In this paper, we present m3MIMO a mmWave fully-digital multi-user multi-input multi-output (MU-MIMO) testbed for advanced wireless research. m3MIMO operates in the 57-64 GHz frequency range and supports up to 1 GHz of bandwidth enabling large data multiplexing in the frequency domain through orthogonal frequency-division multiplexing (OFDM). The testbed features three custom-designed Zynq UltraScale+ RFSoC-based Software Defined Radios (SDRs) empowered with the Pi-Radio fully digital transceivers. Two of these SDRs support eight transmit and receive streams each (8 × 8 MIMO), while the third SDR supports up to four channels. m3MIMO supports three different communication modes: (i) point-to-point (P2P) transmissions; (ii) single-user multi-input multi-output (SU-MIMO), where multiple streams are transmitted to a single end-device; and (iii) MU-MIMO, where two devices are simultaneously served by a single transmitter. To showcase the m3MIMO's versatility, we present two research use cases: tracking-based beamforming and mmWave-based sensing. We will open-source the m3MIMO code along with the relevant use-case datasets, facilitating further analysis.

Emerging mobile virtual reality (VR) systems are required to continuously perform complex computer vision tasks needing computational power that is excessive for mobile devices. Thus, techniques based on wireless edge computing (WEC) have been recently proposed. However, existing WEC methods require the transmission and processing of a high amount of video data which may ultimately saturate the wireless link. In this paper, we propose a novel sensing-assisted edge computing (ISAC-EC) approach to address this issue. ISAC-EC leverages knowledge about the physical environment to reduce the end-to-end latency and overall computational burden by transmitting to the edge server only the relevant data for the delivery of the service. Our intuition is that the transmission of the portion of the video frames where there are no changes with respect to the previous frames can be avoided. Through wireless sensing, only the part of the frames where any environmental change is detected is transmitted and processed. We evaluated ISAC-EC by using a 10K 360°camera with a Wi-Fi 6 sensing system operating at 160 MHz and performing localization and tracking. Experimental results show that ISAC-EC reduces both the channel occupation and end-to-end latency by more than 90% while improving the instance segmentation and object detection performance with respect to state-of-the-art WEC approaches. For reproducibility purposes, we pledge to share our dataset and code repository.

Distributed deep neural networks (DNNs) have emerged as a key technique to reduce communication overhead without sacrificing performance in edge computing systems. Recently, entropy coding has been introduced to further reduce the communication overhead. The key idea is to train the distributed DNN jointly with an entropy model, which is used as side information during inference time to adaptively encode latent representations into bit streams with variable length. To the best of our knowledge, the resilience of entropy models is yet to be investigated. As such, in this paper we formulate and investigate the resilience of entropy models to intentional interference (e.g., adversarial attacks) and unintentional interference (e.g., weather changes and motion blur). Through an extensive experimental campaign with 3 different DNN architectures, 2 entropy models and 4 rate-distortion trade-off factors, we demonstrate that the entropy attacks can increase the communication overhead by up to 95%. By separating compression features in frequency and spatial domain, we propose a new defense mechanism that can reduce the transmission overhead of the attacked input by about 9% compared to unperturbed data, with only about 2% accuracy loss. Importantly, the proposed defense mechanism is a standalone approach which can be applied in conjunction with approaches such as adversarial training to further improve robustness. Code will be shared for reproducibility.