Abstract
Open Radio Access Networks (Open RANs), realized fully in software, require excessive computing resources to support time-sensitive signal-processing algorithms in the physical layer. Among them, multiple-input-multiple-output (MIMO) processing is a key functionality used to drive higher connectivity in the uplink, but it is computationally intensive, triggering the need for hardware acceleration to overcome the processing inefficiency of software-based solutions. Additionally, energy efficiency is becoming a key focus in Open RAN to enable sustainable deployments that utilize available resources efficiently. Because channel-inversion complexity increases polynomially with the number of users in linear detectors, such as zero-forcing (ZF) and minimum-mean-square-error (MMSE), acceleration based on channel-inverse approximations has gained significant attention. However, they unnecessarily multiply the number of base station (BS) antennas to ensure accurate detection, leading to a drastic increase in power consumption owing to the additional radio frequency (RF) chains employed. In contrast, linear detectors achieve a sufficiently good performance with only twice the number of BS antennas as users. This work introduces an exact-MMSE and soft-output hardware accelerator that includes an inversion-free, highly-parallel QR decomposition (QRD) architecture and a low-complexity detector stage with per-cycle soft-output generation, significantly improving the processing latency and throughput. The proposed architecture is fully scalable to support diverse MIMO configurations. Implementation evaluations on a Xilinx Virtex Ultrascale+ field-programmable gate array (FPGA) demonstrate that the proposed exact solution can achieve more than 2x improvement in hardware throughput over existing approximate designs. Moreover, the peak throughput can be increased around 10-fold in slowly fading channels.