Optimal Power Allocation Solution Research Articles

An Efficient Block Successive Upper-Bound Minimization Algorithm for Caching a Reconfigurable Intelligent Surface-Assisted Downlink Non-Orthogonal Multiple Access System

With the booming rollout of 5G communication, abundant new technologies have been proposed for quality of service requirements. In terms of the betterment in transmission coverage, mobile edge caching (MEC) has shown potential in reducing the transmission outage. The performance of MEC, meanwhile, can be promisingly enhanced by reconfigurable intelligent surfaces (RIS). Under this context, we explore a system comprising a small base-station (SBS) with limited cache capacity, two users, and one RIS. The SBS transmits the contents from the cache or fetches them from the remote backhaul hub to communicate with users through directional and possibly reflective channels. In this point-to-multipoint connection, non-orthogonal multiple access (NOMA) is applied, improving the capacity of the system. To minimize the outage probability, we first propose a caching policy from entropy perspective, based on which we investigate the beamforming and power allocation problem. The issue, however, is non-convex and involves multi-dimensional optimization. To address this, we introduce an efficient block successive upper-bound minimization algorithm, grounded in Gershgorin’s circle theorem. This algorithm aims to find the globally optimal solution for power allocation and RIS beamformer, considering both the channel condition and content popularity. Numerical studies are performed to verify the effectiveness of the proposed algorithm.

FedSSC: Joint client selection and resource management for communication-efficient federated vehicular networks

As a promising distributed technology, federated learning (FL) has been widely used in vehicular networks involving large amounts of IoT-enabled sensor data, which derives federated vehicular networks (FVNs). However, the efficiency of FVN is generally limited by vehicle selection policy and communication conditions, which leads to high communication costs and latency. The original FVN transmits model parameters from a random subset of vehicles to the roadside unit (RSU) and ignores the diversity of learning quality among vehicles. In addition, a few vehicles with poor wireless channel conditions may prolong communication latency. To address these two issues, we propose a communication-efficient federated learning approach, composed of Vehicle Selection, Student–Project Allocation (SPA) matching model, and Convex Optimization, called FedSSC, to improve the communication efficiency in FVN. The parameter variations for the same vehicle in two consecutive rounds are used to quantify the quality of the learning results by cosine distance and Affinity Propagation (AP) clustering. Moreover, a subchannel allocation algorithm based on the SPA matching model, as well as a convex optimal power allocation solution are integrated to minimize the communication latency of each training round. According to extensive experiments, the proposed FedSSC reduces the communication overhead by 26.32% on average compared with the benchmarks, whereas the communication latency decreases by 21.84%.

Differentiated power rationing or seasonal power price? Optimal power allocation solution for Chinese industrial enterprises based on the CSW-DEA model

Seasonal power price (SPP) and differentiated power rationing (DPR) are two solutions for tackling power shortages as they adjust power allocation, alleviate power supply–demand contradictions, and promote the sustainable development of the energy industry. Based on microdata from 9593 industrial enterprises in China, this study analyzes the effects of SPP and DPR on allocating power resources optimally. The results reveal the following. (1) Under SPP, the price of power fluctuates in summer between 0.4627 and 0.5047 yuan/kWh, while spring-autumn and winter prices remain stable at 0.3785 yuan/kWh and 0.4627 yuan/kWh, respectively. (2) Under DPR, according to industrial efficiency and the optimal enterprise power rationing ratio, power rationing solutions can be summarized into forced, moderate, and incentive types. (3) SPP and DPR can effectively alleviate power shortages. However, SPP is superior in maintaining balanced industrial development and reducing power costs by guiding enterprises toward off-peak production through price adjustment mechanisms. Finally, policy implications are illustrated in implementing SPP and DPR in the case of power shortages.

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.

Energy Efficiency Maximization for UAV-Assisted Full-Duplex NOMA System: User Clustering and Resource Allocation

In this paper, the problem of energy efficiency maximization of an unmanned-aerial-vehicle (UAV)-assisted full-duplex non-orthogonal-multiple-access (FD-NOMA) system is investigated. In order to reduce the interference among users in the considered system, we first propose a novel and less complex adaptive-geometric distribution (AGD) which adaptively maintain equal cluster size and allocate resource optimally among the users. Later, we formulate an optimization problem of energy efficiency maximization subject to the transmit power budget at the UAV for downlink (DL) users and for each uplink (UL) user and UAV placement. To transform the non-convex problem into convex one, we decouple the original optimization problem into different sub-problems in which the problems of power allocation and UAV placement are alternatively solved using Dinkelbach’s algorithm with successive convex approximation (SCA) and particle swarm optimization (PSO), respectively. Later, the unified solution for optimal power allocation and UAV placement is proposed based on block-coordinate descent (BCD) algorithm. Extensive simulation results validate the superiority of the proposed AGD clustering based proposed algorithm for UAV-assisted FD-NOMA system over the conventional clustering and other orthogonal schemes.

Resource allocation for cellular device‐to‐device‐aided vehicle‐to‐everything networks with partial channel state information

AbstractIn Vehicle‐to‐Everything (V2X), cellular Device‐to‐Device (D2D) communication can not only enhance system capacity and spectral utilization but also reduce the traffic load and communication delay. However, due to the fast variations of the channel state information (CSI) caused by high mobility of vehicles, the accurate CSI is difficult to be obtained in real‐time. In this paper, different from other previous researches that are subject to the perfect CSI, a more realistic channel state model is adopted that only the partial CSI of the communication links is available. Then we study the joint power allocation and spectrum sharing problem to maximize the total weighted ergodic capacity of all vehicular links, namely, Vehicle‐to‐Infrastructure (V2I) links and Vehicle‐to‐Vehicle (V2V) links. Meanwhile, the transmission reliability guarantees the quality of service (QoS) requirements of V2I and V2V. Since the formulated problem is a nonconvex mixed integer nonlinear programming (MINLP) problem, we first derive the strategy for obtaining the optimal solution of power allocation under arbitrary weighted factor and quantify different cases of feasible regions. Then we propose a two‐stage algorithm to complete the optimal resource allocation in polynomial time. Moreover, we further propose a low‐complexity spectrum sharing method based on the principles of Gale‐Shapley mechanism. The simulation results show that the proposed algorithms outperform the other baseline schemes significantly, and the low‐complexity scheme can effectively achieve a tradeoff between performance and complexity.

Joint subcarrier and power allocation with proportional fairness in multicarrier nonorthogonal multiple access systems

SummaryIn this paper, we investigate joint subcarrier and power allocation in multicarrier nonorthogonal multiple access (MC‐NOMA) systems aims to guarantee rate proportional fairness among all users. To this end, we adopt the sum logarithmic rate function as the objective and formulate a resource optimization problem which is a mixed‐integer nonlinear programming (MINLP) problem. Based on variable relaxation and replacement, we convert this problem into a convex optimization problem. On this basis, the expressions of joint subcarrier and power allocation solutions are obtained by using convex optimization theory and the Karush–Kuhn–Tucker (KKT) condition. However, power and subcarrier allocation solutions are highly coupled, and it is difficult to derive both solutions simultaneously. Fortunately, when the subcarrier allocation strategy is given, the optimal power allocation solution can be derived by solving the equations. Furthermore, we develop an iterative resource allocation algorithm to implement joint subcarrier and power allocation among users. Numerical results reveal that the proposed algorithm is effective in general multiuser and multicarrier scenarios, and a substantial performance improvement can be achieved by the proposed algorithm in contrast with the traditional orthogonal multiple access (OMA) schemes. In specific, the proposed algorithm achieves 63% and 59% gains in terms of utility and fairness performance, respectively.

Rate-Splitting Multiple Access to Mitigate the Curse of Mobility in (Massive) MIMO Networks

Rate-Splitting Multiple Access (RSMA) is a robust multiple access scheme for downlink multi-antenna wireless networks. RSMA relies on multi-antenna Rate-Splitting (RS) at the transmitter and Successive Interference Cancellation (SIC) at the receivers. In this work, we study the performance of RSMA under the important setup of imperfect Channel State Information at the Transmitter (CSIT) originating from user mobility and latency/delay (between CSI acquisition and data transmission) in the network. We derive a lower bound on the ergodic sum-rate of RSMA for an arbitrary number of transmit antennas, number of users, user speed and transmit power. Then, we study the power allocation between common and private streams and obtain a closed-form solution for optimal power allocation that maximizes the obtained lower bound. The proposed power allocation greatly reduces precoder design complexity for RSMA. By Link-Level Simulations (LLS), we demonstrate that RSMA with the proposed power allocation is robust to the degrading effects of user mobility and has significantly higher performance compared to conventional multi-user (massive) Multiple-Input Multiple-Output (MIMO) strategies. The work has important practical significance as results demonstrate that, in contrast to conventional multi-user (massive) MIMO whose performance collapse under mobility, RSMA can maintain reliable multi-user connectivity in mobile deployments.

Training Deep Neural Network for Optimal Power Allocation in Islanded Microgrid Systems: A Distributed Learning-Based Approach.

Currently, numerical optimization methods are used to solve distributed optimal power allocation (OPA) problems for islanded microgrid (MG) systems. Most of them are developed based on rigorous mathematical derivation. However, the complexity of such optimization algorithms inevitably creates a gap between theoretical analysis and real-time implementation. In order to bridge such a gap, in this article we provide a new distributed learning-based framework to solve the real-time OPA problem. Specifically, inspired by the human-thinking scheme, distributed deep neural networks (DNNs) together with a dynamic average consensus algorithm are first employed to obtain an approximate OPA solution in a distributed manner. Then a distributed balance generation and demand algorithm is designed to fine-tune it to obtain the final optimal feasible solution. In addition, it is theoretically proved that the proposed DNN can well approximate one existing OPA algorithm (Guo et al. 2018), where quantitative numbers of at most how many hidden layers and neurons are provided. Several experimental case studies show that our proposed distributed learning framework can achieve similar optimal results to those obtained by using typical existing distributed numerical optimization methods while it is superior in terms of simplicity and real-time capability.

Efficient Downlink Power Allocation Algorithms for Cell-Free Massive MIMO Systems

Cell-free Massive MIMO systems consist of a large number of geographically distributed access points (APs) that serve the users by coherent joint transmission. The spectral efficiency (SE) achieved by each user depends on the power allocation: which APs that transmit to which users and with what power. In this article, we revisit the max-min and sum-SE power allocation policies, which have previously been approached using high-complexity general-purpose solvers. We develop and compare several different high-performance low-complexity power allocation algorithms that are appropriate for use in large systems. We propose two new algorithms for sum-SE power optimization inspired by weighted minimum mean square error (WMMSE) minimization and fractional programming (FP). Further, one new FP-based algorithm is proposed for max-min fair power allocation. The alternating direction method of multipliers (ADMM) is used to solve specific convex subproblems in the proposed algorithms. Our ADMM reformulations lead to multiple small-sized subproblems with closed-form solutions. The proposed algorithms find global or local optimal power allocation solutions for large-scale systems but with reduced computational time compared to previous work.

Novel Resource Allocation Techniques for Downlink Non-Orthogonal Multiple Access Systems

Non-orthogonal multiple access (NOMA) plays an important role in achieving high capacity for fifth-generation (5G) networks. Efficient resource allocation is vital for NOMA system performance to maximize the sum rate and energy efficiency. In this context, this paper proposes optimal solutions for user pairing and power allocation to maximize the system sum rate and energy efficiency performance. We identify the power allocation problem as a nonconvex constrained problem for energy efficiency maximization. The closed-form solutions are derived using Karush–Kuhn–Tucker (KKT) conditions for maximizing the system sum rate and the Dinkelbach (DKL) algorithm for maximizing system energy efficiency. Moreover, the Hungarian (HNG) algorithm is utilized for pairing two users with different channel condition circumstances. The results show that with 20 users, the sum rate of the proposed NOMA with optimal power allocation using KKT conditions and HNG (NOMA-PKKT-HNG) is 6.7% higher than that of NOMA with difference of convex programming (NOMA-DC). The energy efficiency with optimal power allocation using DKL and HNG (NOMA-PDKL-HNG) is 66% higher than when using NOMA-DC.

Impact of Imperfect Channel State Information on the Physical Layer Security in D2D Wireless Networks Using Untrusted Relays

In this paper, we investigate the impact of channel estimation errors on the physical layer security of an overlaying device-to-device (D2D) wireless network with an amplify-and-forward untrusted relay. An untrusted relay assists D2D communication while may capture the confidential data. Under the practical assumption of imperfect channel state information (ICSI) for the relay-to-receiver D2D link, we take into account optimal power allocation (OPA) problem to maximize the achievable secrecy rate of two different scenarios which are without jamming and with friendly jamming. Based on these OPA solutions, we study the secrecy performance of the two scenarios by driving closed-form expressions for the ergodic secrecy rate (ESR) in Rayleigh fading channel. We also calculate the high signal-to-noise ratio (SNR) slope and high SNR power offset of the optimized scenarios by finding the asymptotic ESR. Numerical results confirm the accuracy of our proposed theoretical analysis. The results also demonstrate that our proposed OPAs enhance the ESR performance compared with other power allocation techniques. Moreover, they show the effect of ICSI on the ESR such that as channel estimation error grows, the ESR performance reduction is occurred.

Energy-efficient user selection and resource allocation in mobile edge computing

Mobile edge computing (MEC) as a new type of computing model can expand the computing power of cloud computing to the edge of radio access network (RAN), which brings a large number of applications close for end user. Compared to traditional cloud computing, computation tasks being offloaded to edge clouds nearby to execute can reduce transmission delay and energy consumption. However, how to select the best edge cloud in a dense cell to execute tasks remains challenging. To address this challenge, in this paper we propose joint user selection and resource allocation algorithm in MEC to maximize the user’s energy efficiency, defined as the ratio of user throughput to its energy consumption. We formulate the energy efficiency maximization problem as a mixed integer fractional nonlinear optimization problem, which involves both users’ offloading selection and uplink transmission power. To solve this non-convex optimization problem, we transform it into an equivalent subtractive convex optimization problem by relaxation transformation method, and furthermore provide the corresponding optimal solution of user selection and power allocation. Numerical results show that compared with other selection schemes, the proposed optimal scheme has a significant improvement in energy efficiency.

Secure mm‐Wave communications with imperfect hardware and uncertain eavesdropper location

AbstractThis article examines the secrecy performance of millimeter‐wave (mm‐Wave) communications with imperfect hardware and uncertain eavesdropper (Eve) location. We consider a multiple‐antenna source communicating with a single‐antenna destination using masked beamforming to transmit the information signals with artificial noise (AN) in the presence of a passive Eve. For this system, we derive new expressions for the secrecy outage probability (SOP) and secrecy throughput with mm‐Wave multipath propagation under slow‐fading channel conditions and hardware imperfections. Based on this, optimal power allocation (OPA) solutions are derived for the information and AN signals aimed at minimizing the SOP and maximizing the secrecy throughput. Our results reveal that it is non‐trivial to achieve an OPA solution for the general scenario of imperfect hardware. We also highlight that our proposed masked beamforming with OPA scheme significantly enhances the secrecy throughput compared with the benchmark schemes of maximal‐ratio transmission and equal power allocation.

Optimal power allocation for non‐orthogonal multiple access in wireless backhaul networks

In this study, the authors adopt the non-orthogonal multiple access (NOMA) technique to improve the spectrum efficiency in the wireless backhaul networks, whereby the downlink and uplink NOMA techniques are applied for the backhaul and access links, respectively. Due to the coupling between the backhaul and access transmission stages, the transmission power should be carefully allocated in both stages so that the overall throughput can be maximised. They start with the single user equipment (UE) case and consider different scenarios and analyse the tradeoff between the access and backhaul links, and the optimal power allocation solutions are obtained accordingly. They then extend the analysis to the multi-UE case and formulate the optimal power allocation problem, which is solved using the Lagrangian dual decomposition algorithm. Simulation results demonstrate that the proposed schemes are effective in improving the throughput and outperforms the conventional orthogonal multiple access technique under different network settings.

Joint backhaul bandwidth and power allocation in heterogeneous cellular networks: A two‐level approach

SummaryWe investigate the problem of joint downlink wireless backhaul bandwidth (WBB) and power allocation in heterogeneous cellular networks (HCNs). A WBB partitioning scheme is considered, which allocates the whole bandwidth between the macrocell and small cells for data transmission and backhauling. We formulate an optimization problem to maximize the weighted sum logarithmic utility function by jointly optimizing WBB portion and fronthaul power allocation of each base station with consideration of the backhaul capacity limitation on each small cell. In order to solve this joint optimization problem, we propose a hierarchical two‐level approach and decompose the original problem into two independent subproblems: the WBB allocation at the macrocell base station (MBS) and the power allocation at both the MBS and small cell base stations (SBSs). Accordingly, the optimal WBB portion and power allocation solutions are obtained, respectively. Furthermore, we develop a distributed algorithm to implement the joint WBB and power allocation. Numerical results verify the effectiveness of the proposed approach and analyze the impact of the weighted coefficient and backhaul capacity limitation on the network performance. In addition, significant performance gains can be achieved by the proposed approach over the benchmark.

Integrated Resource Management for Terrestrial-Satellite Systems

As data traffic in terrestrial-satellite systems surges, the integration of power allocation for caching, computing, and communication (3C) has attracted much research attention. However, previous works on 3C power allocation in terrestrial-satellite systems mostly focus on maximizing the overall system throughput. In this paper, we aim to guarantee both throughput fairness and data security in terrestrial-satellite systems. Specifically, we first divide the system implementation into three steps, i.e., data accumulation, blockchain computing, and wireless transmission. Then, we model and analyze the delay and power consumption in each step by proposing several theorems and lemmas regarding 3C power allocation. Based on the theorems and lemmas, we further formulate the problem of 3C power allocation as a Nash bargaining game and construct an optimization model for the game. Last, we solve the optimization problem using dual decomposition and obtain the optimal period of the satellite serving the ground stations as well as the optimal 3C power allocation solution. The optimal solution can provide guidelines for parameter configuration in terrestrial-satellite systems. The performance of the proposed terrestrial-satellite architecture is verified by extensive simulations.

Proactive eavesdropping in UAV-aided mobile relay systems

This paper studies a new wireless surveillance scenario, where a legitimate monitor attempts to eavesdrop on the dubious messages forwarded by an unmanned aerial vehicle (UAV)-aided suspicious relay. Specifically, we consider two relaying protocols commonly used by the UAV-aided relay, namely, amplify-and-forward (AF) and decode-and-forward (DF). For each relaying protocol, we maximize the average effective eavesdropping rate by optimizing the legitimate monitor’s jamming power allocation, subject to the average power constraint. The optimal jamming power allocation solutions are derived in closed-form for each of the two protocols, respectively. Numerical results show that the proposed proactive eavesdropping approach can noticeably improve the eavesdropping rate compared to the reference schemes.

Optimal spectral and energy efficiency trade-off for massive MIMO technology: analysis on modified lion and grey wolf optimization

As the technology makes progress towards the era of fifth generation (5G) communication networks, energy efficiency (EE) becomes an significant design criterion, because it guarantees sustainable evolution. In this regard, the massive multiple-input multiple-output (MIMO) technology, where the base stations are outfitted with enormous count of antennas so as to reach multiple orders of spectral and energy efficiency gains, will be a fundamental technology enabler for 5G. This paper plans to implement a massive MIMO model considering the spectral efficiency (SE) and EE. Here, the main goal is to generate the optimal solutions for beam-forming vectors and power allocation. The optimal solution is formed in such a way that both the SE and EE are maximum through resource efficiency metric model. The beam-forming vectors and power allocations are generated by two modified meta-heuristic algorithm to frame a valuable analysis. The first algorithm uses the modified grey wolf optimization (GWO) termed as improved random vector-based GWO (IRV-GWO), and the second algorithm uses the modified lion algorithm (LA) termed as improved random vector-based LA (IRV-LA). Both the algorithms have the ability to solve the complex optimization problems under different applications with respect to better convergence rate, which in turn performs well for pertaining better trade-off between the SE and EE in massive MIMO technology.

Fair Energy-Efficient Resource Allocation for Downlink NOMA Heterogeneous Networks

The increasing in energy consumptions of the current wireless networks, leads towards designing energy-efficient 5G networks. The application of non-orthogonal multiple access (NOMA) in the heterogeneous networks (HetNets) improves the spectrum utilization with the cost of efficient resource allocation. Hence, this article proposes optimal user-pairing and power allocation solutions towards achieving fair energy-efficient resource allocation in downlink femtocell NOMA-HetNets. In the proposed optimization process, the considered constraints are the user's transmission rate, transmit power budget at the base station (BS), and the interference. The energy consumption of both the transmitter and the receiver are considered to simulate the real system design. The Greedy Algorithm (GA) is used to achieve a low-complex optimal solution during the user-pairing process. Simultaneously, the max-min energy efficiency optimization approach is employed to maximize the minimum energy efficiency of the femtocell users to achieve the optimal power allocation solution. The mathematical formulation of the max-min energy efficiency is a non-convex fractional programming problem and is intractable. Thus, the fractional programming theory is adopted to transform the problem into a sequence of subtractive form, followed by the Sequential Convex Programming (SCP) approach to determine the optimal solution. Simulation results show that the proposed NOMA with optimal power allocation method using SCP and GA (NOMA-SCP-GA) achieves fair energy efficiency performance with lower complexity compared to the benchmark methods. Moreover, the minimum energy efficiency of the femtocell user is 38.22% higher than NOMA with Difference of Convex programming (NOMA-DC). The NOMA-SCP-GA method can assure 5G capability demands.