Sum Rate Maximization Problem Research Articles

Manifold Learning Inspired Dynamic Hybrid Precoding With Antenna Partitioning Algorithm for Dual-Hop Hybrid FSO-RF Systems

The decode-and-forward (DF) based free-space optical-radio frequency (FSO-RF) hybrid system combines the advantages of both FSO links and RF links, which makes the system easy to be deployed and enables the extended transmission coverage. In the case of multiple users, the unreasonable dynamic antenna selection algorithm will lead to multi-user interference (MUI) and reduce the spectral efficiency of the system. Therefore, we propose hybrid precoding based on manifold learning with the antenna partitioning algorithm for dual-hop hybrid FSO-RF systems. In the hybrid precoding of dynamic subarray structure, the high-dimensional channels are embedded into the low-dimensional manifolds by the optimized multidimensional scaling (MDS). The potential spatial correlations of the high-dimensional channel are preserved by the scaling by majorizing a complicated function (SMACOF) algorithm. Through proper user clustering, the hybrid precoding is investigated for the sum-rate maximization problem by manifold quasi-conjugate gradient methods. Meanwhile, an antenna subarray partitioning algorithm is proposed, so that each antenna unit can be assigned to an RF chain based on the increment of the user’s maximum signal to interference noise ratio (SINR). By calculating the simulated equivalent channel SINR for the selected users, the antenna division can greatly reduce the computational complexity and the size of the search space, and ensure fairness among users. Simulation results show that this method can obtain almost the best summation rate and higher spectral efficiency compared with the conventional method.

Joint User Selection and Transceiver Design for Cell-Free With Network-Assisted Full Duplexing

In this paper, we investigate the problem of sum rate maximization by guaranteeing the quality of service (QoS) of both uplink and downlink in cell-free with network-assisted full Duplexing (NAFD), where the users operate in half-duplex mode and access points (APs) operate in either full-duplex mode or half-duplex mode. In the considered network, the central processor unit (CPU) sends the compressed beamformed signals to the transmit-APs (T-APs) over the downlink fronthaul, and the T-APs forward the signals to downlink users. At the same time, the receive-APs (R-APs) compress the signals transmitted by uplink users and forward them to the CPU via uplink fronthaul. We aim to maximize the spectral efficiency and the number of users that should be admitted by the network, where users’ requirements of both downlink and uplink signal-to-interference-plus-noise (SINR) constraints, fronthaul capacity constraints, energy harvesting constraints and simultaneous wireless information and power transfer (SWIPT) ratio design are considered. A successive convex approximation-based algorithm is proposed to solve the highly coupled problem, which is guaranteed to converge to the Karush-Kuhn-Tucker (KKT) conditions. We conduct a comprehensive comparison between the NAFD scheme and the traditional co-frequency co-time full duplex (CCFD) scheme and time division duplex (TDD) scheme and offer some valuable opinions about the system design.

Manifold Discriminative Learning Inspired Hybrid Beamforming for Millimeter-Wave Massive MIMO Systems

Millimeter-wave (mmWave) massive MIMO (multiple-input multiple-output) is a promising technology as it provides significant beamforming gains and interference reduction capabilities due to the large number of antennas. However, mmWave massive MIMO is computationally demanding, as the high antenna count results in high-dimensional matrix operations when conventional MIMO processing is applied. Hybrid precoding is an effective solution for the mmWave massive MIMO systems to significantly decrease the number of radio frequency (RF) chains without an apparent sum-rate loss. In this paper, we propose user clustering hybrid precoding to enable efficient and low-complexity operation in high-dimensional mmWave massive MIMO, where a large number of antennas are used in low-dimensional manifolds. By modeling each user set as a manifold, we formulate the problem as clustering-oriented multi-manifolds learning. The manifold discriminative learning seek to learn the embedding low-dimensional manifolds, where manifolds with different user cluster labels are better separated, and the local spatial correlation of the high-dimensional channels within each manifold is enhanced. Most of the high-dimensional channels are embedded in the low-dimensional manifolds by manifold discriminative learning, while retaining the potential spatial correlation of the high-dimensional channels. The nonlinearity of high-dimensional channel is transformed into global and local nonlinearity to achieve dimensionality reduction. Through proper user clustering, the hybrid precoding is investigated for the sum-rate maximization problem by manifold quasi conjugate gradient methods. The high signal to interference plus noise ratio (SINR) is achieved and the computational complexity is reduced by avoiding the conventional schemes to deal with high-dimensional channel parameters. Performance evaluations show that the proposed scheme can obtain near-optimal sum-rate and considerably higher spectral efficiency than some existing solutions

Deep Reinforcement Learning for Channel Selection and Power Allocation in D2D Communications

Device-to-device (D2D) communication is regarded as a key technical component of the fifth-generation (5G), D2D communication usually reuses spectrum resources with cellular users (CUs). To mitigate interference to cellular links and improve spectrum efficiency, this paper investigates a sum-rate maximization problem in the underlay of D2D communication. Particularly, a joint channel selection and power allocation framework based on multi-agent deep reinforcement learning is proposed, named Double Deep Q-Network (DDQN). It can adeptly select the channel and allocate power in a dynamic environment. The proposed scheme only requires local information and some outdated nonlocal information, which reduces signaling overheads significantly. Simulation results show that the proposed scheme can improve the D2D sum rate and ensure quality-of-service (QoS) of CUs compared with other benchmarks.

Novel Convolutional Restricted Boltzmann Machine manifold learning inspired dynamic user clustering hybrid precoding for millimeter-wave massive multiple-input multiple-output systems

Millimeter-wave massive multiple-input multiple-output is a key technology in 5G communication system. In particular, the hybrid precoding method has the advantages of being power efficient and less expensive than the full-digital precoding method, so it has attracted more and more attention. The effectiveness of this method in simple systems has been well verified, but its performance is still unknown due to many problems in real communication such as interference from other users and base stations, and users are constantly on the move. In this article, we propose a dynamic user clustering hybrid precoding method in the high-dimensional millimeter-wave multiple-input multiple-output system, which uses low-dimensional manifolds to avoid complicated calculations when there are many antennas. We model each user set as a novel Convolutional Restricted Boltzmann Machine manifold, and the problem is transformed into cluster-oriented multi-manifold learning. The novel Convolutional Restricted Boltzmann Machine manifold learning seeks to learn embedded low-dimensional manifolds through manifold learning in the face of user mobility in clusters. Through proper user clustering, the hybrid precoding is investigated for the sum-rate maximization problem by manifold quasi-conjugate gradient methods. This algorithm avoids the traditional method of processing high-dimensional channel parameters, achieves a high signal-to-noise ratio, and reduces computational complexity. The simulation result table shows that this method can get almost the best summation rate and higher spectral efficiency compared with the traditional method.

Joint Beamforming for Integrated Mmwave Satellite-Terrestrial Self-Backhauled Networks

Satellite communication is considered as an efficient way to achieve the reliable communication for worldwide users. To further enhance the achievable rate of the satellite system, we propose joint beamforming schemes of an integrated satellite-terrestrial self-backhauled (ISTS) system, in which the satellite backhaul and terrestrial access links are operating at the same millimeter wave band. Specifically, the earth station and the base station (BS) constitute a self-backhauled relay to forward the satellite signals to the terrestrial users. A sum rate maximization problem is formulated under the constraints of the maximum power requirements of the satellite and the BS, the minimum quality-of-service requirements of the terrestrial users, and the maximum satellite backhaul capacity. To obtain some insights, we first assume that the perfect channel state information (CSI) on the satellite and terrestrial links is known and transform the formulated optimization problem to a standard difference of convex problem. Then we solve it iteratively by adopting the convex-concave procedure approach. Furthermore, when only imperfect CSI on the satellite and terrestrial links is available, we employ a iterative penalty function algorithm associated with the sequential convex approximation method and the S-procedure approach to obtain suboptimal beamforming vectors. Finally, simulation results demonstrate that the proposed joint beamforming schemes have better performance compared with other benchmark schemes and the ISTS architecture is suitable for the satellite systems in various orbits.

Hybrid Beamforming, User Scheduling, and Resource Allocation for Integrated Terrestrial-Satellite Communication

In this paper, we investigate hybrid beamforming, user scheduling, and resource allocation optimization based on spectrum coexisting forward transmission in integrated terrestrial-satellite network (ITSN) with the purpose of improving system sum rate and energy efficiency. Considering the limitation of on-board beamforming, a hybrid analog-digital beamforming scheme is designed under the scenario of millimeter wave (mmWave) coexisting in the ITSN framework. Besides, in order to further mitigate intra-beam and inter-beam interference, we propose an adaptive user scheduling scheme, which first determines the cluster center based on adaptive threshold, and then selects users with less channel correlation into a scheduling cluster. Moreover, we model system sum rate maximization problem that incorporates maximum power constrains and minimum data rate requirements. Combined with the aforementioned hybrid beamforming and user scheduling strategy, we formulate the sum rate maximizing problem to a pure power allocation issue. In view of the non-convexity and high complexity, we propose a feasible optimization method based on the minimum mean square error (MMSE) criterion and logarithmic linearization to optimize the power allocation for each user terminal (UT). Simulation results show that our proposed joint beamforming and resource allocation optimization scheduling scheme can achieve an attractive gain in system sum rate and energy efficiency compared with conservative beamforming and allocations.

Optimization of Rate Allocation and Power Control for Rate Splitting Multiple Access (RSMA)

In this paper, the sum-rate maximization problem is studied for wireless networks that use downlink rate splitting multiple access (RSMA). In the considered model, the base station (BS) divides the messages that can be transmitted to its users into a “private” part and a “common” part. Here, the common message is a message that multiple users want to receive and the private message is a message that is dedicated to only a specific user. The RSMA mechanism enables a BS to adjust the split of common and private messages so as to control the interference by decoding and treating interference as noise and, thus optimizing the data rate of users. To maximize the users' sum-rate, the network can determine the rate allocation for the common message to meet the rate demand, and adjust the transmit power for the private message to reduce the interference. This problem is formulated as an optimization problem whose goal is to maximize the sum-rate of all users. To solve this nonconvex maximization problem with a single-antenna BS, the optimal power used for transmitting the private message to the users is first obtained in closed form for a given rate allocation and common message power. Based on the optimal private message transmit power, the optimal rate allocation is then derived under a fixed common message transmit power. Subsequently, an iterative algorithm is proposed to obtain a suboptimal solution of common message transmit power. To solve this nonconvex maximization problem with a multiple-antenna BS, a successive convex approximation method is utilized. Simulation results show that the RSMA can achieve up to 15.6% and 21.5% gains in terms of data rate compared to non-orthogonal multiple access (NOMA) and orthogonal frequency-division multiple access (OFDMA), respectively.

Sum Rate Maximization for Cooperative NOMA with Hardware Impairments

This paper investigates the sum rate (SR) maximization problem for downlink cooperative non-orthogonal multiple access (C-NOMA) systems with hardware impairments (HIs). The source node communicates with users via a half-duplex amplified-and-forward (HD-AF) relay with HIs. First, we derive the SR expression of the systems under HIs. Then, SR maximization problem is formulated under maximum power of the source, relay, and the minimum rate constraint of each user. As the original SR maximization problem is a non-convex problem, it is difficult to find the optimal resource allocation directly by tractional convex optimization method. We use variable substitution method to convert the non-convex SR maximization problem to an equivalent convex optimization problem. Finally, a joint power and rate allocation based on interior point method is proposed to maximize the SR of the systems. Simulation results show that the algorithm can improve the SR of the C-NOMA compared with the cooperative orthogonal multiple access (C-OMA) scheme.

Mitigating the Doubly Near–Far Effect in UAV-Enabled WPCN

In this letter, we investigate an unmanned aerial vehicle (UAV) enabled wireless powered communication network (WPCN), in which the UAV first employs the radio frequency (RF) signal to power ground users, and then the users communicate with the UAV by using the harvested energy. The sum rate maximization problem in WPCN suffers doubly near-far effect caused by the double signal propagation loss in both the energy harvesting and communication stage. It is generally believed that this effect can be alleviated by integrating UAV into WPCN, thanks to its flexible deployment. However, due to the lack of analytical solution in state-of-art research, it is still unclear how to quantify the advantage that the mobility of UAV brings to migrating the doubly near-far effect. To address this issue, by showing that the optimal UAV trajectory has a multi-location hovering structure, we first derive the closed-form optimal solution of resource allocation, as a function of the UAV hovering location for downlink wireless power transfer (WPT). We then prove that the UAV in our case only needs to hover at a single location for WPT. More importantly, it is explicitly shown that the doubly near far effect can be alleviated into near-far effect. Simulation results validate our analysis.

Joint Transmitter-Receiver Optimization and Self-Interference Suppression in Full-Duplex MIMO Systems

In this paper, we study the effects of joint transmit and receive antennas’ selection on full-duplex (FD) multiple-input-multiple-output (MIMO) networks’ performance. The antennas’ selection problem is, in general, a combinatorial problem whose complexity grows exponentially with the number of antennas. To fully understand the effects of antennas’ selection, we study the sum rate maximization problem in a single-cell network with an FD-MIMO base-station (BS). First, we consider a system with a normal-scale MIMO full-duplex BS, i.e., a normal-scale FD MIMO system. The sum rate maximization problem is studied for two different scenarios; in the first scenario, we consider jointly optimizing the transmit and receive antennas’ selection with the precoder and the receiver weights. A Generalized Bender's Decomposition based algorithm is proposed to solve the mixed-integer nonlinear sum rate maximization problem. In the second scenario, we consider self-interference cancellation via zero-forcing (ZF) transmission. A heuristic algorithm is proposed to solve the sum rate maximization problem by optimizing the selection of the transmit antennas, receive antennas, and the receive antennas at which self-interference is nulled. Second, in a very-large scale, i.e., massive MIMO system, we derive lower bounds for the uplink and downlink rates with ZF receiver and precoder, respectively. The sum rate is maximized by jointly optimizing the transmit to receive antennas ratio and the ratio of the receive antennas at which self-interference is nulled. Finally, via numerical analysis, we evaluate the performance of the formulated sum rate maximization problems.

Reconfigurable Holographic Surface: Holographic Beamforming for Metasurface-Aided Wireless Communications

The future sixth generation (6G) wireless communications look forward to constructing a ubiquitous intelligent information network with high data rates. To fulfill such challenging visions, the reconfigurable holographic surface (RHS) is developed as a promising solution due to its capability of accurate multi-beam steering with low power consumption and hardware cost. Different from the conventional phase-controlled antennas, the RHS can control the radiation amplitude of the reference wave propagating on the metasurface by leveraging the holographic technique. The desired object waves can then be generated without complex phase-shifting circuits, enabling the convenient implementation of the transceiver. Such amplitude-controlled holographic beamforming triggers new challenges since a new beamforming scheme needs to be developed to handle the complex-domain optimization problem subject to the unconventional real-domain amplitude constraints, which makes the superposition of the radiation waves from different antenna elements difficult to tackle. In this letter, we consider an RHS-aided multi-user communication system with a base station equipped with an RHS. We formulate a sum rate maximization problem and design a novel amplitude-controlled algorithm to solve the problem. Simulation results verify the effectiveness of the proposed scheme.

Low-Complexity Heuristic Algorithm for Power Allocation and Access Mode Selection in M2M Networks

A hybrid or coexisting orthogonal frequency-division multiple access (OFDMA) and nonorthogonal multiple access (NOMA) scheme is a promising approach to greatly enhance network capacity and reduce the interference in machine-to-machine (M2M) communication networks. In this article, we study the power allocation and access mode selection problem of machine-type communication devices (MTCDs), which are allowed to transmit their data packets to the base station (BS) in direct transmission mode (DTM) or cluster head forwarding mode (CHFM). Considering the transmit power optimization and data rate maximization of the MTCDs, we formulate the power allocation and access mode selection problem as a sum-rate maximization problem. Since the original maximization problem is a nonlinear fractional problem that cannot be solved conveniently, we transform the optimization problem into two subproblems, i.e., power allocation subproblem and access mode selection subproblem. The power allocation subproblem is solved for both OFDMA and NOMA schemes by applying the Lagrange dual method. To solve the access mode selection subproblem, we further divide the subproblem into DTM subproblem and CHFM subproblem and solve these subproblems successively. In particular, for the solution of the CHFM subproblem, we first propose a greedy method-based algorithm, and then, to tackle the issue of high computational complexity, we present a low complexity heuristic algorithm. In the end, we present simulation results to demonstrate the effectiveness of the proposed algorithms.

Joint Deployment and Multiple Access Design for Intelligent Reflecting Surface Assisted Networks

The fundamental intelligent reflecting surface (IRS) deployment problem is investigated for IRS-assisted networks, where one IRS is arranged to be deployed in a specific region for assisting the communication between an access point (AP) and multiple users. Specifically, three multiple access schemes are considered, namely non-orthogonal multiple access (NOMA), frequency division multiple access (FDMA), and time division multiple access (TDMA). The weighted sum rate maximization problem for joint optimization of the deployment location and the reflection coefficients of the IRS as well as the power allocation at the AP is formulated. The non-convex optimization problems obtained for NOMA and FDMA are solved by employing monotonic optimization and semidefinite relaxation to find a performance upper bound. The problem obtained for TDMA is optimally solved by leveraging the time-selective nature of the IRS. Furthermore, for all three multiple access schemes, low-complexity suboptimal algorithms are developed by exploiting alternating optimization and successive convex approximation techniques, where a local region optimization method is applied for optimizing the IRS deployment location. Numerical results are provided to show that: 1) near-optimal performance can be achieved by the proposed suboptimal algorithms; 2) asymmetric and symmetric IRS deployment strategies are preferable for NOMA and FDMA/TDMA, respectively; 3) the performance gain achieved with IRS can be significantly improved by optimizing the deployment location.

Sum Rate Maximization of Massive MIMO NOMA in LEO Satellite Communication System

In this letter, we consider non-orthogonal multiple access (NOMA) applying into massive multiple input multiple output (mMIMO) low earth orbit (LEO) satellite communication system (SCS) to improve spectral efficiency. Specifically, we investigate sum rate maximization problem with transmit power, quality of service (QoS) constraints, imperfect successive interference cancelation (SIC) and imperfect channel state information (CSI) considered. We decouple the original problem into two parts: precoding vectors design and transmit power optimization. Precoding vectors are derived for maximizing the average signal power in each beam and eliminating inter-beam interference (IBI). Then, transmit power optimization problem is transformed into a convex optimization problem by utilizing the first order Taylor expansion, an iterative algorithm is proposed to get the optimal sum rate. Finally, simulation results verify the convergence of the proposed algorithm and our proposed mMIMO NOMA approach is better than other approaches.

Sum-Rate Maximization of Wireless Powered Primary Users for Cooperative CRNs: NOMA or TDMA at Cognitive Users?

Recently, wireless powered cooperative cognitive radio networks (CRNs), which combine the technologies of radio frequency (RF) energy harvesting and CR, have drawn great attention. In such networks, energy cooperation between the cognitive users (CUs) and the wireless powered primary users (PUs) can be performed, where the CUs can charge the PUs wirelessly in exchange for the spectrum access. Specifically, energy cooperation and information transmission is executed in two phases, where the CUs transmit their data signals and the PUs harvest energy from these signals in the first phase, and the PUs transmit their data using the harvested energy in the second phase. In particular, we consider two multiple access schemes for the CUs, namely non-orthogonal multiple access (NOMA) and time-division multiple access (TDMA). For both NOMA and TDMA, the PU sum-rate maximization problems under the minimum CU sum-rate constraint are first simplified by exploring particular problem structure, then are transformed to convex problems, and finally are solved optimally. The PU sum-rates of the two schemes are compared theoretically as well as numerically. It is revealed that the circuit power consumption at the CUs, the required minimum CU sum-rate, and the PU energy harvesting sensitivity and saturation thresholds play key roles in the PU performance comparison of the two schemes.

Artificial intelligence techniques for rate maximization in interference channels

Enabling multiple users to transmit on the same channel, simultaneously, is an essential part of the future generation of communication systems. Optimization of transmit power has a significant impact on the performance of communication systems. Under spectrum re-use, the transmit power of one user causes interference to the other users on the shared channels, also known as interference channels (ICs). Under IC model, the structure of power allocation problems becomes complex. Recently, artificial intelligence (AI) techniques have shown promising results in various wireless communication problems. This paper aims to explore various AI techniques to optimize power allocation in multi-user IC transmission. Sum rate maximization problem subject to guaranteed per user performance under limited available power budget at each transmitting node is considered. Specifically, we first investigate the performance of Particle Filter and Particle Swarm Optimization based search algorithm. Then, Linear Regression and Neural Network based predictors are designed to reduce the overall time complexity. Finally, extensive simulations are presented to compare the performance of obtained solutions.

Blockage-Aware Reliable mmWave Access via Coordinated Multi-Point Connectivity

The fundamental challenge of the millimeter-wave (mmWave) frequency band is the sensitivity of the radio channel to blockages, which gives rise to unstable connectivity and impacts the reliability of a system. To this end, multi-point connectivity is a promising approach for ensuring the desired rate and reliability requirements. A robust beamformer design is proposed to improve the communication reliability by exploiting the spatial macro-diversity and a pessimistic estimate of rates over potential link blockage combinations. Specifically, we provide a blockage-aware algorithm for the weighted sum-rate maximization (WSRM) problem with parallel beamformer processing across distributed remote radio units (RRUs). Combinations of non-convex and coupled constraints are handled via successive convex approximation (SCA) framework, which admits a closed-form solution for each SCA step, by solving a system of Karush-Kuhn-Tucker (KKT) optimality conditions. Unlike the conventional coordinated multi-point (CoMP) schemes, the proposed blockage-aware beamformer design has, per-iteration, computational complexity in the order of RRU antennas instead of system-wide joint transmit antennas. This leads to a practical and computationally efficient implementation that is scalable to any arbitrary multi-point configuration. In the presence of random blockages, the proposed schemes are shown to significantly outperform baseline scenarios and result in reliable mmWave communication.

Energy-Efficient Resource Allocation in C-RANs with Capacity-Limited Fronthaul

Cloud Radio Access Network (C-RAN) is a key architecture for 5G cellular wireless network that aims at improving spectral and energy efficiency of the network by uniting traditional RAN with cloud computing. In this paper, a novel resource allocation scheme that optimizes the network energy efficiency of a C-RAN is designed. First, an energy consumption model that characterizes the computation energy of the BaseBand Unit (BBU) is introduced based on empirical results collected from a programmable C-RAN testbed. Then, an optimization problem is formulated to maximize the energy efficiency of the network, subject to practical constraints including Quality of Service (QoS) requirement, radio remote head transmit power, and fronthaul capacity limits. The formulated Network Energy Efficiency Maximization (NEEM) problem jointly considers the tradeoff among the network accumulated data rate, BBU power consumption, fronthaul cost, and beamforming design. To deal with the non-convexity and mixed-integer nature of the problem, we utilize successive convex approximation methods to transform the original problem into the equivalent Weighted Sum-Rate (WSR) maximization problem. We then propose a provably-convergent iterative method to solve the resulting WSR problem. Extensive simulation results coupled with real-time experiments on a small-scale C-RAN testbed show the effectiveness of our proposed resource allocation scheme and its advantages over existing approaches.

Joint Iterative Optimization-Based Low-Complexity Adaptive Hybrid Beamforming for Massive MU-MIMO Systems

This paper proposes a joint iterative optimization based hybrid beamforming technique for massive MU-MIMO systems. The proposed technique jointly and iteratively optimizes the transmitter precoders and combiners, aiming to approach the global optimum solution for the system sum-rate maximization problem. The proposed technique develops an adaptive algorithm exploiting the stochastic gradients (SG) of the local beamformers and provides low-complexity closed-form solutions. Furthermore, an efficient adaptive scheme is developed based on the proposed adaptive algorithm and the closed-form solutions. The proposed algorithm requires the signal-to-interference-plus-noise ratio (SINR) feedback from each user and a limited size transition vector to be exchanged between the transmitter and receivers at each step to update beamformers locally. Analytic result shows that the proposed adaptive algorithm achieves low-complexity when the array size is large and is able to converge within a small number of iterations. Simulation result shows that the proposed technique is able to achieve superior performance comparing to the existing state-of-art techniques. In addition, the knowledge of instantaneous channel state information (CSI) is not required as the channels are also adaptively estimated with each coherence time which is a practical assumption since the CSI is usually unavailable or have time-varying nature in real-time applications.