Vector Quantization Methods for Access Point Placement in Cell-Free Massive MIMO Systems

We examine the problem of uplink cell-free access point (AP) placement in the context of optimal throughput. In this regard, we formulate two main placement problems, namely the sum rate and minimum rate maximization problems, and discuss the challenges associated with solving the underlying optimization problem with the help of some simple scenarios. As a practical solution to the AP placement problem, we suggest a vector quantization (VQ) approach. The suitability of the VQ approach to cell-free AP placement is investigated by examining three VQ-based solutions. First, the standard VQ approach, that is the Lloyd algorithm (using the squared error distortion function) is described. Second, the tree-structured VQ (TSVQ), which performs successive partitioning of the distribution space is applied. Third, a probability density function optimized VQ (PDFVQ) procedure is outlined, which enables efficient, low complexity, and scalable placement, and is aimed at a massive distributed multiple-input-multiple-output (MIMO) scenario. While the VQ-based solutions do not solve the cell-free AP placement problems explicitly, numerical experiments show that their sum and minimum rate performances are good enough, and offer a good starting point for gradient-based optimization methods. Among the VQ solutions, PDFVQ, with advantages over the other VQ methods, offers a good trade-off between sum and minimum rates.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

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

Adaptive Communication for Wireless Massive MIMO Systems

The demand for high data rates in wireless communications is increasing rapidly. One way to provide reliable communication with increased rates is massive multiple-input multiple-output (MIMO) systems where a large number of antennas is deployed. We analyze three systems utilizing a large number of antennas to provide enhancement in the performance of wireless communications. First, we consider a general form of spatial modulation (SM) systems where the number of transmitted data streams is allowed to vary and we refer to it as generalized spatial modulation with multiplexing (GSMM). A Gaussian mixture model (GMM) is shown to accurately model the transmitted spatially modulated signal using a precoding framework. Using this transmit model, a general closed-form expression for the achievable rate when operating over Rayleigh fading channels is evaluated along with a tight upper and a lower bounds for the achievable rate. The obtained expressions are flexible enough to accommodate any form of SM by adjusting the precoding set. Followed by that, we study quantized distributed wireless relay networks where a relay consisting of many geographically dispersed nodes is facilitating communication between unconnected users. Due to bandwidth constraints, distributed relay networks perform quantization at the relay nodes, and hence they are referred to as quantized distributed relay networks. In such systems, users transmit their data simultaneously to the relay nodes through the uplink channel that quantize their observed signals independently to a few bits and broadcast these bits to the users through the downlink channel. We develop algorithms that can be employed by the users to estimate the uplink channels between all users and all relay nodes when the relay nodes are performing simple sign quantization. This setup is very useful in either extending coverage to unconnected regions or replacing the existing wireless infrastructure in case of disasters. Using the uplink channel estimates, we propose multiple decoders that can be deployed at the receiver side. We also study the performance of each of these decoders under different system assumptions. A different quantization framework is also proposed for quantized distributed relay networking where the relay nodes perform vector quantization instead of sign quantization. Applying vector quantization at the relay nodes enables us to propose an algorithm that allocates quantization resources efficiently among the relay nodes inside the relay network. We also study the beamforming design at the users’ side in this case where beamforming design is not trivial due to the quantization that occurs at the relay network. Finally, we study a different setup of distributed communication systems called cell-free massive MIMO. In cell-free massive MIMO, regular cellular communication is replaced by multiple access points (APs) that are placed randomly over the coverage area. All users in the coverage area are sharing time and frequency resources and all APs are serving all UEs while power allocation is done in a central processor that is connected to the APs through a high speed backhaul network. We study the power allocation in cell-free massive MIMO system where APs are equipped with few antennas and how the distribution of the available antennas among access points affects both the performance and the infrastructure cost