Regularized Approximate Message Passing for Overloaded Discrete Linear Inversion

We propose regularized approximate message passing (RAMP), a low-complexity algorithm for discrete signal detection in overloaded multiple-input multiple-output (MIMO) systems where the number of transmit antennas exceeds the number of receive antennas. While the state-of-the-art (SotA) iterative discrete least squares (IDLS) framework achieves near-optimal discrete-aware performance, its iterative matrix inversions impose a prohibitive $\mathcal{O}(M^3)$ complexity. RAMP resolves this by deriving an adaptive, state-dependent scalar denoiser that enforces arbitrary discrete constellation constraints within the approximate message passing (AMP) framework, reducing per-iteration complexity to $\mathcal{O}(NM)$. A robust variant is further proposed by incorporating an $\ell_2$-norm penalty, analogous to a linear minimum mean squared error (LMMSE) estimator, to enhance noise resilience. Simulation results under uncorrelated Rayleigh fading demonstrate that both proposed algorithms closely track their exact IDLS counterparts while avoiding the catastrophic failure of standard AMP in the overloaded regime, achieving steep bit error rate (BER) waterfall curves at a fraction of the computational cost.

1-bit Quantized Continuous Aperture Arrays

Continuous aperture arrays (CAPAs) have emerged as a promising physical-layer paradigm for sixth generation (6G) systems, offering spatial degrees of freedom beyond those of conventional discrete antenna arrays. This paper investigates the interaction between the CAPA receive architecture and low-cost 1-bit analog-to-digital converters (ADCs), which impose a severe nonlinear distortion penalty in conventional discrete systems. For Rayleigh fading, we derive a moment matching approximation (MMA)-based closed-form symbol error probability (SEP) approximation based on Gamma moment-matching of the spatial eigenvalue distribution, and show that CAPAs incur a diversity-order penalty governed by Jensen's inequality on the mode eigenvalues. For line-of-sight (LoS) propagation, we prove that CAPA achieves exactly the unquantized additive white Gaussian noise (AWGN) performance bound under perfect spatial and phase alignment, completely eliminating the 1-bit penalty that forces discrete systems to double their antenna count. Monte Carlo simulations under Rayleigh, Rician, and LoS conditions validate all analytical results.

Flexible Continuous Aperture Arrays

A novel electromagnetic (EM) structure termed flexible continuous aperture array (FCAPA) is proposed, which incorporates inherent surface flexibility into typical continuous aperture array (CAPA) systems, thereby enhancing the degrees-of-freedom (DoF) of multiple-input multiple-output (MIMO) systems equipped with this technology. By formulating and solving a downlink multi-user beamforming optimization problem to maximize the weighted sum rate (WSR) of the multiple users with FCAPA, it is shown that the proposed structure outperforms typical CAPA systems by a wide margin, with performance increasing with increasing morphability.

Fractional Programming and Manifold Optimization for Reciprocal BD-RIS Scattering Matrix Design

We investigate the problem of maximizing the sum-rate performance of a beyond-diagonal reconfigurable intelligent surface (BD-RIS)-aided multi-user (MU)-multiple-input single-output (MISO) system using fractional programming (FP) techniques. More specifically, we leverage the Lagrangian Dual Transform (LDT) and Quadratic Transform (QT) to derive an equivalent objective function which is then solved iteratively via a manifold optimization framework. It is shown that these techniques reduce the complexity of the optimization problem for the scattering matrix solution, while also providing notable performance gains compared to state-of-the-art (SotA) methods under the same system conditions. Simulation results confirm the effectiveness of the proposed method in improving sum-rate performance.

The IEEE Signal Processing Society's Leading Role in Developing Standards for Computational Imaging and Sensing: Part II

In every imaging or sensing application, the physical hardware creates constraints that must be overcome or they limit system performance. Techniques that leverage additional degrees of freedom can effectively extend performance beyond the inherent physical capabilities of the hardware. An example includes synchronizing distributed sensors so as to synthesize a larger aperture for remote sensing applications. An additional example is integrating the communication and sensing functions in a wireless system through the clever design of waveforms and optimized resource management. As these technologies mature beyond the conceptual and prototype phase they will ultimately transition to the commercial market. Here, standards play a critical role in ensuring success. Standards ensure interoperability between systems manufactured by different vendors and define industry best practices for vendors and customers alike. The Signal Processing Society of the Institute for Electrical and Electronics Engineers (IEEE) plays a leading role in developing high-quality standards for computational sensing technologies through the working groups of the Synthetic Aperture Standards Committee (SASC). In this column we highlight the standards activities of the P3383 Performance Metrics for Integrated Sensing and Communication (ISAC) Systems Working Group and the P3343 Spatio-Temporal Synchronization of a Synthetic Aperture of Distributed Sensors Working Group.

Computing on Dirty Paper: Interference-Free Integrated Communication and Computing

Inspired by Costa's pioneering work on dirty paper coding (DPC), this paper proposes a novel scheme for integrated communication and computing (ICC), named Computing on Dirty Paper, whereby the transmission of discrete data symbols for communication, and over-the-air computation (AirComp) of nomographic functions can be achieved simultaneously over common multiple-access channels. In particular, the proposed scheme allows for the integration of communication and computation in a manner that is asymptotically interference-free, by precanceling the computing symbols at the transmitters (TXs) using DPC principles. A simulation-based assessment of the proposed ICC scheme under a single-input multiple-output (SIMO) setup is also offered, including the evaluation of performance for data detection, and of mean-squared-error (MSE) performance for function computation, over a block of symbols. The results validate the proposed method and demonstrate its ability to significantly outperform state-of-the-art (SotA) ICC schemes in terms of both bit error rate (BER) and MSE.

Integrated Communication and Computing in Time-Varying mmWave Channels

We propose a novel framework for integrated communication and computing (ICC) transceiver design in time-varying millimeter-wave (mmWave) channels. In particular, in order to cope with the dynamics of time-varying mmWave channels, the detection of communication symbols and the execution of an over-the-air computing (AirComp) operation are performed in parallel with channel tracking, as opposed to existing state-of-the-art (SotA) on ICC where perfect knowledge of the channel at all time instances is typically assumed. For clarity of exposition, we consider a single-input multiple-output (SIMO) uplink scenario where multiple single-antenna user equipment (UE) transmit to a base station (BS) equipped with multiple antennas, such that each UE, or edge device (ED), precodes its own transmit signal, while the BS, or access points (APs), also performs receive beamforming. The proposed transceiver framework then estimates channel state information (CSI) and data symbols in parallel, using a bilinear Gaussian belief propagation (BiGaBP) algorithm for joint channel and data detection (JCDE), aided by a channel prediction (CP) algorithm executed before each estimation window at the BS. The AirComp operation is then executed by means of an optimal combination of the residual signal. Simulation results demonstrate the effectiveness of the proposed scheme in performing ICC in challenging time-varying mmWave channels, with minimal degradation to both communication and computing performance.

Metasurfaces-Integrated Doubly-Dispersive MIMO: Channel Modeling and Optimization

The doubly-dispersive (DD) channel structure has played a pivotal role in wireless communications, particularly in high-mobility scenarios and integrated sensing and communications (ISAC), due to its ability to capture the key fading effects experienced by a transmitted signal as it propagates through a dynamic medium. However, extending the DD framework to multiple-input multiple-output (MIMO) systems, especially in environments artificially enhanced by reconfigurable intelligent surfaces (RISs) and stacked intelligent metasurfaces (SIM), remains a challenging open problem. In this chapter, a novel metasurfaces-parametrized DD (MPDD) channel model that integrates an arbitrary number of RISs, while also incorporating SIM at both the transmitter and receiver is introduced. Next, the application of this model to some key waveforms optimized for DD environments -- namely orthogonal frequency division multiplexing (OFDM), orthogonal time frequency space (OTFS), and affine frequency division multiplexing (AFDM) -- is discussed. Finally, the programmability of the proposed model is highlighted through an illustrative application, demonstrating its potential for enhancing waveform performance in SIM-assisted wireless systems.

A Flexible Design Framework for Integrated Communication and Computing Receivers

We propose a framework to design integrated communication and computing (ICC) receivers capable of simultaneously detecting data symbols and performing over-the-air computing (AirComp) in a manner that: a) is systematically generalizable to any nomographic function, b) scales to a massive number of user equipments (UEs) and edge devices (EDs), c) supports the computation of multiple independent functions (streams), and d) operates in a multi-access fashion whereby each transmitter can choose to transmit either data symbols, computing signals or both. For the sake of illustration, we design the proposed multi-stream and multi-access method under an uplink setting, where multiple single-antenna UEs/EDs simultaneously transmit data and computing signals to a single multiple-antenna base station (BS)/access point (AP). Under the communication functionality, the receiver aims to detect all independent communication symbols while treating the computing streams as aggregate interference which it seeks to mitigate; and conversely, under the computing functionality, to minimize the distortion over the computing streams while minimizing their mutual interference as well as the interference due to data symbols. To that end, the design leverages the Gaussian belief propagation (GaBP) framework relying only on element-wise scalar operations coupled with closed-form combiners purpose-built for the AirComp operation, which allows for its use in massive settings, as demonstrated by simulation results incorporating up to 200 antennas and 300 UEs/EDs. The efficacy of the proposed method under different loading conditions is also evaluated, with the performance of the scheme shown to approach fundamental limiting bounds in the under/fully loaded cases.

Affine Filter Bank Modulation: A New Waveform for High Mobility Communications

We propose a new waveform suitable for integrated sensing and communications (ISAC) systems facing doubly-dispersive (DD) channel conditions, as typically encountered in high mobility scenarios. Dubbed Affine Filter Bank Modulation (AFBM), this novel waveform is designed based on a filter-bank structure, known for its ability to suppress out-of-band emissions (OOBE), while integrating a discrete affine Fourier transform (DAFT) precoding stage which yields low peak-to-average power ratio (PAPR) and robustness to DD distortion, as well as other features desirable for ISAC. Analytical and simulation results demonstrate that AFBM maintains quasi-orthogonality similar to that of affine frequency division multiplexing (AFDM) in DD channels, while achieving PAPR levels 3 dB lower, in addition to OOBE as low as -100 dB when implemented with PHYDYAS prototype filters.