Integrated sensing and communication have drawn great research attention in recent years. Specifically, 5G mmWave has demonstrated its capabilities not only in high-speed communications but also in perceiving the physical environment. Apart from providing locationing services for user equipment (UE), 5G mmWave can also estimate the position of target objects that does not carry any equipment (i.e., device-free). Existing works of device-free wireless localization often employs a single monostatic radar or a few transceivers in fixed positions. In this work, we examine a cooperative sensing case, where multiple UEs cooperate with the infrastructure of transmit/receive points (TRPs) to jointly locate device-free objects. This new setting brings in new challenges for existing locationing algorithms as the number and the locations of the UEs and sensing targets are all dynamic. Our work proposes a novel procedure that uses visualization methods to jointly represent the information in the mmWave channel impulse responses and the locations of UEs and TRPs. We then introduce an end-to-end deep learning transformer architecture inspired by popular models in the computer vision domain to estimate the target objects’ locations from the visualizations. On a dataset generated using 3D ray-tracing simulations, our system can locate multiple device-free objects with an average error of 0.47 meters within a 20 meterby-40 meter experiment area.

Ray tracing is one of the de-facto standard method-ologies for radio channel modelling, given the geographical map of the layout. However, the channel generated by ray-tracing cannot be adapted to incorporate knowledge from real-world channel measurements. Several recent papers have proposed training a deep neural network (DNN) to compute the radio map for a given input layout. Such techniques typically require a large number of measurements, transmitters and receivers to generate the dataset needed for training the DNN, and hence can only be trained on simulated data from ray tracing. We propose an extension to these techniques, whereby we first train our DNN on simulated data, and then use a small number of measurements from a given setting to predict the path loss at all locations of interest, borrowing from a generative modelling technique called Deep Image Prior. Our simulations show that Radio DIP can achieve a RMSE of 5 dB in predicting the path loss of 50k outdoor locations, given less than 100 measurements.

Estimating path loss for a transmitter-receiver location is key to many use-cases including network planning and handover. Machine learning has become a popular tool to predict wireless channel properties based on map data. In this work, we present a transformer-based neural network architecture that enables predicting link-level properties from maps of various dimensions and from sparse measurements. The map contains information about buildings and foliage. The transformer model attends to the regions that are relevant for path loss prediction and, therefore, scales efficiently to maps of different size. Further, our approach works with continuous transmitter and receiver coordinates without relying on discretization. In experiments, we show that the proposed model is able to efficiently learn dominant path losses from sparse training data and generalizes well when tested on novel maps.

Machine-learning based transceivers have received increasing attention for next-generation wireless systems. We investigate the application of two meta-learning algorithms – Model Agnostic Meta Learning (MAML) and Reptile – to a deep learning-based Wi-Fi channel estimation and tracking system, called DeepWiPHY. The meta-learning algorithms were compared against conventional methods such as random initialization, cross-evaluation, and retraining on multiple channel models with varying severity of multipath fading. Comparisons were made fairly with respect to the complexity of the adaptation of the model necessary for a new environment. The results indicate that perhaps surprisingly, conventional training methods are adequate and in fact can outperform meta-learning methods over a wide variety of channels. The key is to train the receiver using the worst-case (most severe) multipath channel model, which then allows strong performance across a wide class of channels without requiring the additional burden of meta-learning.

This paper presents a novel compressed sensing (CS) approach to high dimensional wireless channel estimation by optimizing the input to a deep generative network. Channel estimation using generative networks relies on the assumption that the reconstructed channel lies in the range of a generative model. Channel reconstruction using generative priors outperforms conventional CS techniques and requires fewer pilots. It also eliminates the need of a priori knowledge of the sparsifying basis, instead using the structure captured by the deep generative model as a prior. Using this prior, we also perform channel estimation from one-bit quantized pilot measurements, and propose a novel optimization objective function that attempts to maximize the correlation between the received signal and the generator's channel estimate while minimizing the rank of the channel estimate. Our approach significantly outperforms sparse signal recovery methods such as Orthogonal Matching Pursuit (OMP) and Approximate Message Passing (AMP) algorithms such as EM-GM-AMP for narrowband mmWave channel reconstruction, and its execution time is not noticeably affected by the increase in the number of received pilot symbols.

The increasing number of wireless devices operating in unlicensed spectrum motivates the development of intelligent adaptive approaches to spectrum access. We consider decentralized contention-based medium access for base stations (BSs) operating on unlicensed shared spectrum, where each BS autonomously decides whether or not to transmit on a given resource. The contention decision attempts to maximize not its own downlink throughput, but rather a network-wide objective. We formulate this problem as a decentralized partially observable Markov decision process with a novel reward structure that provides long term proportional fairness in terms of throughput. We then introduce a two-stage Markov decision process in each time slot that uses information from spectrum sensing and reception quality to make a medium access decision. Finally, we incorporate these features into a distributed reinforcement learning framework for contention-based spectrum access. Our formulation provides decentralized inference, online adaptability and also caters to partial observability of the environment through recurrent Q-learning. Empirically, we find its maximization of the proportional fairness metric to be competitive with a genie-aided adaptive energy detection threshold, while being robust to channel fading and small contention windows.

In this work, we develop DeepWiPHY, a deep learning-based architecture to replace the channel estimation, common phase error (CPE) correction, sampling rate offset (SRO) correction, and equalization modules of IEEE 802.11ax based orthogonal frequency division multiplexing (OFDM) receivers. We first train DeepWiPHY with a synthetic dataset, which is generated using representative indoor channel models and includes typical radio frequency (RF) impairments that are the source of nonlinearity in wireless systems. To further train and evaluate DeepWiPHY with real-world data, we develop a passive sniffing-based data collection testbed composed of Universal Software Radio Peripherals (USRPs) and commercially available IEEE 802.11ax products. The comprehensive evaluation of DeepWiPHY with synthetic and real-world datasets (110 million synthetic OFDM symbols and 14 million real-world OFDM symbols) confirms that, even without fine-tuning the neural network's architecture parameters, DeepWiPHY achieves comparable performance to or outperforms the conventional WLAN receivers, in terms of both bit error rate (BER) and packet error rate (PER), under a wide range of channel models, signal-to-noise (SNR) levels, and modulation schemes.

This paper proposes a deep learning-based channel estimation method for multi-cell interference-limited massive MIMO systems, in which base stations equipped with a large number of antennas serve multiple single-antenna users. The proposed estimator employs a specially designed deep neural network (DNN) based on the deep image prior (DIP) network to first denoise the received signal, followed by conventional least-squares (LS) estimation. We analytically prove that our LS-type deep channel estimator can approach minimum mean square error (MMSE) estimator performance for high-dimensional signals, while avoiding complex channel inversions and knowledge of the channel covariance matrix. This analytical result, while asymptotic, is observed in simulations to be operational for just 64 antennas and 64 subcarriers per OFDM symbol. The proposed method also does not require any training and utilizes several orders of magnitude fewer parameters than conventional DNNs. The proposed deep channel estimator is also robust to pilot contamination and can even completely eliminate it under certain conditions.