While neural lossy compression techniques have markedly advanced the efficiency of Channel State Information (CSI) compression and reconstruction for feedback in MIMO communications, efficient algorithms for more challenging and practical tasks—such as CSI compression for future channel prediction and reconstruction with relevant side information—remain underexplored, often resulting in suboptimal performance when existing methods are extended to these scenarios. To that end, we propose a novel framework for compression with side information, featuring an encoding process with fixed-rate compression using a trainable codebook for codeword quantization, and a decoding procedure modeled as a backward diffusion process conditioned on both the codeword and the side information. Experimental results show that our method significantly outperforms existing CSI compression algorithms, often yielding over twofold performance improvement by achieving comparable distortion at less than half the data rate of competing methods in certain scenarios. These findings underscore the potential of diffusion-based compression for practical deployment in communication systems.

This paper investigates the channel aging problem of mobile light-fidelity (LiFi) systems. In the LiFi physical layer, the majority of the optimization problems for mobile users are non-convex and require the use of dual decomposition or heuristics techniques. Such techniques are based on iterative algorithms, and often cause a high processing delay at the physical layer. Hence, the obtained solutions are rendered sub-optimal since the LiFi channels are evolving. In this paper, a proactive-optimization (PO) approach that can alleviate the LiFi channel aging problem is proposed. The core idea is to design a long-short-term-memory (LSTM) network that is capable of predicting posterior positions and orientations of mobile users, which can be then used to predict their channel coefficients. Consequently, the obtained channel coefficients can be exploited to derive near-optimal transmission-schemes prior to the intended service-time, which enables real-time service. Through various simulations, the performance of the designed LSTM model is evaluated in terms of prediction error and inference complexity, as well as its application in a practical LiFi optimization problem.

This paper investigates the potential of integrating reconfigurable intelligent surface (RIS) and cooperative non-orthogonal multiple access (C-NOMA) in preserving the freshness of information in real-time Internet of Things (IoT) applications. The system model comprises one base stations (BS), one RIS, and two IoT devices (IoTDs), in an uplink setting, where the IoTD with poor channel quality is assisted by the RIS and by the IoTD with the strong quality through a full duplex (FD) device-to-device (D2D) communication. In this setup, an optimization problem has been formulated to minimize the average sum Age of Information (AoI) by optimizing the transmit power of the IoTDs and the RIS phase shift matrix, which is non-convex and is hard to solve directly. In order to resolve this issue, the formulated optimization problem is divided into a power control sub-problem and a RIS configuration sub-problem. Capitalizing on that, a closed-form solution has been derived for the power control sub-problem and the RIS configuration sub-problem is solved by resorting to difference-of-convex (DC) along with successive convex approximation (SCA). The simulation results demonstrate that the proposed RIS-empowered uplink C-NOMA scheme achieves higher AoI-reduction compared to all considered baseline schemes.

In this paper, we consider a wireless network consisting of a base station that is serving multiple real-time traffic streams forwarding information updates to their destinations in order to sustain the freshness of information for time-critical applications. Since the wireless channels may be unreliable due to the impurities of the propagation environments, such as deep fading, blockages, etc., we integrate a reconfigurable intelligent surface to the wireless system in order to mitigate the propagation-induced impairments, enhance the quality of the wireless links, and ensure that the required freshness of information is achieved for these real time applications. For this network set-up, we investigate the joint optimization of the traffic streams scheduling and the reconfigurable intelligent surface phase-shift matrix with the goal of minimizing the long-term average Age of Information. The formulated optimization problem is a mixed integer non-convex optimization problem, which is difficult to solve. To circumvent the high-coupled optimization variables, and with the aid of bi-level optimization, we decompose the original problem into an outer traffic stream scheduling problem and an inner reconfigurable intelligent surface phase-shift matrix problem. For the outer problem, owing to its complexity and stochastic nature of packet arrivals, we resort to deep reinforcement learning solution where the traffic stream scheduling is modeled as a Markov Decision Process, and Proximal Policy Optimization is invoked to solve it. Whereas, the inner problem that determines the reconfigurable intelligent surface configuration is solved through semi-definite relaxation. Finally, we show through extensive simulations that our approach evaluates the combined impact of scheduling policy and reconfigurable intelligent surface configuration on the long term average Age of Information, where we demonstrate its superiority against other baseline schemes.

This paper investigates the benefits of integrating multiple reconfigurable intelligent surfaces (RISs) in enhancing the timeliness performance of uplink Internet-of-Things (IoT) network, where IoT devices (IoTDs) upload their time-stamped status update information to a base station (BS) using non-orthogonal multiple access (NOMA). Accounting to the potential unreliable wireless channels due to the impurities of the propagation environments, such as deep fading, blockages, etc., multiple RISs are deployed in the considered IoT network to mitigate the propagation-induced impairments, to enhance the quality of the wireless links, and to ensure that the required freshness of information is achieved. In this setup, an optimization problem has been formulated to minimize the average sum Age of Information (AoI) by optimizing the transmit power of the IoTDs, the IoTDs clustering policy, and the RISs configurations. The formulated problem ends up to be a mixed-integer non-convex problem. In order to tackle this challenge, the RISs configurations are first obtained by adopting a semi-definite relaxation (SDR) approach. Then, the joint power allocation and user-clustering problem is solved using the concept of bi-level optimization, where the original problem is decomposed into an outer IoTDs clustering problem and an inner power allocation problem. Optimal closed-form expressions are derived for the inner problem and the Hungarian method is invoked to solve the outer problem. Numerical results demonstrate that our proposed approach achieves lowest AoI compared to the other baseline approaches.

Cooperative-Autonomous-Driving (CAD) systems stringently require that vehicle status information (e.g, speed, position, etc) be timely disseminated for safety reasons. CAD systems rely on real-time information to make critical decisions; hence, the paramount criticality of temporally valid information generation and dissemination. However, the timely information update messages’ delivery faces numerous challenges due to the highly alternating wireless signal propagation in vehicular environments as a result of, for instance, shadowing and blockage, which lead to the unavailability of reliable communication links between cooperating vehicles. Under such harsh conditions, Reconfigurable-Intelligent-Surfaces (RISs) have been proven to highly contribute in mitigating the propagation-induced impairments of the wireless environments and, hence, promoting more robust communication links, which, in turn, allow for maintaining the required freshness of information. In the above-context, this paper revolves around the minimization of the Age-of-Information (AoI) perceived by each of a CAD system’s destination node. The problem is formulated as an Integer-Linear-Program (ILP), which turns out to be quite complex. To work around this complexity, it is proposed herein to use decomposition based on the Lagrangian relaxation method, which largely facilitates the problem’s resolution following typical dynamic programming methodologies. Consequently a feasible solution is extracted using a relatively simple heuristic. An analytical framework is established to reveal insights into the proposed solution and gauge its merits through the establishment of a thorough simulation framework involving various scenarios aiming at verifying its correctness, validity and superiority as compared to other solutions derived using the state-of-the-art branch-and-cut method implemented by CPLEX.

This paper investigates the integration of reconfigurable intelligent surface (RIS) with downlink non-orthogonal-multiple-access (NOMA) in a multi-user two-cell network assisted by the joint-transmission coordinated multipoint (JT-CoMP). Specifically, the RIS is deployed at the edge of two adjacent cells to assist the JT-CoMP from these two cells to multiple far NOMA users located at their edges. Under this setup, we jointly optimize the power allocation (PA) coefficients at the base stations (BSs), the user clustering (UC) policy, and the phase-shift (PS) matrix of the RIS with the objective of maximizing the network sum-rate subject to a target quality-of-service, defined in terms of the minimum required data rate at each cellular user, and the successive interference cancellation (SIC) constraints. The formulated problem ends to be a non-convex mixed-integer non-linear program that is difficult to be solved in a straightforward manner. To alleviate this issue, and with the aid of alternating optimization (AO), the original optimization problem is decomposed into two sub-problems, a joint PA and UC sub-problem and a PS sub-problem, that are solved in an alternating way. For the first sub-problem, we invoke the bi-level optimization approach to decouple the PA sub-problem from the UC sub-problem. For the PA sub-problem, closed-form expressions for the optimal PA coefficients are derived. On the other hand, the UC problem is projected to multiple 2-dimensional assignment problems, each of which is solved using the Hungarian method. Finally, the PS sub-problem is formulated as a difference-of-convex problem and an efficient solution is obtained using the successive convex approximation technique. The numerical results reveal that the network sum-rate of the proposed RIS-assisted CoMP NOMA networks outperforms the conventional CoMP NOMA scheme without the assistance of the RIS, the RIS-assisted CoMP orthogonal multiple access (OMA) scheme, and RIS-assisted NOMA scheme, especially for low transmit power from the BSs.

Ultra-Reliable and Low Latency Communications (URLLC) is one of the essential services in 5G networks and beyond. The coexistence of URLLC alongside other services, namely, enhanced Mobile BroadBand (eMBB) and massive Machine-Type Communications (mMTC), calls for developing spectrally efficient multiplexing techniques. In this work, we study the problem of scheduling URLLC traffic in a downlink system in the presence of eMBB traffic. Based on the proposed superposition/puncturing scheme, a resource allocation problem is formulated with the objective to minimize the rate loss of the eMBB service and URLLC packet segmentation loss while satisfying the eMBB and URLLC quality of service (QoS) constraints. The resulting problem is formulated as a mixed-integer non-linear program (MINLP) which is generally very hard to solve in polynomial time. Hence, we reformulate the problem as a one-to-one pairing problem and we derive its feasibility region as well as the optimal solutions for the power and spectral resource allocation. Subsequently, we propose a low complexity algorithm to support the many-to-many pairing. Simulation results show that the proposed algorithm achieves higher URLLC packet admission rate and lower rate loss for eMBB. For instance, the URLLC packet admission rate, unlike baseline methods, is shown to be preserved under the proposed method even at higher URLLC load. It is shown that at least 30% more URLLC users can be served without degrading their QoS, while keeping the impact on eMBB rate minimal. Detailed numerical evaluation is presented to quantify the benefits of the proposed method.

This paper studies the coexistence of enhanced mobile broadband (eMBB) and ultra-reliable and low-latency communication (URLLC) services in a cellular network that is assisted by a reconfigurable intelligent surface (RIS). The system model consists of one base station (BS) and one RIS that is deployed to enhance the performance of both eMBB and URLLC in terms of the achievable data rate and reliability, respectively. We formulate two optimization problems, a time slot basis eMBB allocation problem and a mini-time slot basis URLLC allocation problem. The eMBB allocation problem aims at maximizing the eMBB sum rate by jointly optimizing the power allocation at the BS and the RIS phase-shift matrix while satisfying the eMBB rate constraint. On the other hand, the URLLC allocation problem is formulated as a multi-objective problem with the goal of maximizing the URLLC admitted packets and minimizing the eMBB rate loss. This is achieved by jointly optimizing the power and frequency allocations along with the RIS phase-shift matrix. In order to avoid the violation in the URLLC latency requirements, we propose a novel framework in which the RIS phase-shift matrix that enhances the URLLC reliability is proactively designed at the beginning of the time slot. For the sake of solving the URLLC allocation problem, two algorithms are proposed, namely, an optimization-based URLLC allocation algorithm and a heuristic algorithm. The simulation results show that the heuristic algorithm has a low time complexity, which makes it practical for real-time and efficient multiplexing between eMBB and URLLC traffic. In addition, using only 60 RIS elements, we observe that the proposed scheme achieves around 99.99% URLLC packets admission rate compared to 95.6% when there is no RIS, while also achieving up to 70% enhancement on the eMBB sum rate.

In this paper, we investigate the dynamic power allocation for a visible light communication (VLC) cellular system consisting of two coordinating attocells, each equipped with one access-point (AP). The coordinated multipoint (CoMP) between the two cells is introduced to assist users experiencing high inter-cell-interference (ICI). Specifically, the coordinated zero-forcing (ZF) precoding is used to cancel the ICI at the users located near the centers of the cells, whereas the joint transmission (JT) is employed to eliminate the ICI at the users located at the edge of both cells and to improve their receptions as well. Furthermore, two multiple access techniques are invoked within each cell, namely, non-orthogonal-multiple-access (NOMA) and cooperative non-orthogonal-multiple-access (C-NOMA). Hence, two multiple access techniques are proposed for the considered multi-user multi-cell system, namely, the CoMP-assisted NOMA scheme and the CoMP-assisted C-NOMA scheme. For each scheme, two power allocation frameworks are formulated each as an optimization problem, where the objective of the former is maximizing the network sum data rate while guaranteeing a certain quality-of-service (QoS) for each user, whereas the goal of the latter is to maximize the minimum data rate among all coexisting users. The formulated optimization problems are not convex, and hence, difficult to be solved directly unless using heuristic methods, which comes at the expense of high computational complexity. To overcome this issue, optimal and low complexity power allocation schemes are derived. In the simulation results, the performance of the proposed CoMP-assisted NOMA and CoMP-assisted C-NOMA schemes are compared with those of the CoMP-assisted orthogonal-multiple-access (OMA) scheme, the C-NOMA scheme and the NOMA scheme, where the superiority of the proposed schemes are demonstrated. Finally, the performance of the proposed schemes and the considered baselines is evaluated while varying various system parameters.

We consider a wireless network consisting of two adjacent cells, where the joint transmission (JT) coordinated multipoint (CoMP) is established to assist the user equipments (UEs) located at the edge of each cell. In addition, full-duplex (FD) cooperative non-orthogonal-multiple-access (C-NOMA) is invoked within each cell to improve the data rates of the UEs and to assist those at the cell edge. The UEs are categorized into two groups, namely, cell-center UEs ($CUs$) and cell-edge UEs ($EUs$). The$CUs$are the UEs located around the center of each cell. Meanwhile, the$EUs$are the UEs located at the edge of each cell, where the JT-CoMP is applied since they have less distinctive received power from two cells. In this paper, a framework to jointly optimize the power control and the UEs clustering of CoMP-assisted FD C-NOMA system is formulated as an optimization problem to maximize the network sum-rate while guaranteeing the required quality-of-service of UEs. The formulated problem is a non-convex mixed-integer non-linear program that cannot be solved in a straightforward manner. To tackle this issue, the formulated problem is decomposed into an inner power allocation problem and an outer UEs clustering problem. For the inner problem, a computational-efficient solution is obtained. Meanwhile, the outer problem is reformulated as a one-to-one three-sided matching game. Then, a low-complexity near-optimal clustering algorithm is proposed. The simulation results demonstrate that 1) the optimality of the power control solution; 2) the CoMP-assisted FD C-NOMA has a superior performance compared to CoMP-assisted half-duplex (HD) C-NOMA and CoMP NOMA schemes for moderate values of self-interference. It has been also shown that the proposed solution achieves around 96.5% of the average achievable network sum-rate of the optimal solution.

This paper investigates the downlink transmission of reconfigurable intelligent surface (RIS)-aided cooperative non-orthogonal-multiple-access (C-NOMA), where both half-duplex (HD) and full-duplex (FD) relaying modes are considered. The system model consists of one base station (BS), two users and one RIS. The goal is to minimize the total transmit power at both the BS and at the user-cooperating relay for each relaying mode by jointly optimizing the power allocation coefficients at the BS, the transmit power coefficient at the relay user, and the passive beamforming at the RIS, subject to power budget constraints, the successive interference cancellation constraint and the minimum required quality-of-service at both cellular users. To address the high-coupled optimization variables, an efficient algorithm is proposed by invoking an alternating optimization approach that decomposes the original problem into a power allocation sub-problem and a passive beamforming sub-problem, which are solved alternately. For the power allocation sub-problem, the optimal closed-form expressions for the power allocation coefficients are derived. Meanwhile, with the aid of difference-of-convex rank-one representation and successive convex approximation, an efficient solution for the passive beamforming is obtained. The simulation results validate the accuracy of the derived power control closed-form expressions and demonstrate the gain in the total transmit power brought by integrating the RIS in C-NOMA networks.

Ultra-Reliable and Low Latency Communications (URLLC) is one of the essential services in 5G networks and beyond. The coexistence of URLLC alongside other service classes, namely, enhanced Mobile BroadBand (eMBB) and massive Machine-Type Communications (mMTC), calls for developing spectrally efficient multiplexing techniques. In this work, we study the problem of scheduling URLLC traffic in a downlink system with the presence of eMBB traffic class. Based on the superposition/puncturing scheme, a resource allocation problem is formulated with the objective to minimize the eMBB data rate loss while satisfying eMBB and URLLC quality of service (QoS) constraints. The resulting problem is formulated as a mixed integer non-linear programming (MINLP) which is generally NP hard and hence complex to solve. Hence, we derive its feasibility region as well as the optimal solutions for the power and spectral resource allocation. Subsequently, we propose a low complexity algorithm to serve URLLC traffic. Simulation results show that the proposed algorithm achieves higher reliability for URLLC and higher eMBB data rate compared to the puncturing schemes. The results also show that the eMBB QoS requirements, which are represented by the eMBB rate loss threshold, has a negative effect on the URLLC reliability for high URLLC load. Therefore, the eMBB rate and the eMBB loss threshold should be jointly optimized considering QoS of both eMBB and URLLC. Index Terms—eMBB, multiplexing, puncturing, superposition, URLLC, 6G.

Light-fidelity (LiFi) is a fully-networked bidirectional optical wireless communication (OWC) technology that is considered as a promising solution for high-speed indoor connectivity aimed for future sixth generation (6G) wireless networks. In the LiFi physical layer, the majority of the power allocation problems for mobile users investigated and reported in the literature are non-convex. These problems may be solved using dual decomposition techniques or heuristics that require iterative algorithms, and often, cannot be computed in real time due to the high computational load. In this paper, a proactive power allocation (PPA) approach that can alleviate the aforementioned issues is proposed. The core of the PPA approach is two cascaded neural networks consisting of one convolution neural network (CNN) and one long-short-term-memory (LSTM) network that are jointly capable of predicting posterior positions and orientations of mobile users following random trajectories in indoor environments. Afterwards, the predicted parameters are fed into the expression of the channel coefficients of the mobile users. Finally, the resulting predicted channel coefficients are exploited for deriving near-optimal power allocation schemes prior to the intended service time, which enables near-optimal and real-time service for mobile LiFi users.

This paper proposes a novel framework to emerge the reconfigurable intelligent surface (RIS) in cellular networks wherein enhanced mobile broadband (eMBB) and ultra-reliable and low-latency communication (URLLC) services coexist. In order to avoid the violation in the URLLC latency requirements, the framework proposes RIS phase shift matrix that enhances the URLLC reliability is proactively designed at the beginning of the time slot. The system model consists of a single base station (BS) and a single RIS which deployed to enhance the channel environments of the eMBB and the URLLC users. To allocates the eMBB users, we formulate a time-slot basis eMBB allocation problem which has the goal of maximizing the eMBB sum-rate by jointly optimizing the power allocation at the BS and the RIS phase shift matrix while satisfying the eMBB rate constraint. Since the formulated problem is a non-convex problem which hard to be solved directly, we adopt the alternating optimization approach to decompose the eMBB allocation problem optimization problem into a power allocation and a RIS phase shift matrix sub-problems. Then, the URLLC allocation problem is formulated as a multi-objective problem with the goal of maximizing the URLLC admitted packets and minimizing the eMBB rate loss by jointly optimizing the power and frequency allocation. Then, we proposed a heuristic algorithm to allocate the URLLC load. The proposed algorithm has a low time complexity which makes it a efficient method for multiplexing URLLC and eMBB traffics. Finally, simulation results show that using only 60 RIS elements, we observe that the proposed scheme achieves around 99.99% URLLC packets admission rate compared to 95.6% when there is no RIS, while also achieving up to 70% enhancement on the eMBB rates.

Light-fidelity (LiFi) is a fully-networked bidirectional optical wireless communication (OWC) technology that is considered as a promising solution for high-speed indoor connectivity. In this paper, the joint estimation of user 3D position and user equipment (UE) orientation in indoor LiFi systems with unknown emission power is investigated. Existing solutions for this problem assume either ideal LiFi system settings or perfect knowledge of the UE states, rendering them unsuitable for realistic LiFi systems. In addition, these solutions consider the non-line-of-sight (NLOS) links of the LiFi channel gain as a source of deterioration for the estimation performance instead of harnessing these components in improving the position and the orientation estimation performance. This is mainly due to the lack of appropriate estimation techniques that can extract the position and orientation information hidden in these components. In this paper, and against the above limitations, the UE is assumed to be connected with at least one access point (AP), i.e., at least one active LiFi link. Fingerprinting is employed as an estimation technique and the received signal-to-noise ratio (SNR) is used as an estimation metric, where both the line-of-sight (LOS) and NLOS components of the LiFi channel are considered. Motivated by the success of deep learning techniques in solving several complex estimation and prediction problems, we employ two deep artificial neural network (ANN) models, one based on the multilayer perceptron (MLP) and the second on the convolutional neural network (CNN), that can map efficiently the instantaneous received SNR with the user 3D position and the UE orientation. Through numerous examples, we investigate the performance of the proposed schemes in terms of the average estimation error, precision, computational time, and the bit error rate. We also compare this performance to that of the k-nearest neighbours (KNN) scheme, which is widely used in solving wireless localization problems. It is demonstrated that the proposed schemes achieve significant gains and are superior to the KNN scheme.

Light-fidelity (LiFi) is a fully-networked bidirectional optical wireless communication (OWC) technology that is considered as a promising solution for high-speed indoor connectivity. Unlike in conventional radio frequency wireless systems, the OWC channel is not isotropic, meaning that the device orientation affects the channel gain significantly. However, due to the lack of proper channel models for LiFi systems, many studies have assumed that the receiver is vertically upward and randomly located within the coverage area, which is not a realistic assumption from a practical point of view. In this paper, novel realistic and measurement-based channel models for indoor LiFi systems are proposed. Precisely, the statistics of the channel gain are derived for the case of randomly oriented stationary and mobile users. For stationary users, two channel models are proposed, namely, the modified truncated Laplace (MTL) model and the modified Beta (MB) model. For mobile users, two channel models are proposed, namely, the sum of modified truncated Gaussian (SMTG) model and the sum of modified Beta (SMB) model. Based on the derived models, the impact of random orientation and spatial distribution of users is investigated, where we show that the aforementioned factors can strongly affect the channel gain and the system performance.

In this paper, we investigate the integration between the coordinated multipoint (CoMP) transmission and the non-orthogonal multiple access (NOMA) in downlink heterogeneous cloud radio access networks (H-CRANs). In H-CRAN, low-power high-density small remote radio heads (SRRHs) are underlaid by high-power low-density macro RRH (MRRH). However, co-channel deployment of the different RRHs gives rise to the problem of inter-cell interference that significantly affects system performance especially the cell-edge users. Thus, the users are first categorized into Non-CoMP users and CoMP users based on the relation between the useful signal to the dominant interference signal. The Non-CoMP user is the user equipment (UEs) having high signal-to-interference-plus-noise-ratio (INR) and hence associates with only one RRH. On the other hand, the CoMP user, cell-edge user, is the UE that experiences less distinctive received power with the best two RRHs. In the proposed CoMP-NOMA framework, each RRH schedules CoMP-UE and non-CoMP-UE over the same transmission channel using NOMA. We first design an analytical framework based on tools from the stochastic geometry to evaluate the performance of the proposed framework (CoMP-NOMA) which is based on H-CRAN in terms of the average achievable data rate for each NOMA UE. We then examine the spectral efficiency of the proposed CoMP-NOMA based H-CRAN. Simulation results are provided to validate the accuracy of the analytical models and to reveal the superiority of the proposed CoMP-NOMA framework compared with conventional CoMP orthogonal multiple access (CoMP-OMA) techniques. By reaping the benefits of both JT-CoMP and NOMA, we prove that the proposed framework can successfully deal with the inter-cell interference by using CoMP and improve the network's spectral efficiency through NOMA technique. We also show that, with an appropriate power allocation coefficient setting at the Non-CoMP-UEs, a fairness performance can be achieved between the CoMP-UEs and the Non-CoMP-UEs.

This paper investigates the performance of cooperative non-orthogonal multiple access (C-NOMA) in downlink communication systems. Using C-NOMA, users with more favorable channel conditions can assist communication between the base station (BS) and the users with less favorable channel conditions using either full-duplex (FD) or half-duplex (HD) device-to-device (D2D) relaying and successive interference cancellation (SIC). To maximize the benefits of C-NOMA, we formulate and solve a novel optimization problem that jointly determines the optimal D2D user pairing and the optimal power control scheme in a downlink cellular system consisting of a BS that communicates with a set of spatially dispersed users. The formulated problem is a non-convex mixed-integer non-linear program (MINLP) which is difficult to solve due to the dependency between power control and user pairing. Thus, we decompose the problem into an inner power control problem and an outer pairing problem. For the inner problem, we derive the optimal closed-form expressions for both HD and FD relaying modes, while the outer problem of user pairing can be solved using the well-known Hungarian method. The simulation results show that the proposed framework outperforms a variety of proposed schemes in the literature and that it can obtain the optimal pairing and power control policies for a network with 100 users in negligible computational time.

We study in this paper the secrecy performance of the multiple-input multiple-output (MIMO) visible light communication (VLC) wiretap channel. The underlying system model comprises three nodes: one transmitter, equipped with multiple fixtures of LEDs, one legitimate receiver and one eavesdropper, each equipped with multiple photo-diodes (PDs). The VLC channel is modeled as a real-valued amplitude-constrained Gaussian channel and the eavesdropper is assumed to be randomly located in the coverage area. We propose a low-complexity precoding scheme that aims at enhancing the secrecy performance of the system. Specifically, assuming discrete input signaling, we derive an average achievable secrecy rate for the underlying system in a closed-form, and the derived expression is a function of the precoding matrix and the input distribution using stochastic geometry. Then, we propose a low-complexity design of the precoding matrix based on the generalized singular value decomposition (GSVD) of the channel matrices of the system. We examine the resulting average achievable secrecy rate using the truncated discrete generalized normal (TDGN) distribution, which is the best-known discrete distribution available in the literature. Finally, we validate the proposed scheme through extensive simulations and we demonstrate its superiority when compared to other schemes reported in the literature.

This paper investigates the performance of cooperative non-orthogonal multiple access (C-NOMA) in cellular downlink systems. The system model consists of a base station (BS) that needs to serve multiple users within a region of service. A subset of the users, especially those located close to the cell edge, undergo severe fading and suffer from poor channel quality and low achievable rates. To overcome this problem, CNOMA is proposed as the system design methodology, in which users that have the capability of full-duplex (FD) communication can assist the transmissions between the BS and users with poor channel quality through device-to-device (D2D) communications. To harness both the multiplexing gain from NOMA and the diversity gain from FD-D2D communications, we formulate and solve a novel optimization problem that jointly determines D2D user pairing and power allocation. The formulated problem is a mixed-integer non-linear program (MINLP) with prohibitively high complexity. To overcome this issue, a two-step policy is proposed to solve the problem in polynomial time. Our simulation results show that with reasonable assumptions, the proposed scheme always outperforms some existing schemes in the literature, and that, under undesirable conditions, e.g., poor D2D channel conditions or imperfect self-interference (SI) cancellation, the proposed scheme is reduced to conventional NOMA.

Among the challenges of realizing the full potential of light-fidelity (LiFi) cellular networks are user mobility, random device orientation, and blockage. In this paper, we study the impact of those challenges on the performance of LiFi networks in an indoor environment using measurement-based channel models, unlike existing studies that rely on theoretical channel models. In our paper, we adopt spatial modulation (SM) and consider two configurations for the user equipment (TIE). A multidirectional receiver (MDR) structure is proposed, in which the PDs are located on different sides of the TIE, e.g., a smartphone. This configuration is motivated by the fact that conventional structures exhibit poor performance in the presence of random device orientation and blockage. In fact, we show that the MDR outperforms the benchmark structure by over 10 dB at bit-error ratio (BER) of 3.8 × 10-3. Moreover, an adaptive access point (AP) selection scheme for the SM is considered, where the number of APs is chosen adaptively in an effort to achieve the lowest energy requirement for a target BER and spectral efficiency. The user performance with random orientation and blockage in the entire room is evaluated for sitting and walking activities, for which the orientation-based random waypoint (ORWP) mobility model is invoked. Furthermore, we demonstrate that the proposed adaptive technique with SM outperforms the conventional spatial multiplexing system. We also study the performance of the underlying system on the uplink channel where we apply the same techniques used for the downlink channel. It is shown analytically that the multidirectional transmitter (MDT) with adaptive SM is highly energy efficient.

This paper investigates the secrecy performance of the multiple-input single-output visible light communication (VLC) wiretap channel. The considered system model comprises three nodes: a transmitter (Alice) equipped with multiple fixtures of LEDs, a legitimate receiver (Bob), and an eavesdropper (Eve), each equipped with one photo-diode. The VLC channel is modeled as a real-valued amplitude-constrained Gaussian channel. Eve is assumed to be randomly located in the same area as Bob. Due to this, artificial noise-based beamforming is adopted as a transmission strategy in order to degrade Eve's signal-to-noise ratio. Assuming discrete input signaling, we derive an achievable secrecy rate in a closed-form expression as a function of the beamforming vectors and the input distribution. We investigate the average secrecy performance of the system using stochastic geometry to account for the location randomness of Eve. We also adopt the truncated discrete generalized normal (TDGN) as a discrete input distribution. We present several examples through which we confirm the accuracy of the analytical results via Monte Carlo simulations. The results also demonstrate that the TDGN distribution, albeit being not optimal, yields performance close to the secrecy capacity.

We study in this paper the achievable secrecy rate of the Gaussian multiple-input single-output (MISO) visible light communication (VLC) in the presence of randomly located eavesdroppers. We consider a system model comprising a transmitter (Alice) equipped with multiple fixtures of LEDs, one legitimate receiver (Bob) equipped with a single photo-diode (PD) and a group of randomly located eavesdroppers, each equipped with a single PD. The channel is modeled as deterministic, real- valued and subject to an amplitude constraint, through which Alice wants to communicate privately with Bob. We consider the case where the eavesdroppers are colluding together, i.e., they act jointly in eavesdropping on the communication between Alice and Bob. We adopt the truncated generalized normal (TGN) as input signaling and beamforming as transmission strategy. At first, we derive a closed-form expression of the average achievable secrecy rate as a function of the density of eavesdroppers using stochastic geometry. Then, we investigate the optimal beamformer that maximizes the average achievable secrecy rate of the system. Finally, the analysis is verified by Monte Carlo simulations.

We study in this paper the secrecy performance of the multiple-input single-output (MISO) visible light communication (VLC) channel in the presence of randomly located eavesdroppers. We consider a system model comprising a transmitter, equipped with multiple fixtures of LEDs, one legitimate receiver equipped with a single photo-diode (PD) and a group of randomly located (and colluding) eavesdroppers, each equipped with a single PD. We derive a closed-form expression for the average achievable secrecy rate as a function of the beamforming vector, the input distribution, and the location density of the eavesdroppers using stochastic geometry. We then investigate the optimal beamformer that maximizes the average achievable secrecy rate. We corroborate the analytical results through Monte Carlo simulations and we demonstrate substantial improvements provided by the proposed scheme over existing ones.

We study, in this paper, the secrecy performance of a multi-user (MU) multiple-input single-output visible light communication broadcast channel with confidential messages. The underlying system model comprises K +1 nodes: a transmitter (Alice) equipped with N fixtures of LEDs and K spatially dispersed users, each equipped with a single photo-diode. The MU channel is modeled as deterministic and real-valued and assumed to be perfectly known to Alice, since all users are assumed to be active. We consider typical secrecy performance measures, namely, the max-min fairness, the harmonic mean, the proportional fairness, and the weighted fairness. For each performance measure, we derive an achievable secrecy rate for the system as a function of the precoding matrix. As such, we propose algorithms that yield the best precoding matrix for the derived secrecy rates, where we analyze their convergence and computational complexity. In contrast, what has been considered in the literature so far is zero-forcing (ZF) precoding, which is suboptimal. We present several numerical examples through which we demonstrate the substantial improvements in the secrecy performance achieved by the proposed techniques compared with those achieved by the conventional ZF. However, this comes at a slight increase in the complexity of the proposed techniques compared with ZF.

We investigate in this paper the achievable secrecy sum-rate of the multiple-input single-output (MISO) visible light communication (VLC) broadcast Gaussian channel with confidential messages. We consider a system model comprising (K +1) nodes: a transmitter (Alice) equipped with N fixtures of LEDs and K (K ≤ N) spatially dispersed users, each equipped with a single photo-diode (PD). The channel is modeled as deterministic and real-valued, subject to amplitude and power constraints, through which Alice wants to transmit in one channel use K confidential messages. We propose a new precoding scheme based on the eigenvalues of the pencils of the different K MISO VLC channels in order to maximize the achievable secrecy sum-rate of the overall system. The motivation behind this scheme is based on the fact that the optimal precoding scheme maximizing the achievable secrecy rate of the single user MISO VLC wiretap channel is expressed through the eigenvector associated to the largest eigenvalue of the wiretap channel's pencil. We investigate both cases, namely, when the locations of users are known, i.e., perfect channel state information (CSI) is available to Alice, or unknown, i.e., imperfect CSI. Furthermore, we adopt the truncated generalized normal (TGN) distribution as input signaling. We present several examples which demonstrate the substantial improvements in the secrecy sum-rates achieved by the proposed techniques compared to those achieved by zero-forcing (ZF) precoding.

We investigate in this paper the achievable secrecy rate of the multiple-input multiple-output (MIMO) visible light communication (VLC) Gaussian wiretap channel. We consider a system model comprising three nodes: one transmitter (Alice) equipped with multiple fixtures of LEDs, one legitimate receiver (Bob) and one eavesdropper (Eve), each equipped with multiple photo-diodes (PDs). We study at first the problem of optimal signaling scheme that maximizes the achievable secrecy rate of the MIMO VLC wiretap channel. We consider the cases where the location of Eve is known (i.e. perfect channel state information (CSI)) or unknown (i.e. imperfect CSI). Finally, we derive an upper bound on the secrecy capacity that we used to assess the closeness of the achievable secrecy rate to the derived bound.

In this paper, we study the achievable secrecy rate of visible light communication (VLC) links for discrete input distributions. We consider single user single eavesdropper multiple-input single-output (MISO) links. In addition, both beamforming and robust beamforming are considered. In the former case, the location of the eavesdropper is assumed to be known, whereas in the latter case, the location of the eavesdropper is unknown. We compare the obtained results with those achieved by some continuous distributions including the truncated generalized normal (TGN) distribution and the uniform distribution. We numerically show that the secrecy rate achieved by the discrete input distribution with a finite support is significantly improved as compared to those achieved by the TGN and the uniform distributions.

We study the secrecy capacity of the multiple- input single-output (MISO) Gaussian wiretap visible light communication (VLC) channel. We study a typical VLC scenario with one transmitter, one legitimate receiver, and one eavesdropper. Specifically, we compute the achievable secrecy rate for various input signaling distributions, including the truncated generalized normal (TGN) and uniform distributions. The transmitter is equipped with multiple light sources, while the legitimate and unauthorized receivers are each equipped with a single photodetector. We analyze the achievable secrecy rates via transmit beamforming and artificial noise. In addition, both zero-forcing beamforming and robust beamforming are considered. In the former case, the location of the eavesdropper is assumed to be known, whereas in the latter case, the location of the eavesdropper is unknown. Our numerical results show that the secrecy rate achieved by the TGN distribution is significantly improved as compared to those achieved by the truncated Gaussian and uniform distributions, for both zero-forcing beamforming and robust beamforming. We also derive an upper bound on the achievable secrecy capacity that we used to assess the closeness of the achievable secrecy rates to the derived bound.