Energy Efficiency Optimization Based on Power Allocation in Massive MIMO Downlink Systems

Abstract

To solve the energy efficiency (EE) optimization in a multi-cell (MU) massive multiple-input multiple-output (MIMO) downlink system, of which channels are of symmetry in the Time-Division Duplex (TDD) protocol, we utilize a spatially correlated channel model and adopt the minimum mean-squared error (MMSE) estimator to tailor linear precoding vectors. Then, we derive the expression of downlink spectral efficiency (SE), taking interference into account. Subsequently, we establish the EE optimization function, which is defined as the average capacity divided by power consumption. In an interference-limited scenario, the EE optimization is of high complexity to solve globally as it is not jointly concave. To this end, we propose the Dinkelbach-like power allocation algorithm to obtain a suboptimal solution. We transform the EE problem in a fractional form into a subtractive optimization form called an auxiliary subproblem. Then, we relax the sub-problem to a concave problem by initializing the interference and omitting the dynamic power term about throughput. Lastly, we solve iteratively the Karush–Kuhn–Tucker (KKT) conditions by bisection search. Consequently, we obtain a sub-solution with modest complexity. The simulation results justify the rationality of the Dinkelbach-like algorithm and demonstrate that the proposal outperforms the reference schemes and effectively improves the performance metrics EE and SE.

Multi-Objective Reinforcement Learning for Power Allocation in Massive MIMO Networks: A Solution to Spectral and Energy Trade-Offs

“…A large fraction of the power consumed in the network comprises the power consumed at the BS [30]. The power consumption of the BS comprises circuit powers required in operations such as the number of transmit antennas, channel estimation, and encoding and decoding [31].…”

“…, O} do Update multi-critic networks for ϕ ′ o using (31). Update single actor network for θ ′ using (30).…”

Maximizing the Downlink Data Rates in Massive Multiple Input Multiple Output with Frequency Division Duplex Transmission Mode Using Power Allocation Optimization Method with Limited Coherence Time

“…The existing wireless communications systems will face significant challenges due to the rapid increase in the applications of the Internet of Things (IoT) that need ultrareliable and low-latency communication (URLLC) [1]. In particular, widely expected future services, such as smart healthcare systems, factory automation, and autonomous vehicles, will be critically reliant on providing URLLC while maximizing the data rates [2][3][4]. This necessitates finding massive communication systems with advanced techniques to meet the extremely high data rate requirements.…”