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Reconfigurable intelligent surfaces (RISs) have been proposed to increase coverage in millimeter-wave networks by providing an indirect path from transmitter to receiver when the line-of-sight (LoS) path is blocked. In this paper, the problem of optimizing the locations and orientations of multiple RISs is considered for the first time. An iterative coverage expansion algorithm based on gradient descent is proposed for indoor scenarios where obstacles are present. The goal of this algorithm is to maximize coverage within the shadowed regions where there is no LoS path to the access point. The algorithm is guaranteed to converge to a local coverage maximum and is combined with an intelligent initialization procedure to improve the performance and efficiency of the approach. Numerical results demonstrate that, in dense obstacle environments, the proposed algorithm doubles coverage compared to a solution without RISs and provides about a 10% coverage increase compared to a brute force sequential RIS placement approach. 

Millimeter-wave (mmWave) communications have been regarded as one of the most promising solutions to deliver ultra-high data rates in wireless local-area networks. A significant barrier to delivering consistently high rate performance is the rapid variation in quality of mmWave links due to blockages and small changes in user locations. If link quality can be predicted in advance, proactive resource allocation techniques such as link-quality-aware scheduling can be used to mitigate this problem. In this paper, we propose a link quality prediction scheme based on knowledge of the environment. We use geometric analysis to identify the shadowed regions that separate LoS and NLoS scenarios, and build LoS and NLoS link-quality predictors based on an analytical model and a regression-based approach, respectively. For the more challenging NLoS case, we use a synthetic dataset generator with accurate ray tracing analysis to train a deep neural network (DNN) to learn the mapping between environment features and link quality. We then use the DNN to efficiently construct a map of link quality predictions within given environments. Extensive evaluations with additional synthetically generated scenarios show a very high prediction accuracy for our solution. We also experimentally verify the scheme by applying it to predict link quality in an actual 802.11ad environment, and the results show a close agreement between predicted values and measurements of link quality. 

This paper studies the effects of millimeter-wave (mm-wave) beam alignment errors on the downlink achievable rate of a heterogeneous network (HetNet), which consists of sub-6 GHz macro-cells and mm-wave small-cells. The alignment error is modeled as a function of the underlying mm-wave link parameters. The conventional maximum biased received power criterion, where the bias is used for mm-wave small-cells, is adopted for cell associations. By varying the value of the bias factor, we investigate the changes in the downlink rate coverage probability. Our simulation results indicate that high values (of the order of 30 dB) for the bias, while beneficial in the case of perfect alignment, are actually disadvantageous for the low-rate users in the case of imperfect beam alignment. The low-rate users are better served by a moderate value (of the order of 20 dB) of the bias when the beam alignment errors are accounted for. We also show that the above disparity can be narrowed down by increasing by mm-wave base station (BS) antennas and/or the mm-wave BS density. 

Although the millimeter wave (mmWave) band has great potential to address ever-increasing demands for wireless bandwidth, its intrinsically unique propagation characteristics call for different scheduling strategies in order to minimize performance drops caused by blockages. A promising approach to mitigate the blockage problem is proactive scheduling, which uses blockage predictions to schedule users when they are experiencing good channel conditions. In this paper, we formulate an optimal scheduling problem with fairness constraints that allows us to find a schedule with maximum aggregate rate that achieves approximately the same fairness as the classic proportional fair scheduler. The results show that, for the problem settings studied, up to around 30% increase in aggregate rate compared to classic proportional fair scheduling (PFS) is possible with no decrease in fairness when blockages can be accurately predicted 0.5 seconds in advance. Furthermore, aggregate rate could be doubled compared to PFS if blockages can be accurately predicted 5 seconds in advance. While these results demonstrate the very promising potential of proactive scheduling, we also discuss several future research directions that must be pursued to effectively realize the approach. 

Beam alignment is a critical aspect in millimeter wave (mm-wave) cellular systems. However, the inherent limitations of channel estimation result in beam alignment errors, which degrade the system performance. For systems with a large number of antennas at the base station, downlink channel estimation is performed using uplink pilot signals. The beam alignment errors, thus, depend on the user equipment (UE) transmit power, which needs to be managed properly as the UEs are battery powered. This paper investigates how the use of uplink power control for the transmission of pilot signals in a mm-wave network affects the downlink beam alignment errors, which depend on various link parameters. We use stochastic geometry and statistics of the Student's t -distribution to develop an analytical model, which captures the interplay between the uplink power control and downlink signal-to-noise ratio (SNR) coverage probability. Our results indicate that using uplink power control significantly reduces UE power consumption without adversely affecting the downlink SNR coverage. 

Internet traffic load is not uniformly distributed through the day; it is significantly higher during peak-periods, and comparatively idle during off-peak periods. In this context, we present CacheFlix, a time-shifted edge-caching solution that prefetches Netflix content during off-peak periods of network connectivity. We specifically focus on Netflix since it contributes to the largest percentage of global Internet traffic by a single application. We analyze a real-world dataset of Netflix viewing activity that we collected from 1060 users spanning a 1-year period and comprised of over 2.2 million Netflix TV shows and documentary series; we restrict the scope of our study to Netflix series that account for 65% of a typical user's Netflix load in terms of bytes fetched. We present insights on users' viewing behavior, and develop an accurate and efficient prediction algorithm using LSTM networks that caches episodes of Netflix series on storage constrained edge nodes, based on the user's past viewing activity. We evaluate CacheFlix on the collected dataset over various cache eviction policies, and find that CacheFlix is able to shift 70% of Netflix series traffic to off-peak hours. 

Wi-Fi is one of the key wireless technologies for the Internet of things (IoT) owing to its ubiquity. Low-power operation of commercial Wi-Fi enabled IoT modules (typically powered by replaceable batteries) is critical in order to achieve a long battery life, while maintaining connectivity, and thereby reduce the cost and frequency of maintenance. In this work, we focus on commonly used sparse periodic uplink traffic scenario in IoT. Through extensive experiments with a state-of-the-art Wi-Fi enabled IoT module (Texas Instruments SimpleLink CC3235SF), we study the performance of the power save mechanism (PSM) in the IEEE 802.11 standard and show that the battery life of the module is limited, while running thin uplink traffic, to ~30% of its battery life on an idle connection, even when utilizing IEEE 802.11 PSM. Focusing on sparse uplink traffic, a prominent traffic scenario for IoT (e.g., periodic measurements, keep-alive mechanisms, etc.), we design a simulation framework for single-user sparse uplink traffic on ns-3, and develop a detailed and platform-agnostic accurate power consumption model within the framework and calibrate it to CC3235SF. Subsequently, we present five potential power optimization strategies (including standard IEEE 802.11 PSM) and analyze, with simulation results, the sensitivity of power consumption to specific network characteristics (e.g., round-trip time (RTT) and relative timing between TCP segment transmissions and beacon receptions) to present key insights. Finally, we propose a standard-compliant client-side cross-layer power saving optimization algorithm that can be implemented on client IoT modules. We show that the proposed optimization algorithm extends battery life by 24%, 26%, and 31% on average for sparse TCP uplink traffic with 5 TCP segments per second for networks with constant RTT values of 25 ms, 10 ms, and 5 ms, respectively.