Analog Approximate-FFT 8/16-Beam Algorithms, Architectures and CMOS Circuits for 5G Beamforming MIMO Transceivers

Abstract

Emerging millimeter-wave (mmW) wireless systems require beamforming and multiple-input multipleoutput (MIMO) approaches in order to mitigate path loss, obstructions, and attenuation of the communication channel. Sharp mmW beams are essential for this purpose and must support baseband bandwidths of at least 1 GHz to facilitate higher system capacity. This paper explores a baseband multibeamforming method based on the spatial Fourier transform. Approximate computing techniques are used to propose a lowcomplexity fast algorithm with sparse factorizations that neatly map to integer W/L ratios in CMOS current mirrors. The resulting approximate fast Fourier transform (FFT) can thus be efficiently realized using CMOS analog integrated circuits to generate multiple, parallel mmW beams in both transmit and receive modes. The paper proposes both 8-and 16-point approximate-FFT algorithms together with circuit theory and design information for 65-nm CMOS implementations. Postlayout simulations of the 8-point circuit in Cadence Spectre provide well-defined mmW beam shapes, a baseband bandwidth of 2.7 GHz, a power consumption of 70 mW, and a dynamic range >42.2 dB. Preliminary experimental results confirm the basic functionality of the 8-beam circuit. Schematic-level analysis of the 16-beam I/Q version show worst-case and average side lobe levels of −10.2 dB and −12.2 dB at 1 GHz bandwidth, and −9.1 dB and −11.3 dB at 1.5 GHz bandwidth. The proposed multibeam architectures have the potential to reduce circuit area and power requirements while meeting the bandwidth requirements of emerging 5G baseband systems.

Wireless Communications and Applications Above 100 GHz: Opportunities and Challenges for 6G and Beyond

“…These computations can be efficiently performed using a spatial Fast Fourier transform (FFT), which can be implemented using both analog as well as digital approaches [88] and reduces the brute force computational complexity of the DFT from O(N 2 ) down to O(N log N ). Moreover, approximate versions of the DFT can be computed with even lower complexity than the FFT, as shown by Cintra, Bayer, and their collaborators [89]- [93]. The Cintra-Bayer approach to approximate-DFT computation allows significantly smaller arithmetic complexity over FFT-based approaches (in some cases, reducing the computation burden by an order of magnitude for large N ) at the cost of only ∼2 dB penalty in worst-case sidelobe level [89]- [93].…”

“…Moreover, approximate versions of the DFT can be computed with even lower complexity than the FFT, as shown by Cintra, Bayer, and their collaborators [89]- [93]. The Cintra-Bayer approach to approximate-DFT computation allows significantly smaller arithmetic complexity over FFT-based approaches (in some cases, reducing the computation burden by an order of magnitude for large N ) at the cost of only ∼2 dB penalty in worst-case sidelobe level [89]- [93]. Note that while today's mobile devices do not concern themselves with the power savings due to FFT computations (because fidelity of the modulation is vital for bit error rate), the same may not be true for future wireless devices at mm-wave and THz.…”

“…Note that while today's mobile devices do not concern themselves with the power savings due to FFT computations (because fidelity of the modulation is vital for bit error rate), the same may not be true for future wireless devices at mm-wave and THz. In fact, the availability of massive channel bandwidths and likely increase in sensitivity to power consumption (e.g., prolonged battery life) may remove the present-day need for perfect fidelity in DFT/FFT processing and making approximate versions attractive [93].…”

Beamformer development challenges for 5G and beyond

“…In this study, the importance of beam misalignment due to beamwidth reduction and its impact on the power loss is also shown. It is also shown that the signal bandwidth of around 1 GHz is required for fixed beamformer to be able to match the theoretical capacity [99].…”

Fast Radix-32 Approximate DFTs for 1024-Beam Digital RF Beamforming

“…In particular, the adoption of approximate DFT (ADFT) computations opens up new possibilities for fast algorithms which do not compute the DFT in the strictest mathematical sense, but nevertheless can be good enough for digital multi-beam RF beamforming applications, particularly at mmwave frequencies and above, where reproducibility of antenna patterns become more problematic. Because ADFT applications are able to realize much greater efficiencies than the theoretical lower bound N for an N -point DFT proposed in [15], ADFT computations allow greater reductions in computational complexity than traditional FFTs, albeit at the cost of a deterministic loss in performance, namely a small increase in worst-case side-lobe level [16].…”