Towards Optimal Energy Efficiency in Cell-Free Massive MIMO Systems (original) (raw)

Maximizing Energy Efficiency for Consumption Circuit Power in Downlink Massive MIMO Wireless Networks

International Journal of Electrical and Computer Engineering (IJECE), 2017

Massive multi-input-multi-output (MIMO) systems are crucial to maximizing energy efficiency (EE) and battery-saving technology. Achieving EE without sacrificing the quality of service (QoS) is increasingly important for mobile devices. We first derive the data rate through zero forcing (ZF) and three linear precodings: maximum ratio transmission (MRT), zero forcing (ZF), and minimum mean square error (MMSE). Performance EE can be achieved when all available antennas are used and when taking account of the consumption circuit power ignored because of high transmit power. The aim of this work is to demonstrate how to obtain maximum EE while minimizing power consumed, which achieves a high data rate by deriving the optimal number of antennas in the downlink massive MIMO system. This system includes not only the transmitted power but also the fundamental operation circuit power at the transmitter signal. Maximized EE depends on the optimal number of antennas and determines the number of active users that should be scheduled in each cell. We conclude that the linear precoding technique MMSE achieves the maximum EE more than ZF and MRT because the MMSE is able to make the massive MIMO system less sensitive to SNR at an increased number of antennas. Keyword: 5G cellular systems Enrgy efficiency Multi-input Multi-output Quality of service Signal-to-noise ratio

On the Energy Efficiency of Limited-Backhaul Cell-Free Massive MIMO

ICC 2019 - 2019 IEEE International Conference on Communications (ICC), 2019

We investigate the energy efficiency performance of cell-free Massive multiple-input multiple-output (MIMO), where the access points (APs) are connected to a central processing unit (CPU) via limited-capacity links. Thanks to the distributed maximum ratio combining (MRC) weighting at the APs, we propose that only the quantized version of the weighted signals are sent back to the CPU. Considering the effects of channel estimation errors and using the Bussgang theorem to model the quantization errors, an energy efficiency maximization problem is formulated with per-user power and backhaul capacity constraints as well as with throughput requirement constraints. To handle this non-convex optimization problem, we decompose the original problem into two sub-problems and exploit a successive convex approximation (SCA) to solve original energy efficiency maximization problem. Numerical results confirm the superiority of the proposed optimization scheme.

Designing Multi-User MIMO for Energy Efficiency: When is Massive MIMO the Answer

—Assume that a multiuser multiple-input multiple-output (MIMO) communication system must be designed to cover a given area with maximal energy efficiency (bit/Joule). What are the optimal values for the number of antennas, active users, and transmit power? By using a new model that describes how these three parameters affect the total energy efficiency of the system, this work provides closed-form expressions for their optimal values and interactions. In sharp contrast to common belief, the transmit power is found to increase (not decrease) with the number of antennas. This implies that energy efficient systems can operate at high signal-to-noise ratio (SNR) regimes in which the use of interference-suppressing precoding schemes is essential. Numerical results show that the maximal energy efficiency is achieved by a massive MIMO setup wherein hundreds of antennas are deployed to serve relatively many users using interference-suppressing regularized zero-forcing precoding.

Cell-Free Massive Mimo Systems with Multi-Antenna Users

2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2018

This paper studies a cell-free massive multiple-input multiple-output (MIMO) system where its access points (APs) and users are equipped with multiple antennas. Two transmission protocols are considered. In the first transmission protocol, there are no downlink pilots, while in the second transmission protocol, downlink pilots are proposed in order to improve the system performance. In both transmission protocols, the users use the minimum mean-squared error-based successive interference cancellation (MMSE-SIC) scheme to detect the desired signals. For the analysis, we first derive a general spectral efficiency formula with arbitrary side information at the users. Then analytical expressions for the spectral efficiency of different transmission protocols are derived. To improve the spectral efficiency (SE) of the system, max-min fairness power control (PC) is applied for the first protocol by using the closed-form expression of its SE. Due to the computation complexity of deriving the closed-form performance expression of SE for the second protocol, we apply the optimal power coefficients of the first protocol to the second protocol. Numerical results show that two protocols combining with multi-antenna users are prerequisites to achieve the suboptimal SE regardless of the number of user in the system. Index terms-Cell-free massive MIMO, massive MIMO, spectral efficiency, MMSE-SIC, power control. I. INTRODUCTION Cellular massive multiple-input multiple-output (MIMO) is currently considered as a key wireless access technology for 5G because it can provide high spectral efficiency (SE) and high energy efficiency (EE) with simple signal processing [2], [3]. In cellular massive MIMO, the BS with massive antenna arrays simultaneously serves all users in its cell on the same time-frequency resource [4]-[7]. Since cellular massive MIMO is based on cellular topology, its inherent limitation is inter-cell interference. To overcome this limitation, cell-free massive MIMO is introduced [8]. Cell-free massive MIMO can be considered as a useful and scalable version of network MIMO [9], [10] (much in the same way as cellular Massive MIMO is scalable version of multiuser MIMO). In cell-free massive MIMO, a large number of access points (APs), which are geographically distributed over a large area, coherently serve all users on same timefrequency resource [8], [11]. Cell-free massive MIMO can

Cell-Free Massive MIMO with Finite Fronthaul Capacity: A Stochastic Geometry Perspective

IEEE Transactions on Wireless Communications

In this work, we analyze the downlink performance of a cell-free massive multiple-input-multipleoutput system with finite capacity fronthaul links between the centralized baseband unit and the access point (APs). Conditioned on the user and AP locations, we first derive an achievable rate for a randomly selected user in the network that captures the effect of finite fronthaul capacity as a compression error. From this expression, we establish that for the traditional cell-free architecture where each AP serves all the users in the network, the achievable rate becomes zero as the network size grows. Hence, to have a meaningful analysis, for the traditional architecture, we model the user and AP locations as two independent binomial point processes over a finite region and provide an accurate theoretical result to determine the user rate coverage. For a larger (possibly infinite) network, we consider a user-centric architecture where each user in the network is served by a specified number of nearest APs that limits the fronthaul load. For this architecture, we model the AP and user locations as two independent Poisson point processes (PPPs). Since the rate expression is a function of the number of users served by an AP, we statistically characterize the load in terms of the number of users per AP. As the exact derivation of the probability mass function of the load is intractable, we first present the exact expressions for the first two moments of the load. Next, we approximate the load as a negative binomial random variable through the moment matching method. Using the load results along with appropriate distance distributions of a PPP, we present an accurate theoretical expression for the rate coverage of the typical user. From the analyses we conclude that for the traditional architecture the average system sum-rate is a quasi-concave function of the number of users. Further, for the user-centric architecture, there exists an optimal number of serving APs that maximizes the average user rate.

Optimal Pilot and Data Power Allocation in the Cell-Free Massive MIMO Systems

It is well known that joint pilot and data power control, often known as JPDPC, has a significant effect on cell-free massive multiple-input multiple-output (CFmMIMO) system performance. In this paper, the uplink performance of the CFmMIMO system is evaluated under two conflicting optimization objectives (sum spectral efficiency and proportional fairness) by considering JPDPC as an optimization variable. In the CFmMIMO system with imperfect channel state information (CSI), we first formulate a multi-objective optimization (MOO) problem that can handle the fairness sum-SE trade-off problems. Then, taking into account a total energy budget for each access point, we propose a power control approach that optimally allocates power between data symbols and pilot symbols. Due to the fact that the problem is non-convex, new algorithms based on a combination of successive convex approximation and geometric programming are used to handle it. The numerical results demonstrate the benefit of op...

Optimization of energy and spectral efficiency of massive MIMO-small cell system

2015

Current wireless world demands energy efficiency networks due to limited power resources at mobile nodes in a contingency of universal energy saving. This is achieved by employing more effective and efficient resource allocation algorithms for power minimization. This paper considers mainly minimizing power by the process of non coherent convex optimization for massive Multi input and Multi output (MIMO) antenna systems. We presented small cell access (SCA) points combined with massive-MIMO networks to improve spectral efficiency in a macro cell environment. We proposed a novel non convex low complexity RZF beamforming technique for power optimization with soft cell coordination. The total power consumption includes both dynamic and static power allocation. Thus, there is need for minimizing and optimizing the total power consumption without compromising Quality of service(QOS) constraints. The results were simulated by using with MATLAB 2014a.

Energy-Efficient Resource Optimization for Massive MIMO Networks Considering Network Load

Computers, Materials & Continua

This paper investigates the resource optimization problem for a multi-cell massive multiple-input multiple-output (MIMO) network in which each base station (BS) is equipped with a large number of antennas and each base station (BS) adapts the number of antennas to the daily load profile (DLP). This paper takes into consideration user location distribution (ULD) variation and evaluates its impact on the energy efficiency of load adaptive massive MIMO system. ULD variation is modeled by dividing the cell into two coverage areas with different user densities: boundary focused (BF) and center focused (CF) ULD. All cells are assumed identical in terms of BS configurations, cell loading, and ULD variation and each BS is modeled as an M/G/m/m state dependent queue that can serve a maximum number of users at the peak load. Together with energy efficiency (EE) we analyzed deployment and spectrum efficiency in our adaptive massive MIMO system by evaluating the impact of cell size, available bandwidth, output power level of the BS, and maximum output power of the power amplifier (PA) at different cell loading. We also analyzed average energy consumption on an hourly basis per BS for the model proposed for data traffic in Europe and also the model proposed for business, residential, street, and highway areas.

Massive MIMO: Achievable Energy Efficiency for 5G systems with Multi-user Environment

International Journal of Recent Technology and Engineering (IJRTE), 2019

Massive Multi-Input and Multi-Output (MIMO) antenna system potentially provides a promising solution to improve energy efficiency (EE) for 5G wireless systems. The aim of this paper is to enhance EE and its limiting factors are explored. The maximum EE of 48 Mbit/Joule was achieved with 15 user terminal (UT)s. This problem is related to the uplink spectral efficiency with upper bound for future wireless networks. The maximal EE is obtained by optimizing a number of base station (BS) antennas, pilot reuse factor, and BSs density. We presented a power consumption model by deriving Shannon capacity calculations with closed-form expressions. The simulation result highlights the EE maximization with optimizing variables of circuit power consumption, hardware impairments, and path-loss exponent. Small cells achieve high EE and saturate to a constant value with BSs density. The MRC scheme achieves maximum EE of 36 Mbit/Joule with 12 UTs. The simulation results show that peak EE is obtained...