Energy Efficiency and Spectral Efficiency Tradeoff in Downlink Distributed Antenna Systems

Multiuser MIMO (MU-MIMO) in which the base station transmits multiple streams to multiple users, has received significant importance as a way of achieving spectral efficiency (SE) and energy efficiency (EE). However, these two important design criteria conflict with each other and a careful study of their trade-off is mandatory for designing future wireless communication systems. This paper investigates the trade-off between EE and SE in downlink MU-MIMO system which is adopted by 3GPP Long Term Evolutions (LTE). The EE is measured as, “throughput (bits) per Joule” while both RF transmit power and circuit power consumptions are considered. In this paper, given the SE requirement, a constrained optimization problem where a constraint redefined with cubic inequality is formulated to maximize EE. Then, a novel resource allocation algorithm is proposed to achieve maximum EE. Simulation results demonstrate the effectiveness of the proposed scheme and illustrate the fundamental trade-offs between energy efficient and spectral efficient transmission.

Multiuser MIMO (MU-MIMO), in which the base station transmits multiple streams to multiple users, has received significant importance as a way of achieving spectral efficiency (SE) and energy efficiency (EE). However, these two important design criteria conflict with each other and a careful study of their trade-off is mandatory for designing future wireless communication systems. This paper investigates the trade-off between EE and SE in downlink MU-MIMO system which is adopted by 3GPP LTE-Advanced to meet IMT-Advanced targets. The EE is measured as, "throughput (bits) per Joule" while both RF transmit power and circuit power consumptions are considered. In this paper, given the SE requirement, a constrained optimization problem where constraints redefined with cubic inequality is formulated to maximize EE. Then, a novel resource allocation algorithm is proposed to achieve maximum EE. Simulation results demonstrate the effectiveness of the proposed scheme and illustrate the fundamental trade-offs between energy efficient and spectral efficient transmission. Our analytical results shed light on future "green" network planning in downlink MU-MIMO systems.

Non-orthogonal multiple access (NOMA) deployment in future wireless networks has been recently considered a promising radio access technology to enhance spectral efficiency (SE). However, gain in SE comes always with the cost of energy efficiency (EE). In this paper, we investigate the SE and EE tradeoff in downlink NOMA with the consideration of quality of service (QoS) requirements based on three population-based multi-objective evolutionary algorithms (MOEAs): multi-objective particle swarm optimization (MOPSO), non-dominated sorting genetic algorithm-II (NSGA-II) and strength Pareto evolutionary algorithm-2 (SPEA2). The tradeoff is optimized and Pareto optimal solutions are obtained through MOEAs. The effectiveness of the algorithms is evaluated based on the hypervolume metric and the capability of solving multi-objective optimization problems. Simulation results reveal that SPEA2 outperforms NSGA-II and MOPSO. Furthermore, NSGA-II is the loser among all algorithms in terms of finding Pareto optimal results.

In this paper, we propose an optimal scheme for a distributed antenna system (DAS) to maximize energy efficiency (EE) under a constraint of overall transmit power of each remote access unit (RAU). We exploit the multicriteria optimization method to systematically investigate the relationship between EE and spectral efficiency (SE). Using the weighted sum method, we first convert the multicriteria optimization function, which is extremely complex, into a simpler single objective optimization function. Then an optimal algorithm is developed to allocate the available power to tradeoff EE and SE effectively. Furthermore, we also illustrate the effectiveness of the proposed method and demonstrate there is a tradeoff between energy-efficient and spectral-efficient transmission through computer simulation of a downlink multiuser DAS.

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.

Energy efficiency(EE) has caught more and more attention in future wireless communications due to steadily rising energy costs and environmental concerns. In this paper, we propose an EE scheme with proportional fairness for the downlink multiuser distributed antenna systems (DAS). Our aim is to maximize EE, subject to constraints on overall transmit power of each remote access unit (RAU), bit-error rate (BER), and proportional data rates. We exploit multi-criteria optimization method to systematically investigate the relationship between EE and spectral efficiency (SE). Using the weighted sum method, we first convert the multi-criteria optimization problem, which is extremely complex, into a simpler single objective optimization problem. Then an optimal algorithm is developed to allocate the available power to balance the tradeoff between EE and SE. We also demonstrate the effectiveness of the proposed scheme and illustrate the fundamental tradeoff between energy- and spectral-efficient transmission through computer simulation.

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.

Two main concerns for designing a wireless system are more network capacity and less energy consumption. Recently, distributed antenna system (DAS) has received considerable attention due to its potential to provide higher spectral efficiency (SE) and uniform coverage for cellular networks. In this regard, this paper compares the performance of DAS with centralized antenna system (CAS) in LTE-A system in terms of energy efficiency (EE), where practical restrictions such as out-of-cell interference, path loss, and small scale fading are taken into account. Furthermore, the EE and system power consumption are investigated under three different cell-load scenarios (high, moderate, and low load) where different numbers of antennas are activated and remaining of antennas are under sleep mode. Finally, based on the tradeoff between power-saving and EE, two optimal DAS antenna deployments are proposed for low and moderate cell-load scenarios. The results reveal that DAS considerably outperforms CAS in terms of EE and by optimal deploying antennas of DAS significant power-saving and EE are achievable. The proposed methodology achieved savings of up to 27.63% in terms of energy savings in a macrocell with guarantee of a high capacity of data.

Spectral efficiency and energy efficiency of distributed antenna systems with virtual cells

In this paper, the power allocation problem for downlink massive multiple-input multiple-output (MIMO) systems with inter-user interference is investigated considering the tradeoff between energy efficiency (EE) and spectral efficiency (SE). The minimum mean-square-error channel estimate and the rigorous closed-form expression of achievable downlink rate are first derived. Then, the multi-objective optimization problem (MOP) is formulated subject to maximum total transmission power constrain, where conflicting EE and SE are maximized simultaneously. To obtain the solution set characterized as a Pareto set, the MOP is transformed into a single-objective problem (SOP) using weighted sum method. We further convert the SOP into a convex optimization problem by adding an additional interference constraint, and the optimal power allocation algorithm is proposed by using dual method to balance EE and SE efficiently. Simulation results demonstrate the effectiveness of the proposed algorithm and illustrate the fundamental tradeoff between EE and SE for different parameter settings.

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.

In recent years, a lot of power allocation algorithms have been proposed to maximize spectral efficiency (SE) and energy efficiency (EE) for the distributed antenna systems (DAS). However, the traditional iterative power allocation algorithms are difficult to be implemented in reality because of their high computational complexity. With the development of machine learning algorithms, it has been proved that the machine learning method has excellent learning ability and low computational complexity, which can approximate the traditional iterative power allocation well and be easily to be implemented in reality. In this paper, we propose a new deep neural network (DNN) model for DAS. From the perspective of machine learning, traditional iterative algorithms can be regarded as a nonlinear mapping between user channel realizations and optimal power allocation schemes. Therefore, we train the DNN to learn the nonlinear mapping between the user channel realizations and the corresponding power allocation schemes based on the traditional iterative algorithm. Then, a power allocation schemes based on DNN method is developed to maximize SE and EE for DAS. The simulation results show that the proposed scheme can not only obtain the almost similar performance as the traditional iterative algorithm, but also reduce much online computational time.

In fifth-generation cellular networks (5G), a large multiple-input multiple-output (MIMO) technique is critical for optimizing the trade-off between energy efficiency (EE) and spectral efficiency (SE). The challenges for the next generation depend on increasing the high data traffic in the wireless communication system for both EE and SE. The trade-off between EE and SE in downlink massive MIMO systems has been investigated in this paper using the first derivative of transmit antennas and transmit power. The EE-SE trade-off has been investigated using a multi-objective optimization problem to reduce transmit power. The EE and SE based on constraint maximum transmits power allocation and a number of antennas by computing the first derivative of transmit power to maximize the trade-off EE - SE has been improved. The optimal trade-off between EE and SE can be obtained based on the first derivative by selecting the optimal antennas with a low cost of transmit power showing good simulation results. Therefore, based on an optimal optimization problem is flexible to make trade-offs between EE-SE for distinct preferences.

Underlay in-band device-to-device (D2D) multicast communication, where the same content is disseminated via direct links in a group, has the potential to improve the spectral and energy efficiencies of cellular networks. However, most of the existing approaches for this problem only address either spectral efficiency (SE) or energy efficiency (EE). We study the tradeoff between SE and EE in a single cell D2D integrated cellular network, where multiple D2D multicast groups (MGs) may share the uplink channel with multiple cellular users. We explore SE-EE tradeoff for this problem by formulating the EE maximization problem with constraint on SE and maximum available transmission power. A power allocation algorithm is proposed to solve this problem and its efficacy is demonstrated via extensive numerical simulations. The tradeoff between SE and EE as a function of density of D2D MGs, and maximum transmission power of an MG is characterized.

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.