Maximizing Energy Efficiency in Heterogeneous Cellular Network with Massive MIMO and Small Cells

Abstract

With the rapid development of wireless cellular network, massive multiple input and multiple output (MIMO) and Small Cells (SCs) are both suitable candidates for the future fifth generation (5G) networks. Energy Efficiency (EE) has become increasingly concerned along with the two powerhungry candidates. This paper herein analyzed the energy efficiency of heterogeneous cellular network with massive MIMO and SCs. We maximized the EE by assigning the antennas of macro base station and SCs. We studied the relationship among the macro base station antennas, SCs and users by simulation and curve-fitting. Due to the fact that the Minimum Mean Square Error (MMSE) algorithm has better performance than the convex operation for total power minimization, we chose the MMSE algorithm to acquire the maximum energy efficiency with different amount of macro base station antennas, SCs and users. From the numerous simulation results, it can be seen that the number of antennas in the macro base station, the number of SCs and the user numbers have linear relationship when EE reaches the maximum value.

Sparsity‐based MIMO interference suppression technique in the presence of imperfect channel state information

“…The simulation benchmarks are selected as MRT, ZF, Multiflow‐RZF precoding in [9] and the MMSE precoder presented in [10] which is in the form of where denotes the precoding matrix, is a diagonal matrix which contains the transmit power of each antenna. In order to have a fair comparison, the diagonal elements of are set to .…”

“…In another test, we compare the performance of the proposed SISP scheme with MRT, ZF and the two precoding schemes in [9, 10] for various SNR values. The EE versus SNR curves of various precoding schemes have been depicted in Fig.…”

“…The channel vectors include small‐scale and large‐scale fading coefficients. We consider Rayleigh fading distribution for the small‐scale components, while the path loss, shadowing, penetration loss, and the antenna correlation loss effects are modelled as large‐scale fading [9, 10].…”

“…For example, in [7], the signals of the macro users are mapped to the null space of the interference channels from the macro BS to the pico users. The EE of a heterogeneous cellular network has been improved in [9, 10] by minimising the transmit power using a multiflow‐regularised zero‐forcing beamforming or MMSE precoding, respectively. The bit error rate (BER) performances of the SIC, multiple feedback SIC, and PIC algorithms have been investigated in [11, 12] for a point to point MIMO communication system.…”

Energy Efficient Power Allocation in Massive MIMO Systems with Power Limited Users

“…A power allocation scheme is obtained for a set of parallel channels while the transmitter has partial channel status information [11]. The energy efficiency of a heterogeneous cellular network with massive MIMOs and Small Cells (SCs) is discussed in [12]. Furthermore, the energy-efficient power allocation for a multi-user massive MIMO system in a single cell is investigated by using the SIF method in [13].…”