# Evaluation of Multi-Beam Massive MIMO Considering MAC Layer Using IEEE802.11 ac and FDD-LTE
## 1 Introduction  
Cellular network data traffic volumes have increased significantly with the advent of smart devices. This trend will continue to grow as Internet of Things (IoT) equipment and big-data services become more common. To meet this demand, recent research and development have focused on achieving a 20-Gbps or more standard for future wireless communication [1; 2]. Multiple-input multiple-output (MIMO) systems can be used to improve the transmission rate within a limited frequency band [3]. To this end, multi-user MIMO (MU-MIMO) has been developed to enable MIMO transmission to multiple users [4]. MU-MIMO technologies have been standardized for LTE-advanced and IEEE802.11 ac [5; 6]. Accordingly, massive MIMO is viewed as a fifth-generation (5G) technology, and it is expected to play an important role in achieving 5G targets [1; 7]. In a MIMO system, a base station (BS) acquires channel state information (CSI) between itself and a user terminal (UT) [8]. As CSI acquisition under massive MIMO involves numerous UT-BS antenna pair channels, one of the most important challenges in implementing it is the acquisition of large amounts of CSI with a small overhead. To achieve this, it is necessary to evaluate not only the physical (PHY) layer but also the medium access control (MAC) layer. The MAC layer is a communication protocol of a part of the data link layer corresponding to the second layer of the OSI reference model in IEEE802, and it is located in a layer that is one layer above the physical layer. Implicit beamforming (BF) has been proposed as an approach for eliminating CSI feedback in MU-MIMO systems [9]. However, the communication efficiency of short-packet communication decreases even if implicit BF is applied in a massive MIMO system [10]. CSI estimation and initial UT tracking, which require a significant amount of overhead, are necessary in hybrid BF massive MIMO systems [1]. A hybrid BF approach referred to as multi-beam massive MIMO transmission has been proposed to solve this problem. CSI estimation is unnecessary in this approach. The effectiveness of multi-beam massive MIMO has been demonstrated through computer simulation [11; 12; 13]. In multi-beam massive MIMO systems, a number of analog multi-beams are created for initial UT tracking and a subset of these beams with high received power are selected for use. As multi-beams have narrow beam widths, interference signals can be mitigated and residual interference can be cancelled by applying a blind array algorithm to digital beam components using only the information pertaining to a received signal [14; 15; 16]. This paper proposes a multi-beam massive MIMO approach that achieves high transmission efficiency and flexible communication using asynchronous UT transmission. As the proposed system does not require timing synchronization among UTs, all UTs can freely transmit signals while avoiding collision between their signals. The diminished signal transmission time resulting from this configuration reduces the total power consumption by the UTs. In our previous studies, we proposed multi-beam massive MIMO beam-selection methods [13] that utilized received-signal information such as the power difference and amplitude correlation between beams. This approach was capable of appropriately performing beam-selection at a high signal-to-interference noise ratio (SINR). Furthermore, this method could be performed through signal processing using a simple configuration and was highly suitable for hybrid analog-digital massive MIMO. Channel quality indicator (CQI) values, which can serve as an index of modulation scheme determination, cannot be obtained under multi-beam massive MIMO because it does not perform CSI estimation. In this paper, we propose a simple adaptive modulation scheme for multi-beam massive MIMO transmission based on amplitude correlation and received power. Under this method, an appropriate modulation scheme can be simply determined based on the relationship between amplitude correlation and the SINR.