Joint user grouping and power allocation in VLC-NOMA system

With the energy consumption of wireless networks increasing, visible light communication (VLC) has been regarded as a promising technology to realize energy conservation. Due to the massive terminals access and increased traffic demand, the implementation of non-orthogonal multiple access (NOMA) technology in VLC networks has become an inevitable trend. In this paper, we aim to maximize the energy efficiency in VLC-NOMA networks. Assuming perfect knowledge of the channel state information of user equipment, the energy efficiency maximization problem is formulated as a mixed integer nonlinear programming problem. To solve this problem, the joint user grouping and power allocation (JUGPA) is proposed including user grouping and power allocation. In user grouping phase, we utilize the average of channel gain among all user equipment and propose a dynamic user grouping algorithm with low complexity. The proposed scheme exploits the channel gain differences among users and divides them into multiple groups. In power allocation phase, we proposed a power allocation algorithm for maximizing the energy efficiency for a given NOMA group. Thanks to the objective function is fraction form and non-convex, we firstly transform it to difference form and convex function. Then, we derive the closed-form optimal power allocation expression that maximizes the energy efficiency by Dinkelbach method and Lagrange dual decomposition method. Simulation results show that the JUGPA can effectively improve energy efficiency of the VLC-NOMA networks.     

Deep transfer reinforcement learning for resource allocation in hybrid multiple access systems

This paper proposes a resource allocation scheme for hybrid multiple access involving both orthogonal multiple access and non-orthogonal multiple access (NOMA) techniques. The proposed resource allocation scheme employs multi-agent deep reinforcement learning (MA-DRL) to maximize the sum-rate for all users. More specifically, the MA-DRL-based scheme jointly allocates subcarrier and power resources for users by utilizing deep Q networks and multi-agent deep deterministic policy gradient networks. Meanwhile, an adaptive learning determiner mechanism is introduced into our allocation scheme to achieve better sum-rate performance. However, the above deep reinforcement learning adopted by our scheme cannot optimize parameters quickly in the new communication model. In order to better adapt to the new environment and make the resource allocation strategy more robust, we propose a transfer learning scheme based on deep reinforcement learning (T-DRL). The T-DRL-based scheme allows us to transfer the subcarrier allocation network and the power allocation network collectively or independently. Simulation results show that the proposed MA-DRL-based resource allocation scheme can achieve better sum-rate performance. Furthermore, the T-DRL-based scheme can effectively improve the convergence speed of the deep resource allocation network.     

A novel power allocation strategy for cooperative PDMA systems

Cooperative communication technology is of great importance for increasing the user reachable rate, further improving throughput and reducing the outage probability of non-orthogonal multiple access (NOMA) systems. This paper mainly studies the power allocation optimization method based on amplify-and-forward (AF) pattern division multiple access (PDMA) to obtain the maximum achievable throughput. We formulate an optimization problem of user power allocation in a downlink PDMA system with cooperative relaying, the exact expressions of system throughput and user outage probability of the AF-PDMA system are derived, and a novel power allocation optimization method based on uniform distribution and restricted constraints is proposed. The effectiveness of the restricted constraints and optimization method is verified by theoretical analysis and simulation. The studies we have performed showed that the proposed scheme with uniform distribution and restricted constraints can be significantly improved in terms of the system throughput in comparison to the case with a genetic algorithm (GA) and fixed power allocation scheme. Concerning the proposed method, the search space is reduced to 1/3 of the original feasible region, and the runtime of the algorithm accounts for only 20% of the GA runtime.     

URLLC-oriented secure communication for UAV relay-assisted network

In this paper, we investigate the unmanned aerial vehicle (UAV) relay-assisted secure short packet communication. The UAV acts as a decode-and-forward relay to transmit control signals from the source to the actuators in the presence of a ground eavesdropper (EV) whose imperfect channel state information is available at the UAV. Specially, non-orthogonal multiple access is adopted in our work to achieve more connections and improve the fairness of communication and the short packets are employed for data transmission to reduce the latency. Explicitly, we maximize the minimum average secrecy throughput among all actuators by jointly optimizing the UAV trajectory, transmit power and blocklength allocation, which generates a challenging optimization problem. Therefore, we propose an iterative algorithm based on block coordinate descent method and successive convex approximation technique to handle the non-convex problem. Numerical results show that the proposed scheme has better performance compared to the benchmark schemes.     

Intelligent radio resource management in reconfigurable IRS-enabled NOMA networks

Intelligent reflecting surfaces (IRSs) are anticipated to provide reconfigurable propagation environment for next generation communication systems. In this paper, we investigate a downlink IRS-aided multi-carrier (MC) non-orthogonal multiple access (NOMA) system, where the IRS is deployed to especially assist the blocked users to establish communication with the base station (BS). To maximize the system sum rate under network quality-of-service (QoS), rate fairness and successive interference cancellation (SIC) constraints, we formulate a problem for joint optimization of IRS elements, sub-channel assignment and power allocation. The formulated problem is mixed non-convex. Therefore, a novel three stage algorithm is proposed for the optimization of IRS elements, sub-channel assignment and power allocation. First, the IRS elements are optimized using the bisection method based iterative algorithm. Then, the sub-channel assignment problem is solved using one-to-one stable matching algorithm. Finally, the power allocation problem is solved under the given sub-channel and optimal number of IRS elements using Lagrangian dual-decomposition method based on Lagrangian multipliers. Moreover, in an effort to demonstrate the low-complexity of the proposed resource allocation scheme, we provide the complexity analysis of the proposed algorithms. The simulated results illustrate the various factors that impact the optimal number of IRS elements and the superiority of the proposed resource allocation approach in terms of network sum rate and user fairness. Furthermore, we analyze the proposed approach against a new performance metric called computational efficiency (CE).     

基于鱼群算法的OFDMA自适应资源分配

针对多用户正交频分多址系统自适应资源分配问题, 提出了一种新的子载波和基于鱼群算法的功率自适应分配算法. 该算法首先对总功率在子载波间均等分布的条件下进行子载波分配,然后引入鱼群算法并根据给出的兼顾用户公平性与系统容量的适应度函数,通过全局搜索实现用户间的功率分配. 仿真结果表明,新算法在保证用户公平性的同时, 还实现了系统总的传输速率最大化. 关键词： 多用户正交频分多址 资源分配 鱼群算法 速率最大化     

Energy efficient collaborative computation for double-RIS assisted mobile edge networks

Computation offloading in mobile edge computing (MEC) systems emerges as a novel paradigm of supporting various resource-intensive applications. However, the potential capabilities of MEC cannot be fully unleashed when the communication links are blocked by obstacles. This paper investigates a double-reconfigurable-intelligent-surfaces (RISs) assisted MEC system. To efficiently utilize the limited frequency resource, the users can partially offload their computational tasks to the MEC server deployed at base station (BS) by adopting non-orthogonal multiple access (NOMA) protocol. We aim to minimize the energy consumption of users with limited resource by jointly optimizing the transmit power of users, the offloading fraction of users and the phase-shifts of RISs. Since the problem is non-convex with highly coupled variables, the block coordinate descent (BCD) method is leveraged to alternatively optimize the decomposed four subproblems. Specifically, we invoke successive convex approximation for low complexity (SCALE) and Dinkelbach technique to tackle the fractional programming of power optimization. Then the offloading fraction is obtained by closed-form solution. Further, we leverage semidefinite relaxation (SDR) and bisection method to address the phase-shifts design of double RISs. Finally, numerical results illustrate that the proposed double-RIS assisted NOMA scheme is capable of efficiently reducing the energy consumption and achieves significant performance gain over the benchmark schemes.     

Profit maximization in cache-aided intelligent computing networks

This paper focuses on the profit maximization problem in a reconfigurable intelligent surfaces (RIS) aided computing network, where multiple heterogeneous users offload their computational tasks to one computational access point (CAP) for seeking computing acceleration at the cost of profit. In particular, the CAP can also pre-store a part of the computing task to speed up computing, and the system has limited communication and computing resources, where heterogeneous users have different offloading requirements and the CAP can dynamically allocate the system resources to meet the requirements of users to earn profits. To maximize the system profit, we devise the system by proposing a resource allocation scheme which employs a genetic algorithm (GA), based on statistical channel state information (CSI) of wireless links. The proposed algorithm maximizes the long-term profit of the system by optimizing resource allocation among users. Finally, simulation results are provided to verify the proposed scheme. The results show that our proposed resource allocation scheme outperforms the conventional ones.     

Joint terminal-AP association and power allocation for NOMA-enabled space–air–ground integrated networks

This paper considers a space–air–ground integrated network (SAGIN) to provide network access services for aerial and terrestrial terminals. The non-orthogonal multiple access (NOMA) is used for improving spectral efficiency in the uplink transmission between terminals and access points (APs) in SAGIN. A sum rate maximization optimization problem is formulated by optimizing terminal-AP association and power allocation, while simultaneously satisfying the constraints of transmit power, network coverage characteristics, and quality-of-service (QoS) requirements of both aerial and terrestrial terminals. To deal with the formulated mixed integer nonlinear programming (MINLP) optimization problem, we first decouple it into separated terminal-AP association and power allocation problems. Then, we adopt the Q-learning algorithm to solve the terminal-AP association subproblem. Based on the obtained terminal-AP association solution, an iterative power allocation algorithm is developed by exploiting the Lagrange dual method. Moreover, the computational complexity of the proposed algorithm is further analyzed. Simulation results demonstrate that, compared with other schemes, our proposed algorithm can achieves a better performance in terms of the achievable sum rate, average achievable rate, and outage probability.     

A power allocation method for $$2 \times 2$$ VLC-MIMO indoor communication

Visible light communication (VLC) has been a promising field of optical communications which focuses on visible light spectrum that humans can see. Unlike existing studies which mainly discuss point-to-point communication, in this paper, we consider a VLC network, in particular a \(2 \times 2\) system. Our focus is on dealing with interference in this network. The objective is to maximize the signal to interference plus noise ratio (SINR) of one receiver for a given SINR of another receiver. We formulate a power allocation optimization problem to deal with such interference, and introduce dichotomy to solve this optimization problem. Simulation results have twofold meaning: First, \(\mathrm{SINR}_1\) increases with the growth of \(\mathrm{SINR}_2\), which are the SINR of the two receivers, respectively. Second, our proposed scheme outperforms the classical time-division multiple access technique in terms of transmit powers of both light sources when the data rate for these two schemes are set to be identical for each user, respectively.     

Investigation of RIS Enabled Mixed RF/VLC Relaying with Interference

The present research work aims at analyzing a variable gain amplify-and-forward (AF) relaying system with reconfigurable intelligent surface (RIS) systems to set-up a mixed communication system with visible light communication (VLC). The RIS and VLC forms of communication will hold utmost importance in the future generations of wireless communication. However, while implementation, various practical challenges are to be dealt with. One such challenge is the impact of interference on the reliability of the communication setup. To evaluate this, it has been assumed that the channel state information (CSI) aided amplify-and-forward (AF) relay node operates in the vicinity of multiple co-channel interferers (CCIs). For the considered system model, closed-form expressions have been derived for outage probability and bit error rate (BER) of the system. The formulation of symbol error rate (SER) has also been presented to generalize the findings. The analysis has been taken forward to showcase the impact of various system parameters such as number of elements in RIS, length and inclination angles of the VLC system along with number and strength of the interferers in system. It has been observed during investigation that the increment in the number of reflecting elements in the RIS has a major contribution in minimizing the signal degradation caused due to the multiple CCIs. Numerical results have been verified using the Monte-Carlo simulations.     

Efficient user access and lamp selection in LED-based visible light communication network

We present a network-level signaling mechanism for user access and service setup in light emitting diode(LED)-based visible light communication(VLC) networks and define the corresponding signaling messages.In this mechanism,lamp selection is an important step for realizing flexible user access and efficient resource allocation.Two basic selection schemes are proposed,and an enhanced bandwidth-based scheme is presented.Simulation results show the different advantages among these schemes.     

An effective approach for initial access in 5G-millimeter wave-based Vehicle to Everything (V2X) communication using Improved Genetic Algorithm

The process of establishing a directional communication link between the vehicle (VE) on the road and a roadside unit (RSU) is known as initial access process in 5G-millimeter wave(mmWave)-Vehicle to Everything (V2X) communications. Initial access is a tricky problem because substantial interruption or delay can be experienced where the RSU and the VE tries to discover the suitable beam alignment for establishing a direct communication link. Thus, it is very important to resolve this initial access problem in an effective way. Moreover, with the popularity of 5G-mmWave based V2X communication many researchers are trying to address this problem. Therefore, in this paper, we will be presenting a novel beam refinement technique that uses Improved Genetic Algorithm which is quite useful when the system is comprised of large number of antenna arrays. In this work we have considered that RSU and VE are equipped with multiple input multiple output (MIMO) antenna system. Proposed improved genetic algorithm includes some key improvements in terms of selection procedure, elitism, crossover and mutation operations. The effectiveness of the proposed work is investigated in terms of Capacity achieved vs number of transmit and receive antennas at RSU and VE, codebook size, outage probability and total transmitted power. Moreover, a detail analysis has been performed with previous state of the art works in terms of key performance metrics like: capacity achieved by 5G-V2X system, outage probability, and total transmitted power utilization. The proposed work has shown improved beam refinement by solving the initial access problem effectively.     

Fairness-aware resource allocation for D2D-enabled IoT in NOMA-based cellular networks with mutual successive interference cancellation

Device-to-device (D2D) communications and non-orthogonal multiple access (NOMA) are promising technologies to meet the growing demand for IoT-connected devices. However, they bring about new challenges including the co-channel interference, that can limit the performance improvement. To manage the co-channel interference, we address the problem of joint power allocation and sub-channel assignment for D2D-enabled IoT devices (IoTDs) underlaying a NOMA-based cellular network, in which the successive interference cancellation (SIC) decoding is enabled at the level of IoTDs and cellular user equipment (CUE)to increase the number of connected devices and the capacity. This problem is modeled as a mixed-integer nonconvex optimization problem which includes the concept of fairness with respect to the data rates of IoTDs. To solve the problem, a semi-distributed algorithm is developed, which is of polynomial time complexity. The proposed algorithm leverages the successive convex approximation and a heuristic approach. Evaluation results demonstrate the efficiency of the proposed scheme with respect to the sum rate, fairness, access rate and computational complexity.     

Fast frequency modulation optical code division multiple access communication system

An optical fast frequency modulation code division multiple access communication system is proposed. In this system, an electrically controlled tunable optical filter (TOF) is used to encode the modulated broadband light source. The code depends on the function set to the controller. Two-dimensional code named functional code is also proposed based on a shifted sine function. The function defines the dynamic coding pattern of the central wavelength of the transmitted narrowband optical signal. Thus, the system will allow for an easy reconfiguration of the transmitter without the need for sophisticated encoder. At the receiver, a synchronized TOF with the same function is used as a decoder. The performance of this system is shown to be better compared with a fast frequency hopping and a spectral amplitude coding systems.     

LDPC coded DFT/DCT-spread SC-FDMA for image communication: A compressed sensing approach

A compressed sensing (CS)-based detector is proposed for the low-density parity-check (LDPC) coded single carrier frequency division multiple access (SC-FDMA) scheme. The proposed CS-based detector can be employed at the receiver of LDPC-coded SC-FDMA systems for efficient image communications over vehicular channels. The proposed detector employs a suitable sparse recovery algorithm. We have considered both the discrete Fourier transform (DFT)-based and the discrete cosine transform (DCT)-based SC-FDMA for mitigating the channel-induced dispersion at a low peak to average power ratio (PAPR). Additionally, both the linear equalizer (LE) and the decision feedback equalizer (DFE)-based SC-FDMA have been considered for image communication. The performance of the proposed technique is investigated using a number of image quality metrics. The qualities of the received images are also compared visually. The complexity of the proposed detector and that of the benchmark detectors are quantified. Furthermore, the performance and the complexity of the proposed system using some of the sparse recovery techniques are investigated and compared. Our simulations demonstrate that LDPC coded SC-FDMA using the compressed sampling matching pursuit (CoSaMP)-based CS detector can significantly improve the performance of image communication over vehicular channels.     

Energy efficiency maximization for IRS-assisted NOMA networks

In this paper, we consider a non-orthogonal multiple access (NOMA) system assisted by intelligent reflecting surface (IRS). As an emerging technology that has received widespread attention, IRS can reconfigure the wireless channel environment by adjusting the relationship between the incident angle and the exit angle, thereby improving system performance. Our goal is to use the flexible assistance of IRS to achieve the maximum energy efficiency of the NOMA system. Our design objects are the beam vector design of the base station and the phase matrix design of the IRS. The original problem is highly non-convex. We consider using the block coordinate descent method to design the phase matrix and beam vector separately. Simulation results show that our proposed scheme has better performance than traditional OMA and systems without IRS assistance.     

Deployment and trajectory design of fixed-wing UAVs in NOMA assisted wireless networks

This paper proposes a deployment and trajectory scheme for fixed-wing unmanned aerial vehicles (UAVs) deployed as flying base stations in multi-UAV enabled non-orthogonal multiple access (NOMA) downlink communication. Specifically, the deployment of UAVs and power allocation of users are jointly optimized to maximize the sum-rate. Thereafter, the energy efficiency maximization problem is formulated to optimize the trajectory of UAVs by jointly considering the quality of service (QoS) requirement of users, various flight constraints, limited on-board energy, and users’ mobility. Initially, the existing users are divided into clusters by k-means clustering, where each cluster is served by a single UAV. Then, the clusters are further divided into multiple sub-clusters, each having a pair of near and far users. Orthogonal multiple access (OMA) is applied among sub-clusters and NOMA is applied to intra sub-cluster users. Lastly, the Balanced-grey wolf optimization (B-GWO) algorithm is proposed for solving the non-convex optimization problems. Simulation results prove the superiority of the B-GWO based deployment and trajectory algorithms compared to the benchmarks. In addition, the proposed B-GWO based trajectory algorithm achieves a near-optimal performance with an optimality gap of less than 1.5% compared to the exhaustive search.     

Metasurface aided intelligent reflecting surfaces for indoor GSSK-VLC downlink

To provide high-quality communication in the indoor generalized space shift keying (GSSK) aided visible light communications (VLC) downlink transmission, especially when the line-of-sight (LoS) link is blocked, a metasurface aided intelligent reflecting surfaces (mIRS) scheme is proposed. By controlling the reflection characteristics of incident light in a deliberate manner provided in this paper, the proposed mIRS-assisted indoor GSSK-VLC downlink can significantly enhance the signal quality at the receiver end. Furthermore, the maximum likelihood (ML) and efficient preprocessing enabled sparsity orthogonal matching pursuit (OMP) detectors are respectively presented for the considered system. Finally, simulations are demonstrated to verify the effectiveness of the proposed mIRS-assisted indoor GSSK-VLC downlink transmission.     

Secrecy sum rate maximization for a MIMO-NOMA uplink transmission in 6G networks

Non-orthogonal multiple access (NOMA), as a well-qualified candidate for sixth-generation (6G) mobile networks, has been attracting remarkable research interests due to high spectral efficiency and massive connectivity. The aim of this study is to maximize the secrecy sum rate (SSR) for a multiple-input multiple-output (MIMO)-NOMA uplink network under the maximum total transmit power and quality of service (QoS) constraints. Thanks to the generalized singular value decomposition method, the SSR of NOMA is compared with conventional orthogonal multiple access and other baseline algorithms in different MIMO scenarios. Due to the subtractive and non-convex nature of the SSR problem, the first-order Taylor approximation is exploited to transform the original problem into a suboptimal concave problem. Simulation results are provided and compared with some other benchmarks to evaluate the efficacy of the proposed method.