A recent study on the interpretability of real-valued convolutional neural networks (CNNs) [1] has revealed a direct and physically meaningful link with the task of finding features in data through matched filters. However, applying this paradigm to illuminate the interpretability of complex-valued CNNs meets a formidable obstacle: the extension of matched filtering to a general class of noncircular complex-valued data, referred to here as the widely linear matched filter (WLMF), has been only implicit in the literature. To this end, to establish the interpretability of the operation of complex-valued CNNs, we introduce a general WLMF paradigm, provide its solution and undertake analysis of its performance. For rigor, our WLMF solution is derived without imposing any assumption on the probability density of noise. The theoretical advantages of the WLMF over its standard strictly linear counterpart (SLMF) are provided in terms of their output signal-to-noise-ratios (SNRs), with WLMF consistently exhibiting enhanced SNR. Moreover, the lower bound on the SNR gain of WLMF is derived, together with condition to attain this bound. This serves to revisit the convolution-activation-pooling chain in complex-valued CNNs through the lens of matched filtering, which reveals the potential of WLMFs to provide physical interpretability and enhance explainability of general complex-valued CNNs. Simulations demonstrate the agreement between the theoretical and numerical results.

In the past decade, there has been substantial progress at training increasingly deep neural networks. Recent advances within the teacher–student training paradigm have established that information about past training updates show promise as a source of guidance during subsequent training steps. Based on this notion, in this paper, we propose Long Short-Term Sample Distillation, a novel training policy that simultaneously leverages multiple phases of the previous training process to guide the later training updates to a neural network, while efficiently proceeding in just one single generation pass. With Long Short-Term Sample Distillation, the supervision signal for each sample is decomposed into two parts: a long-term signal and a short-term one. The long-term teacher draws on snapshots from several epochs ago in order to provide steadfast guidance and to guarantee teacher–student differences, while the short-term one yields more up-to-date cues with the goal of enabling higher-quality updates. Moreover, the teachers for each sample are unique, such that, overall, the model learns from a very diverse set of teachers. Comprehensive experimental results across a range of vision and NLP tasks demonstrate the effectiveness of this new training method.

Although the widely linear minimum mean-square error (WLMMSE) receiver has been widely applied in multiple-input-multiple-output (MIMO) systems, there has been no theoretical analysis to quantify its distribution of signal-to-interference-plus-noise ratio (SINR) in arbitrary fading environments. In this paper, the closed-from expression of SINR at the output of the WLMMSE detection is presented, which, in its essence, can be interpreted as the sum of a series of gamma distributed random variables. The general probability density function of SINR is derived for the first time, which is explicitly expressed in terms of the confluent Lauricella hypergeometric function. Simulations on MIMO transmission systems over Rayleigh fading channels support the analytic results.

This paper presents a new course design to enhance students' interest in our graduate curriculum of morden digital signal processing (MDSP). The familiar radio frequency identification (RFID) technology is introduced to reveal the fundamentals of the least-mean-square (LMS) algorithm. Students are encouraged to encode their customised data into their respective RFID tags, and to use the software-defined radio (SDR) platform to monitor the inventory process integrated within RFID communication systems. After acquiring the digital signal from the SDR hardware, they need to perform adaptive channel estimation and signal detection to identify the customised data. By applying appropriate mathematical tools in the Matlab programming environment to solve the signal detection problem in two different communication scenarios, students reported to have had a deeper understanding of the signal processing concepts of adaptive filtering techniques in a self-directed manner.

An augmented nonlinear complex LMS (ANCLMS) algorithm is proposed to adaptively mitigate both the linear and nonlinear self-interference (SI) components in a full-duplex direct-conversion transceiver (DCT). A data prewhitening scheme, which exploits the known SI signal distributions, is also adopted to accelerate the convergence. Theoretical mean and mean square performance evaluations of the proposed SI canceller are performed and fully support the proposed approach. Computer simulations on wireless local area network (WLAN) standard compliant waveforms in practical full-duplex (FD) direct-conversion transceiver settings support the analysis.