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In this paper, we investigate an anti-jamming hybrid beamforming (HBF) design in millimeter-wave (mmWave) massive MIMO systems for reliable wireless communications. Different from the conventional schemes designed by assuming perfect channel state information (CSI) of both communication channels and jamming channels. We aim to design the HBF scheme by minimizing the dispersion of the output signals in statistics without the knowledge of the jamming channel information. Two partially-connected HBF architectures are considered. Specifically, we propose a two-stage robust HBF design by solving the formulated non-convex problem. First, we introduce an auxiliary vector that represents the product of an analog beamformer (ABF) and a digital beamformer (DBF), and propose a pseudo-Newton method assisted beamforming algorithm by relaxing the non-convex constraint on the ABF. In the second stage, we separately optimize the ABF and DBF for two different partially-connected HBF architectures by proposing a matrix decomposition-based alternating minimization method. Finally, simulation results are provided for demonstrating the superiority of our proposed robust HBF schemes over other benchmark schemes by considering the security threat of potential jamming attacks, especially when the number of snapshots is small.

Frequency hopping has been proved to be effective against radio jamming attacks in wireless communications. In this article, deep reinforcement learning algorithm is applied for providing frequency hopping strategies against jamming attacks in wideband communication systems. We first model the frequency hopping communication system in a dynamic jamming environment, where a two-dimensional pattern with a certain number of time slots and channels are formulated. In particular, a jammer using multi-channel blocking jamming is considered, where the some frequency bands are attacked via probabilistic jamming patterns. In this case, an intelligent frequency hopping strategy is desired, especially when perfect knowledge of the jamming patterns is not known at the transmitter and receiver sides. In order to tackle this issue, the interaction between the users and the jammer are modeled as a Markov decision process and a deep Q-learning algorithm is proposed to solve the frequency hopping decision making problem. Finally, the system performance is evaluated by simulations. Our simulation results have shown that in comparison to Q-learning assisted frequency hopping strategy, the proposed deep Q-learning assisted frequency hopping strategy is capable of attaining a better anti-jamming performance, especially for a large number of frequency bands and long transmission time. Furthermore, the proposed deep Q-learning assisted frequency hopping strategy is able to provide robust anti-jamming performance when jamming patterns are unknown.

Mobile edge computing (MEC) technology can reduce the computational burden of Internet of Things (IoT) devices by offloading computationally intensive tasks to physically proximate edge servers. The combination of millimeter-wave (mmWave) massive multiple input multiple output (MIMO) and non-orthogonal multiple access (NOMA) technique has draw significant attention for its high spectral efficiency. This paper merges the computation offloading and mmWave massive MIMO-NOMA communication to seek energy efficient performance for IoT devices in MEC network. First, we propose an IoT device clustering algorithm to schedule all IoT devices into limited beams. Following this, we minimize the total energy consumption of computing and offloading in the NOMA-MEC network by jointly optimizing hybrid beamforming, offloading task assignment (OTA) ratio and power allocation. Then, the non-convex joint optimization problem is solved by proposing a suboptimal solution with two stages. In the first stage, a cyclic block-coordinate update algorithm is proposed for analog beamforming design, and then the digital beamforming vectors are obtained by zero-forcing method. In the second stage, an alternating minimization algorithm is proposed to optimize the OTA ratio and power allocation. Finally, simulation results are provided to validate the convergence of the proposed algorithms and the superiority of our proposed schemes over other benchmark schemes, which demonstrate the outstanding performance leveraging the massive MIMO-NOMA technique during computational offloading.