A Deep-Unfolding-Optimized Coordinate-Descent Data-Detector ASIC for mmWave Massive MIMO

We present a 22 nm FD-SOI (fully depleted silicon-on-insulator) application-specific integrated circuit (ASIC) implementation of a novel soft-output Gram-domain block coordinate descent (GBCD) data detector for massive multi-user (MU) multiple-input multiple-output (MIMO) systems. The ASIC simultaneously addresses the high throughput requirements for millimeter wave (mmWave) communication, stringent area and power budget per subcarrier in an orthogonal frequency-division multiplexing (OFDM) system, and error-rate performance challenges posed by realistic mmWave channels. The proposed GBCD algorithm utilizes a posterior mean estimate (PME) denoiser and is optimized using deep unfolding, which results in superior error-rate performance even in scenarios with highly correlated channels or where the number of user equipment (UE) data streams is comparable to the number of basestation (BS) antennas. The fabricated GBCD ASIC supports up to 16 UEs transmitting QPSK to 256-QAM symbols to a 128-antenna BS, and achieves a peak throughput of 7.1 Gbps at 367 mW. The core area is only 0.97 mm$^2$ thanks to a reconfigurable array of processing elements that enables extensive resource sharing. Measurement results demonstrate that the proposed GBCD data-detector ASIC achieves best-in-class throughput and area efficiency.

A Software-Defined and Distributed Wi-Fi Channel-State Information Acquisition Testbed

We propose a software-defined testbed for Wi-Fi channel-state information (CSI) acquisition. This testbed features distributed software-defined radios (SDRs) and a custom IEEE 802.11a software stack that enables the passive collection of CSI data from commercial off-the-shelf (COTS) devices that connect to an existing Wi-Fi network. Unlike commodity Wi-Fi sniffers or channel sounders, our software-defined testbed enables a quick exploration of advanced CSI estimation algorithms in real-world scenarios from naturally-generated Wi-Fi traffic. We explore the effectiveness of two advanced algorithms that denoise CSI estimates, and we demonstrate that CSI-based positioning of COTS Wi-Fi devices with a multilayer perceptron is feasible in an indoor office/lab space in which people are moving.

Efficient and Physically-Consistent Modeling of Reconfigurable Electromagnetic Structures

Reconfigurable electromagnetic structures (REMSs), such as reconfigurable reflectarrays (RRAs) or reconfigurable intelligent surfaces (RISs), hold significant potential to improve wireless communication and sensing systems. Even though several REMS modeling approaches have been proposed in recent years, the literature lacks models that are both computationally efficient and physically consistent. As a result, algorithms that control the reconfigurable elements of REMSs (e.g., the phase shifts of an RIS) are often built on simplistic models that are inaccurate. To enable physically accurate REMS-parameter tuning, we present a new framework for efficient and physically consistent modeling of general REMSs. Our modeling method combines a circuit-theoretic approach with a new formalism that describes a REMS's interaction with the electromagnetic (EM) waves in its far-field region. Our modeling method enables efficient computation of the entire far-field radiation pattern for arbitrary configurations of the REMS reconfigurable elements once a single full-wave EM simulation of the non-reconfigurable parts of the REMS has been performed. The predictions made by the proposed framework align with the physical laws of classical electrodynamics and model effects caused by inter-antenna coupling, non-reciprocal materials, polarization, ohmic losses, matching losses, influence of metallic housings, noise from low-noise amplifiers, and noise arising in or received by antennas. In order to validate the efficiency and accuracy of our modeling approach, we (i) compare our modeling method to EM simulations and (ii) conduct a case study involving a planar RRA that enables simultaneous multiuser beam- and null-forming using a new, computationally efficient, and physically accurate parameter tuning algorithm.

Optimizing Puncturing Patterns of 5G NR LDPC Codes for Few-Iteration Decoding

Rate-matching of low-density parity-check (LDPC) codes enables a single code description to support a wide range of code lengths and rates. In 5G NR, rate matching is accomplished by extending (lifting) a base code to a desired target length and by puncturing (not transmitting) certain code bits. LDPC codes and rate matching are typically designed for the asymptotic performance limit with an ideal decoder. Practical LDPC decoders, however, carry out tens or fewer message-passing decoding iterations to achieve the target throughput and latency of modern wireless systems. We show that one can optimize LDPC code puncturing patterns for such few-iteration-constrained decoders using a method we call swapping of punctured and transmitted blocks (SPAT). Our simulation results show that SPAT yields from 0.20 dB up to 0.55 dB improved signal-to-noise ratio performance compared to the standard 5G NR LDPC code puncturing pattern for a wide range of code lengths and rates.

A 1.2 mm$^2$ 416 mW 1.44 Mmat/s 64$\times$16 Matrix Preprocessing ASIC for Massive MIMO in 22FDX

Massive multiuser (MU) multiple-input multiple-output (MIMO) enables concurrent transmission of multiple users to a multi-antenna basestation (BS). To detect the users' data using linear equalization, the BS must perform preprocessing, which requires, among other tasks, the inversion of a matrix whose dimension equals the number of user data streams. Explicit inversion of large matrices is notoriously difficult to implement due to high complexity, stringent data dependencies that lead to high latency, and high numerical precision requirements. We propose a novel preprocessing architecture based on the block-LDL matrix factorization, which improves parallelism and, hence, reduces latency. We demonstrate the effectiveness of our architecture through (i) massive MU-MIMO system simulations with mmWave channel vectors and (ii) measurements of a 22FDX ASIC, which is, to our knowledge, the first fabricated preprocessing engine for massive MU-MIMO with 64 BS antennas and 16 single-antenna users. Our ASIC reaches a clock frequency of 870 MHz while consuming 416 mW. At its peak throughput, the ASIC preprocesses 1.44 M 64$\times$16 matrices per second at a latency of only 0.7 $\mu$s.

Low-Coherence Sequence Design Under PAPR Constraints

Low-coherence sequences with low peak-to-average power ratio (PAPR) are crucial for multi-carrier wireless communication systems and are used for pilots, spreading sequences, and so on. This letter proposes an efficient low-coherence sequence design algorithm (LOCEDA) that can generate any number of sequences of any length that satisfy user-defined PAPR constraints while supporting flexible subcarrier assignments in orthogonal frequency-division multiple access (OFDMA) systems. We first visualize the low-coherence sequence design problem under PAPR constraints as resolving collisions between hyperspheres. By iteratively adjusting the radii and positions of these hyperspheres, we effectively generate low-coherence sequences that strictly satisfy the imposed PAPR constraints. Simulation results (i) confirm that LOCEDA outperforms existing methods, (ii) demonstrate its flexibility, and (iii) highlight its potential for various application scenarios.

PyJama: Differentiable Jamming and Anti-Jamming with NVIDIA Sionna

Despite extensive research on jamming attacks on wireless communication systems, the potential of machine learning for amplifying the threat of such attacks, or our ability to mitigate them, remains largely untapped. A key obstacle to such research has been the absence of a suitable framework. To resolve this obstacle, we release PyJama, a fully-differentiable open-source library that adds jamming and anti-jamming functionality to NVIDIA Sionna. We demonstrate the utility of PyJama (i) for realistic MIMO simulations by showing examples that involve forward error correction, OFDM waveforms in time and frequency, realistic channel models, and mobility; and (ii) for learning to jam. Specifically, we use stochastic gradient descent to optimize jamming power allocation over an OFDM resource grid. The learned strategies are non-trivial, intelligible, and effective.

A Jammer-Mitigating 267 Mb/s 3.78 mm$^2$ 583 mW 32$\times$8 Multi-User MIMO Receiver in 22FDX

We present the first multi-user (MU) multiple-input multiple-output (MIMO) receiver ASIC that mitigates jamming attacks. The ASIC implements a recent nonlinear algorithm that performs joint jammer mitigation (via spatial filtering) and data detection (using a box prior on the data symbols). Our design supports 8 user equipments (UEs) and 32 basestation (BS) antennas, QPSK and 16-QAM with soft-outputs, and enables the mitigation of single-antenna barrage jammers and smart jammers. The fabricated 22 nm FD-SOI ASIC includes preprocessing, has a core area of 3.78 mm$^2$, achieves a throughput of 267 Mb/s while consuming 583 mW, and is the only existing design that enables reliable data detection under jamming attacks.

EVM Analysis of Distributed Massive MIMO with 1-Bit Radio-Over-Fiber Fronthaul

We analyze the uplink performance of a distributed massive multiple-input multiple-output (MIMO) architecture in which the remotely located access points (APs) are connected to a central processing unit via a fiber-optical fronthaul carrying a dithered and 1-bit quantized version of the received radio-frequency (RF) signal. The innovative feature of the proposed architecture is that no down-conversion is performed at the APs. This eliminates the need to equip the APs with local oscillators, which may be difficult to synchronize. Under the assumption that a constraint is imposed on the amount of data that can be exchanged across the fiber-optical fronthaul, we investigate the tradeoff between spatial oversampling, defined in terms of the total number of APs, and temporal oversampling, defined in terms of the oversampling factor selected at the central processing unit, to facilitate the recovery of the transmitted signal from 1-bit samples of the RF received signal. Using the so-called error-vector magnitude (EVM) as performance metric, we shed light on the optimal design of the dither signal, and quantify, for a given number of APs, the minimum fronthaul rate required for our proposed distributed massive MIMO architecture to outperform a standard co-located massive MIMO architecture in terms of EVM.

Jammer-Resilient Time Synchronization in the MIMO Uplink

Spatial filtering based on multiple-input multiple-output (MIMO) processing is a promising approach to jammer mitigation. Effective MIMO data detectors that mitigate smart jammers have recently been proposed, but they all assume perfect time synchronization between transmitter(s) and receiver. However, to the best of our knowledge, there are no methods for resilient time synchronization in the presence of smart jammers. To remedy this situation, we propose JASS, the first method that enables reliable time synchronization for the single-user MIMO uplink while mitigating smart jamming attacks. JASS detects a randomized synchronization sequence based on a novel optimization problem that fits a spatial filter to the time-windowed receive signal in order to mitigate the jammer. We underscore the efficacy of the proposed optimization problem by proving that it ensures successful time synchronization under certain intuitive conditions. We then derive an efficient algorithm for approximately solving our optimization problem. Finally, we use simulations to demonstrate the effectiveness of JASS against a wide range of different jammer types.