A Reduced Complexity Ungerboeck Receiver for Quantized Wideband Massive SC-MIMO

Employing low resolution analog-to-digital converters in massive multiple-input multiple-output (MIMO) has many advantages in terms of total power consumption, cost and feasibility of such systems. However, such advantages come together with significant challenges in channel estimation and data detection due to the severe quantization noise present. In this study, we propose a novel iterative receiver for quantized uplink single carrier MIMO (SC-MIMO) utilizing an efficient message passing algorithm based on the Bussgang decomposition and Ungerboeck factorization, which avoids the use of a complex whitening filter. A reduced state sequence estimator with bidirectional decision feedback is also derived, achieving remarkable complexity reduction compared to the existing receivers for quantized SC-MIMO in the literature, without any requirement on the sparsity of the transmission channel. Moreover, the linear minimum mean-square-error (LMMSE) channel estimator for SC-MIMO under frequency-selective channel, which do not require any cyclic-prefix overhead, is also derived. We observe that the proposed receiver has significant performance gains with respect to the existing receivers in the literature under imperfect channel state information.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

All-Digital Massive MIMO Uplink and Downlink Rates under a Fronthaul Constraint

We characterize the rate achievable in a bidirectional quasi-static link where several user equipments communicate with a massive multiple-input multiple-output base station (BS). In the considered setup, the BS operates in full-digital mode, the physical size of the antenna array is limited, and there exists a rate constraint on the fronthaul interface connecting the (possibly remote) radio head to the digital baseband processing unit. Our analysis enables us to determine the optimal resolution of the analog-to-digital and digital-to-analog converters as well as the optimal number of active antenna elements to be used in order to maximize the transmission rate on the bidirectional link, for a given constraint on the outage probability and on the fronthaul rate. We investigate both the case in which perfect channel-state information is available, and the case in which channel-state information is acquired through pilot transmission, and is, hence, imperfect. For the second case, we present a novel rate expression that relies on the generalized mutual-information framework.Comment: 5 pages, 5 figure

Multiantenna Wireless Architectures with Low Precision Converters

One of the main key technology enablers of the next generation of wireless communications is massive multiple input multiple output (MIMO), in which the number of antennas at the base station (BS) is scaled up to the order of tens or hundreds. It provides considerable energy and spectral efficiency by spatial multiplexing, which enables serving multiple user equipments (UEs) on the same time and frequency resource. However, the deployment of such large-scale systems could be challenging and this thesis is aimed at studying one of the challenges in the optimal implementation of such systems. More specifically, we consider a fully digital setup, in which each antenna at the BS is connected to a pair of data converters through a radio-frequency (RF) chain, all located at the remote radio head (RRH), and there is a limitation on the capacity of the fronthaul link, which connects the RRH to the baseband unit (BBU), where digital signal processing is performed. The fronthaul capacity limitation calls for a trade-off between some of the design parameters, including the number of antennas, the resolution of data converters and the over-sampling ratio. In this thesis, we study the aforementioned trade-off considering the first two design parameters.First, we consider a quasi-static scenario, in which the fading coefficients do not change throughout the transmission of a codeword. The channel state information (CSI) is assumed to be unknown at the BS, and it is acquired through pilot transmission. We develop a framework based on the mismatched decoding rule to find lower bounds on the achievable rates. The bi-directional rate at 10% outage probability is selected as the performance metric to determine the recommended architecture in terms of number of antennas and the resolution of data converters. Second, we adapt our framework to a finite blocklength regime, considering a realistic mm-wave multi-user clustered MIMO channel model and a well suited channel estimation algorithm. We start our derivations by considering random coding union bound with parameter s (RCUs) and apply approximations to derive the corresponding normal approximation and further, an easy to compute outage with correction bound. We illustrate the accuracy of our approximations, and use the outage with correction bound to investigate the optimal architecture in terms of the number of antennas and the resolution of the data converters.Our result show that at low signal to noise (SNR) regime, we benefit from lowering the resolution of the data converters and increasing the number of antennas, while at high SNR for a practical scenario, the optimal architecture could move to 3 or 4 bits of resolution since we are not in demand of large array gain anymore

Massive MIMO with Non-Ideal Arbitrary Arrays: Hardware Scaling Laws and Circuit-Aware Design

Massive multiple-input multiple-output (MIMO) systems are cellular networks where the base stations (BSs) are equipped with unconventionally many antennas, deployed on co-located or distributed arrays. Huge spatial degrees-of-freedom are achieved by coherent processing over these massive arrays, which provide strong signal gains, resilience to imperfect channel knowledge, and low interference. This comes at the price of more infrastructure; the hardware cost and circuit power consumption scale linearly/affinely with the number of BS antennas $N$. Hence, the key to cost-efficient deployment of large arrays is low-cost antenna branches with low circuit power, in contrast to today's conventional expensive and power-hungry BS antenna branches. Such low-cost transceivers are prone to hardware imperfections, but it has been conjectured that the huge degrees-of-freedom would bring robustness to such imperfections. We prove this claim for a generalized uplink system with multiplicative phase-drifts, additive distortion noise, and noise amplification. Specifically, we derive closed-form expressions for the user rates and a scaling law that shows how fast the hardware imperfections can increase with $N$ while maintaining high rates. The connection between this scaling law and the power consumption of different transceiver circuits is rigorously exemplified. This reveals that one can make the circuit power increase as $\sqrt{N}$, instead of linearly, by careful circuit-aware system design.Comment: Accepted for publication in IEEE Transactions on Wireless Communications, 16 pages, 8 figures. The results can be reproduced using the following Matlab code: https://github.com/emilbjornson/hardware-scaling-law

Massive MIMO Systems with Non-Ideal Hardware: Energy Efficiency, Estimation, and Capacity Limits

The use of large-scale antenna arrays can bring substantial improvements in energy and/or spectral efficiency to wireless systems due to the greatly improved spatial resolution and array gain. Recent works in the field of massive multiple-input multiple-output (MIMO) show that the user channels decorrelate when the number of antennas at the base stations (BSs) increases, thus strong signal gains are achievable with little inter-user interference. Since these results rely on asymptotics, it is important to investigate whether the conventional system models are reasonable in this asymptotic regime. This paper considers a new system model that incorporates general transceiver hardware impairments at both the BSs (equipped with large antenna arrays) and the single-antenna user equipments (UEs). As opposed to the conventional case of ideal hardware, we show that hardware impairments create finite ceilings on the channel estimation accuracy and on the downlink/uplink capacity of each UE. Surprisingly, the capacity is mainly limited by the hardware at the UE, while the impact of impairments in the large-scale arrays vanishes asymptotically and inter-user interference (in particular, pilot contamination) becomes negligible. Furthermore, we prove that the huge degrees of freedom offered by massive MIMO can be used to reduce the transmit power and/or to tolerate larger hardware impairments, which allows for the use of inexpensive and energy-efficient antenna elements.Comment: To appear in IEEE Transactions on Information Theory, 28 pages, 15 figures. The results can be reproduced using the following Matlab code: https://github.com/emilbjornson/massive-MIMO-hardware-impairment