Spectral Efficiency Maximization of a Single Cell Massive MU-MIMO Down-Link TDD System by Appropriate Resource Allocation

This paper deals with the problem of maximizing the spectral efficiency in a massive multi-user MIMO downlink system, where a base station is equipped with a very large number of antennas and serves single-antenna users simultaneously in the same frequency band, and the beamforming training scheme is employed in the time-division duplex mode. An optimal resource allocation that jointly selects the training duration on uplink transmission, the training signal power on downlink transmission, the training signal power on uplink transmission, and the data signal power on downlink transmission is proposed in such a way that the spectral efficiency is maximized given the total energy budget. Since the spectral efficiency is the main concern of this work, and its calculation using the lower bound on the achievable rate is computationally very intensive, in this paper, we also derive approximate expressions for the lower bound of achievable downlink rate for the maximum ratio transmission (MRT) and zero-forcing (ZF) precoders. The computational simplicity and accuracy of the approximate expressions for the lower bound of achievable downlink rate are validated through simulations. By employing these approximate expressions, experiments are conducted to obtain the spectral efficiency of the massive MIMO downlink time-division duplexing system with the optimal resource allocation and that of the beamforming training scheme. It is shown that the spectral efficiency of the former system using the optimal resource allocation is superior to that yielded by the latter scheme in the cases of both MRT and ZF precoders

Spectral Efficiency Maximization of a Massive Multiuser MIMO System via Appropriate Power Allocation

Massive multiuser multiple-input multiple-output (MU-MIMO) systems are being considered for the next generation wireless networks in view of their ability to increase both the spectral and energy efficiencies. For such systems, linear detectors such as zero-forcing (ZF) and maximum-ratio combining (MRC) detectors on the uplink (UL) transmission have been shown to provide near optimal performance. As well, linear precoders such as ZF and maximum-ratio transmission (MRT) precoders on the downlink (DL) transmission offer lower complexity along with a near optimal performance in these systems. One of the most challenging problems in massive MU-MIMO systems is obtaining the channel state information (CSI) at the transmitter as well as the receiver. In such systems, the base station (BS) obtains CSI using pilot sequences, which are transmitted by the users. Due to the channel reciprocity between the UL and DL channels in the time-division duplex (TDD) mode, BS employs CSI obtained to precode the data symbols in DL transmission. To accurately decode the received symbols in the DL transmission, the users also need to acquire CSI. In view of this, a beamforming training (BT) scheme has been proposed in the literature to obtain the estimates of CSI at each user. In this scheme, BS transmits a short pilot sequence to the users in a way such that each user estimates the effective channel gain. Conventionally, the power of the pilot symbols has been considered equal to the power of data symbols for all the users. In this thesis, we pose and answer a basic question about the operation of a base station: How much the spectral efficiency could be improved if the transmit power allocated to the pilot and data symbols of each user are chosen in some optimal fashion? In answering this question and in order to maximize the spectral efficiency for a given total energy budget, some methods of power allocation are proposed. First, we derive a closed-form approximate expression for the achievable downlink rate for the maximum ratio transmission precoder based on small-scale fading in order to evaluate the spectral efficiency in the BT scheme. Then, we propose three methods of power allocation in order to maximize the spectral efficiency for a given total power budget among the users. In the first proposed method, we allocate equal pilot power as well as equal data power for all users in order to maximize the spectral efficiency. In the second proposed method, we allow for the allocation of different data powers among the users, whereas the pilot power for each user is kept the same and is specified. In the third method, we optimally allocate equal pilot power and a different data power for each user in such a way that the spectral efficiency is maximized. Numerical results are obtained showing that all the three proposed methods are superior to the existing methods in terms of spectral efficiency. In addition, they also show that the third proposed method of power allocation outperforms the other two proposed methods in terms of the spectral efficiency. Next, we derive a closed-form approximate expression for the achievable downlink rate for the maximum ratio transmission precoder based on large-scale fading in order to evaluate the spectral efficiency in the BT scheme. Then, we propose four methods of power allocation in order to maximize the spectral efficiency for a given total power budget among the users. In the first method, power is allocated among the pilot and data symbols in such a way that the pilot power as well as the data power for each user is the same. In the second method, power is allocated among the data symbols of the various users, whereas the pilot power for each user is the same and is specified. In this method, the data power for each user is optimally determined to maximize the spectral efficiency. In the third method, power is allocated among the pilot and data symbols of the various users, whereas the pilot power for each user is the same but determined. In this method, the same pilot power along with the various data powers is optimized to maximize the spectral efficiency. Finally, in the fourth method, power is allocated optimally among each of the pilot and data symbols of the various users so as to maximize the spectral efficiency. Numerical results are obtained showing that the performance of the first proposed method is approximately the same as that of the conventional approach. In addition, they also show that the second, third and fourth methods of power allocation yield similar performance in terms of spectral efficiency, and that the spectral efficiency of these methods is much superior to that of the first method or of the conventional method. Finally, we investigate the spectral efficiency of massive MU-MIMO systems on an UL transmission with a very large number of antennas at the base station serving single-antenna users. A practical physical channel model is proposed by dividing the angular domain into a finite number of distinct directions. A lower bound on the achievable rate of the uplink data transmission is derived using a linear detector for each user and employed in defining the spectral efficiency. The lower bound obtained is further modified for the maximum-ratio combining and zero-forcing receivers. A power control scheme based on the large-scale fading is also proposed to maximize the spectral efficiency under the peak power constraint. Experiments are conducted to evaluate the lower bounds obtained and the performance of the proposed method. The numerical results show that the proposed power control method provides a spectral efficiency which is the same as that of the maximum power criterion using the ZF receiver. Further, the proposed method provides a spectral efficiency that is higher than that provided by the maximum power criterion using the MRC receiver

Resource Allocation in Collocated Massive MIMO for 5G and Beyond

Massive multiuser multiple-input multiple-output (MIMO) systems have been recently introduced as a promising technology for the next generation of wireless networks. It has been proven that linear precoders/detectors such as maximum ratio transmitting/maximum ratio combining (MRT/MRC), zero forcing (ZF), and linear minimum mean square error (LMMSE) on the downlink (DL)/uplink (UL) transmission can provide near optimal performance in such systems. Acquiring channel state information (CSI) at the transmitter as well as the receiver is one of the challenges in multiuser massive MIMO that can affect the network performance. Any data transmission in multiuser massive MIMO systems starts with the user transmitting UL pilots. The base station (BS) then uses the MMSE estimation method to accurately estimate the CSI from the pilot sequences. Since the UL and DL channels are reciprocal in time division duplex (TDD) mode, the BS employs the obtained CSI to precode the data symbols prior to DL transmission. The users also need the CSI knowledge to accurately decode the DL signals. Beamforming training (BT) scheme is one of the methods that is proposed in the literature to provide the CSI knowledge for the users. In this scheme, the BS precodes and transmits a pilot sequence to the users such that each user can estimate its effective channel coefficients. Developing an optimal resource distribution method that enhances the system performance is another challenging issue in multiuser massive MIMO. As mentioned earlier, CSI acquisition is one of the requirements of multiuser massive MIMO, and UL pilot transmission is the common method to achieve that. Conventionally, equal powers have been considered for the pilot transmission phase and data transmission phase. However, it can be shown that the performance of the system under this method of power distribution is not optimal. Therefore, to further improve the performance of multiuser massive MIMO technology, especially in cases where the antenna elements are not well separated and the propagational dispersion is low, optimal resource allocation is required. Hence, the main objective of this M.A.Sc. thesis is to develop an optimal resource allocation among pilot and data symbols to maximize the spectral efficiency, assuming different receivers such as MRC, ZF, and LMMSE are employed at the BS. Since the calculation of spectral efficiency using the lower bound on the achievable rate is computationally very intensive, we first obtain closed-form expressions for the achievable UL rate of users, assuming the angular domain in the physical channel model is divided into a finite number of separate directions. An approximate expression for spectral efficiency is then developed using the aforementioned closed-form rates. Finally, we propose a resource allocation scheme in which the pilot power, data power, and training duration are optimally chosen in order to maximize the spectral efficiency in a given total power budget. Extensive simulations are conducted in MATLAB and the results are presented that illustrate the notable improvement in the achievable spectral efficiency through the proposed power allocation scheme. Moreover, the results show that the performance of the proposed method is much superior when the number of channel directions or the number of antennas at BS increases. Furthermore, while the advantage of the proposed method is more notable in the case of ZF and LMMSE receivers, it still outperforms the equal power allocation method for the MRC receiver in terms of spectral efficiency

Harmonized Cellular and Distributed Massive MIMO: Load Balancing and Scheduling

Multi-tier networks with large-array base stations (BSs) that are able to operate in the "massive MIMO" regime are envisioned to play a key role in meeting the exploding wireless traffic demands. Operated over small cells with reciprocity-based training, massive MIMO promises large spectral efficiencies per unit area with low overheads. Also, near-optimal user-BS association and resource allocation are possible in cellular massive MIMO HetNets using simple admission control mechanisms and rudimentary BS schedulers, since scheduled user rates can be predicted a priori with massive MIMO. Reciprocity-based training naturally enables coordinated multi-point transmission (CoMP), as each uplink pilot inherently trains antenna arrays at all nearby BSs. In this paper we consider a distributed-MIMO form of CoMP, which improves cell-edge performance without requiring channel state information exchanges among cooperating BSs. We present methods for harmonized operation of distributed and cellular massive MIMO in the downlink that optimize resource allocation at a coarser time scale across the network. We also present scheduling policies at the resource block level which target approaching the optimal allocations. Simulations reveal that the proposed methods can significantly outperform the network-optimized cellular-only massive MIMO operation (i.e., operation without CoMP), especially at the cell edge

A survey on hybrid beamforming techniques in 5G : architecture and system model perspectives

The increasing wireless data traffic demands have driven the need to explore suitable spectrum regions for meeting the projected requirements. In the light of this, millimeter wave (mmWave) communication has received considerable attention from the research community. Typically, in fifth generation (5G) wireless networks, mmWave massive multiple-input multiple-output (MIMO) communications is realized by the hybrid transceivers which combine high dimensional analog phase shifters and power amplifiers with lower-dimensional digital signal processing units. This hybrid beamforming design reduces the cost and power consumption which is aligned with an energy-efficient design vision of 5G. In this paper, we track the progress in hybrid beamforming for massive MIMO communications in the context of system models of the hybrid transceivers' structures, the digital and analog beamforming matrices with the possible antenna configuration scenarios and the hybrid beamforming in heterogeneous wireless networks. We extend the scope of the discussion by including resource management issues in hybrid beamforming. We explore the suitability of hybrid beamforming methods, both, existing and proposed till first quarter of 2017, and identify the exciting future challenges in this domain