Secure ultra-reliable low-latency communication (URLLC) has been recently investigated with the fundamental limits of finite block length (FBL) regime in mind. Analysis has revealed that when eavesdroppers outnumber BS antennas or enjoy a more favorable channel condition compared to the legitimate users, base station (BS) transmit power should increase exorbitantly to meet quality of service (QoS) constraints. Channelinduced impairments such as shadowing and/or blockage pose a similar challenge. These practical considerations can drastically limit secure URLLC performance in FBL regime. Deployment of an intelligent reflecting surface (IRS) can endow such systems with much-needed resiliency and robustness to satisfy stringent latency, availability, and reliability requirements. We address this problem and propose a joint design of IRS platform and secure URLLC network. We minimize the total BS transmit power by simultaneously designing the beamformers and artificial noise at the BS and phase-shifts at the IRS, while guaranteeing the required number of securely transmitted bits with the desired packet error probability, information leakage, and maximum affordable delay. The proposed optimization problem is nonconvex and we apply block coordinate descent and successive convex approximation to iteratively solve a series of convex subproblems instead. The proposed algorithm converges to a suboptimal solution in a few iterations and attains substantial power saving and robustness compared to baseline schemes.

Broadband millimeter wave (mmW) systems are a promising pioneer of cellular communication for next generation which is utilizing the hybrid baseband/ analog beamforming structures along with the miniature massive antenna arrays at both sides of the communication link. mmW channel with an available unlicensed spread spectrum is frequency selective because the signal bandwidth can be larger than the coherence bandwidth. Due to the sparse nature of mmW channel, extracting compressive sensing model of the system is preferable. In fact, exploiting the sparse structure will lead to the reduction of the computational complexity, because there is a reduction in the channel training length compared with the conventional methods such as least square estimation. Most of the prior works have considered on-grid quantized departure/arrival angles in the input/output antennas to obtain a sparse virtual channel model. However, the sparse angles in the physical channel model are continuous where this continuity indicates a mismatch between the physical angles and the on-grid angles. Such a mismatch will contribute to unwanted components in the virtual channel model. Given these extra components, the conventional compressive sensing tools are unable to recover the channel. In this paper, we propose two solutions for overcoming the problem caused by off-grid angle selection. The first is based on the vector shaping, and the second one is based on the sparse total least square concepts. Simulation results demonstrate that the proposed methods both could obtain an adequate channel recovery and are preferable regarding computational complexity concerning the newly developed surrogate method. KEYWORDScompressive sensing (CS), millimeter wave (mmW), off-grid, on-grid quantization, total least square (TLS), vector shaping (VS)

Summary In millimeter wave (mmW) communication systems, hybrid architecture, including the analog‐digital precoder and combiner matrices, is employed to take advantage of the multistream transceiver. In practice, mmW channel is assumed to be frequency‐selective, since the signal bandwidth is larger than the coherence bandwidth. Hence, orthogonal frequency‐division multiplexing signaling can be remedial. So far, most of the previous works on the frequency‐selective channel estimation have focused on the single measurement vector (SMV) form, whereas finding and exploiting the proper multimeasurement vector (MMV) model can improve upon the estimation procedure based on compressive sensing (CS) concepts. In fact, the estimation procedure based on the MMV model has a faster convergence speed than the SMV method specially, when the training frames are small. In this paper, we first extract the MMV model of the channel. In this model, the rank‐deficiency occurs as the number of training frames is less or equal to the sparsity level. Thus, the conventional estimation methods fail to provide the desirable performance. To overcome this issue, we propose two rank‐aware algorithms based on the enhancement of the observed signal subspace. The first algorithm assumes to know the sparsity level, while the second faces to the lack of knowledge about the sparsity level. The simulation results corroborate the fact that the proposed methods outperform the conventional CS algorithms such as Simultaneous Orthogonal Matching Pursuit.

Secure ultra-reliable low-latency communication (URLLC) has been recently investigated with the fundamental limits of finite block length (FBL) regime in mind. Analysis has revealed that when eavesdroppers outnumber BS antennas or enjoy a more favorable channel condition compared to the legitimate users, base station (BS) transmit power should increase exorbitantly to meet quality of service (QoS) constraints. Channelinduced impairments such as shadowing and/or blockage pose a similar challenge. These practical considerations can drastically limit secure URLLC performance in FBL regime. Deployment of an intelligent reflecting surface (IRS) can endow such systems with much-needed resiliency and robustness to satisfy stringent latency, availability, and reliability requirements. We address this problem and propose to minimize the total BS transmit power by simultaneously designing the beamformers and artificial noise at the BS and phase-shifts at the IRS, while guaranteeing the required number of securely transmitted bits with the desired packet error probability, information leakage, and maximum affordable delay. The proposed optimization problem is nonconvex and we apply block coordinate descent and successive convex approximation to iteratively solve a series of convex subproblems instead. The proposed algorithm converges to a suboptimal solution in a few iterations and attains substantial power saving and robustness compared to baseline schemes.