George Ntavazlis Katsaros

Neuromorphic computing has so far been driven predominantly by machine-learning workloads, yet its underlying properties also make it particularly well suited to combinatorial optimization problems expressed in Ising or QUBO form. While neuromorphic Ising solvers have been demonstrated, how a given problem should be formulated to best suit neuromorphic dynamics has received far less attention. Maximum-likelihood (ML) channel decoding can be expressed as an Ising/QUBO problem, and two distinct formulations already exist in the quantum-annealing literature: a squared-penalty formulation that uses few spins but produces dense intra-check couplings, and a chain-product formulation that improves locality at the cost of additional auxiliary spins. Both place the ML codeword at the ground state under sufficient constraint enforcement, but they have not been compared under the constraints that neuromorphic hardware imposes. This work provides the first systematic side-by-side comparison of QUBO/Ising formulations of ML decoding for linear codes. We show that the two formulations impose fundamentally different tradeoffs in neuron count, synaptic density, locality, and convergence behavior. The preferred formulation is inseparable from the choice of solver, and the two must be considered jointly. Finally, we show that ground-state correctness alone is an insufficient design criterion, and that signal processing tasks should ideally be co-formulated with their neuromorphic hardware models if neuromorphic computing is to extend into the receiver pipeline.

This demo showcases how nonlinear processing can enable massive, scalable connectivity, demonstrating that even with a single antenna access point, we can support multiple concurrently transmitted information streams at the same time-frequency resources.

Multi-user multiple-input, multiple-output (MU-MIMO) designs can substantially increase the achievable throughput and connectivity capabilities of wireless systems. However, existing MU-MIMO deployments typically employ linear processing that, despite its practical benefits, can leave capacity and connectivity gains unexploited. On the other hand, traditional non-linear processing solutions (e.g., sphere decoders) promise improved throughput and connectivity capabilities, but can be impractical in terms of processing complexity and latency, and with questionable practical benefits that have not been validated in actual system realizations. At the same time, emerging new Open Radio Access Network (Open-RAN) designs call for physical layer (PHY) processing solutions that are also practical in terms of realization, even when implemented purely on software. This work demonstrates the gains that our highly efficient, massively parallelizable, non-linear processing (MPNL) framework can provide, both in the uplink and downlink, when running in real-time and over-the-air, using our new 5G-New Radio (5G-NR) and Open-RAN compliant, software-based PHY. We showcase that our MPNL framework can provide substantial throughput and connectivity gains, compared to traditional, linear approaches, including increased throughput, the ability to halve the number of base-station antennas without any performance loss compared to linear approaches, as well as the ability to support a much larger number of users than base-station antennas, without the need for any traditional Non-Orthogonal Multiple Access (NOMA) techniques, and with overloading factors that can be up to 300%.