Butler-Matrix Beamspace Front-Ends for Massive MIMO: Architecture, Loss Budget, and Capacity Impact

Massive Multiple-Input Multiple-Output (MIMO) systems with hundreds or thousands of antenna elements are fundamental to next-generation wireless networks, promising unprecedented spectral efficiency through spatial multiplexing and beamforming. However, the computational burden of channel state information (CSI) acquisition and processing scales dramatically with array size, creating a critical bottleneck for practical deployments. While previous works demonstrated that Fast Fourier Transform (FFT)-based beamspace processing can exploit the inherent angular sparsity of wireless channels to compress CSI feedback, the digital implementation requires intensive computations that become prohibitive for ultra-large arrays. This paper presents an analog alternative using Butler matrices—passive beamforming networks that realize the Discrete Fourier Transform in hardware—combined with RF switching circuits to select only dominant angular components. We provide a comprehensive analysis of Butler matrix architectures for arrays up to 32 × 32 elements, characterizing insertion losses across different technologies (microstrip, substrate-integrated waveguide, and waveguide) and operating frequencies (10–30 GHz). The proposed system incorporates parallel power sensing with Winner-Take-All circuits for sub-microsecond beam selection, drastically reducing the number of active RF chains. Full-wave simulations and capacity evaluations at 12 and 30 GHz demonstrate that the Butler-based approach achieves comparable performance to FFT methods while offering significant advantages in power consumption and processing latency. For a 256 × 256 array, FFT computation requires 0.36 ms compared to near-instantaneous analog processing, making Butler matrices particularly attractive for real-time massive MIMO systems. These findings establish Butler matrix front-ends as a practical pathway toward scalable, energy-efficient beamspace processing in 6G networks.