wireless hardware engineers

Abstract: We present SIXPPAC, the first streaming machine-learning engine for real-time integrated sensing and communication (ISAC) in wireless systems. SIXPPAC integrates two SAR ADCs and a digital binary-weight six-layer neural network (NN). The 0.765 mm$^\text{2}$ 12 nm FinFET engine stores all NN parameters on chip, enabling sustained 105 MS/s processing of 8b I/Q baseband samples at 0.8 V. By deploying bipolar inner products using AND instead of XNOR gates, SIXPPAC consumes 4.18 nJ/inference (0.42 POPS/W) at 0.8 V. We evaluate SIXPPAC on three example applications to demonstrate its versatility.

Abstract: This paper considers nonlinear digital self-interference (SI) cancellation and uplink-channel estimation for single-antenna in-band full-duplex radios when a desired uplink pilot is present during SI training. In this setting, a rich nonlinear SI dictionary can absorb part of the structured pilot response, while its large and correlated set of basis functions can make reliable SI estimation difficult, particularly with limited training. During SI-coefficient estimation, we treat the unknown uplink channel as a nuisance parameter and eliminate it to obtain an equivalent reduced SI estimation problem on the orthogonal complement of the pilot convolution subspace. Since the projected nonlinear dictionary can remain ill conditioned, we develop two complementary full-dictionary estimators: a pilot-projected ridge estimator with Bayesian-information-criterion tuning (PP-RidgeBIC), which uses global ridge shrinkage, and a pilot-projected group-sparse Bayesian estimator (PP-GSBE), which learns branch-dependent Bayesian shrinkage from the data. These estimators offer two complementary operating points: PP-RidgeBIC favors computational efficiency, whereas PP-GSBE provides greater adaptivity to heterogeneous nonlinear SI components. Simulations show substantial gains in SI cancellation and uplink reception over non-projected and hard-selection alternatives; notably, PP-GSBE suppresses the residual SI below the noise floor with moderate training and consistently provides the strongest performance under nonlinear model mismatch.

Abstract: Conventional wireless communication systems employ antennas with fixed radiation patterns. Enhancing system performance by increasing the number of antennas would push the limits of cost and size for communication devices. To overcome this issue, novel antenna technologies are urgently desired to break through the fundamental performance bottleneck and engineering limits. Pixel antennas provide a general framework for highly reconfigurable antennas, which can flexibly adjust radiation patterns via controlling variable lumped elements placed across adjacent pixels, unlocking novel degrees of freedom (DoF) in the beamspace domain. Recently, an emerging antenna coding technique links wireless system performance to the antenna configurations. This enables the use of pixel antennas to enhance system performance by leveraging the DoF from the antenna side. In this article, we provide a comprehensive overview on antenna coding by pixel antennas. Specifically, based on the pixel antenna model, the concept of antenna coding is firstly introduced, followed by a discussion on the classifications of antenna coding. Two cases for channel gain and multiplexing gain enhancement are highlighted to show the effectiveness of antenna coding in wireless communications. Besides, various optimization approaches for antenna coding design are investigated. Finally, the challenges and opportunities are outlined, providing guidance for future works.

Abstract: Pinching-antenna systems (PASS) reconfigure wireless channels by activating radiating elements at selected locations along dielectric waveguides. Port selection is a core issue in improving the performance of multiuser PASS systems. In this paper, we study joint port selection and precoder design for multiuser sum rate maximization, which leads to a large-scale nonconvex mixed-integer problem coupling discrete port selection with continuous precoder design. We propose a discrete diffusion method that does not require optimal or near-optimal port selection solutions as training labels and recasts the resulting optimization problem as sampling from a target distribution. Instead, we learn a potential function from local objective differences between neighboring feasible realizations. The learned potential is incorporated into a Metropolis--Hastings sampling rule to guide a parallel diffusion process while preserving the port selection constraints. Simulation results show that the proposed method approaches exhaustive search within a $0.30\%$ sum rate gap while achieving over $150\times$ speedup, and outperforms state-of-the-art methods including greedy and beam search. In particular, it also achieves an $18.8\times$ speedup over greedy search.

Abstract: This paper proposes an advanced realization principle for wideband multiuser multiple-input multiple-output orthogonal frequency-division multiplexing (MU-MIMO OFDM) transmitters, where the conventional transmitter-side baseband chain is physically synthesized in the wave domain. For design and optimization purposes, this fully wave-domain wideband MU-MIMO OFDM transmitter implemented by a cascaded SIM structure is functionally partitioned into two cascaded SIM blocks. The first block, denoted as SIM_1, integrates symbol loading and channel-adaptive MU-MIMO precoding updated at the channel-coherence timescale, mapping the user streams to a virtual port-subcarrier representation. The second block, denoted as SIM_2, acts as an offline-configured sampling-rate modulator that materializes the inverse discrete Fourier transform (IDFT) and cyclic prefix (CP) insertion directly in the wave domain. This baseband-free architecture establishes a virtual-to-physical transition from information bits to radiated CP-extended OFDM waveforms. To account for practical nonidealities, SIM_2 is optimized to fit the ideal multi-port CP-OFDM operator, and its residual response is mapped into an effective coupling matrix. Then, SIM_1 is optimized in a communication-oriented manner by jointly adapting discrete phase shifts and stream-subcarrier power loading to maximize the sum spectral efficiency. Results demonstrate the convergence, architecture trade-off, wave-domain OFDM materialization accuracy, and competitive performance of the proposed baseband-free transmitter.