB signal-to-noise ratio (SNR) loss with respect to HS/MRC for practical number of antennas. However, SC-MRC incurs much smaller insertion loss. Numerical results show that the analytical solutions can provide accurate estimation of error rate and outage probability in large SNR region.

Optimal relay placement is studied for free-space optical multi-hop communication when obstacles and infeasible regions exist. We propose an optimization framework to find the optimal relay locations in the specified available regions and make laser links bypass obstacles of various shapes. A grouping optimization technique is proposed to reduce the optimization time when the number of relays is large, and we numerically demonstrate that this technique can provide solutions close to the optimal solutions, and its average searching time grows linearly with the number of relays. Simulation results show that our proposed optimization framework can effectively provide good solution to the problem of optimal relay placement in limited available regions, and enable the laser links to bypass obstacles in order to achieve minimum end-to-end outage probability.

A unified capacity analysis under weak and composite turbulences of a free-space optical (FSO) link that accounts for pointing errors and both types of detection techniques (i.e. intensity modulation/direct detection as well as heterodyne detection) is addressed in this work. More specifically, a unified exact closed-form expression for the moments of the end-to-end signal-to-noise ratio (SNR) of a single link FSO transmission system is presented in terms of well-known elementary functions. Capitalizing on these new moments expressions, unified approximate and simple closed- form results are offered for the ergodic capacity at high SNR regime as well as at low SNR regime. All the presented results are verified via computer- based Monte-Carlo simulations.

Block error rate performance of subcarrier intensity modulation based optical wireless communication systems is analysed over Gamma–Gamma and lognormal atmospheric turbulence channels. The analysis is applicable to non‐coherent binary modulations and coherent binary phase shift keying. The special cases of K ‐distributed strong turbulence channel and negative exponential turbulence channel are also considered. The closed‐form expressions of block error rate in the Gamma–Gamma turbulence channels are derived using a series expansion of the modified Bessel function. For the lognormal turbulence channels, the Gauss–Laguerre quadrature method can be used to estimate the block error rate accurately.

The performance of free-space optical (FSO) communication systems is compromised by atmospheric fading and pointing errors. The pointing errors are widely considered as a combination of two components: boresight and jitter. A statistical model is investigated for pointing errors with nonzero boresight by taking into account the laser beamwidth, detector aperture size, and jitter variance. A novel closed-form probability density function (PDF) is derived for this new nonzero boresight pointing error model. Furthermore, we obtain closed-form PDF for the composite lognormal turbulence channels and finite series approximate PDF for the composite Gamma-Gamma turbulence channels, which is suitable for terrestrial FSO applications impaired by building sway. We conduct error rate analysis of on-off keying signaling with intensity modulation and direct detection over the lognormal and Gamma-Gamma fading channels. Asymptotic error rate analysis and outage probability of such a system are also presented based on the derived composite PDFs. It is shown that the boresight can only affect the coding gain, while the diversity order is determined by the atmospheric fading effect as well as the pointing error effect.

Although relatively simple exact error rate expressions are available for selection combining (SC) and equal gain combining (EGC) with independent fading channels, results for correlated channels are highly complex, requiring multiple levels of integration when more than two branches are considered. Asymptotic analysis has been used to derive simple error expressions valid in the high signal-to-noise ratio (SNR) region. However, it is not clear at what SNR value the asymptotic results are an accurate approximation of the exact solution. In this paper, we derive asymptotic results for SC and EGC in correlated generalized Rician fading channels. Furthermore, the asymptotic results for SC are expanded into an exact infinite series. Although this series grows quickly in complexity as more terms are included, truncation to even two or three terms has much greater accuracy than the first (asymptotic) term alone. Finally, we derive asymptotically tight lower and upper bounds on the error rate for EGC. Using these bounds, we are able to show at what SNR value the asymptotic results are valid.

The asymptotic performances of multibranch diversity reception systems employing the popular and important combining techniques over arbitrarily correlated Rician channels are studied. Based on an integral representation of the joint probability density function of multivariate Rician random variables with arbitrary correlations, closed-form expressions for asymptotic error rate and asymptotic outage probability are derived for multibranch receiver diversity receptions. The important diversity schemes studied are maximal ratio combining, equal gain combining, selection combining, and hybrid maximal ratio combining as well as hybrid equal gain combining. Numerical results indicate that these analytical solutions can provide accurate estimation of error rate and outage probability in large-signal-to-noise-ratio regimes. The analysis provides some important theoretical and practical results concerning the performance characteristics of multibranch diversity receivers operating on arbitrarily correlated Rician fading channels.

An adaptive subcarrier intensity modulation employing rectangular quadrature amplitude modulation is investigated for optical wireless communication over the Gamma-Gamma turbulence channels. The adaptive scheme is implemented by adapting the modulation order according to the received signal-to-noise ratio and a pre-defined target bit-error rate requirement. Highly accurate series approximations for the achievable spectral efficiency, average bit-error rate, and outage probability are derived using a series expansion of the modified Bessel function. In addition, asymptotic bit-error rate and outage probability analyses are also presented. Our asymptotic analysis reveals that the diversity order of the considered system depends only on the smaller channel parameter of the Gamma-Gamma turbulence.

Exact error rate analysis for pre-detection equal gain combining over arbitrarily correlated fading branches has proven elusive. Even specialized correlation models result in complex error rate expressions with multiple nested infinite series or integrals. For this reason, asymptotic analysis is a useful tool due to the simplicity of the error rate expressions it produces. However, one major shortcoming of this approach is that the asymptotic technique can not predict at what signal-to-noise ratio the asymptotic approximation is accurate. In this paper, we derive asymptotically tight single-integral lower and upper bounds on the error probability for a correlated generalized Rician fading model. These lower and upper bounds help determine when the asymptotic solutions approach the exact results.

Free-space optical links are vulnerable to atmospheric fading and pointing errors. When a laser beam wanders, most previous works model the random radial displacement distance of the pointing error as a Rayleigh distributed random variable. However, for a non-ideally fast-tracked laser beam, the radial displacement distance at the receiver aperture may not obey the Rayleigh distribution, since the displacements in vertical and horizontal directions may not always follow a zero-mean Gaussian distribution. In this paper, we generalize a widely used pointing error model by assuming that the radial displacement distance follows a Rician distribution and derive a closed-form probability density function (PDF) of the generalized pointing error model. Furthermore, we derive closed-form PDFs for the composite lognormal and Gamma-Gamma turbulence channels using this generalized pointing error model. Error rate performance is also studied for an on-off keying signaling system with intensity modulation and direct detection in the presence of this generalized pointing error.

A recently published lower bound on the probability of a finite union of events, which is referred to in this paper as the new FLS lower bound, is applied to find a lower bound of the average symbol detection error rate of nonuniform M-ary modulated signals transmitted over the additive white Gaussian noise channels. An optimum detector based on the maximum a posteriori principle is assumed. Numerical results are presented to show that, when compared with the best known de Caen lower bound and the KAT lower bound in the literature, the new FLS lower bound always provides the tightest estimation of the average symbol detection error rate for both uniform and nonuniform signallings.

Coherent free-space optical communication employing M-ary phase-shift keying is investigated for space-time coded multiple-input multiple-output (MIMO) systems over Gamma-Gamma turbulence channels. The proposed space-time coded system offers an efficient utilization of coherent free-space optical channel capacity by exploiting the degree of freedom at both the transmitter and receiver ends. Such a system design is particularly suitable for turbulence channels. Novel closed-form error rate expressions are presented for the proposed MIMO system using a series expansion approach. In addition, asymptotic symbol-error rate and truncation error analyses are performed. It is shown that our proposed system is a viable solution for coherent optical MIMO applications and achieves the full diversity benefits of MIMO links. Numerical results further demonstrate the high accuracy of the derived error rate solutions.

A free-space optical communication system using subcarrier binary phase-shift keying modulation is analyzed for Gamma-Gamma turbulence channels with pointing errors. We study the bit-error rate performance of such system and obtain highly accurate bit-error rate approximations using a series expansion approach. Asymptotic error rate analysis is also presented. Our asymptotic analysis reveals some unique transmission characteristics of such system.

We investigate a variable-rate, constant-power adaptive subcarrier intensity modulation employing M-ary phase shift keying and rectangular quadrature amplitude modulation for optical wireless communication over the Gamma-Gamma turbulence channels. The adaptive schemes offer efficient utilization of optical wireless communication channel capacity by adapting the modulation order according to the received signal-to-noise ratio and a pre-defined target bit-error rate requirement. Novel closed-form series solutions are presented for the achievable spectral efficiency, average bit-error rate, and outage probability using a series expansion approach of the modified Bessel function. In addition, asymptotic bit-error rate and outage probability analyses are presented. Our asymptotic bit-error rate analysis shows that the diversity order of both non-adaptive and adaptive systems depends only on the smaller channel parameter of the Gamma-Gamma turbulence. Numerical results demonstrate high accuracy of our series solutions with finite number of terms and improved spectral efficiency achieved by the adaptive systems without increasing the transmitter power or sacrificing bit-error rate requirements.

Subcarrier intensity modulation is analyzed for general order rectangular quadrature amplitude modulation over the Gamma-Gamma turbulence channels. Closed-form expressions of average symbol error rate are derived using a series expansion of the modified Bessel function and a moment generating function approach. Truncation error analysis and asymptotic error rate analysis are also presented. Numerical results demonstrate that our closed-form expressions are highly accurate.

We study the error rate of a subcarrier intensity modulated multiple-input multiple-output wireless optical communication system employing phase-shift keying and Alamouti code. Closed-form error rate expressions are derived using a moment generating function approach with a series expansion of the modified Bessel function. Truncation error analysis and asymptotic error rate analysis are also presented. Our asymptotic analysis shows that the diversity order of the system depends only on the smaller channel parameter. The numerical results demonstrate that our series solutions are efficient and highly accurate.

From the canonical representation of the Marcum Q-function, a simple and highly accurate small argument approximation for the Marcum Q-function is first obtained. Utilizing this approximation, we derive the asymptotic error rate and outage probability expressions for multi-branch selection combining over generalized correlated Nakagami-m fading channels. These closed-form solutions can be used to provide rapid and accurate estimation of the error rates and outage probabilities in large signal-to-noise ratio regimes. It is shown that asymptotic error rates and outage probability over correlated Nakagami-m branches can be obtained by scaling the asymptotic error rates and outage probability over independent branches with a factor detm(M), where det(M) is the determinant of matrix M whose elements are the square root of the corresponding elements in the branch power covariance coefficient matrix.

The generalized method of moments (GMM) is introduced in the framework of estimating the Nakagami-m fading parameter. This GMM approach provides a systematic procedure for finding the moment-based m parameter estimators. Using the multivariate delta method, we present a derivation for the asymptotic variance of the GMM Nakagami m parameter estimators. Monte Carlo simulation results show that the GMM approach can lead to estimators outperforming existing moment-based m parameter estimators over a wide range of channel conditions. It is shown that the asymptotic performance of these GMM estimators are close to that of the maximum-likelihood based estimator. The proposed method can be easily applied to both noiseless and noisy environments.

The generalized method of moments (GMM) is introduced in the framework of estimating the Nakagami-m fading parameter. This GMM approach provides a systematic procedure for finding the moment-based m parameter estimators. Using the multivariate delta method, we demonstrate a derivation for the asymptotic variance of the GMM Nakagami m parameter estimators. Monte Carlo simulation results show that the GMM approach can lead to estimators outperforming existing moment-based m parameter estimators over a wide range of channel conditions. It is shown that the asymptotic performance of these GMM estimators are close to that of the maximum-likelihood based estimation. The proposed method can be easily applied to both noiseless and noisy environments.

Coherent free-space optical (FSO) communications is analyzed for long-range, high turbulence FSO systems operating under K-distributed turbulence conditions. Exact error rates are presented for M-ary phase-shift keying (MPSK) and M-ary quadrature amplitude modulation (MQAM) using a closed-form moment generating function. Maximum ratio combining (MRC) reception is employed in the system with MPSK and MQAM to mitigate the turbulence effects, and asymptotic error rate analysis is presented for this MRC reception. The error rate performance degradation caused by phase compensation error is also studied, and it is found that the impact of phase compensation error on the error rate performance can be small when the standard deviation of the phase compensation error is kept below twenty degrees.

Coherent modulation is more effective than differential modulation for orthogonal frequency division multiplexing (OFDM) systems requiring high data rate and spectral efficiency. Channel estimation is therefore an integral part of the receiver design. Two iterative maximum likelihood (ML) based channel estimation algorithms are proposed for OFDM uplinks in dispersive time-varying channels. The uplink multipath fading channel is modeled such that the channel state can be determined by estimating the unknown channel parameters. A second-order Taylor series expansion is adopted to simplify the channel estimation problem. Based on the system model, an iterative ML-based algorithm is first proposed to estimate the discrete-time channel parameters. The mean square error performance of the proposed algorithm is analyzed using a small perturbation technique. Based on a convergence rate analysis, an improved iterative ML channel estimation algorithm is presented using a successive overrelaxation method. Numerical experiments are performed to confirm the theoretical analyses and show the improvement in convergence rate of the improved algorithm.

Diversity reception can be used over free-space optical links to mitigate atmospheric turbulence effects on system performance. In this paper, we present the error rate analysis for equal gain combining with heterodyne detection in coherent free-space optical communication systems over K-distributed atmospheric turbulence channels. The signal-to-noise ratio statistics of the combiner output are analyzed, and a new closed-form characteristic function is derived for the square root of instantaneous signal-to-noise ratio. Based on this characteristic function, error rates for binary phase-shift keying and outage probability are presented for equal gain combining schemes. Comparing to the maximum ratio and selection diversity reception techniques, our results demonstrate the usefulness of equal gain combining reception in coherent free-space optical communications. Simulation results are presented to further verify the accuracy of the analytical results.

Asymptotic error rate expressions are derived for multi-branch equal gain combining and selection combining operating on arbitrarily correlated Rayleigh fading channels. These closed-form solutions are used to provide rapid and accurate estimation of the error rates in large signal-to-noise ratio regions. More importantly, they reveal additional insights into the transmission characteristics of linear diversity combining schemes operating on correlated Rayleigh fading channels. It is shown that the asymptotic error rates over correlated branches can be obtained by scaling the asymptotic error rates over independent branches with a factor, det(M), where det(M) is the determinant of the normalized channel correlation matrix. This relationship is valid for both coherent and noncoherent signalings. A similar relationship is also established for the outage probabilities of fading wireless systems employing multi-branch diversity reception.

Asymptotic error rate expressions are derived for multi-branch equal gain combining and selection combining operating on arbitrarily correlated Rayleigh fading channels. These closed-form solutions are used to provide rapid and accurate estimation of the error rates in large signal-to-noise ratio regions. More importantly, it is shown that the asymptotic error rates over correlated branches can be obtained by scaling the asymptotic error rates over independent branches with a factor, det(M), where det(M) is the determinant of the normalized channel correlation matrix.

Error performance of a coherent free space optical system with K-distributed turbulence is studied. A closed-form expression of the moment generating function for K-distributed turbulence is derived, and the moment generating function is used to obtain the exact error rates for binary phase shift keying (BPSK), BPSK with spatial diversity, as well as differential phase-shift keying. Based on our analytical moment generating function expression, asymptotic error rates in large signal-to-noise ratio regions are obtained.

Maximum-likelihood based channel estimation is considered for orthogonal frequency division multiplexing systems in dispersive time-varying channels. A successive overrelaxation approach is adopted to analyze the convergence rate of an iterative channel estimation algorithm. Based on the analysis, an improved fast converging iterative channel estimation algorithm is proposed. Numerical tests are performed to show the improvement of the proposed algorithm over a recently proposed channel estimation algorithm.

Asymptotic bit-error rate expressions are derived for binary phase shift keying with multi-branch equal gain combining (EGC) and selection combining (SC) on equally correlated Rician channels. Numerical results indicate that these analytical error rate expressions can provide accurate error rate estimation at large signal-to-noise ratio (SNR). Furthermore, these analytical results can be used to reveal some important insights into the performance characteristics of EGC and SC operating on equally correlated Rician fading channels.

Asymptotic error rate expressions are derived for equal gain combining and selection combining on dual-branch correlated Rician channels. The asymptotic error rate performances of three popular diversity combining techniques are compared and some insights are obtained. The impacts of various channel parameters on the asymptotic performances of these diversity combining techniques are studied comprehensively. Some previous observations on diversity performances on correlated Rician channels are unified and further extended.

The bit error rate of a binary phase shift keying (BPSK) signal in Ricean-faded cochannel interference is studied. A precise bit error rate expression is derived for a bandlimited BPSK signal corrupted by an arbitrary number of asynchronous Ricean-faded interfering signals. An error rate estimation based on a saddlepoint approximation is also provided. It is shown that this approximation is highly accurate. For the special case, when there is one synchronous Ricean-faded interfering signal in an interference-limited environment, an asymptotic error rate analysis based on the saddle point approximation reveals that fading in the cochannel interfering signal can have different impacts on the desired user's error rate performance. Our study shows that when the signal-to-interference ratio (SIR) is sufficiently large, the error floor of the desired user signal decreases with an increase of the Rice factor in the interfering user's fading channel. However, the opposite phenomenon can happen when the SIR is small.

An asymptotic error rate performance analysis has been reported recently for maximum ratio combining on correlated Rician channels; however, a similar approach cannot be applied to equal gain combining or selection combining. A novel approach is introduced to analyze the asymptotic error rate performances of equal gain combining and selection combining on dual-branch correlated Rician channels. The asymptotic error rate performances of popular diversity combining techniques are compared and some insights are obtained that explain some behaviors previously considered unexpected. The impacts of various channel parameters on the asymptotic performances of these diversity combining techniques are studied comprehensively. Some previous observations on diversity performances on correlated Rician channels are unified and further extended.

Error rates of orthogonal frequency-division multiplexing (OFDM) signals in multipath slow fading Nakagami-m fading channels are considered. The exact probability density function of a sum of Nakagami-m random phase vectors is used to derive a closed-form expression for the error rates of OFDM signals. The precise error-rate analysis is extended to a system using multichannel reception with maximal ratio combining. An asymptotic error-rate analysis is also provided. For a two-tap channel with finite values of Nakagami-m fading parameters, our analysis and numerical results show that the asymptotic error-rate performance of an OFDM signal is similar to that of a single carrier signal transmitted over a Rayleigh fading channel. On the other hand, our analysis further shows that a frequency-selective channel that can be represented by two constant taps has similar asymptotic error-rate performance to that of a one-sided Gaussian fading channel. It is observed that, depending on the number of channel taps, the error-rate performance does not necessarily improve with increasing Nakagami-m fading parameters.

The bit error rate of a binary phase shift keying signal in Ricean-faded cochannel interference is studied. A precise bit error rate expression based on a characteristic function method is derived for a bandlimited binary phase shift keying signal corrupted by an arbitrary number of asynchronous Ricean-faded interfering signals. For the special case when there is one synchronous Ricean-faded interfering signal, a Chernoff bound analysis is performed and it predicts that the error floor of the desired user signal decreases with an increase of the Rice factor in the interfering user's fading channel. However, our precise bit error rate analysis results reveal that the opposite phenomenon can also happen, in particular when the signal-to-interference power ratio is low. A saddle-point approximation based error rate analysis is also provided. It is shown that this approximation is highly accurate. An asymptotic analysis based on the saddle-point approximation further reveals that a minimum signal-to-interference power ratio is required to have the desired user's error rate performance improved by a less-faded interfering signal.

An accurate bit-error-rate (BER) calculation method is derived for a binary direct-sequence (DS) spread-spectrum multiple-access (SSMA) system using conventional matched filter receivers and random sequences on flat-fading Nakagami channels. A closed-form expression for the characteristic function of a sum of multiple Nakagami interfering signals is found. The accuracy of the standard Gaussian approximation (SGA) is assessed for DS code-division multiple-access (CDMA) systems operating on Nakagami fading channels.

Space diversity is an effective method to combat fading and cochannel interference (CCI) in wireless systems. In this paper, outage performances of several diversity schemes, including a practical variation of maximal-ratio combining that does not require signal-to-noise ratios at different antennas, equal-gain combining, and selection combining, are compared analytically for an interference-limited environment in a Rayleigh fading channel. Our analysis provides insight into the performance of diversity schemes in the presence of CCI as well as assesses the impact of cochannel interferer power distributions.

The error rates of orthogonal frequency division multiplexing (OFDM) signals on frequency selective Nakagami-m fading channels are considered. The exact probability density function of a sum of Nakagami-m random vectors is used to derive a closed-form expression for the error rate of OFDM signals. The exact error rate analysis is also extended to a system using multichannel reception with maximal ratio combining. Our analysis and numerical results show that the error rates obtained using a previous Nakagami-m approximation can be unreliable. It is observed that, depending on the number of channel taps, the error rate performance may degrade with increasing values of Nakagami fading parameters.

The bit-error rate (BER) of bandlimited binary phase-shift keying in a fading and cochannel interference (CCI) environment is derived for the case of perfect coherent detection. The fading-and-interference model assumed is general and of interest for microcellular system studies. The model allows both desired signal and interfering signals to experience arbitrary amounts of fading severity. A precise BER expression is derived using a characteristic function method. Using this accurate analytical result, the impact of the interfering users' fading severity on the desired user-error rate is examined. The BERs obtained under perfect coherent detection are also valid as lower performance bounds for practical realizable receivers where ideal coherent detection is difficult to implement. The error-rate performance of a novel bandwidth-efficient pulse shape is determined for the general fading and CCI environment. Analysis and numerical results show that the new pulse can provide better BER performance than the widely used raised-cosine pulse.

A closed-form expression is derived for the moment-generating function of the Weibull distribution, valid when its fading parameter assumes integer values. Expressions for average signal-to-noise ratio, signal outage, and average symbol-error rate are derived for single-channel reception and independent multichannel diversity reception operating on flat Weibull slow-fading channels.

Equal gain combining (EGC) diversity has performance close to that of maximal ratio combining but at lower implementation complexity. We present a new outage performance analysis for EGC in mobile cellular radio systems that are limited by cochannel interference and undergo Rayleigh fading. We utilize a new model where interfering signals add in amplitude and phase across antenna array elements. In addition, the interfering signals may each have a different power. In comparing our analysis to an existing method, we find that: 1) as much as 1.5 dB difference in signal-to-interference ratio may exist at the same probability of outage; 2) the existing method can lead to overly optimistic outage performance prediction in certain situations.

An exact average bit error rate expression is derived for binary phase shift keying in a generalized Nakagami/Nakagami fading channel. The accuracy of an Gaussian approximation is assessed using this exact expression.

A binary direct-sequence spread-spectrum multiple-access system with random sequences in flat Rayleigh fading is considered. A new explicit closed-form expression is obtained for the characteristic function of the multiple-access interference signals. It is shown that the overall error rate can be expressed by a single integral whose integrand is nonnegative and exponentially decaying. Bit-error rates (BERs) are obtained with this expression to any desired accuracy with minimal computational complexity. The dependence of the system BER on the number of transitions in the target user signature chip sequence is explicitly derived. The results are used to examine definitively the validity of three Gaussian approximations and to compare the performances of synchronous systems to asynchronous systems.

Accurate bit error rate calculation is considered for a binary, direct sequence spread spectrum multiple access system using conventional matched filter receivers and long code sequences in flat fading Nakagami channels. To this end, a closed-form expression for the characteristic function of a sum of multiple interfering signals is found. The exact average bit error rate is expressed as a single integral with a well-behaved integrand function. Numerical results obtained from the new accurate bit error rate expression are compared with the standard Gaussian approximation.