Beamspace processing is an emerging paradigm to reduce hardware complexity in all-digital millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) basestations. This approach exploits sparsity of mmWave channels but requires spatial discrete Fourier transforms (DFTs) across the antenna array, which must be performed at the baseband sampling rate. To mitigate the resulting DFT hardware implementation bottleneck, we propose a fully-unrolled Streaming MUltiplierLess (SMUL) fast Fourier Transform (FFT) engine that performs one transform per clock cycle. The proposed SMUL-FFT architecture avoids hardware multipliers by restricting the twiddle factors to a sum-of-powers-of-two, resulting in substantial power and area savings. Compared to state-ofthe-art FFTs, our SMUL-FFT ASIC designs in 65 nm CMOS demonstrate more than 45% and 17% improvements in energyefficiency and area-efficiency, respectively, without noticeably increasing the error-rate in mmWave massive MIMO systems. I. INTRODUCTIONMillimeter-wave (mmWave) communication [1], [2] promises significantly increased data-rates due to the availability of large contiguous frequency bands. Massive multiuser multipleinput multiple-output (MU-MIMO) [3] is a key technology to combat the high path loss of mmWave propagation [2] while enabling simultaneous communication with multiple user equipments (UEs) in the same frequency band. The higher baseband sampling rates needed to support larger bandwidths at mmWave frequencies, combined with the large number of antennas in massive MU-MIMO, result in new challenges for analog and digital hardware design. A. Fast Fourier Transforms for Beamspace ProcessingMmWave channels typically comprise only a few dominant propagation paths [1], [2], making them sparse in the beamspace domain [4]- [9]. Beamspace processing exploits this sparsity to reduce the computational complexity of baseband processing [10]-[12]. This approach, which is described in detail in Section II-A, calls for spatial discrete Fourier transforms (DFTs) operated at the baseband sampling rate in order to convert the received signals at the antenna array into the beamspace domain-in high-bandwidth mmWave communication systems, billions of spatial DFTs must be computed per second.
Massive multi-user multiple-input multiple-output (MU-MIMO) wireless systems operating at millimeter-wave (mmWave) frequencies enable simultaneous wideband data transmission to a large number of users. In order to reduce the complexity of MU precoding in all-digital basestation architectures, we propose a two-stage precoding architecture that first performs precoding using a sparse matrix in the beamspace domain, followed by an inverse fast Fourier transform that converts the result to the antenna domain. The sparse precoding matrix requires a small number of multipliers and enables regular hardware architectures, which allows the design of hardwareefficient all-digital precoders. Simulation results demonstrate that our methods approach the error-rate of conventional Wiener filter precoding with more than 2ˆreduced complexity.
Orthogonal frequency-division multiplexing (OFDM) time-domain signals exhibit high peak-to-average (power) ratio (PAR), which requires linear radio-frequency chains to avoid an increase in error-vector magnitude (EVM) and out-of-band (OOB) emissions. In this paper, we propose a novel joint PAR reduction and precoding algorithm that relaxes these linearity requirements in massive multiuser (MU) multiple-input multipleoutput (MIMO) wireless systems. Concretely, we develop a novel alternating projections method, which limits the PAR and transmit power increase while simultaneously suppressing MU interference. We provide a theoretical foundation of our algorithm and provide simulation results for a massive MU-MIMO-OFDM scenario. Our results demonstrate significant PAR reduction while limiting the transmit power, without causing EVM or OOB emissions.
Wireless communication systems that rely on orthogonal frequency-division multiplexing (OFDM) suffer from a high peak-to-average (power) ratio (PAR), which necessitates powerinefficient radio-frequency (RF) chains to avoid an increase in error-vector magnitude (EVM) and out-of-band (OOB) emissions. The situation is further aggravated in massive multiuser (MU) multiple-input multiple-output (MIMO) systems that would require hundreds of linear RF chains. In this paper, we present a novel approach to joint precoding and PAR reduction that builds upon a novel p − q -norm formulation, which is able to find minimum PAR solutions while suppressing MU interference. We provide a theoretical underpinning of our approach and provide simulation results for a massive MU-MIMO-OFDM system that demonstrate significant reductions in PAR at low complexity, without causing an increase in EVM or OOB emissions.
Recent channel state information (CSI)-based positioning pipelines rely on deep neural networks (DNNs) in order to learn a mapping from estimated CSI to position. Since real-world communication transceivers suffer from hardware impairments, CSI-based positioning systems typically rely on features that are designed by hand. In this paper, we propose a CSI-based positioning pipeline that directly takes raw CSI measurements and learns features using a structured DNN in order to generate probability maps describing the likelihood of the transmitter being at pre-defined grid points. To further improve the positioning accuracy of moving user equipments, we propose to fuse a time-series of learned CSI features or a timeseries of probability maps. To demonstrate the efficacy of our methods, we perform experiments with real-world indoor line-ofsight (LoS) and non-LoS channel measurements. We show that CSI feature learning and time-series fusion can reduce the mean distance error by up to 2.5× compared to the state-of-the-art. I. INTRODUCTIONThe need for low-cost but accurate positioning systems is driven by recent trends in virtual reality, asset tracking, robotics, and industrial automation [2], [3]. Existing outdoor positioning solutions mostly rely on global navigation satellite systems (GNSS) that provide meter-level accuracy but require line-of-sight (LoS) satellite connectivity. High-precision indoor positioning solutions typically require specialized hardware that uses visible or infra-red light to localize objects with either active IR transmitting markers [4] or passive reflectors [1]. Such systems require unobstructed views, are affected by sunlight and reflective surfaces, and are costly. A. CSI-Based Positioning with Neural NetworksLow-cost indoor positioning can be achieved with existing communication infrastructure that utilizes orthogonal frequency division multiplexing (OFDM) [5]. OFDM receivers must acquire channel state information (CSI) to suppress intersymbol interference caused by multi-path propagation [5].
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