Transceiver Architectures for Future Wireless Systems with Hardware Constraints

QC 20260327

Abstract

In the upcoming era of communication systems, there is an anticipated shift towards using lower-grade hardware components to optimize size, cost, and power consumption. This shift is particularly beneficial for multiple-input multiple-output (MIMO) systems and Internet of Things devices, which require numerous components and extended battery lives. However, using lower-grade components introduces impairments, including various non-linear and time-varying distortions affecting communication signals. Traditionally, these impairments have been treated as additional noise due to the lack of a rigorous theory. This thesis explores a new perspective on how the structure of impairments can be exploited to optimize communication performance. To address these challenges, this thesis presents impairments-aware beamforming in various scenarios. 

Initially, we investigate the systems with limited fronthaul capacity. We propose an optimized linear precoding for advanced antenna systems (AAS) operating at a 5G base station (BS) within the constraints of a limited fronthaul capacity, modeled by a quantizer. The proposed novel precoding minimizes the mean-squared error (MSE) at the receiver side using a sphere decoding (SD) approach. 

After analyzing MSE minimization, a new linear precoding design is proposed to maximize the sum rate of the same system in the second part of this thesis. The latter problem is solved by a novel iterative algorithm inspired by the classical weighted minimum mean square error (WMMSE) approach. Additionally, a quantization-aware low-complexity algorithm expectation propagation (EP) is presented for large massive MIMO setups, which is more practical for nowadays systems. Besides, the heuristic quantization-aware precoding method with lower computational complexity is presented, showing that it outperforms the quantization-unaware baseline. This baseline is an optimized infinite-resolution precoding, which is then quantized. This study reveals that it is possible to double the sum rate at high SNR by selecting weights and precoding matrices that are quantization-aware. 

Next, we adopt a splitting precoding architecture tailored to fronthaul-constrained systems for practical deployments. In modern systems, the AAS can perform part of the beamforming locally, for example, through beam-space selection. The remaining lower-dimensional interference-cancelation precoder can then be transmitted over the limited-capacity fronthaul link. Compared to the previous fully centralized setup under the same fronthaul constraint, this approach enables higher quantization resolution for the precoder coefficients. Moreover, since both the uplink pilot signals used for channel estimation and the downlink precoding matrix must be transmitted over the limited-capacity fronthaul link, we design a joint uplink–downlink bit allocation scheme to determine the optimal distribution of fronthaul resources between the two directions.

In the final part of this thesis, we focus on the signaling problem in mobile millimeter-wave (mmWave) communication. The challenge of mmWave systems is the rapid fading variations and extensive pilot signaling. We explore the frequency of updating the combining matrix in a wideband mmWave point-to-point MIMO under user equipment (UE) mobility. The concept of beam coherence time is introduced to quantify the frequency at which the UE must update its downlink receive combining matrix. The study demonstrates that the beam coherence time can be even hundreds of times larger than the channel coherence time of small-scale fading. Simulations validate that the proposed lower bound on this defined concept guarantees no more than 50 \% loss of received signal gain (SG). Based on these results, beam-coherence-aware two-stage digital combining is proposed for the mmWave single-user point-to-point MIMO and multi-user MIMO systems. We also propose time-domain channel estimation.

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