Achievable Sum Rate Research Articles

A Study of Downlink Power-Domain Non-Orthogonal Multiple Access Performance in Tactile Internet Employing Sensors and Actuators

The Tactile Internet (TI) characterises the transformative paradigm that aims to support real-time control and haptic communication between humans and machines, heavily relying on a dense network of sensors and actuators. Non-Orthogonal Multiple Access (NOMA) is a promising enabler of TI that enhances interactions between sensors and actuators, which are collectively considered as users, and thus supports multiple users simultaneously in sharing the same Resource Block (RB), consequently offering remarkable improvements in spectral efficiency and latency. This article proposes a novel downlink power domain Single-Input Single-Output (SISO) NOMA communication scenario for TI by considering multiple users and a base station. The Signal-to-Interference Noise Ratio (SINR), sum rate and fair Power Allocation (PA) coefficients are mathematically derived in the SISO-NOMA system model. The simulations are performed with two-user and three-user scenarios to evaluate the system performance in terms of Bit Error Rate (BER), sum rate and latency between SISO-NOMA and traditional Orthogonal Multiple Access (OMA) schemes. Moreover, outage probability is analysed with varying fixed Power Allocation (PA) coefficients in the SISO-NOMA scheme. In addition, we present the outage probability, sum rate and latency analyses for fixed and derived fair PA coefficients, thus promoting dynamic PA and user fairness by efficiently utilising the available spectrum. Finally, the performance of 4 × 4 Multiple-Input Multiple-Output (MIMO) NOMA incorporating zero forcing-based beamforming and a round-robin scheduling process is compared and analysed with SISO-NOMA in terms of achievable sum rate and latency.

Precoder Design for Network Massive MIMO Optical Wireless Communications.

Precoding is a technique employed to enhance transmission rates in various communication systems, including massive multiple-input multiple-output (MIMO) and optical wireless communication (OWC). In this study, we focus on network massive MIMO OWC (NM-MIMO-OWC) systems and investigate the precoder design to enhance the sum rate and improve the system performance. We present the network's massive MIMO OWC framework. By utilizing this framework, we are able to calculate the achievable sum rate. Subsequently, we consider the precoding design for maximizing the sum rate while adhering to the total power constraint. To solve this optimization problem, we provide a necessary condition of the optimal solution based on the Karush-Kuhn-Tucker (KKT) conditions, and propose a low-complexity algorithm to further enhance the efficiency of the proposed precoding technique. The numerical results demonstrate that the proposed precoder design significantly improves the transmission rate and effectively maximizes the sum rate.

Dynamic RIS partitioning in NOMA systems using deep reinforcement learning

The rapid evolution of wireless communication technologies necessitates innovative solutions to meet the increasing performance requirements of future networks, particularly in terms of spectral efficiency, energy efficiency, and computational efficiency. Reconfigurable Intelligent Surfaces (RIS) and Non-Orthogonal Multiple Access (NOMA) are emerging as promising technologies to enhance wireless communication systems. This paper explores the dynamic partitioning of RIS elements in NOMA systems using Deep Reinforcement Learning (DRL) to optimize resource allocation and overall system performance. We propose a novel DRL-based framework that dynamically adjusts the partitioning of RIS elements to maximize the achievable sum rate and ensure fair resource distribution among users. Our architecture leverages the flexibility of RIS to create an intelligent radio environment, while NOMA enhances spectral efficiency. The DRL model is trained online, adapting to real-time changes in the communication environment. Empirical results demonstrate that our approach closely approximates the performance of the optimal iterative algorithm (exhaustive search) while reducing computational time by up to 90 percent. Furthermore, our method eliminates the need for an offline training phase, providing a significant advantage in dynamic environments by removing the requirement for retraining with every environmental change. These findings highlight the potential of DRL-based dynamic partitioning as a viable solution for optimizing RIS-aided NOMA systems in future wireless networks.

Proximal Policy Optimization for RIS-Assisted Full Duplex 6G-V2X Communications

In this work, we consider a novel reconfigurable intelligent surface (RIS)-assisted full-duplex (FD) sixth generation (6G)-vehicle-to-everything (V2X) communication network having a FD base station (BS) simultaneously communicating with a uplink (UL) and a downlink (DL) mobile vehicles with the aide of two RISs, one for each link. We provide an analytical framework to investigate the performance of this network and, consequently, formulate an optimization problem to jointly optimize the phase shift matrices at both the RISs that maximize the achievable sum-rate. Thereafter, due to the non-convex nature of the problem, we propose a low complexity proximal policy optimization (PPO)-based deep reinforcement learning (DRL) algorithm that solves the problem in continuous action spaces by reducing the overall training overhead and provides optimum values of phase-shift matrix at each RIS. Further, we extend the analysis considering multiple transmit and receive antennas at the BS that simultaneously serves multiple UL and DL vehicles. We present exhaustive simulations-based graphical results to validate the effectiveness and accuracy of the proposed algorithm compared to deep deterministic policy gradient (DDPG) and successive refinement (SR)-based solutions. Accordingly, we demonstrate the dominance of the considered FD system over its half-duplex (HD) counterpart. We also discuss the impact of the number of elements at each RIS, each vehicle's velocity and their distance from the RIS and the BS on the performance of the respective links. Moreover, we also highlight the impact of imperfect self-interference cancellation and discuss the trade-off between the UL and DL performances due to this imperfection. Additionally, it is shown that the proposed algorithm is approximately <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$10 \times$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$ 40 \times$</tex-math></inline-formula> faster than DDPG and SR, respectively. Finally, for the multiple vehicle case, we demonstrate the effect of co-channel interference on the DL rate and the impact of RIS elements, the number of UL and DL vehicles on the system performance.

Sum rate maximization in UAV-assisted data harvesting network supported by CF-mMIMO system exploiting statistical CSI

Unmanned Aerial Vehicles (UAVs) have been considered to have great potential in supporting reliable and timely data harvesting for Sensor Nodes (SNs) from an Internet of Things (IoT) perspective. However, due to physical limitations, UAVs are unable to further process the harvested data and have to rely on terrestrial servers, thus extra spectrum resource is needed to convey the harvested data. To avoid the cost of extra servers and spectrum resources, in this paper, we consider a UAV-based data harvesting network supported by a Cell-Free massive Multiple-Input-Multiple-Output (CF-mMIMO) system, where a UAV is used to collect and transmit data from SNs to the central processing unit of CF-mMIMO system for processing. In order to avoid using additional spectrum resources, the entire bandwidth is shared among radio access networks and wireless fronthaul links. Moreover, considering the limited capacity of the fronthaul links, the compress-and-forward scheme is adopted. In this work, in order to maximize the ergodically achievable sum rate of SNs, the power allocation of ground access points, the compression of fronthaul links, and also the bandwidth fraction between radio access networks and wireless fronthaul links are jointly optimized. To avoid the high overhead introduced by computing ergodically achievable rates, we introduce an approximate problem, using the large-dimensional random matrix theory, which relies only on statistical channel state information. We solve the nontrivial problem in three steps and propose an algorithm based on weighted minimum mean square error and Dinkelbach's methods to find solutions. Finally, simulation results show that the proposed algorithm converges quickly and outperforms the baseline algorithms.

RSMA-aided V2X communications: A multi-layer perspective

Rate-splitting multiple access (RSMA), a novel technology in next-generation mobile communications, has garnered increasing attention for its powerful and flexible interference management when compared to traditional multiple access methods. However, considering classical vehicle-to-everything (V2X) communication networks with multiple cells, the flexibility of decoding increases with an increasing number of users having different requests, which results in higher outage performance and additional energy consumption. In order to address these problems, this paper proposes a novel multi-layer (ML) RSMA scheme, aiming to enhance the system performance and decrease the decoding flexibility simultaneously. For the first layer, users enjoying optimal quality of service (QoS) are selected to aid the other cluster members. For the second layer, the cluster members are grouped following the QoS and request similarity. In addition, with respect to our proposed ML-V2X RSMA system, this paper investigates the performance over Rayleigh fading channels, where we derive closed-form expressions of achievable sum rate and outage probability, respectively. Numerical results reveal that: (1) For low SNR scenarios, the ML-V2X RSMA system achieves higher achievable sum rates than classical NOMA-aided V2X systems. (2) The outage probability of ML-V2X RSMA system is superior to that of NOMA-aided V2X systems and traditional cooperative communication systems.

Energy efficiency enhancement in millimetre-wave MIMO-NOMA using three layer user grouping and adaptive power allocation algorithm

Massive multi-input multi-output (MIMO) is realized as the principal technology in the emerging fifth generation communication network system. Hybrid structure uplink communication is considered for the MIMO Non-orthogonal multiple access (MIMO-NOMA) system’s beam forming and power efficiency improvement through the novel three-layer user grouping. In the three-layer user grouping, the K-means algorithm is adopted in the initial layer for grouping users among different clusters and rectifying clustering errors in the third layer. The second layer used the agglomerative nesting (AGNES) algorithm for merging smaller clusters based on the channel correlation and angles of arrival similarity. The beam selection is carried out to minimize the intrusion of defined beam elements and to overcome beam overlapping problems. The non-convex optimization of the power allocating problem is modified as a convex problem by introducing a Quadratic transform (QT) to minimize each user’s data rate requirement. The algorithm of coati optimization is proposed to iteratively optimize the power allocation problem. The simulation results show that our proposed methodology goes beyond the existing schemes in terms of energy efficiency beyond the maximum power and achievable sum rate can be achieved.

Spectrum-efficient user grouping and resource allocation based on deep reinforcement learning for mmWave massive MIMO-NOMA systems

Millimeter-wave (mmWave) massive multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) is proven to be a primary technique for sixth-generation (6G) wireless communication networks. However, the great increase in users and antennas brings challenges for interference suppression and resource allocation for mmWave massive MIMO-NOMA systems. This study proposes a spectrum-efficient and fast convergence deep reinforcement learning (DRL)-based resource allocation framework to optimize user grouping and allocation of subchannel and power. First, an enhanced K-means grouping algorithm is proposed to reduce the multi-user interference and accelerate the convergence. Then, a dueling deep Q-network (DQN) structure is proposed to perform subchannel allocation, which further improves the convergence speed. Moreover, a deep deterministic policy gradient (DDPG)-based power resource allocation algorithm is designed to avoid the performance loss caused by power quantization and improve the system’s achievable sum-rate. The simulation results demonstrate that our proposed scheme outperforms other neural network-based algorithms in terms of convergence performance, and can achieve higher system capacity compared with the greedy algorithm, the random algorithm, the RNN algorithm, and the DoubleDQN algorithm.

Linear feedback coding scheme for multiple-access fading channels with degraded message sets.

Channel coding technology plays an important role in wireless communication systems, and it serves as a crucial mechanism to reduce interference during the transmission process. As the fifth-generation (5G) and sixth-generation (6G) wireless communication systems rapidly advance, requirements of the users on the quality and security of wireless service are increasing. To solve these problems, it calls for us to explore the new channel coding technologies. In this paper, a linear feedback coding scheme for fading multiple-access channels with degraded message sets (FMAC-DMS) is proposed. In this scheme, the transmitting beamforming and channel splitting are used to transform the channel with complex signals into scalar equivalent sub-channels. Then, the extended Schalkwijk-Kailath coding scheme (SK) is further applied to each sub-channel. The channel capacity, finite blocklength (FBL) sum-rate and FBL secrecy achievable sum-rate of the FMAC-DMS in single-input single-output (SISO) and multi-input single-output (MISO) cases are derived. Finally, we show that the proposed scheme not only provides a FBL coding solution but also guarantees physical layer security(PLS). The numerical and simulation results show the effectiveness of the proposed scheme as a channel coding solution. The study of this paper provides a new method to construct a practical FBL scheme for the FMAC-DMS.

User scheduling for non-orthogonal multiple access-enabled 5G aeronautical mobile airport communications system

The recent concept of aeronautical mobile airport communications system with the 5th generation mobile communication technology (5G AeroMACS) for enhancing real-time and efficient flight information transmission has attracted wide attention from the civil aviation industry. In addition, non-orthogonal multiple access (NOMA) is also a promising solution to further improve the sum rate. However, the fundamental limit of the conventional 5G AeroMACS is that, the base station (BS) equipped with dozens of antennas cannot support more users than its number of antennas using the same time-frequency resources. To break this limit, for the first time, we investigate user scheduling policies for the integration of NOMA in 5G AeroMACS, i.e., 5G AeroMACS-NOMA, employing a half-duplex (HD) BS for serving multiple HD downlink users simultaneously. The two proposed user scheduling policies are obtained from the sub-optimal solution of mixed combinatorial non-convex optimization problems for the maximization of the achievable sum rate of the entire system. To mitigate the inter-beam interference (IBI), a user clustering algorithm based on the principle of maximizing the channel spatial correlation is designed, and an iterative power allocation algorithm is utilized for maximizing the achievable sum rate of the entire system. Furthermore, to mitigate the serious error propagation caused by the nature in NOMA, a scenario with user division is considered, and an user scheduling policy based on successive convex approximation is designed to strike a balance between computational complexity and optimality. Simulation results show that the proposed 5G AeroMACS-NOMA schemes can achieve a higher achievable sum rate compared with conventional 5G AeroMACS.

Joint wireless resource allocation and bitrate adaptation for QoE improvement in IRS-aided RSMA-enabled IoMT streaming systems

In the context of the increasing demand for Internet of Multimedia Things (IoMT) services, rate splitting multiple access (RSMA) and intelligent reflecting surface (IRS) technologies have been considered potential networking enablers to provide ultra-throughput wireless access. However, challenges arise due to the heterogeneity of IoMT devices and arbitrary network quality changes, resulting in unwanted service quality fluctuations and downgradation. This study addresses this problem by jointly optimizing wireless resource allocation and bitrate adaptation with Deep Reinforcement Learning (DRL)-based QoE management for IRS-aided RSMA-enabled IoMT streaming systems. We formulated the problem as a Markov decision process (MDP) and apply Proximal Policy Optimization (PPO) method to flexibly adjust IoMT bitrate, transmission beamforming, IRS phase shift, and RSMA parameters. As a result, our algorithm mitigates overestimation of client-side bandwidth, leading to smoother playback and reduced quality fluctuations. Simulations show that our approach outperforms baseline methods in terms of video resolution (up to 2.5 times) and achievable sum-rate (up to 50%), contributing to a superior streaming experience in IoMT systems.

Performance analysis of inverse successive interference cancellation in NOMA-based communications

AbstractNon-orthogonal multiple access (NOMA) is a promising solution to enhance the spectral efficiency of sixth generation (6G) networks. NOMA enables multiple users to be concurrently served, relying on superposition coding at the transmitter and successive interference cancellation (SIC) at the receivers. However, in practice, hardware impairments and channel estimation errors will lead to incorrect signal ordering. This study analyses inverse successive interference cancellation (ISIC) in downlink power-domain NOMA networks. In the worst case, ISIC will inversely order the N received signals, compared to conventional SIC. First, ISIC will decode the signal with the minimum allocated power, while treating the other $$N-1$$ N - 1 signals as interference. This process is repeated $$N-1$$ N - 1 times and in the last step, the signal with the maximum allocated power will be decoded without interference. Various key performance metrics for ISIC are analytically derived, including the outage probability, throughput, error probability, ergodic rate, achievable sum-rate, energy and spectral efficiency. In order to provide further insights on ISIC, an asymptotic analysis is also provided. Comparisons with SIC reveal that for the signal with the second-lowest power allocation level, ISIC achieves better performance, while the performance of the information signal with the minimum allocated power is degraded. Overall, our findings shed light on the performance of NOMA in practical settings where SIC can not be correctly performed and focuses on the worst-case where the decoding order at the receiver is completely inversed.

RIS-Assisted Robust Beamforming for UAV Anti-Jamming and Eavesdropping Communications: A Deep Reinforcement Learning Approach

The reconfigurable intelligent surface (RIS) has been widely recognized as a rising paradigm for physical layer security due to its potential to substantially adjust the electromagnetic propagation environment. In this regard, this paper adopted the RIS deployed on an unmanned aerial vehicle (UAV) to enhance information transmission while defending against both jamming and eavesdropping attacks. Furthermore, an innovative deep reinforcement learning (DRL) approach is proposed with the purpose of optimizing the power allocation of the base station (BS) and the discrete phase shifts of the RIS. Specifically, considering the imperfect illegitimate node’s channel state information (CSI), we first reformulated the non-convex and non-conventional original problem into a Markov decision process (MDP) framework. Subsequently, a noisy dueling double-deep Q-network with prioritized experience replay (Noisy-D3QN-PER) algorithm was developed with the objective of maximizing the achievable sum rate while ensuring the fulfillment of the security requirements. Finally, the numerical simulations showed that our proposed algorithm outperformed the baselines on the system rate and at transmission protection level.

6G-Powered Efficient Resource Control through IRS-UE Association.

The widespread popularity of live streaming, cloud gaming, mobile video streaming, and many real-time applications relies on high-speed data to ensure low latency and seamless user experience. This large high-speed data demand has led to the development of next-generation or sixth-generation (6G) communication technology. It aims to offer high-speed communication support to multiple applications and interactive services simultaneously. But the vulnerability of node communication to the changing propagation environment often leads to call drops, data loss, and high latency. This paper presents a 6G-enabled wireless network that makes use of multiple intelligent reflecting surfaces (IRSs). The distributed IRSs enhance the robustness of transmission but the increased overhead owing to multiple IRSs is the main challenge. To overcome this, efficient resource control is introduced, which associates sets of IRSs to user equipment (UE). An algorithm, namely IUABP (IRS-UE association based on pilots), is proposed; it offers selective resource control. Furthermore, the performance of the distributed IRS system is evaluated based on the achievable sum rate for different IRS numbers, reflecting elements, and transmit powers. We observed that the proposed association scheme offers an improvement of 30% in the achieved sum rate using N = 50 and R = 5 at a transmit power of 12 dBm. We also discuss the comparison with two other association schemes, namely, distance-based association and random association.

Beamforming and data detection in intelligent reflecting surfaces‐assisted communication using deep learning

SummaryIn this article, we propose two deep neural network (DNN) architectures for an intelligent reflecting surface (IRS)‐assisted network with a single transmitter and multiple receivers. The first DNN is designed to obtain an optimal beamforming weight vector that maximizes the achievable sum rate over the IRS‐aided network. The beamforming vector is designed based on a pilot‐based channel estimation procedure, which requires only a small fraction of the available number of reflecting surfaces on the IRS to be active. The obtained vector is fed to the second DNN, which is designed for detecting information symbols from the transmitter. Our simulation study shows that the achievable rate obtained using the designed beamforming vector approaches the maximum achievable rate quickly, as the number of active elements increases. The impact of the number of active elements and the beamforming vector on the performance of the detector is also studied, in terms of the bit error rate (BER). We show that the BER performance of the second DNN is close to that of the optimal maximum likelihood detector. Further, we study the effect of the channel estimation errors on the performance of the DNNs. Moreover, we study the effect of the hyperparameters of the architectures on the training time and performance, in terms of root mean squared error and accuracy.

AI-Based Q-Learning Approach for Performance Optimization in MIMO-NOMA Wireless Communication Systems

In this paper, we investigate the performance enhancement of Multiple Input, Multiple Output, and Non-Orthogonal Multiple Access (MIMO-NOMA) wireless communication systems using an Artificial Intelligence (AI) based Q-Learning reinforcement learning approach. The primary challenge addressed is the optimization of power allocation in a MIMO-NOMA system, a complex task given the non-convex nature of the problem. Our proposed Q-Learning approach adaptively adjusts power allocation strategy for proximal and distant users, optimizing the trade-off between various conflicting metrics and significantly improving the system’s performance. Compared to traditional power allocation strategies, our approach showed superior performance across three principal parameters: spectral efficiency, achievable sum rate, and energy efficiency. Specifically, our methodology achieved approximately a 140% increase in the achievable sum rate and about 93% improvement in energy efficiency at a transmitted power of 20 dB while also enhancing spectral efficiency by approximately 88.6% at 30 dB transmitted Power. These results underscore the potential of reinforcement learning techniques, particularly Q-Learning, as practical solutions for complex optimization problems in wireless communication systems. Future research may investigate the inclusion of enhanced channel simulations and network limitations into the machine learning framework to assess the feasibility and resilience of such intelligent approaches.

Learning-Based Reconfigurable-Intelligent-Surface-Aided Rate-Splitting Multiple Access Networks

Rate-splitting multiple access (RSMA) and reconfigurable intelligent surface (RIS) techniques show promise in enhancing spectral efficiency in sixth-generation Internet of Things (IoT) networks. However, optimizing the synergy between these two methods is challenging due to the complex and dynamic environment. This study focuses on maximizing the sum-rate metric in RIS-assisted uplink multiantenna RSMA IoT networks to address this problem. We jointly optimized the base station beamforming design, power allocation, and RIS phase shifts to enhance the spectral efficiency with multiple mobile IoT devices present. The controlled parameters are continuous variables and the mathematical problem is non-concave, Therefore, we formulated the problem as a Markov decision process and used the deep deterministic policy gradient (DDPG) to determine the optimal joint actions. We proposed a safe action shaping process for the decision-making actor network to address constraint violations. Through a rigorous performance evaluation, we demonstrated that the DDPG approach with action shaping outperforms the current DDPG algorithm regarding the maximum achievable sum rate.

STAR-RIS-Enabled Short-Packet NOMA Systems

This paper analyzes the performance of simultaneous transmitting and reflecting (STAR) reconfigurable intelligent surface (RIS)-enabled short-packet non-orthogonal multiple access (NOMA) systems. A mode-switching protocol combined with partition strategies (MSPS) for the STAR-RIS design is proposed to simultaneously serve multiple actuators along with the discrete phase-shift alignment in order to reduce the STAR-RIS implementation costs. Approximate and asymptotic closed-form expressions for the average block error rate (BLER) and achievable rate have been derived to reveal some useful insights, such as the actuators' diversity order, coding gain, the power allocation factor, packet length, and the number of information bits. Numerical results confirm that: i) the considered system achieves better BLER balance among the actuators than classical RIS-based short-packet NOMA systems and higher achievable sum rate than RIS-based short-packet orthogonal multiple access ones; ii) the STAR-RIS with discrete phase shift design using 3-bit quantization achieves near-optimal performance with the continuous phase shift; iii) there is a trade-off between using bit quantizer and the total number of STAR-RIS elements; and iv) the power allocation strategy provides better average sum rate improvement than block-length increasing strategy.

Framework for fast and low-complexity deployment of UAVs-assisted communication

Unmanned Aerial Vehicles (UAVs) can work as aerial base stations along with the ground base stations to enhance capacity and provide ubiquitous coverage in 5G and beyond communication systems. UAVs have numerous features that distinguish them from the traditional fixed ground network infrastructure. These features include more flexibility, lower costs, faster integration, and better channel conditions with users owing to the UAVs maneuverability and adaptability to varying network conditions. This paper addresses UAVs positioning and radio resource management, which are two critical design issues that greatly impact the system performance and user experience in UAVs-assisted communication scenarios. Our objective is maximizing the achieved sum rate of a system consisting of a ground base station and a number of UAVs operating as aerial base stations under constraints on the minimum rate requirements of users, the capacity of wireless backhaul links between the UAVs and the ground base station, and the power budgets of the base stations. In doing so, we optimize the UAVs locations, the users-to-base stations association, the access link channels assignment, and the power allocation. The problem is a mixed-integer non-convex optimization, which is NP-hard to solve, and consequently we propose a fast low-complexity algorithm that achieves high performance compared to the common block coordinate descent-based solution approach and at much less execution time.

SLIPT-Enabled Multi-LED MU-MISO VLC Networks: Joint Beamforming and DC Bias Optimization

This paper studies a simultaneous lightwave information and power transfer (SLIPT)-enabled multi-user (MU) multiple input single output (MISO) visible light communication (VLC) network, where multiple user equipments (UEs) are simultaneously allowed to receive information by the photodiode (PD) and harvest energy by the solar panel, respectively. To improve the system spectral efficiency, an achievable sum rate maximization problem is formulated subject to the minimum energy harvesting (EH) requirement of each UE, the total transmit power constraint of the LEDs and the dimming control constraint of the LEDs, by jointly optimizing the beamforming vector and the direct current (DC) bias vector of the LEDs. To be general, the practical non-linear EH model and the information capacity model of the VLC channel are adopted. To solve the non-convex optimization problem, the objective function is equivalently transformed at first by using the epigraph reformulation, and then the transformed problem is relaxed with the semi-definite relaxation (SDR) method. Based on this, an iterative algorithm is designed to approximately obtain the maximal achievable sum rate based on the successive convex approximation (SCA). Simulation results indicate that there exists a non-trivial trade-off between the achievable sum rate and the harvested energy, and that DC bias vector dominates the amount of the harvested energy compared with the beamforming vector. Besides, for a given relatively high EH requirement, the achievable sum rate increases with the increment of the dimming level, especially for relatively small dimming level. Once the dimming level is larger than a specific value, with the increment of the dimming level, more transmit power will be allocated for illumination, resulting in the decrement of the achievable sum rate.