Accurate channel state information (CSI) acquisition is indispensable for mmWave massive MIMO systems to perform sophisticated signal processing. The existing channel estimation methods such as beam training and compressed sensing either have low accuracy or require considerably high training overhead. To achieve a fast yet accurate channel estimation, we propose a parallel hierarchical multi-beam search method in this paper. Unlike the existing beam search methods, which search multipath components (MPCs) one by one, the proposed method searches all the MPCs simultaneously by scanning all the beam pairs with MPCs in each layer of codebook in parallel. Therefore, the beam search of different MPCs does not interfere with each other. To improve the estimation accuracy and reduce the beam training overhead, the length of each training sequence under different codebook layers and different SNR is obtained by beam detection. Moreover, we particularly design an optimal beam pattern synthesis codebook for the hierarchical multi-beam search by using the design method of digital filter, which is applicable in other hierarchical search methods as well. Simulation results show that the proposed codebook is superior to other codebooks and the proposed multi-beam search method has higher channel estimation accuracy, but requires less training overhead, compared with the state-of-the-art alternatives.

In massive multi-user multi-input–multi-output (MU-MIMO) downlink transmission systems, the total power consumption of the base station is mainly attributed to high-resolution digital-to-analog converters (DACs). Therefore, utilizing one-bit DACs can effectively reduce the system power consumption. This work investigates the one-bit DACs precoding problem in massive MU-MIMO systems. Typically, the one-bit DACs precoding problem based on the minimum mean square error criterion is an NP-hard problem, making it challenging to solve. By exploiting the structural properties of nonconvex constraints, the nonconvex optimization problem is transformed into an equivalent continuous-domain mathematical programming problem with equilibrium constraints. The projected gradient method is used for obtaining the quantized precoding signals, and the method is optimized by introducing a step-size adjustment coefficient. Simulation results demonstrate that the improved projected gradient method can effectively reduce the uncoded bit error rate and achieve a performance gain of approximately $\text{2}\,\text{dB}$ with 16 quadrature amplitude modulation signaling. The simulations also demonstrate the better robustness of the suggested method with imperfect channel state information. Specifically, we evaluate the power consumption of the proposed algorithm to that of the zero-forcing precoder with different resolution DACs, illustrating its superior power efficiency. Furthermore, we demonstrate the effectiveness of the proposed algorithm through a theoretical analysis of the convergence, complexity, and exact property of the optimal solution.

Mobile edge cache (MEC)-enabled air-to-ground integrated Internet of Vehicles (IoV) technology can solve wireless network backhaul congestion and high latency, but security problems such as eavesdropping are often ignored when designing cache strategies. In this paper, we propose a joint design of cache strategy and physical layer transmission to improve the security offloading ratio of MEC-enabled air-to-ground IoV. By using the random geometry theory and Laplace transform, we derive the closed-form expression of the network security offloading ratio, which is defined as the probability that the request vehicle (RV) successfully finds the required file around it and obtains the file with a data rate larger than a given threshold. During the file acquisition process, we collectively consider the impact of the successful connection and secure transmission in the vehicle wireless communication. Then, we establish an optimization problem for maximizing the network security offloading ratio, in which the cache strategy and the secure transmission rate are jointly optimized. Furthermore, we propose an alternating optimization algorithm to solve the joint optimization problem. Simulation experiments verify the correctness of our theoretical derivation, and prove that the proposed cache strategy is superior to other existing cache strategies.

In a massive multiuser multiple-input multiple-output (MU-MIMO) downlink communication system, it is observed that the primary power consumption is attributed to high-resolution digital-to-analog converters (DACs). Under the need to build the next generation of low-power mobile communications, we study the low-resolution DACs precoding problem in massive MU-MIMO communication systems. A novel symbol-level precoding technique is developed for the low-resolution $b$ -bit DACs system where $b \ge 2$ . Specifically, we first equivalently reconstruct the low-resolution DACs constraints, and then formulate the low-resolution DACs precoding problem as a directly solvable discrete binary optimization problem. By adding a penalty term to relax the discrete binary constraints, the alternating direction multiplier method can effectively solve the problem. In particular, we use an iterative subprocess that directly projects the low-resolution DACs constraints, avoiding the inversion of the quantized precoding signal in the alternating optimization. Furthermore, based on the matrix theory, the proposed algorithm avoids high-dimensional matrix inversion in each iteration, resulting in a reduction in algorithmic complexity. Simulations evaluate the performance of the proposed low-resolution DACs precoding design for different resolutions, where it is shown that using low-resolution DACs can lead to higher power efficiency. In particular, we focus on setup with low-resolution DACs and shows that compared with existing schemes, the proposed algorithm can achieve a performance gain of approximately $\text{0.5}\,\text{dB}$ in the 3-bit DACs case. We further analyze the convergence and computational complexity of the algorithm and verify it through simulation.

Notice of Violation of IEEE Publication Principles "An Efficient Nonlinear Quantized Constant Envelope Precoding for Massive MU-MIMO Systems," by R. Liang, H. Li, W. Zhang, C. Liu and Y. Guo, in IEEE Systems Journal, Early Access After a careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE’s Publication Principles. This paper contains content from the papers cited below. Credit notices were used, but due to the absence of quotation marks or offset text, the copied content is not clearly referenced or specifically identified. "VLSI Design of a 3-bit Constant-Modulus Precoder for Massive MU-MIMO," by O. Castañeda, S. Jacobsson, G. Durisi, T. Goldstein and C. Studer, in the Proceedings of the IEEE International Symposium on Circuits and Systems (ISCAS), May 2018 "Quantized Precoding for Massive MU-MIMO," by S. Jacobsson, G. Durisi, M. Coldrey, T. Goldstein and C. Studer, in IEEE Transactions on Communications, vol. 65, no. 11, pp. 4670-4684, November 2017 Massive multiuser multiple-input–multiple-output (MU-MIMO) systems are foreseen as the key technology in next-generation wireless communication systems. However, many radio-frequency (RF) chains will require high power consumption and lots of hardware cost. One of the practical solutions is using low-resolution discrete phase shifters (PSs) for each antenna and RF chain. This article designs a nonlinear quantized constant envelope precoding algorithm for MU-MIMO systems with low-resolution discrete PS at the base station. In this algorithm, the quantized constant envelope precoding problem is presented as a nonconvex optimization problem. The multiuser interference is minimized by solving the optimal precoded vector and the associated precoding factors. First, we consider relaxing the nonconvex constraint on the 1-bit quantized constant envelope precoding to a continuous set of convex constraints on the unit circle. Afterwards, the semidefinite relaxation method is used to tackle the equivalent 1-bit quantized precoding problem. We further extend the algorithm to provide multi-bit quantized constant envelop precoding. Finally, the simulations are carried out under various conditions and compared with the most advanced precoding methods. The results demonstrate that this algorithm achieves a comparable performance to the existing precoding methods. At the same time, the corresponding performance-complexity tradeoff is more conducive to the expansion of the proposed algorithm to the multibit case.