Recently, it has been reported that one-bit massive MIMO (mMIMO) detection is equivalent to a binary classification problem that can be solved efficiently using support vector machine (SVM). Inspired by this result, we first reformulate SVM in a decentralized form consisting of multiple classifiers. This enables the use of the consensus alternating direction method of multipliers (CADMM), a technique that can improve robustness and performance through its inherent consensus making. We further update CADMM to output only valid constellation points and achieve significantly improved detection performance. In our method, by changing the size of the grouped classifiers, we balance the number of classifiers for consensus accuracy with sufficient data per group to ensure classifier robustness. Ultimately, we demonstrate that our proposed method significantly outperforms existing practically feasible methods for one-bit mMIMO detection.
We study a dynamic strategic communication problem in which a sender controls the timing of truthful updates from binary continuous-time Markov sources. The receiver chooses between a zero-order-hold estimator that follows the sender's updates and a prior-only default estimator,…
Covariance steering provides an efficient framework for designing linear stochastic feedback policies, but its extension to nonlinear systems relies on a Gaussian surrogate obtained through local linearization. Because this surrogate may differ substantially from the true nonline…
Classical rate-distortion (RD) theory has long established the fundamental limits of lossy compression by quantifying the minimum number of bits required to represent a source under a prescribed distortion constraint. However, widely used distortion measures such as mean-squared…
A 140-GHz transmitter with direct raised-cosine-like envelope shaping and wide-range phase tuning is presented in 90-nm SiGe BiCMOS. The proposed architecture relaxes conventional baseband pulse shaping and high-speed digital-to-analog converters by directly synthesizing 3-level…
Over-the-air (OTA) computation has recently gained significant attentions as an effective approach to enhance the communication efficiency of wireless federated learning (FL). By enabling simultaneous transmission and aggregation of local model updates, OTA-FL can substantially r…
Belief propagation (BP) is widely used for data association (DA) in target tracking. Existing convergence analyses of BP for DA address only the two-way correspondence between targets and measurements, where each target generates at most one measurement per scan. Multipath DA (MP…