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.

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.

This paper proposes a blind beamforming strategy for intelligent reflecting surface (IRS), aiming to boost the signal-to-noise ratio (SNR) by coordinating phase shifts across the reflective elements in the absence of channel information. Differing from most existing approaches that first estimate channels and then optimize phase shifts, the proposed blind beamforming method explores the wireless environment by extracting statistical features directly from random samples of the received signal power, without acquiring channel station information (CSI). This new method just requires a polynomial number of random samples to provide a quadratic SNR boost in the number of reflective elements without CSI, whereas the standard random-max sampling algorithm can only achieve a linear boost under the same condition. Moreover, we interpret blind beamforming from a least-squares point of view. Field tests demonstrate the significant advantages of the proposed blind beamforming approach over the benchmark methods in enhancing wireless transmission.

An Internet of Things (IoT) system can include several different types of service providers, who sell IoT service, network service, and computation service to customers, either jointly or separately. The complicated coupling among these providers in terms of pricing and service decisions is an under-explored research area, the understanding of which is critical to the success of IoT networks. This paper studies the impact of the provider interaction structures on the overall IoT system with massive heterogeneous customers. Specifically, we consider three interaction structures: coordinated, vertically-uncoordinated, and horizontally-uncoordinated structures. Despite the challenging non-convex optimization problems involved in modeling and analyzing these structures, we successfully obtain the closed-form optimal pricing strategies of providers in each interaction structure. We prove that the coordinated structure is better than two uncoordinated structures for both providers and customers, as it avoids selfish price markup behaviors in uncoordinated structures. When customers’ demand variance is large and utility-cost ratio is medium, vertically-uncoordinated structure is better than horizontal one for both providers and customers, due to the complementary providers’ competition in horizontally-uncoordinated structure. Counter-intuitively, we identify that providers’ optimal prices do not change with their costs at the critical point of customers’ full participation in the vertically-uncoordinated structure.