Optimal Sum Rate Research Articles

Fairness-Constrained Rate Optimization of Multi-Link Slotted Aloha

This letter optimizes the data rate performance of multi-link slotted Aloha networks, in which Multi-Link Devices (MLDs) coexist with multiple groups of Single-Link Devices (SLDs), where different groups reside in distinct channels. MLDs can transmit/receive packets via multiple channels, causing interference to all groups of SLDs and severe rate unfairness issue. To ensure SLDs in different channels can acquire target ratios of data rate, explicit expressions of sum rate and group data rate are derived, based on which the optimal transmission probabilities of MLDs/SLDs for maximizing the sum rate without or with fairness constraints are obtained. The analysis reveals the coupling effect of the behavior of devices in different channels, the tradeoff between the optimal sum rate performance and the fairness, and the effect of channel fading on benefit of using multi-link transmission capability.

Distributed Noncoherent Joint Transmission Based on Multi-Agent Reinforcement Learning for Dense Small Cell Networks

In dense small cell networks, the coordinated multi-point noncoherent joint transmission (JT) is a key technique to mitigate inter-cell interference and enhance network capacity. However, the capacity-maximizing power control and beamforming problem subject to a total transmit power constraint at each individual small cell base station (BS) is inherently nonconvex and NP-hard. To solve this problem, most existing algorithms require global channel state information (CSI) and a lot of computations, which are infeasible and impractical in dynamic wireless networks with limited computing power and link capacity. In this paper, we characterize a low-dimensional solution structure for the power control of the sum-rate maximization problem in time division duplex (TDD) dense small cell networks. Taking advantage of this low-dimensional structure, a distributed noncoherent JT scheme based on multi-agent reinforcement learning (MARL) is proposed to maximize the sum-rate of the dense small cell networks with reduced information overhead. In the proposed scheme, each BS acts as an agent and makes decisions locally. It is proved that the optimal sum-rate can be achieved for single-transmit-antenna BSs by using the proposed scheme. Compared to the best method presently known, the proposed scheme achieves a similar sum-rate with considerably lower computational complexity and information overhead, which makes it more appealing for practical implementations.

A novel user clustering and a low-complexity power allocation in multi-user and multi-cluster NOMA system via Stackelberg game competition

Non-orthogonal multiple access (NOMA) has drawn wide attentions in multi-user, resource-constrained mobile systems to improve the system spectral efficiency. In the conventional resource assignment approaches, the optimal power allocation solutions are usually performed among multiple clusters, which, however, incur a high computational complexity due to the diversity of multiple users. In this work, we aim at the practical scenarios of multi-user and multi-cluster NOMA networks with the proposed a low-complexity power allocation and a suboptimal user clustering approach. By using Stackelberg game competition, a closed-form solution with no iterative operations is given first to the power allocation problem. The main idea of the authors’ proposed scheme is to have the base station (BS) allocating resources to all the users freely to guarantee their required minimum transmission-rates, and obtaining the maximal revenue through selling the remaining resources. Then, a suboptimal user clustering approach is further proposed to obtain more revenue for selling the resource. Numerical results show that the proposed power allocation scheme approximates the optimal sum-rates performance, but with lower computational floating-point operations (FLOPs) for the multi-user scene. And, the given user clustering scheme can pursue more revenue of the BS.

Hardware Impaired RIS Assisted Multipair FD Communication With Spatial Correlation

We investigate a multi-pair full-duplex (FD) two-way communication system assisted by a reconfigurable intelligent surface (RIS), where each user-pair exchanges information via RIS. Unlike most existing works related to FD systems with RIS, we take into account an inevitable electromagnetic interference (EMI) and also consider a practical Rician channel between RIS and users, and spatial correlation owing to size constraint at the RIS. In addition we consider low-cost hardware at RIS and users resulting in impairments. We first derive an approximate expression for the achievable sum-rate of the system. We then optimize RIS phases to achieve the optimum sum-rate using particle swarm optimization (PSO) algorithm. We, through the simulation results, show the optimality of PSO algorithm and the impact of EMI and hardware impairments on the sum-rate.

Sum Rate Optimization of IRS-Aided Uplink Muliantenna NOMA with Practical Reflection.

Recently, intelligent reflecting surfaces (IRSs) have drawn huge attention as a promising solution for 6G networks to enhance diverse performance metrics in a cost-effective way. For massive connectivity toward a higher spectral efficiency, we address an intelligent reflecting surface (IRS) to an uplink nonorthogonal multiple access (NOMA) network supported by a multiantenna receiver. We maximize the sum rate of the IRS-aided NOMA network by optimizing the IRS reflection pattern under unit modulus and practical reflection. For a moderate-sized IRS, we obtain an upper bound on the optimal sum rate by solving a determinant maximization (max-det) problem after rank relaxation, which also leads to a feasible solution through Gaussian randomization. For a large number of IRS elements, we apply the iterative algorithms relying on the gradient, such as Broyden–Fletcher–Goldfarb–Shanno (BFGS) and limited-memory BFGS algorithms for which the gradient of the sum rate is derived in a computationally efficient form. The results show that the max-det approach provides a near-optimal performance under unit modulus reflection, while the gradient-based iterative algorithms exhibit merits in performance and complexity for a large-sized IRS with practical reflection.

Interplay Between Interference-Aware Resource Allocation Algorithm Design, CSI, and Feedback in Underlay D2D Networks

A key problem in underlay device-to-device (D2D) systems is assigning cellular users and D2D users to subchannels to improve spatial reuse while controlling the interference they cause to each other. We present a unified treatment of this problem for two practically motivated partial and statistical channel state information (CSI) models with quantized feedback. They differ in the CSI available at the D2D receiver. In both models, the nodes only have statistical information of inter-D2D and inter-cell interferences, and employ fractional power control. We present two polynomial-time algorithms to assign multiple D2D pairs to subchannels, namely, relaxation-pruning algorithm (RPA) and cardinality-constrained subchannel assignment algorithm (CCSAA). RPA and CCSAA guarantee a D2D sum rate that is at least one-half and one-third, respectively, of the optimal sum rate. We also propose a novel statistical rate upgradation technique that exploits the allocation information to improve the D2D rates. We observe that inter-D2D interference has a more pronounced effect in the statistical CSI model. The algorithms respond differently to the two CSI models. RPA outperforms CCSAA in the partial CSI model, while CCSAA outperforms RPA in the statistical CSI model despite its weaker performance guarantee.

Achieving Near Optimal Sum Rate in Downlink Massive Multiple Antenna System

In this paper, a massive multiple input multiple output (massive-MIMO) system with up to 600 base station antennas exploiting the channel state information (CSI) to improve the capacity of system while serving multiple UEs was proposed. Two precoding schemes: zero-forcing (ZF) and maximum ratio transmission (MRT) were implemented and used to test the effectiveness of the proposed system. In order to study the effectiveness of the system, its performance was analyzed in terms of achievable sum rate. MATLAB codes were developed for the simulations test conducted to study the sum rate performance of the system. Simulations were carried out by varying the number of base station antennas from 300 to 600 with respect to the number of users to determine the achievable sum rate of the system. Results showed that increasing the number of base station antennas brings about better spectral efficiency for optimal multiuser interference reduction such that highest sum rates: 317.5 Bits/s/Hz and 317.0 Bits/s/Hz, were achieved using ZF considering vector/matrix normalizations for number of base station antennas (M) equal to 600 and number of UEs (K) equal to 200 when sum rate is plotted against number of base station antennas in high transmitted power scenarios. Also, when sum rate is plotted against number of users, the system achieves highest sum rate of 433.7 Bits/s/Hz and 433.3 Bits/s/Hz for ZF considering vector/matrix normalizations at M 600, for K equal to 200 for high transmitted power scenarios. The results show that for low transmitted power, MRT outperforms ZF, while ZF gives better performance than MRT for high transmitted power. Generally, the results obtained revealedthat increasing the number of base station antennas can provide near optimal (or best possible) achievable sum rate and mitigate multiuser interference (MUI).

Fractional Frequency Reuse in Random Hybrid FD/HD Small Cell Networks With Fractional Power Control

Small cells are regarded as a promising solution for improving the coverage and spectral efficiency of the fifth generation (5G) cellular networks. Small cell networks utilize low transmission power levels and thus, are appropriate for deploying full-duplex (FD) technology. In this paper, a hybrid full/half duplex (FD/HD) small cell network exploiting fractional frequency reuse (FFR) and fractional power control (FPC) for interference mitigation is considered. Theoretical expressions for the coverage probability and the average sum-rate in uplink and downlink directions are derived for systems that make use of conventional frequency reuse, hard FFR, and soft FFR. The impact of various system parameters, such as power control parameter, small base station (SBS) density, and self-interference cancellation (SIC) parameters on system performance are investigated. The numerical experiments show that SBSs tend to operate in FD mode for large values of SBS density and SIC level. Also, interestingly, the optimal sum-rate is achieved when all SBSs operate either in FD or in HD mode. Finally, it is observed that exploiting FFR techniques in FD networks making use of FPC, significantly improves downlink coverage probability.

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.

Asynchronous Transmission for Multiple Access Channels: Rate-Region Analysis and System Design for Uplink NOMA

In this work, we thoroughly analyze the rate-region provided by the asynchronous transmission in multiple access channels (MACs). We derive the corresponding capacity-regions, applicable to a wide range of pulse shaping methods. We analytically prove that asynchronous transmission enlarges the capacity-region of MACs. Although successive interference cancellation (SIC) can achieve the optimal sum-rate for the conventional uplink non-orthogonal multiple access (NOMA) methods, it is unable to achieve the boundary of the capacity-region for the asynchronous transmission. We demonstrate that for the asynchronous transmission, the optimal SIC decoding order to achieve the maximum sum-rate is based on the users' channel strengths. This optimal ordering is in contrast to the conventional uplink NOMA, where various decoding orders can result in the maximum sum-rate. Furthermore, we provide practical transceiver designs to approach the capacity-region. The memory induced by asynchronous transmission enables the use of the trellis-based detection methods which improves the performance. In addition, we propose a transceiver design, based on channel diagonalization to exploit the frequency-selectivity introduced by timing offsets. The proposed transceiver design, joint with the turbo principle, enables us to achieve a rate pair that is not achievable by the synchronous transmission.

An Autonomous Transmission Scheme Using Dueling DQN for D2D Communication Networks

In this paper, we investigate device-to-device (D2D) communication networks which are one of the key technologies for next-generation mobile communication networks and many other applications such as unmanned aerial vehicles (UAVs), vehicle-to-vehicle (V2V), and Internet of things (IoT). The overlay D2D communication networks that are considered in our study use dedicated radio resources separate from what cellular networks use and there exists co-channel interference in D2D networks without cross-channel interference between two networks. We propose a new transmission scheme for overlay D2D networks that uses a dueling deep reinforcement learning (DRL) architecture. The DRL is especially effective in environments where actions do not affect subsequent states as in wireless communication networks. The main contribution of this paper is that the proposed architecture is designed to utilize only information that each D2D devices can easily obtain by measuring channels. The proposed scheme thus enables D2D devices to train their own neural networks and to decide autonomously whether to transmit data without any intervention from infrastructures. The performance of the proposed scheme is analyzed in terms of average sum-rates and is compared to three baseline schemes. Simulation results show that the proposed scheme can achieve almost optimal sum-rates with low signal-to-noise (SNR) values without any intervention from infrastructure.

Sum-Rate Maximization of Multicell MISO Networks With Limited Information Exchange

Although there have been extensive studies on transmit beamforming in multi-input single-output (MISO) multicell networks, achieving optimal sum-rate with limited channel state information (CSI) is still a challenge even with a single user per cell. A novel cooperative downlink multicell MISO beamforming scheme is proposed with highly limited information exchange among the base stations (BSs) to maximize the sum-rate. In the proposed scheme, each BS can design its beamforming vector with only local CSI based on limited information exchange on CSI. Unlike previous studies, the proposed beamforming design is non-iterative and does not require any vector or matrix feedback but requires only quantized scalar information. The proposed scheme closely achieves the optimal sum-rate bound in almost all signal-to-noise ratio regime based on non-iterative optimization with lower amount of information exchange than existing schemes, which is justified by numerical simulations.

Optimal Rate-Diverse Wireless Network Coding Over Parallel Subchannels

This paper derives the maximum achievable sum-rate and presents the optimal encoding/decoding framework for rate-diverse wireless network coding (RD-WNC) over broadband channels consisting of multiple parallel subchannels. RD-WNC applies to a communication scenario in which a base station wants to deliver two different messages with different rates to two users. The base station combines the two separate messages into one network-coded message and broadcasts the network-coded message to both users. Each user then extracts its desired message from the network-coded message by subtracting from it the other message, which we assume to be side information available to the user. Deriving the maximum achievable sum-rate for RD-WNC is challenging when the channel consists of multiple parallel subchannels with different channel coefficients (e.g., the subcarrier channels of OFDM systems), since apart from the rate allocation between the two users, optimal power allocation among multiple subchannels needs to be identified. The first contribution of this paper is a new “mountain-leveling” power allocation algorithm to achieve the maximum sum-rate. With the resulting power allocation, we can then achieve the corresponding optimal sum-rate by having a separate encoding/decoding mechanism for each subchannels, but doing so is cumbersome and complex when the number of subchannels is large. The second contribution of this paper is a practical encoding/decoding framework using only one encoder-decoder mechanism for all subchannels without sacrificing sum-rate optimality. We provide numerical results to corroborate our theoretical findings and to demonstrate the benefits of our encoding/decoding framework.

Sum-Rate Maximization of Uplink Rate Splitting Multiple Access (RSMA) Communication

In this paper, the problem of maximizing the wireless users’ sum-rate for uplink rate splitting multiple access (RSMA) communications is studied. In the considered model, the message intended for a single user is split into two sub-messages with separate transmit power and the base station (BS) uses a successive decoding technique to decode the received messages. To maximize each user’s transmission rate, the users must adjust their transmit power and the BS must determine the decoding order of the messages transmitted from the users to the BS. This problem is formulated as a sum-rate maximization problem with proportional rate constraints by adjusting the users’ transmit power and the BS’s decoding order. However, since the decoding order variable in the optimization problem is discrete, the original maximization problem with transmit power and decoding order variables can be transformed into a problem with only the rate splitting variable. Then, the optimal rate splitting of each user is determined. Given the optimal rate splitting of each user and a decoding order, the optimal transmit power of each user is calculated. Next, the optimal decoding order is determined by an exhaustive search method. To further reduce the complexity of the optimization algorithm used for sum-rate maximization in RSMA, a user pairing based algorithm is introduced, which enables two users to use RSMA in each pair and also enables the users in different pairs to be allocated with orthogonal frequency. For comparisons, the optimal sum-rate maximizing solutions with proportional rate constraints are obtained for non-orthogonal multiple access (NOMA), frequency division multiple access (FDMA), and time division multiple access (TDMA). Simulation results show that RSMA can achieve up to 10.0, 22.2, and 81.2 percent gains in terms of sum-rate compared to NOMA, FDMA, and TDMA.

To Sense or Not To Sense: A Comparative Study of CSMA With Aloha

A fundamental difference between the two most representative random-access schemes, Aloha and carrier sense multiple access (CSMA), is sensing. There has been a common belief that the access efficiency can always be improved by the use of carrier sensing, which is indeed based on an implicit assumption that the packet length is much larger than the sensing time. For machine-to-machine (M2M) communications featured with short packets, the benefit of sensing may not overweigh the cost any more. It is therefore of paramount importance to identify the conditions for CSMA to outperform Aloha. In this paper, the sum rate performance of CSMA networks with two representative receiver structures, i.e., the collision model and the capture model, is characterized and optimized, based on which a comparative study of the optimal sum rate performance between Aloha and CSMA is conducted to establish criteria for beneficial sensing. The analysis shows that the maximum sum rates of CSMA with both receiver structures logarithmically increase with the mean received SNR $\rho $ at the high SNR region, and the rate gain of the capture model over the collision model is significant only when $\rho $ is small. The critical threshold for the ratio of the sensing time to the packet length for beneficial sensing is characterized under various scenarios, and found to be close to zero at the low SNR region when the capture model is adopted, indicating that the packet length needs to be extremely large for CSMA to outperform Aloha in that case. The analysis sheds important light on the access design of M2M communications, and suggests that Aloha could be a more favorable option when short packets are sent by a massive amount of low-power machine-type devices.

Asymptotically Optimal Resource Block Allocation With Limited Feedback

Consider a channel allocation problem over a frequency-selective channel.There are K channels (frequency bands) and N users such that K=bN for some positive integer b. We want to allocate b channels (or resource blocks) to each user. Due to the nature of the frequency-selective channel, each user considers some channels to be better than others. The optimal solution to this resource allocation problem can be computed using the Hungarian algorithm. However, this requires knowledge of the numerical value of all the channel gains, which makes this approach impractical for large networks. We suggest a suboptimal approach, that only requires knowing what the M-best channels of each user are. We find the minimal value of M such that there exists an allocation where all the b channels each user gets are among his M-best. This leads to feedback of significantly less than one bit per user per channel. For a large class of fading distributions, including Rayleigh, Rician, m-Nakagami and others, this suboptimal approach leads to both an asymptotically (in K) optimal sum-rate and an asymptotically optimal minimal rate. Our non-opportunistic approach achieves (asymptotically) full multiuser diversity as well as optimal fairness, by contrast to all other limited feedback algorithms.

Low Complexity Beamforming and User Selection Schemes for 5G MIMO-NOMA Systems

This paper investigates the resource allocation (RA) problem for the downlink multiple input multiple output-based non-orthogonal multiple access (MIMO-NOMA) system, which is usually solved through a beamforming (BF) process and a user selection (US) process. In this paper, we propose the multiple-user channel state information (CSI)-based singular value decomposition (MU-CSI-SVD) BF scheme and the minimization of power (Min-Power) US scheme. Under perfect CSI scenarios, the proposed MU-CSI-SVD achieves near optimal sum rate performance with the high-complexity BF scheme while has the same level computational complexity as the low-complexity BF scheme. For imperfect CSI scenarios, the proposed MU-CSI-SVD also achieves better outage probability performance but requires the same level computational complexity as the existing low-complexity BF scheme. Moreover, MU-CSI-SVD is helpful to decrease the computational complexity for solving the Min-Power US problem. Compared with existing US schemes, the proposed Min-Power scheme can achieve larger sum rate or lower outage probability with limited computational complexity increment. Therefore, in this paper, we develop a complete RA scheme for 5G MIMO-NOMA systems, which has excellent performance while with low computational complexity for both perfect and imperfect CSI scenarios.

A Practical Resource Management Scheme for Cellular Underlaid D2D Networks

In this paper, we investigate a resource management scheme for cellular underlaid device-to-device (D2D) communications, which are an integral part of mobile caching networks. D2D communications are allowed to share radio resources with cellular communications as long as the generating interference of D2D communications satisfies an interference constraint to secure cellular communications. Contrary to most of the other studies, we propose a distributed resource management scheme for cellular underlaid D2D communications focusing on a practical feasibility. In the proposed scheme, the feedback of channel information is not required because all D2D transmitters use a fixed transmit power and every D2D transmitter determines when to transmit data on its own without centralized control. We analyze the average sum-rates to evaluate the proposed scheme and compare them with optimal values, which can be achieved when a central controller has the perfect entire channel information and the full control of all D2D communications. Our numerical results show that the average sum-rates of the proposed scheme approach the optimal values in low or high signal-to-noise power ratio (SNR) regions. In particular, the proposed scheme achieves almost optimal average sum-rates in the entire SNR values in practical environments.

On the Capacity and the Optimal Sum-Rate of a Class of Dual-Band Interference Channels

We study a class of two-transmitter two-receiver dual-band Gaussian interference channels (GIC) which operates over the conventional microwave and the unconventional millimeter-wave (mm-wave) bands. This study is motivated by future 5G networks where additional spectrum in the mm-wave band complements transmission in the incumbent microwave band. The mm-wave band has a key modeling feature: due to severe path loss and relatively small wavelength, a transmitter must employ highly directional antenna arrays to reach its desired receiver. This feature causes the mm-wave channels to become highly directional, and thus can be used by a transmitter to transmit to its designated receiver or the other receiver. We consider two classes of such channels, where the underlying GIC in the microwave band has weak and strong interference, and obtain sufficient channel conditions under which the capacity is characterized. Moreover, we assess the impact of the additional mm-wave band spectrum on the performance, by characterizing the transmit power allocation for the direct and cross channels that maximizes the sum-rate of this dual-band channel. The solution reveals conditions under which different power allocations, such as allocating the power budget only to direct or only to cross channels, or sharing it among them, becomes optimal.

Optimal Sum Rate for Different Pre-Coding Schemes in Very Large Scale Mimo Channels

Optimal Sum Rate for Different Pre-Coding Schemes in Very Large Scale Mimo Channels