The incorporation of intelligent reflecting surface (IRS) into wireless communication systems can extend the coverage and enhance the data transmission rate. This paper studies the joint transceiver and IRS designs in IRS-assisted multi-input multi-output (MIMO) systems under both perfect channel state information (CSI) and imperfect CSI. Specifically, the transmit precoder, reflection coefficients at the IRS, and receive equalizer are jointly optimized to minimize the data detection mean square error (MSE), subject to the transmission power constraint and the modulus constraints for IRS reflection coefficients. The design problems, non-convex and challenging, are tackled under the framework of alternating optimization. For the design with perfect CSI, we successively optimize the IRS reflection coefficients given the precoder and present the closed-form optimal angle of one reflection coefficient given the others. For the robust design with imperfect CSI, we first average the detection MSE over channel uncertainties by using a generalized statistical CSI error model. Then, the averaged MSE is approximated by a more tractable upper bound. Subsequently, the robust design problem is elaborately transformed into a form similar to the problem with perfect CSI. Numerical results demonstrate the effectiveness of the proposed designs as compared to various benchmark schemes.

In this paper, we investigate a mobile edge computing (MEC) network with two computational access points (CAPs), where the source is equipped with multiple antennas and it has some computational tasks to be accomplished by the CAPs through Nakagami-m distributed wireless links. Since the MEC network involves both communication and computation, we first define the outage probability by taking into account the joint impact of latency and energy consumption. From this new definition, we then employ receiver antenna selection (RAS) or maximal ratio combining (MRC) at the receiver, and apply selection combining (SC) or switch-and-stay combining (SSC) protocol to choose a CAP to accomplish the computational task from the source. For both protocols along with the RAS and MRC, we further analyze the network performance by deriving new and easy-to-use analytical expressions for the outage probability over Nakagami-m fading channels, and study the impact of the network parameters on the outage performance. Furthermore, we provide the asymptotic outage probability in the low regime of noise power, from which we obtain some important insights on the system design. Finally, simulations and numerical results are demonstrated to verify the effectiveness of the proposed approach. It is shown that the number of transmit antenna and Nakagami parameter can help reduce the latency and energy consumption effectively, and the SSC protocol can achieve the same performance as the SC protocol with proper switching thresholds of latency and energy consumption.

In this article, we investigate a communication and computation problem for industrial Internet of Things (IoT) networks, where K relays can help accomplish the computation tasks with the assist of M computational access points. In industrial IoT networks, latency and energy consumption are two important metrics of interest to measure the system performance. To enhance the system performance, a three-hierarchical optimization framework is proposed to reduce the latency and energy consumption, which involves bandwidth allocation, off-loading, and relay selection. Specifically, we first optimize the bandwidth allocation by presenting three schemes for the second-hop wireless relaying. We then optimize the computation off-loading based on the discrete particle swarm optimization algorithm. We further present three relay selection criteria by taking into account the tradeoff between the system performance and implementation complexity. Simulation results are finally demonstrated to show the effectiveness of the proposed three-hierarchical optimization framework.

In the fifth generation mobile networks, generalized frequency division multiplexing (GFDM) is expected as the candidate waveform which can flexibly meet the requirements of diverse applications and scenarios for the Internet of Things (IoT) because of its advantages over orthogonal frequency division multiplexing (OFDM). In order to achieve the reliable data transmission in GFDM-based IoT systems, channel estimation (CE) is a prerequisite. However, the 2-D block modulation and the nonorthogonality between subcarriers for GFDM make it almost impossible that the conventional CE methods suitable for OFDM are directly applied to GFDM. To cope with this problem, a soft decision control strategy-based iterative CE (SDC-ICE) method is proposed in this paper. First, the received signal is equalized by the channel frequency response (CFR) from the pilot-based CE. After GFDM demodulation and Turbo decoding, the feedback log-likelihood ratio is utilized to rebuild symbols for data-aided CE by a redesigned Turbo receiver. Subsequently, the feedback information of both current and former iterations is used to improve the reliability of rebuilt symbols. The CFR obtained from SDC-ICE is used for equalization in the next iteration. The performance of SDC-ICE can be improved by increasing the iterations. Finally, the bit error rate (BER) and mean square error (MSE) performances of SDC-ICE and hard decision control strategy-based iterative CE (HDC-ICE) are simulated and evaluated. Simulation results demonstrate that the proposed method has better BER and MSE performance than HDC-ICE within fewer iterations.

In this paper, we investigate the security of a cache-aided multi-relay communication network in the presence of multiple eavesdroppers, where each relay can pre-store a part of the requested files in order to assist secure data transmission from source to destination. If the relays have cached the requested file, then they can directly send it to the destination; otherwise, traditional dual-hop data transmission is used. For both cases, relay selection is performed to assist the secure data transmission. We analyze the network secrecy performance in both scenarios ofnon-colludingandcolludingeavesdroppers, and obtain a closed-form expression for the average secrecy outage probability (SOP), as well as an asymptotic expression for the high main-to-eavesdropper ratio (MER). Through minimizing the network SOP, we further optimize the cache placement by proposing a stochastic sampling based cache learning (SacLe) strategy, which can be implemented in parallel and thus reduces the implementation latency substantially. Numerical and simulation results are finally presented to verify the proposed analysis, and show that the caching strategy has a significant impact on the network secrecy performance through affecting the caching diversity gain and signal cooperation gain at the relays. The proposed SacLe strategy is shown to be able to achieve the optimal performance obtained by the brute force (BF) algorithm.

We investigate cache technique from the spatial view and study its impact on the relaying networks. In particular, we consider a dual‐hop relaying network, where decode‐and‐forward (DF) relays can assist the data transmission from the source to the destination. In addition to the traditional dual‐hop relaying, we also consider the cache from the spatial view, where the source can prestore the data among the memories of the nodes around the destination. For the DF relaying networks without and with cache, we study the system performance by deriving the analytical expressions of outage probability and symbol error rate (SER). We also derive the asymptotic outage probability and SER in the high regime of transmit power, from which we find the system diversity order can be rapidly increased by using cache and the system performance can be significantly improved. Simulation and numerical results are demonstrated to verify the proposed studies and find that the system power resources can be efficiently saved by using cache technique.

We consider the optimal precoder design with the assumption that the transmitter only has channel covariance information, for the multi‐input multi‐output (MIMO) information and energy transmission system. The objective of the system design is to maximize the average system information rate, meanwhile meeting the minimum energy requirement of the energy receiver. Following this objective, we formulate the problem as a semidefinite programming (SDP) and further transform it into a dual problem. Two methods are proposed to solve this problem: the first method decomposes the transmission covariance as a product of precoders so that the constrained optimization becomes an unconstrained one, whereas the second method derives the structure of the optimal transmission covariance analytically. Both methods are proved to be convergent and their overheads and complexity are also analyzed. The achievable rate‐energy (R‐E) regions for the proposed methods are presented in the simulation. Under various system settings, the superiority of the proposed methods is shown by comparing with a few existing transmission schemes.