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.

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.

We consider an antenna tuning problem that is quite important to ensure a satisfying user experience in wireless networks. We aim to maximize the coverage ratio of a large service region by properly choosing the antenna angles. In order to embrace the true complexity of the practical networks and the radio channels, a system level simulator is required. The optimization algorithm has to be performed based on the stochastic numerical output of the simulator. We proposed a zeroth order continuation method to solve the challenging stochastic black-box non-convex optimization problem. The basic idea is to use two observations of the simulator output to generate a gradient estimator, that can be applied to optimize the smoothed version of the original objective function. We optimize a series of smoothed functions to make the solution progressively closer to the global optimum. The performance guarantee of the proposed algorithm has been investigated under weaker assumptions compared to those of the state-of-art analysis. Although the proposed algorithm can be applied to general problems, we perform simulations considering an ideal network model and present numerical results to corroborate our claim.

Radio propagation modeling and prediction is fundamental for modern cellular network planning and optimization. Conventional radio propagation models fall into two categories. Empirical models, based on coarse statistics, are simple and computationally efficient, but are inaccurate due to oversimplification. Deterministic models, such as ray tracing based on physical laws of wave propagation, are more accurate and site specific. But they have higher computational complexity and are inflexible to utilize site information other than traditional global information system (GIS) maps.

In this paper, we present deep network analyzer (DNA), a big data analytics platform for anomaly detection (AD) and root cause analysis (RCA) in mobile wireless networks. DNA is motivated by the growing scale and complexity of cellular networks along with the lack of advanced big data analytics tools for effective network management. It abstracts the RCA process into two modules, namely rule (fingerprint) learning and the module of AD and fingerprint matching. We first develop a rare association rule mining method to learn the symptoms of network anomalies and to build a fingerprint knowledge database from the historic data. Then a statistical machine learning approach is employed to identify the anomalies within the incoming dataset collected via various probes in the network and map the fingerprints of the detected anomalies to the rules in the knowledge database. The DNA platform has been tested using the real production data from the field and has been shown to be a highly effective platform for AD and RCA for large-scale cellular systems serving tens of millions of mobile users.