The principal investigators gratefully acknowledge support for this research initiative.

National Science Foundation

This material is based upon work supported, in part, by the National Science Foundation (NSF) under Grants:

Existing wireless systems are designed to support human-generated traffic, which is predominantly a monolithic class with a few dominating applications such as voice telephony, Internet browsing, and video streaming. The success of current cellular systems is in large part due to the design of an efficient connection-based downlink for transferring data from the base station to the user equipment. This has been facilitated by the fact that we have not placed stringent demands on the quality of service. Indeed, design metrics have centered around peak/average downlink data rates and aggregate data throughput rather than on strong statistical guarantees for individual users or resiliency. Two factors are likely to upend the status quo in the design philosophy of wireless systems: the changing profile of traffic and the focus on resilience rather than on average performance measures. Growth in wireless traffic will come from distributed content creators, a massive number of unattended machines enabling distributed learning, mobile robots, and video monitoring devices. These devices will demand a substantially robust and efficient uplink from the user equipment to the base station. In addition, these devices tend to generate a rich diversity of traffic profiles and demand profiles, thereby creating the need for adaptable and resilient infrastructures. With this in mind, this project addresses the need to optimize and improve the robustness of uplink wireless access for heterogeneous devices. From a broad perspective, the project seeks to strengthen wireless network infrastructure. It provides opportunities for collaboration with industry for technology transfer to wireless standards. Concurrently, the project seeks to create educational materials that contribute to the training of a globally competitive science, technology, engineering, and mathematics (STEM) workforce and offers skill-development opportunities to practitioners in the wireless industry.

From a technical viewpoint, the project considers the paradigm of cell-free multiple input multiple output (MIMO) systems with distributed signal processing as a solution to improve resiliency of cellular systems. It explores unsourced multiple access as a means to reduce coordination at the physical and medium access control layers, thereby enabling the operation of an efficient uplink within the cell-free paradigm. It seeks innovative solutions to several key problems that must be addressed in order to design a robust uplink based on cell-free unsourced random access with multiple transmit and receive antennas, especially to enable distributed learning. The problems tackled in this project can be grouped into three interrelated categories: (i) the design of codes and receiver signal processing algorithms for uncoordinated, non-orthogonal multiple access in cell-free MIMO systems; (ii) the design of novel joint compression, coding and multiple access algorithms for federated learning and edge computing; and (iii) the joint design of radio-frequency (RF) front ends and baseband signal processing for mitigating non-linearities that result from multiband receivers that are emblematic of future devices. The proposed solution methodologies are based on promising recent results leveraging connections between multiple access and sparse recovery, over the air federated learning, new RF front end designs based on machine learning, and combining model-based signal processing and machine learning. Some aspects of the solution methodologies are also based on classical and highly-effective results in multi-terminal information theory and rateless codes, but used in the context of distributed learning.

Low-Complexity Algorithms for Unsourced Multiple Access and Compressed Sensing in Large Dimensions

Wireless traffic is increasingly heterogeneous, with growth coming primarily from unattended devices. While early implementations of wireless communication systems have focused on voice telephony, subsequent generations of cellular infrastructures have enabled users to connect more broadly with the Internet, in support of applications such as gaming, browsing, and video watching. Looking into the future, unattended devices are predicted to grow rapidly and to generate a significant portion of the wireless data traffic. This evolution represents a formidable challenge for current infrastructures because such devices interact with the Internet in fundamentally different ways than humans. Individuals tend to establish sustained connections through their phones or computers, whereas machines often sporadically transmit status updates or control decisions with very short payloads. Without a fundamental redesign of the medium access control layer, wireless infrastructures will be unable to efficiently carry machine-type traffic, thereby creating a bottleneck for growth and innovation. The main goal of this research effort is to devise pragmatic random access schemes for machine-type data, with an eye towards addressing the aforementioned issues associated with the digital traffic of tomorrow. Findings from this project are expected to (i) help strengthen digital infrastructures, by now unanimously recognized as a key driver of the economy; (ii) train competent engineers with skills attuned to societal needs; and (iii) broaden participation in science, technology, engineering, and mathematics through recruiting and mentoring.

Close connections will be exploited between multiple-access communication, compressed sensing, and sparse graph inference. The crucial challenges and main innovations arise from the exceedingly large dimensionality of the engineering problems considered, compared to the state-of-the-art. The envisioned structures and algorithms for performing at such scales are rooted in the divide-and-conquer approaches of stochastic binning and splitting data. Techniques from graph-based codes to modern iterative methods and interference management are expected to play important roles in pushing the boundaries of unsourced random access and inference in large dimensions. The fundamental limits of complexity-constrained algorithms in wireless communications will be characterized by leveraging recently developed tools from finite-block-length information theory, statistical physics, and applied probability. Key attributes of the proposed models include uncoordinated access and the ability to operate without explicitly acquiring device identities. This departure from established schemes is crucial for eliminating a reliance on individualized feedback, which has enabled fast connections in the past but would now become cost-prohibitive as a mechanism for machine-type traffic. Likely outcomes for this project include near-optimum, low-complexity schemes for the next-generation of random access wireless systems, which will be broadly applicable to deal with inference in exceedingly large dimensions.

Massive Uncoordinated and Sporadic Multiple Access - Strengthening Connections between Coding and Random Access

The wireless landscape is poised to change, once again, within the next few years due to the emergence of machine-driven communications. This creates new challenges for wireless traffic, with packets originating from sporadic transmissions rather than sustained connections. Currently deployed scheduling policies are ill-equipped to deal with such traffic because they rely on gathering information about channel quality and queue length for every active device. The goal of this research initiative is to address this deficiency and devise novel access schemes tailored to massive uncoordinated and sporadic multiple access, thereby readying wireless infrastructures for the traffic of tomorrow. The broader impacts of this research program include providing pragmatic solutions to some of the challenges posed by an evolving wireless landscape, strengthening wireless infrastructures, and contributing to the training of a globally competitive Science, Technology, Engineering and Math workforce. The research tasks are attuned to societal needs in information technologies, an important economic driver for our nation. The wide dissemination of the findings will enhance the scientific understanding of wireless systems, access strategies, and iterative methods.

The intellectual merit of this research initiative lies in exploiting the close connections between message-passing decoding and serial interference cancellation to create new access strategies. Linking advances in iterative methods to uncoordinated random access embodies the type of crosscutting research that can lead to disruptive technologies and paradigm shifts. This project embraces the evolving perspective of harnessing interference in wireless networks rather than fighting it or avoiding it. This viewpoint underlies many recent successes in network coding and distributed storage. This project brings forth such a perspective in the design of large-scale wireless networks.

Qualcomm Technologies, Inc.

This material is also based upon work supported, in part, by Qualcomm Technologies, Inc. through their University Relations Program.