In this paper, we design a novel two-phase unsourced random access (URA) scheme in massive multiple input multiple output (MIMO). In the first phase, we collect a sequence of information bits to jointly acquire the user channel state information (CSI) and the associated information bits. In the second phase, the residual information bits of all the users are partitioned into sub-blocks with a very short length to exhibit a higher spectral efficiency and a lower computational complexity than the existing transmission schemes in massive MIMO URA. By using the acquired CSI in the first phase, the sub-block recovery in the second phase is cast as a compressed sensing (CS) problem. From the perspective of the statistical physics, we provide a theoretical framework for our proposed URA scheme to analyze the induced problem based on the replica method. The analytical results show that the performance metrics of our URA scheme can be linked to the system parameters by a single-valued free entropy function. An AMP-based recovery algorithm is designed to achieve the performance indicated by the proposed theoretical framework. Simulations verify that our scheme outperforms the most recent counterparts.

In this paper, we propose a novel dynamic unsourced random access (URA) framework for massive multiple-input multiple-output (MIMO) uplink access. Unlike the existing studies, where the quasi-static channel models and the unchanged user states (active or idle) are assumed, we take the dynamics in both the channels and the states of user devices into consideration. Such a framework supports the high mobility of user devices, and facilitates their abrupt terminates and accesses during the whole transmission process. To model the dynamics, we adopt steady-state Gaussian Markov processes for all the channel coefficients of user devices, and introduce a series of latent variables to indicate the user states. We design a two-step algorithm, including the approximate message passing (AMP)-based inner decoding algorithm and the variational message passing (VMP)-based outer decoding algorithm, to decode the information sequences for all the user devices that have accessed the network. Simulation results show that our proposed method outperforms all the baselines when there are dynamics in the channels of user devices, and our proposed method has robustness to deal with the abrupt changes of user states by equipping the large number of antennas at the base station.

In this paper, we propose a novel fully Bayesian approach for the massive multiple-input multiple-output (MIMO) massive unsourced random access (URA). The payload of each user device is coded by the sparse regression codes (SPARCs) without redundant parity bits. A Bayesian model is established to capture the probabilistic characteristics of the overall system. Particularly, we adopt the core idea of the model-based learning approach to establish a flexible Bayesian channel model to adapt the complex environments. Different from the traditional divide-and-conquer or pilot-based massive MIMO URA strategies, we propose a three-layer message passing (TLMP) algorithm to jointly decode all the information blocks, as well as acquire the massive MIMO channel, which adopts the core idea of the variational message passing and approximate message passing. We verify that our proposed TLMP significantly enhances the spectral efficiency compared with the state-of-the-arts baselines, and is more robust to the possible codeword collisions.

This paper considers an uplink massive machine-type communication scenario with a massive number of antennas, where a large number of user devices are connected to a base station (BS) and the user traffic is sporadic. We propose a novel hybrid message passing (HMP) algorithm to achieve joint active detection and channel estimation (JADCE) by exploiting the channel characteristics in both the angular domain and user domain, which is expected to enhance the performance of JADCE compared with the conventional approaches without the knowledge of such a consideration. The user grouping is performed simultaneously with JADCE, which provides the prerequisites of the joint spatial division and multiplexing to achieve significant savings both in the downlink training and feedback of channel state information at the transmitter. Based on the acquired knowledge of user grouping, we further propose a per-group processing based JADCE (PGP-JADCE) approach. It significantly reduces the computational overhead for JADCE and the BS is capable to process all the user groups in parallel. Further, the analysis of detection error probabilities and channel estimation error of PGP-JADCE is provided.

Model-based method and data-based method are two basic approaches for the design of wireless communication systems. Model-based methods suffer from inaccurate modeling assumptions due to excessively complex environment. Recently, data-based methods have achieved remarkable performances in the communication system design without the knowledge of accurate model but encounter some challenges such as, lack of available labelled training data and explainability. In this paper, we propose a novel hybrid idea to integrate the strengths of both data and model-based methods, named model refinement learning, which is training affordable, theoretically interpretable and self-adapting. To show the idea more concretely, a novel channel estimation algorithm is proposed in the multiple-input single-output (MISO) system in the case where the noise model is unknown. In particular, we utilize a universal mixture of Gaussian (MoG) model, which can adaptively adjust the involved parameters to fit the true noise distribution by using observed data. We propose a novel variational inference framework to achieve automatical noise model refinement and design the corresponding online channel estimator. To reduce the online algorithm overhead, we propose a decoupled variational Bayesian method to achieve linear computational complexity. Simulations show that our proposed method outperforms both the model-based and data-based counterparts.

Passive radio frequency identification (RFID) systems raise new transmission secrecy protection challenges against the special proactive eavesdropper, since it is able to both enhance the information wiretap and interfere with the information detection at the RFID reader simultaneously by broadcasting its own continuous wave (CW) signal. To defend against proactive eavesdropping attacks, we propose an artificial noise (AN) aided secure transmission scheme for the RFID reader, which superimposes an AN signal on the CW signal to confuse the proactive eavesdropper. The power allocation between the AN signal and the CW signal are optimized to maximize the secrecy rate. Furthermore, we model the attack and defense process between the proactive eavesdropper and the RFID reader as a hierarchical security game, and prove it can achieve the equilibrium. Simulation results show the superiority of our proposed scheme in terms of the secrecy rate and the interactions between the RFID reader and the proactive eavesdropper.

We propose a spatially spread quint (SS‐quint) of only dipoles or loops, for direction of arrival (DOA) and polarisation estimation. The proposed SS‐quint is spatially centrosymmetric. Based on this centrosymmetry, the authors develop a computationally low‐cost DOA and polarisation estimator via vector‐cross‐product. Compared with the spatially spread electromagnetic vector‐sensor, the proposed SS‐quint consists of only dipoles or loops, and thus, its components have more consistent responses. Compared with a previously proposed SS‐quint configuration, which is required to be strictly uniformly L‐shaped, the proposed SS‐quint has a more flexible array configuration in the sense that it is not restricted to any particular shape. The Cramér–Rao bounds are derived and simulation results are provided, to demonstrate the performance of the proposed SS‐quint array configuration.