Error correction codes are essential for reliability and capacity in wireless networks. By correcting errors in real-time, they reduce re-transmissions, conserve bandwidth, and enhance network performance. However, these advantages come at the price of high decoding complexity and high latency which compels network designers to make sub-optimal deployment choices such as considering approximate decoding algorithms, limiting parallelism, bit-precision, and iteration counts, sacrificing the potential capacity and performance gains. Moreover, the ever-increasing user demand in wireless networks poses additional challenges in managing power consumption, operational costs, and the carbon footprint of base stations and terminals. This highlights the need for continued innovation in wireless network baseband architecture and implementation strategies.

This dissertation introduces quantum computing-based processing architectures for decoding error correction codes, offering new computational paradigms to address these challenges at scale. By harnessing the principles of quantum mechanics, we propose a transformative shift in the way decoding is achieved, benefiting wireless performance and capacity, through the design and implementation of the following systems: (1) QBP, quantum annealing decoder for LDPC codes, (2) HyPD, hybrid classical–quantum annealing decoder for Polar codes, (3) QGateD, quantum amplitude amplification decoder for generic XOR-based error correction codes, (4) FDeQ, quantum gate decoder flexible to both LDPC and Polar codes, (5) QAVP, quantum annealing approach to vector perturbation precoding (a multi-user MIMO downlink baseband optimization problem). These systems collectively fall under the thesis that quantum computing is a promising approach for baseband processing, warranting further justification from an economic and environmental impact perspective. To address this and to make the case for quantum computing in wireless industry, (6) the dissertation presents a comprehensive cost and carbon footprint analysis of quantum hardware, both quantitatively and qualitatively. This may be of potential interest to NextG wireless networks and quantum architectures.

In order to meet mobile cellular users’ ever-increasing data demands, today’s 4G and 5G networks are designed mainly with the goal of maximizing spectral efficiency. While they have made progress in this regard, controlling the carbon footprint and operational costs of such networks remains a long-standing problem among network designers. This paper takes a long view on this problem, envisioning a NextG scenario where the network leverages quantum annealing for cellular baseband processing. We gather and synthesize insights on power consumption, computational throughput and latency, spectral efficiency, operational cost, and feasibility timelines surrounding quantum annealing technology. Armed with these data, we analyze and project the quantitative performance targets future quantum annealing hardware must meet in order to provide a computational and power advantage over CMOS hardware, while matching its whole-network spectral efficiency. Our quantitative analysis predicts that with quantum annealing hardware operating at a 82.32 μs problem latency and 2.68M qubits, quantum annealing will achieve a spectral efficiency equal to CMOS computation while reducing power consumption by 41 kW (45% lower) in a 5G base station scenario with 400 MHz bandwidth and 64 antennas, and a 160 kW power reduction (55% lower) using 8.04M qubits in a C-RAN setting with three 5G base stations.

Tomorrow's massive-scale IoT sensor networks are poised to drive uplink traffic demand, especially in areas of dense deployment. To meet this demand, however, network designers leverage tools that often require accurate estimates of Channel State Information (CSI), which incurs a high overhead and thus reduces network throughput. Furthermore, the overhead generally scales with the number of clients, and so is of special concern in such massive IoT sensor networks. While prior work has used transmissions over one frequency band to predict the channel of another frequency band on the same link, this paper takes the next step in the effort to reduce CSI overhead: predict the CSI of a nearby but distinct link. We propose Cross-Link Channel Prediction (CLCP), a technique that leverages multi-view representation learning to predict the channel response of a large number of users, thereby reducing channel estimation overhead further than previously possible. CLCP's design is highly practical, exploiting existing transmissions rather than dedicated channel sounding or extra pilot signals. We have implemented CLCP for two different Wi-Fi versions, namely 802.11n and 802.11ax, the latter being the leading candidate for future IoT networks. We evaluate CLCP in two large-scale indoor scenarios involving both line-of-sight and non-line-of-sight transmissions with up to 144 different 802.11ax users and four different channel bandwidths, from 20 MHz up to 160 MHz. Our results show that CLCP provides a 2× throughput gain over baseline and a 30% throughput gain over existing prediction algorithms.

This paper presents SoundSticker, a system for steganographic, in-band data communication over an acoustic channel. In contrast with recent works that hide bits in inaudible frequency bands, SoundSticker embeds hidden bits in the audible sounds, making them more reliably survive audio codecs and bandpass filtering, while achieving a higher data rate and remaining imperceptible to a listener. The key observation behind SoundSticker is that the human ear is less sensitive to the audio phase changes than the frequency and amplitude changes, which leaves us an opportunity to alter the phase of an audio clip to convey hidden information. We take advantage of this opportunity and build an OFDM-based physical layer. To make this PHY-layer design work for a variety of end devices with heterogeneous computation resources, SoundSticker addresses multiple technical challenges including perceivable waveform artifacts caused by the phase-based modulation, bit rate adaptation without channel sounding and real-time preamble detection. Our prototype on both smartphones and ESP32 platforms demonstrates SoundSticker’s superior performance against the state of the arts, while preserving excellent sound quality and remaining unaffected by common audio codecs like MP3 and AAC. Audio clips produced by SoundSticker can be found at https://soundsticker.github.io/.

Exploiting (near-)optimal MIMO signal processing algorithms in the next generation (NextG) cellular systems holds great promise in achieving significant wireless performance gains in spectral efficiency and device connectivity, to name a few. However, it is extremely difficult to enable optimal processing methods in the systems, since the required computational amount increases exponentially with more users and higher data rates, while available processing time is strictly limited. In this regard, quantum signal processing has been recently identified as a promising potential enabler of the (near-)optimal algorithms in the systems, since quantum computing could dramatically speed up the computation via non-conventional effects based on quantum mechanics. Given existing quantum decoherence and noise on quantum hardware, parallel quantum optimization could accelerate the process even further at the expense of more qubit usage. In this paper, we discuss the parallelization of quantum MIMO processing and investigate a spin-level preprocessing method for relatively finer-grained decomposition that can support more flexible parallel quantum signal processing, compared to the recently reported symbol-level decomposition method. We evaluate the method on the state-of-the-art analog D-Wave Advantage quantum processor.