4 research outputs found
Energy Efficiency Optimization for MIMO Distributed Antenna Systems
In this paper, we propose a transmit covariance optimization method to maximize the energy efficiency (EE) for a single-user distributed antenna system, where both the remote access units (RAUs) and the user are equipped with multiple antennas. Unlike previous related work, both the rate requirement and the RAU selection are taken into consideration. Here, the total circuit power consumption is related to the number of active RAUs. Given this setup, we first propose an optimal transmit covariance optimization method to solve the EE optimization problem under a fixed set of active RAUs. More specifically, we split this problem into three subproblems, namely, the rate maximization problem, the EE maximization problem without rate constraint, and the power minimization problem, and each subproblem can be efficiently solved. Then, a novel distance-based RAU selection method is proposed to determine the optimal set of active RAUs. Simulation results show that the performance of the proposed RAU selection is almost identical to the optimal exhaustive search method with significantly reduced computational complexity, and the performance of the proposed algorithm significantly outperforms the existing EE optimization methods
Network Coding for Distributed Antenna Systems
The mushroom growth of devices that require connectivity has led to an increase in the demand for spectrum resources as well as high data rates. 5G has introduced numerous solutions to counter both problems, which are inherently interconnected. Distributed antenna systems (DASs) help in expanding the coverage area of the network by reducing the distance between radio access unit (RAU) and the user equipment. DASs that use multiple-input multiple-output (MIMO) technology allow devices to operate using multiple antennas, which lead to spectrum efficiency. Recently, the concept of virtual MIMO (VMIMO) has gained popularity. VMIMO allows single antenna nodes to cooperate and form a cluster resulting in a transmission flow that corresponds to MIMO technology. In this chapter, we discuss MIMO-assisted DAS and its utility in forming a cooperative network between devices in proximity to enhance spectral efficiency. We further amalgamate VMIMO-assisted DAS and network coding (NC) to quantify end-to-end transmission success. NC is deemed to be particularly helpful in energy constrained environments, where the devices are powered by battery. We conclude by highlighting the utility of NC-based DAS for several applications that involve single antenna empowered sensors or devices
On the Total Energy Efficiency of Cell-Free Massive MIMO
We consider the cell-free massive multiple-input multiple-output (MIMO)
downlink, where a very large number of distributed multiple-antenna access
points (APs) serve many single-antenna users in the same time-frequency
resource. A simple (distributed) conjugate beamforming scheme is applied at
each AP via the use of local channel state information (CSI). This CSI is
acquired through time-division duplex operation and the reception of uplink
training signals transmitted by the users. We derive a closed-form expression
for the spectral efficiency taking into account the effects of channel
estimation errors and power control. This closed-form result enables us to
analyze the effects of backhaul power consumption, the number of APs, and the
number of antennas per AP on the total energy efficiency, as well as, to design
an optimal power allocation algorithm. The optimal power allocation algorithm
aims at maximizing the total energy efficiency, subject to a per-user spectral
efficiency constraint and a per-AP power constraint. Compared with the equal
power control, our proposed power allocation scheme can double the total energy
efficiency. Furthermore, we propose AP selections schemes, in which each user
chooses a subset of APs, to reduce the power consumption caused by the backhaul
links. With our proposed AP selection schemes, the total energy efficiency
increases significantly, especially for large numbers of APs. Moreover, under a
requirement of good quality-of-service for all users, cell-free massive MIMO
outperforms the colocated counterpart in terms of energy efficiency