SVM-Based signal detection for low-resolution quantized systems

Abstract

The rapid evolution of wireless communication has led to the advent of next-generation networks, driven by the need for ultra-reliable low-latency communications (URLLC), and massive machine type communications (mMTC). With the future sixth-generation (6G), researchers are trying to move beyond millimeter wave (mmWave) into subTHz bands, [1] to enable the Ultra Massive MIMO (UM-MIMO) systems. Though these emerging technologies have significantly enhanced spectral efficiency and network capacity, they present challenges in power consumption due to the larger number of antennas, each equipped with dedicated analog-to-digital converters (ADCs). The power consumption of ADCs grows exponentially with resolution and linearly with sampling rate [2], resulting in unsustainable power demands. For instance, a system with 256 RF chains can consume over 250 W in ADC power alone, which is impractical for energy-constrained environments. To mitigate this challenge, low-resolution ADCs have emerged, while significantly reducing power consumption by limiting the number of quantization bits [3]. However, this low-resolution ADCs introduces nonlinear distortions, severely impacting receiver performance. Traditional signal processing techniques, which assume a linear input-output relationship, struggle to mitigate these distortions, necessitating novel approaches for effective detection and estimation. To this end, a maximum likelihood (ML) detector was derived [4] for the phase quantization in low-resolution ADCbased single-input single-output (SISO) systems, but it requires channel state information (CSI). Therefore, we are trying to apply Artificial intelligence (AI) to estimate the transmitted signal at the receiver without channel knowledge. Here, we applied support vector machine(SVM) based approach to detect the quantized signals at the receiver in a SISO system with n-bit ADC. It gives nearly optimum results when comparing to the ML detector in [4]. Also in the ML detector, an error in CSI causes a severe performance degradation in the system. Our method overcomes this issue by eliminating the need for CSI while achieving nearly optimal results. This work lays the foundation for AI-driven detection in complex systems like gigantic MIMO, where deriving an ML detector is infeasible and achieving accurate CSI through channel estimation is also impractical due to the presence of over two thousand antennas.