Energy and Spectral Efficiency Tradeoff in NOMA: Multi-Objective Evolutionary Approaches

Research into cutting-edge systems that can satisfy the rising needs for greater data rate and increased reliability is becoming more and more necessary as wireless communication technologies continue to evolve. To enhance the spectral and energy efficiencies, this study examine the performance of massive multiple input multiple output (mMIMO) communication systems using wavelet-order orthogonal frequency division multiplexing (W-OFDM). Also, a communication system was created based on W-OFDM Massive MIMO using MATLAB to model its operation. The simulated system's various stages of operation was examined through detailed analysis and its performance was compare with prior studies and practical networks on OFDM. Key performance metrics, specifically the data throughput, was evaluated to assess the effectiveness and viability of the system. The simulation results indicates that W-OFDM can achieve up to 35% reduction in peak-to-average-power-ratio (PAPR) compared to the traditional OFDM, with a 20% improvement in BER performance under Rayleigh fading conditions. Also, the spectral efficiency of W-OFDM mMIMO outperforms convectional OFDM by 15%, making it a viable technology for future communication networks requiring high data rates and reliability. This implies that, the analytical evaluation confirms that W-OFDM mMIMO provides substantial benefits for improving energy and spectral efficiencies in wireless communication systems, addressing the challenges of increased user demands for high spectrum. This study contributes to the understanding of MIMO systems' performance optimization, highlighting the importance of antenna configuration and cell distance. Comprehensive analysis of spectral efficiency and energy efficiency trade-offs in MIMO systems and identification of optimal antenna configuration for maximum energy efficiency, addressing some of the challenges of future wireless communication systems.

As a key technology for future wireless networks, massive multiple-input multiple-output (MIMO) can significantly improve the energy efficiency (EE) and spectral efficiency (SE), and the performance is highly dependant on the degree of the available channel state information (CSI). While most existing works on massive MIMO focused on the case where the instantaneous CSI at the transmitter (CSIT) is available, it is usually not an easy task to obtain precise instantaneous CSIT. In this paper, we investigate EE-SE tradeoff in single-cell massive MIMO downlink transmission with statistical CSIT. To this end, we aim to optimize the system resource efficiency (RE), which is capable of striking an EE-SE balance. We first figure out a closed-form solution for the eigenvectors of the optimal transmit covariance matrices of different user terminals, which indicates that beam domain is in favor of performing RE optimal transmission in massive MIMO downlink. Based on this insight, the RE optimization precoding design is reduced to a real-valued power allocation problem. Exploiting the techniques of sequential optimization and random matrix theory, we further propose a low-complexity suboptimal two-layer water-filling-structured power allocation algorithm. Numerical results illustrate the effectiveness and near-optimal performance of the proposed statistical CSI aided RE optimization approach.

We consider a point-to-point wireless commu-nication with a multilevel quadrature amplitude modulation (M-QAM) and energy constraints. In this context, this paper addresses the problem of the spectral and energy efficiency trade-off for a realistic system model. Our researches focus on the power amplifier model, which depends on transmission power, drain efficiency and peak-to-average ratio (PAR). While they have been extensively studied in previous research, far too little attention has been paid to the influence of PAR and drain efficiency in the spectral and energy efficiency trade-off. This study provides new insights into the estimation of the system energy consumption and the choice of the optimal spec-tral efficiency while introducing a realistic system model based on a hardware-dependent power amplifier energy. Furthermore, we investigate the influence of PAR and drain efficiency parameters on this trade-off. Our study reveals that this hardware-dependent system model plays a significant role in energy saving, and that the induced reduction of spectral efficiency goes in the direction sought by embedded systems. Moreover, our research underlines the importance of considering the drain efficiency exponent as having a real impact on the energy consumption.

This letter investigates the tradeoff between energy efficiency (EE) and spectral efficiency (SE) in downlink multiuser distributed antenna systems (DAS). Given the SE requirement and maximum power limit, a constrained optimization problem is formulated to maximize EE. Because of the multi-dimensional and non-convex nature of the problem, we first transform the multicriteria optimization problem with high complexity into a simpler single objective optimization problem. Then a novel power allocation algorithm is proposed to achieve maximum EE. Simulation results demonstrate the effectiveness of the proposed scheme as well as illustrate the fundamental tradeoff between energy efficient and spectral efficient transmission in downlink multiuser DAS.

Wireless communications are currently undergoing very rapid development, and as a result, the demand for wireless spectrum has been growing dramatically. In this regard, future wireless solutions have to meet multiple needs in terms of spectrum efficiency (SE). Both 5G, the next generation of mobile networks, and Cognitive Radio (CR) came as promising new technologies dealing with those requirements. In the process of molding a future for those systems, it is becoming increasingly clear that energy efficiency (EE) is a desirable design. In this paper, an innovative architecture will be conceived by combining those two technologies. In addition, a novel approach of technical sensing will be implemented in order to ensure a compromise between SE-EE.

Optimizing a hybrid beamforming transmitter is a non-convex problem and requires channel state information, leading in most cases to nontrivial feedback overhead. We propose a methodology relying on the principles of deep generative models and unfolding to achieve near-optimal hybrid beamforming with reduced feedback and computational complexity. We first represent the channel as a low-dimensional manifold via a generative adversarial network (GAN) and search the optimum digital and analog precoders in this low-dimensional space. To decrease the search complexity, we find an iteration rule by formulating hybrid beamforming as a bi-level optimization problem and then unfold each iteration as a neural layer. This results in a novel model-based deep neural network that incorporates domain knowledge. Our results show that this method (i) approaches the capacity-achieving spectral efficiency, (ii) provides a superior energy and spectral efficiency tradeoff, (iii) decreases feedback overhead, and (iv) reduces the complexity significantly, by optimizing a single low-dimensional vector per channel coherence time, with the neural network itself trained offline. The achieved spectral efficiency is robust when tested with realistic 3GPP channel models, even if the offline training relies on a simple geometric channel model.

A multi-objective particle swarm optimization (MOPSO) approach for multi-objective economic load dispatch problem in power system is presented in this paper. The economic load dispatch problem is a non-linear constrained multi-objective optimization problem. The proposed MOPSO approach handles the problem as a multi-objective problem with competing and non-commensurable fuel cost, emission and system loss objectives and has a diversity-preserving mechanism using an external memory (call “repository”) and a geographically-based approach to find widely different Pareto-optimal solutions. In addition, fuzzy set theory is employed to extract the best compromise solution. Several optimization runs of the proposed MOPSO approach were carried out on the standard IEEE 30-bus test system. The results revealed the capabilities of the proposed MOPSO approach to generate well-distributed Pareto-optimal non-dominated solutions of multi-objective economic load dispatch. Comparison with Multi-objective Evolutionary Algorithm (MOEA) showed the superiority of the proposed MOPSO approach and confirmed its potential for solving multi-objective economic load dispatch.

Solving a tri-objective supply chain problem with modified NSGA-II algorithm

The multi-antenna non-orthogonal multiple access (NOMA) technique is a promising method to enhance energy and spectrum efficiencies of wireless communication systems through advanced precoding algorithms. However, conventional NOMA schemes encounter high complexity issues due to the successive interference cancellation (SIC) process at the receiver end. Moreover, traditional precoding designs for multi-antenna NOMA systems only utilize user channel state information and overlook the modulation details of transmitted data symbols, which may result in suboptimal performance. To overcome these disadvantages, we propose a symbol-level precoding (SLP) scheme to maximize the energy efficiency of the downlink NOMA systems. In particular, by the proposed SLP scheme, the “interference signals” is designed to fall within the decoding region of the “desired signal”, eliminating the need for an SIC receiver, thereby reducing the complexity of the NOMA system in practical applications. For ease of illustrating the idea of the SLP-based NOMA designs, we first focus on a single user group case with two users, and solve the associated problem by developing a fractional programming-based algorithm. Then, we extend the idea to the multi-user scenarios and focus on the joint user grouping and SLP design. For the purpose of computational complexity reduction, a low-complexity (adaptive) user grouping scheme is proposed based on the idea of unsupervised clustering. Finally, numerical simulations are carried out to validate the effectiveness of the proposed SLP scheme and algorithms in improving the energy efficiency of the system.

Due to intense competition in the market place, effective scheduling has now become an important issue for the growth and survival of manufacturing firms. To sustain in the current competitive environment, it is essential for the manufacturing firms to improve the schedule based on simultaneous optimization of performance measures such as makespan, flow time, and tardiness. The current paper presents a novel particle swarm optimization (PSO) algorithm for solving multi-objective flexible job shop scheduling problem with the goal of finding approximations of the optimal Pareto front. The Pareto-optimal solutions obtained through multi-objective particle swarm optimization (MOPSO) have been ranked by the composite scores obtained through maximum deviation theory (MDT) to avoid subjectiveness and impreciseness in the decision-making. The results are compared with non-dominated sorting genetic algorithm-II (NSGA-II) and multi-objective evolutionary algorithm (MOEA) in terms of four performance metrics. Twenty-eight benchmark instances from literature are solved by the proposed algorithm. It is observed that MOPSO outperforms NSGA-II and MOEA in four performance metrics in most of the instances.

Reconfigurable intelligent surface (RIS) aided cell-free (CF) multiple-input multiple-output (MIMO) system is of great significance to enable green communication. Nevertheless, the large number of elements deployed on RIS can result in unexpected computational overhead and extra power consumption. In this work, a sparse RIS-aided CF MIMO system is investigated. Correspondingly, an optimization problem of balancing spectral efficiency (SE) and energy efficiency (EE) is formulated with three matrix variables, i.e., precoding, phase shift and sparse matrices. Since the problem is non-convex and it cannot be solved directly, the problem is divided into three subproblems through fractional programming, and an alternating optimization is proposed by using the Lagrangian dual reformulation (LDR) and quadratic transform (QT). Furthermore, the subproblem of sparse matrix is formulated as binary integer quadratic programming (BIQP), and then transformed and solved by binary integer linear programming (BILP). Simulation results verify the effectiveness of the proposed algorithm in terms of EE and SE tradeoff.

This study compared the performances of three state-of-theart evolutionary multi-objective optimization algorithms: the Non-Dominated Sorted Genetic Algorithm II (NSGA-II), the Epsilon-Dominance Non-Dominated Sorted Genetic Algorithm II (e -NSGA-II) and the Epsilon-Dominance Multi-Objective Evolutionary Algorithm ( e MOEA), on a constrained biobjective crop-mix planning problem (BCP). The BCP test case objectives were to: (i) maximize crop production and (ii) minimize planting area. The BCP problem was enumerated to provide the true Pareto-optimal solution set to facilitate rigorous testing of the evolutionary multi-objective optimization algorithms. The performances of the three algorithms were assessed and compared using three performance metrics (additive epsilon indicator, inverted generational distance and spacing). For the comparative study, a graphical comparison with respect to true Pareto front of optimal crop planning problem was provided. Results of the analyses indicated that the e MOEA greatly outperforms the NSGAII and the e -NSGA-II.

Conventional design of wireless networks mainly focuses on system capacity and spectral efficiency (SE). As green radio (GR) becomes an inevitable trend, energy-efficient design is becoming more and more important. In this paper, the fundamental tradeoff between energy efficiency (EE) and SE in downlink orthogonal frequency division multiple access (OFDMA) networks is addressed. We first set up a general EE-SE tradeoff framework, where the overall EE, SE and per-user quality-of-service (QoS) are all considered, and prove that under this framework, EE is strictly quasiconcave in SE. We then discuss some basic properties, such as the impact of channel power gain and circuit power on the EE-SE relation. We also find a tight upper bound and a tight lower bound on the EE-SE curve for general scenarios, which reflect the actual EE-SE relation. We then focus on a special case that priority and fairness are considered and suggest an alternative upper bound, which is proved to be achievable for flat fading channels. We also develop a low-complexity but near-optimal resource allocation algorithm for practical application of the EE-SE tradeoff. Numerical results confirm the theoretical findings and demonstrate the effectiveness of the proposed resource allocation scheme for achieving a flexible and desirable tradeoff between EE and SE.

In this paper, the tradeoff between spectrum efficiency (SE) and energy efficiency (EE) is investigated in terms of interference management and power allocation for heterogeneous networks (HetNets) with non-orthogonal multiple access (NOMA). The EE and SE tradeoff is modeled as a multi-objective problem (MOP) under the maximum power and quality of service (QoS) constraints, which is non-convex. The MOP is relaxed into a convex single objective problem (SOP) by adopting a weighted sum strategy with the hypograph transformation. The SOP is solved in two steps. In the first step, we propose a power allocation technique based on non-cooperative (NC) game for EE and SE in NOMA HetNets. In the proposed NC game, the macro base station (MBS) and the small BSs (SBSs) compete with an equal priority in order to optimize their transmit powers towards maximizing the weighted sum of SE and EE. In the second step, a closed-form formula is proposed to control the power allocated to users while taking into account both QoS constraint and successive interference cancellation (SIC) condition. From simulations, the proposed technique can, in some dedicated settings, considerably improve the tradeoff between EE and SE over conventional techniques.

Network reconfiguration is an alternative to reduce power losses and optimize the operation of power distribution systems. In this paper, an encoding scheme for evolutionary algorithms is proposed in order to search efficiently for the Pareto-optimal solutions during the reconfiguration of power distribution systems considering multiobjective optimization. The encoding scheme is based on the edge window decoder (EWD) technique, which was embedded in the Strength Pareto Evolutionary Algorithm 2 (SPEA2) and the Nondominated Sorting Genetic Algorithm II (NSGA-II). The effectiveness of the encoding scheme was proved by solving a test problem for which the true Pareto-optimal solutions are known in advance. In order to prove the practicability of the encoding scheme, a real distribution system was used to find the near Pareto-optimal solutions for different objective functions to optimize.