SpikeCSI: CSI Feedback Compression for MIMO Wireless Systems using Spiking Neural Networks

Multi-input, multi-output (MIMO) systems are key for next generation wireless networks to increase capacity without widening the operational bandwidth. The higher the number of antennas, the higher the capacity of the wireless system. However, this requires increasing the amount of control data – the channel state information (CSI) – that the receiver should feed back to the transmitter to properly precode data streams. In this paper, we propose SpikeCSI, a new approach for CSI compression and reconstruction that relies on spiking neural networks (SNNs) to reduce the computational complexity of the procedure. By operating through spikes rather than real numbers, SpikeCSI allows reducing the energy consumption required for MIMO channel sounding. We implement SpikeCSI and compare its performance with state-of-the-art approaches using synthetic data obtained through ray tracing simulations and real data collected through a software-defined radio (SDR)-based mmWave testbed. The results show that SpikeCSI approaches and, in some configurations, even outperform traditional methods while reducing neuron activity in the SNN compared to standard artificial neural networks (ANNs) which rely on dense processing.