This work focuses on the optimal placement of meta-surfaces (MTSs) onto the ceiling of an industrial manufacturing workshop. In particular, we assume that a total ofMMTSs are deployed, and that there areLpossible positions for each MTS. The resulting signal-to-noise (SNR) maximization problem is difficult to tackle directly because of the coupling between the placement decisions of the different MTSs. Mathematically, we are faced with a nonlinear discrete optimization problem withLMpossible solutions. A remarkable result shown in this paper is that the above challenging problem can be efficiently solved withinO(ML2log(ML)) time. There are two key steps in developing the proposed algorithm. First, we successfully decouple the placement variables of different MTSs by introducing a continuous auxiliary variable μ; the discrete primal variables are now easy to optimize when μ is held fixed, but the optimization problem of μ is nonconvex. Second, we show that the optimization of continuous μ can be recast into a discrete optimization problem with onlyLMpossible solutions, so the optimal μ can now be readily obtained. Numerical results show that the proposed algorithm can not only guarantee a global optimum but also reach the optimal solution efficiently.

Configuring intelligent surface (IS) or passive antenna array without any channel knowledge, namely blind beamforming, is a frontier research topic in the wireless communication field. Existing methods in the previous literature for blind beamforming include the RFocus and the CSM, the effectiveness of which has been demonstrated on hardware prototypes. However, this paper points out a subtle issue with these blind beamforming algorithms: the RFocus and the CSM may fail to work in the non-line-of-sight (NLoS) channel case. To address this issue, we suggest a grouping strategy that enables adaptive blind beamforming. Specifically, the reflective elements (REs) of the IS are divided into three groups; each group is configured randomly to obtain a dataset of random samples. We then extract the statistical feature of the wireless environment from the random samples, thereby coordinating phase shifts of the IS without channel acquisition. The RE grouping plays a critical role in guaranteeing performance gain in the NLoS case. In particular, if we place all the REs in the same group, the proposed algorithm would reduce to the RFocus and the CSM. We validate the advantage of the proposed blind beamforming algorithm in the real-world networks at 3.5 GHz aside from simulations.

The beamforming problem of intelligent reflecting surface (IRS) has been extensively considered from an optimization perspective assuming that channel state information (CSI) is available. However, the reality is that the existing prototypes seldom follow this model-based approach because channel estimation is technically difficult and costly for the network protocols and hardware to date. A recent trend is to perform beamforming blindly without channel knowledge, e.g., the so-called CSM method [1], [2]. This work looks at blind beamforming from a reinforcement learning point of view. We first show that CSM boils down to a special case of the greedy algorithm in the reinforcement learning context. We analyze the resulting cumulative regret, and further propose an upper approximation to facilitate the optimization of the exploration probability. Moreover, we show that a gradient sampling scheme can improve the efficiency of reinforcement learning as compared to the uniform sampling scheme used in CSM. Finally, we validate the performance advantage of the proposed methods in a prototype system.

Intelligent reflecting surface (IRS) is an emerging technology to enhance spatial multiplexing in wireless networks. This letter considers the discrete passive beamforming design for IRS in order to maximize the minimum signal-to-interference-plus-noise ratio (SINR) among multiple users in an IRS-assisted downlink network. The main design difficulty lies in the discrete phase-shift constraint. Differing from most existing works, this letter advocates a convex-hull relaxation of the discrete constraints which leads to a continuous reformulated problem equivalent to the original discrete problem. This letter further proposes an efficient alternating projection/proximal gradient descent and ascent algorithm for solving the reformulated problem. Simulation results show that the proposed algorithm outperforms the state-of-the-art methods significantly.

Conventional policy for configuring an intelligent reflecting surface (IRS) typically requires channel state information (CSI), thus incurring substantial overhead costs and facing incompatibility with the current network protocols. This paper proposes a blind beamforming strategy in the absence of CSI, aiming to boost the minimum signal-to-noise ratio (SNR) among all the receiver positions, namely the coverage enhancement. Although some existing works already consider the IRS-assisted coverage enhancement without CSI, they assume certain position-channel models through which the channels can be recovered from the geographic locations. In contrast, our approach solely relies on the received signal power data, not assuming any position-channel model. We examine the achievability and converse of the proposed blind beamforming method. If the IRS has N reflective elements and there are U receiver positions, then our method guarantees the minimum SNR of$\Omega (N^{2}/U)$—which is fairly close to the upper bound$O(N+N^{2}\sqrt {\ln (NU)}/\sqrt [{4}]{U})$. Aside from the simulation results, we justify the practical use of blind beamforming in a field test at 2.6 GHz. According to the real-world experiment, the proposed blind beamforming method boosts the minimum SNR across seven random positions in a conference room by 18.22 dB, while the position-based method yields a boost of 12.08 dB.

Channel acquisition is a major challenge faced by the conventional beamforming methods when dealing with multiple intelligent reflecting surfaces (IRSs), because the number of unknown channels grows exponentially with the number of IRSs. This work proposes to sidestep channel estimation and to configure the IRSs blindly based on the statistical information which is extracted from a set of random samples of the received signal power. The proposed blind beamforming method has provable performance in terms of the signal-to-noise ratio (SNR) boost. For instance, it yields a quartic SNR boost of$\Theta(N^{4})$for a double-IRS system under certain condition, where$N$is the number of reflected elements of each IRS. We remark that the above$\Theta(N^{4})$result is more sophisticated than the existing ones about the double-IRS system in the literature. Furthermore, we numerically demonstrate the advantage of the proposed blind beamforming method through prototype tests with multiple IRSs.

Channel acquisition is a major bottleneck in fully exploiting the potential of intelligent reflecting surfaces (IRSs) to improve the wireless environment. In order to bypass such difficulty, an alternative is to optimize IRS based on the received signal statistics rather than channel state information (CSI), namely blind beamforming. The two recent methods, RFocus and conditional sample mean (CSM), fall into this category, both of which have been shown highly effective in practice. Nevertheless, we find a subtle drawback with the existing blind beamforming methods that they may not work well for the non-line-of-sight (NLoS) case for two reasons. First, many more signal samples are needed when the direct propagation diminishes. Second, if the direct propagation is completely blocked then the existing blind beamforming methods cannot work whatsoever. To address this issue, we propose an adaptive strategy for blind beamforming, which guarantees an approximation ratio of the global optimum. Field tests and simulations show that the proposed blind beamforming method is much more suited for NLoS environment than the existing ones.

Deep neural network (DNN) shows great promise in providing more intelligence to ubiquitous end devices. However, the existing partition-offloading schemes adopt data-parallel or model-parallel collaboration between devices and the cloud, which does not make full use of the resources of end devices for deep-level parallel execution. This article proposes eDDNN (i.e., enabling Distributed DNN), a collaborative inference scheme over heterogeneous end devices using cross-platform Web technology, moving the computation close to ubiquitous end devices, improving resource utilization, and reducing the computing pressure of data centers. eDDNN implements D2D communication and collaborative inference among heterogeneous end devices with WebRTC protocol, divides the data and corresponding DNN model into pieces simultaneously, and then executes inference almost independently by establishing a layer dependency table. Besides, eDDNN provides a dynamic allocation algorithm based on deep reinforcement learning to minimize latency. We conduct experiments on various data sets and DNNs and further employ eDDNN into a mobile Web AR application to illustrate the effectiveness. The results show that eDDNN can achieve the latency decrease by$2.98\times $, reduce mobile energy by$1.8\times $, and relieve the computing pressure of the edge server by$2.57\times $, against a typical partition-offloading approach.