Hardware-Conscious Wireless Communication System Design

The work at hand is a selection of topics in efficient wireless communication system design, with topics logically divided into two groups.One group can be described as hardware designs conscious of their possibilities and limitations. In other words, it is about hardware that chooses its configuration and properties depending on the performance that needs to be delivered and the influence of external factors, with the goal of keeping the energy consumption as low as possible. Design parameters that trade off power with complexity are identified for analog, mixed signal and digital circuits, and implications of these tradeoffs are analyzed in detail. An analog front end and an LDPC channel decoder that adapt their parameters to the environment (e.g. fluctuating power level due to fading) are proposed, and it is analyzed how much power/energy these environment-adaptive structures save compared to non-adaptive designs made for the worst-case scenario. Additionally, the impact of ADC bit resolution on the energy efficiency of a massive MIMO system is examined in detail, with the goal of finding bit resolutions that maximize the energy efficiency under various system setups.In another group of themes, one can recognize systems where the system architect was conscious of fundamental limitations stemming from hardware.Put in another way, in these designs there is no attempt of tweaking or tuning the hardware. On the contrary, system design is performed so as to work around an existing and unchangeable hardware limitation. As a workaround for the problematic centralized topology, a massive MIMO base station based on the daisy chain topology is proposed and a method for signal processing tailored to the daisy chain setup is designed. In another example, a large group of cooperating relays is split into several smaller groups, each cooperatively performing relaying independently of the others. As cooperation consumes resources (such as bandwidth), splitting the system into smaller, independent cooperative parts helps save resources and is again an example of a workaround for an inherent limitation.From the analyses performed in this thesis, promising observations about hardware consciousness can be made. Adapting the structure of a hardware block to the environment can bring massive savings in energy, and simple workarounds prove to perform almost as good as the inherently limited designs, but with the limitation being successfully bypassed. As a general observation, it can be concluded that hardware consciousness pays off

An Energy Efficiency Perspective on Massive MIMO Quantization

One of the basic aspects of Massive MIMO (MaMi) that is in the focus of current investigations is its potential of using low-cost and energy-efficient hardware. It is often claimed that MaMi will allow for using analog-to-digital converters(ADCs) with very low resolutions and that this will result in overall improvement of energy efficiency. In this contribution, we perform a parametric energy efficiency analysis of MaMi uplink for the entire base station receiver system with varyingADC resolutions. The analysis shows that, for a wide variety of system parameters, ADCs with intermediate bit resolutions (4 - 10 bits) are optimal in energy efficiency sense, and that using very low bit resolutions results in degradation of energy efficiency

Modified Forced Convergence Decoding of LDPC Codes with Optimized Decoder Parameters

Reducing the complexity of decoding algorithms for LDPC codes is an important prerequisite for their practical implementation. In this work we propose a reduction of computational complexity targeting the highly reliable codeword bits and show that this approach can be seamlessly merged with the forced convergence scheme. We also show how the minimum achievable complexity of the resulting scheme for given performance constraints can be found by solving a constrained optimization problem, and successfully apply a gradient-descent based stochastic approximation (SA) method for solving this problem. The proposed methods are tested on LDPC codes from the IEEE 802.11n standard. Computational complexity reduction of 55% and a 75% reduction of memory access have been observed

Decentralized Massive MIMO Processing Exploring Daisy-chain Architecture and Recursive Algorithms

Algorithms for Massive MIMO uplink detection and downlink precoding typically rely on a centralized approach, by which baseband data from all antenna modules are routed to a central node in order to be processed. In the case of Massive MIMO, where hundreds or thousands of antennas are expected in the base-station, said routing becomes a bottleneck since interconnection throughput is limited. This paper presents a fully decentralized architecture and an algorithm for Massive MIMO uplink detection and downlink precoding based on the Stochastic Gradient Descent (SGD) method, which does not require a central node for these tasks. Through a recursive approach and very low complexity operations, the proposed algorithm provides a good trade-off between performance, interconnection throughput and latency. Further, our proposed solution achieves significantly lower interconnection data-rate than other architectures, enabling future scalability.Comment: Manuscript accepted for publication in IEEE Transactions on Signal Processin

Impact of Relay Cooperation on the Performance of Large-scale Multipair Two-way Relay Networks

We consider a multipair two-way relay communication network, where pairs of user devices exchange information via a relay system. The communication between users employs time division duplex, with all users transmitting simultaneously to relays in one time slot and relays sending the processed information to all users in the next time slot. The relay system consists of a large number of single antenna units that can form groups. Within each group, relays exchange channel state information (CSI), signals received in the uplink and signals intended for downlink transmission. On the other hand, per-group CSI and uplink/downlink signals (data) are not exchanged between groups, which perform the data processing completely independently. Assuming that the groups perform zero-forcing in both uplink and downlink, we derive a lower bound for the ergodic sumrate of the described system as a function of the relay group size. By close observation of this lower bound, it is concluded that the sumrate is essentially independent of group size when the group size is much larger than the number of user pairs. This indicates that a very large group of cooperating relays can be substituted by a number of smaller groups, without incurring any significant performance reduction. Moreover, this result implies that relay cooperation is more efficient (in terms of resources spent on cooperation) when several smaller relay groups are used in contrast to a single, large group.Comment: Accepted to Globecom 2018. Copyright 2018 IEE

Towards versatile access networks (Chapter 3)

Compared to its previous generations, the 5th generation (5G) cellular network features an additional type of densification, i.e., a large number of active antennas per access point (AP) can be deployed. This technique is known as massive multipleinput multiple-output (mMIMO) [1]. Meanwhile, multiple-input multiple-output (MIMO) evolution, e.g., in channel state information (CSI) enhancement, and also on the study of a larger number of orthogonal demodulation reference signal (DMRS) ports for MU-MIMO, was one of the Release 18 of 3rd generation partnership project (3GPP Rel-18) work item. This release (3GPP Rel-18) package approval, in the fourth quarter of 2021, marked the start of the 5G Advanced evolution in 3GPP. The other items in 3GPP Rel-18 are to study and add functionality in the areas of network energy savings, coverage, mobility support, multicast broadcast services, and positionin

Reducing the Complexity of LDPC Decoding Algorithms: An Optimization-Oriented Approach

This paper presents a structured optimization framework for reducing the computational complexity of LDPC decoders. Subject to specified performance constraints and adaptive to environment conditions, the proposed framework leverages the adjustable performance-complexity tradeoffs of the decoder to deliver satisfying performance with minimum computational complexity. More specifically, two constraint scenarios are studied: the “good-enough” performance and “as good- as-possible performance”. Moreover, we also investigate the effects of different degrees of freedom in performance-complexity tradeoff adjustments. The effectiveness of the proposed method has been verified by simulating a set of LDPC codes used in IEEE 802.11 and IEEE 802.16 standards. Computational complexity reductions of up to 35% have been observed

When Are Low Resolution ADCs Energy Efficient in Massive MIMO?

Massive MIMO (MaMI) is often promoted as a technology that will enable the use of low-quality, cheap hardware. One particular component that has been in the focus of MaMI-related research is the analog-to-digital converter (ADC), and use of very low-resolution ADCs has been proposed. However, studies about whether this strategy is justified from an energy-efficiency point of view have largely been inconclusive. In this paper, we choose system setup and models that reflect the hardware implementation reality as close as possible and perform a parametric analysis of uplink energy efficiency as a function of ADC resolution. If antenna scaling and decrease of ADC resolution are considered independently, the energy efficiency is shown to be maximized at intermediate ADC resolutions, typically in the range of 4–8 bits. Moreover, optimal ADC resolution does not decrease when more antennas are used except in some specific cases, and when it does, the decrease is approximately logarithmic in the number of antennas. In the case when antenna scaling and ADC degradation are coupled through a constant-performance constraint, it is shown that energy efficiency cannot improve with reduced bit resolution unless the power consumption of blocks other than ADCs scales down with the upscaling of antennas at a fast enough rate. Altogether it is concluded that in MaMI, intermediate ADC resolutions are optimal in energy efficiency sense, and, except in some special cases, scaling up the antennas to very large numbers does not change this conclusion

Energy savings using wake-up receivers - An analysis of optimal designs

With increasing number of wirelessly connected devices having limited energy resources, energy efficient solutions become more and more important. Many studies have addressed schemes where low-power wake-up receivers are used to improve energy efficiency in networks with low traffic intensity (per node) and demands on availability. One such scheme is the duty-cycled wake-up receiver medium access (DCW-MAC) scheme, for which we have detailed understanding of achievable energy savings. In this paper we combine these with recent bounds on the characteristics of optimal low-power receiver designs. What we find is that network energy savings are always achievable with optimally designed wake-up receivers. Moreover, potential network energy savings increase as wake-up receiver power consumption decreases, despite the associated degradation in performance. The lowest network energy consumption, for this type of network, is reached for wake-up receivers pushed to the limit of functioning

Deep Convolutional Neural Networks for Massive MIMO Fingerprint-Based Positioning

This paper provides an initial investigation on the application of convolutional neural networks (CNNs) for fingerprint-based positioning using measured massive MIMO channels. When represented in appropriate domains, measured massive MIMO channels have a sparse structure which can be efficiently learned by CNNs for positioning purposes. We evaluate the positioning accuracy of state-of-the-art CNNs with channel fingerprints generated from a channel model with a rich clustered structure: the COST 2100 channel model. We find that moderately deep CNNs can achieve fractional-wavelength positioning accuracies, provided that an enough representative data set is available for training