We propose new low-fidelity (LoFi) user equipment (UE) scheduling algorithms for multiuser multiple-input multiple-output (MIMO) wireless communication systems. The proposed methods rely on an efficient guess-and-check procedure that, given an objective function, performs paired comparisons between random subsets of UEs that should be scheduled in certain time slots. The proposed LoFi scheduling methods are computationally efficient, highly parallelizable, and gradient-free, which enables the use of almost arbitrary, non-differentiable objective functions. System simulations in a millimeter-wave (mmWave) multiuser MIMO scenario demonstrate that the proposed LoFi schedulers outperform a range of state-of-the-art user scheduling algorithms in terms of bit error-rate and/or computational complexity.
* Accepted at the 2024 IEEE International Conference on Acoustics,
Speech, and Signal Processing (ICASSP)
Massive grant-free transmission and cell-free wireless communication systems have emerged as pivotal enablers for massive machine-type communication. This paper proposes a deep-unfolding-based joint activity and data detection (DU-JAD) algorithm for massive grant-free transmission in cell-free systems. We first formulate a joint activity and data detection optimization problem, which we solve approximately using forward-backward splitting (FBS). We then apply deep unfolding to FBS to optimize algorithm parameters using machine learning. In order to improve data detection (DD) performance, reduce algorithm complexity, and enhance active user detection (AUD), we employ a momentum strategy, an approximate posterior mean estimator, and a novel soft-output AUD module, respectively. Simulation results confirm the efficacy of DU-JAD for AUD and DD.
Beamforming is a powerful tool for physical layer security, as it can be used for steering signals towards legitimate receivers and away from eavesdroppers. An active eavesdropper, however, can interfere with the pilot phase that the transmitter needs to acquire the channel knowledge necessary for beamforming. By doing so, the eavesdropper can make the transmitter form beams towards the eavesdropper rather than towards the legitimate receiver. To mitigate active eavesdroppers, we propose VILLAIN, a novel channel estimator that uses secret pilots. When an eavesdropper interferes with the pilot phase, VILLAIN produces a channel estimate that is orthogonal to the eavesdropper's channel (in the noiseless case). We prove that beamforming based on this channel estimate delivers the highest possible signal power to the legitimate receiver without delivering any signal power to the eavesdropper. Simulations show that VILLAIN mitigates active eavesdroppers also in the noisy case.
* Accepted at the 2024 International Zurich Seminar on Information and
Channel charting (CC) applies dimensionality reduction to channel state information (CSI) data at the infrastructure basestation side with the goal of extracting pseudo-position information for each user. The self-supervised nature of CC enables predictive tasks that depend on user position without requiring any ground-truth position information. In this work, we focus on the practically relevant streaming CSI data scenario, in which CSI is constantly estimated. To deal with storage limitations, we develop a novel streaming CC architecture that maintains a small core CSI dataset from which the channel charts are learned. Curation of the core CSI dataset is achieved using a min-max-similarity criterion. Numerical validation with measured CSI data demonstrates that our method approaches the accuracy obtained from the complete CSI dataset while using only a fraction of CSI storage and avoiding catastrophic forgetting of old CSI data.
* Presented at the 2023 Asilomar Conference on Signals, Systems, and
Analog subtractive synthesizers are generally considered to provide superior sound quality compared to digital emulations. However, analog circuitry requires calibration and suffers from aging, temperature instability, and limited flexibility in generating a wide variety of waveforms. Digital synthesis can mitigate many of these drawbacks, but generating arbitrary aliasing-free waveforms remains challenging. In this paper, we present the +-synth, a hybrid digital-analog eight-voice polyphonic synthesizer prototype that combines the best of both worlds. At the heart of the synthesizer is the big Fourier oscillator (BFO), a novel digital very-large scale integration (VLSI) design that utilizes additive synthesis to generate a wide variety of aliasing-free waveforms. Each BFO produces two voices, using four oscillators per voice. A single oscillator can generate up to 1024 freely configurable partials (harmonic or inharmonic), which are calculated using coordinate rotation digital computers (CORDICs). The BFOs were fabricated as 65nm CMOS custom application-specific integrated circuits (ASICs), which are integrated in the +-synth to simultaneously generate up to 32768 partials. Four 24-bit 96kHz stereo DACs then convert the eight voices into the analog domain, followed by digitally controlled analog low-pass filtering and amplification. Measurement results of the +-synth prototype demonstrate high fidelity and low latency.
All-digital massive multiuser (MU) multiple-input multiple-output (MIMO) at millimeter-wave (mmWave) frequencies is a promising technology for next-generation wireless systems. Low-resolution analog-to-digital converters (ADCs) can be utilized to reduce the power consumption of all-digital basestation (BS) designs. However, simultaneously transmitting user equipments (UEs) with vastly different BS-side receive powers either drown weak UEs in quantization noise or saturate the ADCs. To address this issue, we propose high dynamic range (HDR) MIMO, a new paradigm that enables simultaneous reception of strong and weak UEs with low-resolution ADCs. HDR MIMO combines an adaptive analog spatial transform with digital equalization: The spatial transform focuses strong UEs on a subset of ADCs in order to mitigate quantization and saturation artifacts; digital equalization is then used for data detection. We demonstrate the efficacy of HDR MIMO in a massive MU-MIMO mmWave scenario that uses Householder reflections as spatial transform.
* To be presented at Asilomar Conference on Signals, Systems, and
Recent advances in electronic and photonic technologies have allowed efficient signal generation and transmission at terahertz (THz) frequencies. However, as the gap in THz-operating devices narrows, the demand for terabit-per-second (Tbps)-achieving circuits is increasing. Translating the available hundreds of gigahertz (GHz) of bandwidth into a Tbps data rate requires processing thousands of information bits per clock cycle at state-of-the-art clock frequencies of digital baseband processing circuitry of a few GHz. This paper addresses these constraints and emphasizes the importance of parallelization in signal processing, particularly for channel code decoding. By leveraging structured sub-spaces of THz channels, we propose mapping bits to transmission resources using shorter code words, extending parallelizability across all baseband processing blocks. THz channels exhibit quasi-deterministic frequency, time, and space structures that enable efficient parallel bit mapping at the source and provide pseudo-soft bit reliability information for efficient detection and decoding at the receiver.
In multiple-input multiple-output (MIMO) wireless systems with frequency-flat channels, a single-antenna jammer causes receive interference that is confined to a one-dimensional subspace. Such a jammer can thus be nulled using linear spatial filtering at the cost of one degree of freedom. Frequency-selective channels are often transformed into multiple frequency-flat subcarriers with orthogonal frequency-division multiplexing (OFDM). We show that when a single-antenna jammer violates the OFDM protocol by not sending a cyclic prefix, the interference received on each subcarrier by a multi-antenna receiver is, in general, not confined to a subspace of dimension one (as a single-antenna jammer in a frequency-flat scenario would be), but of dimension L, where L is the jammer's number of channel taps. In MIMO-OFDM systems, a single-antenna jammer can therefore resemble an L-antenna jammer. Simulations corroborate our theoretical results. These findings imply that mitigating jammers with large delay spread through linear spatial filtering is infeasible. We discuss some (im)possibilities for the way forward.
Low-resolution analog-to-digital converters (ADCs) in massive multi-user (MU) multiple-input multiple-output (MIMO) wireless systems can significantly reduce the power, cost, and interconnect data rates of infrastructure basestations. Thus, recent research on the theory and algorithm sides has extensively focused on such architectures, but with idealistic quantization models. However, real-world ADCs do not behave like ideal quantizers, and are affected by fabrication mismatches. We analyze the impact of capacitor-array mismatches in successive approximation register (SAR) ADCs, which are widely used in wireless systems. We use Bussgang's decomposition to model the effects of such mismatches, and we analyze their impact on the performance of a single ADC. We then simulate a massive MU-MIMO system to demonstrate that capacitor mismatches should not be ignored, even in basestations that use low-resolution SAR ADCs.
* To be presented at Asilomar Conference on Signals, Systems, and
Cell-free communication has the potential to significantly improve grant-free transmission in massive machine-type communication, wherein multiple access points jointly serve a large number of user equipments to improve coverage and spectral efficiency. In this paper, we propose a novel framework for joint active user detection (AUD), channel estimation (CE), and data detection (DD) for massive grant-free transmission in cell-free systems. We formulate an optimization problem for joint AUD, CE, and DD by considering both the sparsity of the data matrix, which arises from intermittent user activity, and the sparsity of the effective channel matrix, which arises from intermittent user activity and large-scale fading. We approximately solve this optimization problem with a box-constrained forward-backward splitting algorithm, which significantly improves AUD, CE, and DD performance. We demonstrate the effectiveness of the proposed framework through simulation experiments.