Abstract
Massive multiple-input multiple-output (MIMO) systems can be applied to support numerous internet of things (IoT) devices using its excessive amount of transmitter (TX) antennas. However, one of the big obstacles for the realization of the massive MIMO system is the overhead of reference signal (RS), because the number of RS is proportional to the number of TX antennas and/or related user equipments (UEs). It has been already reported that antenna group-based RS overhead reduction can be very effective to the efficient operation of massive MIMO, but the method of deciding the number of antennas needed in each group is at question. In this paper, we propose a simplified determination scheme of the number of antennas needed in each group for RS overhead reduced massive MIMO to support many IoT devices. Supporting many distributed IoT devices is a framework to configure wireless sensor networks. Our contribution can be divided into two parts. First, we derive simple closed-form approximations of the achievable spectral efficiency (SE) by using zero-forcing (ZF) and matched filtering (MF) precoding for the RS overhead reduced massive MIMO systems with channel estimation error. The closed-form approximations include a channel error factor that can be adjusted according to the method of the channel estimation. Second, based on the closed-form approximation, we present an efficient algorithm determining the number of antennas needed in each group for the group-based RS overhead reduction scheme. The algorithm depends on the exact inverse functions of the derived closed-form approximations of SE. It is verified with theoretical analysis and simulation that the proposed algorithm works well, and thus can be used as an important tool for massive MIMO systems to support many distributed IoT devices.
Highlights
Massive multiple-input multiple-output (MIMO) system is a powerful technology that can increase both spectral efficiency (SE) and energy efficiency (EE), and it has been actively discussed to be included in the 3rd generation partnership project (3GPP) standard as core technology for 5G systems [1,2,3]
The massive MIMO system uses a large amount of transmitter (TX) antennas and serves limited number of user equipments (UEs) and/or Internet of things (IoT) devices/sensors, so it is a combination scheme of multi-user (MU) MIMO and beamforming, and its drastic performance gain has already been proven in various literature [4,5,6,7,8]
We propose a simplified determination scheme of the number of antennas needed in each group for massive MIMO to support wireless sensor networks, and present related system structure for the operation
Summary
Massive multiple-input multiple-output (MIMO) system is a powerful technology that can increase both spectral efficiency (SE) and energy efficiency (EE), and it has been actively discussed to be included in the 3rd generation partnership project (3GPP) standard as core technology for 5G systems [1,2,3]. Based on the RF impairment information which is embedded in power headroom, BS performs precoding with the compensation of channel mismatch This is very important scheme to support distributed IoT devices using massive MIMO equipped data center. We propose a simplified determination scheme of the number of antennas needed in each group for massive MIMO to support wireless sensor networks, and present related system structure for the operation. Even though antenna grouping-based RS overhead reduction scheme is quite effective for the operation of massive MIMO and related IoT device support, channel estimation error is inevitable. In a word, based on closed-form approximation of SE, to support distributed IoT devices, we propose a simplified determination scheme of the number of antennas needed in each group. To denote the componentwise product of the matrices. we use diag [d1 , · · · , d N ] for N × N diagonal matrix with di as the ith diagonal element
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