In this paper, we investigate the problem of beam alignment in millimeter wave (mmWave) systems, and design an optimal algorithm to reduce the overhead. Specifically, due to directional communications, the transmitter and receiver beams need to be aligned, which incurs high delay overhead since without a priori knowledge of the transmitter/receiver location, the search space spans the entire angular domain. This is further exacerbated under dynamic conditions (e.g., moving vehicles) where the access to the base station (access point) is highly dynamic with intermittent on-off periods, requiring more frequent beam alignment and signal training. To mitigate this issue, we consider an online stochastic optimization formulation where the goal is to maximize the directivity gain (i.e., received energy) of the beam alignment policy within a time period. We exploit the inherent correlation and unimodality properties of the model, and demonstrate that contextual information improves the performance. To this end, we propose an equivalent structured Multi-Armed Bandit model to optimally exploit the explorationexploitation tradeoff. In contrast to the classical MAB models, the contextual information makes the lower bound on regret (i.e., performance loss compared with an oracle policy) independent of the number of beams. This is a crucial property since the number of all combinations of beam patterns can be large in transceiver antenna arrays, especially in massive MIMO systems. We further provide an asymptotically optimal beam alignment algorithm, and investigate its performance via simulations.
We propose an architecture that integrates RF (i.e., sub-6 GHz) and millimeter wave (mmWave) technologies for 5G cellular systems. Communications in the mmWave band faces significant challenges due to variable channels, intermittent connectivity, and high energy usage. Moreover, speeds for electronic processing of data is of the same order as typical rates for mmWave interfaces which makes the use of complex algorithms for tracking channel variations and adjusting resources accordingly impractical.Our proposed architecture integrates the RF and mmWave interfaces for beamforming and data transfer, and exploits the spatio-temporal correlations between the interfaces. Based on extensive experimentation in indoor and outdoor settings, we demonstrate that an integrated RF/mmWave signaling and channel estimation scheme can remedy the problem of high energy usage and delay associated with mmWave beamforming. In addition, cooperation between two interfaces at the higher layers effectively addresses the high delays caused by highly intermittent mmWave connectivity. We design a scheduler that fully exploits the mmWave bandwidth, while the RF link acts as a fallback mechanism to prevent high delay.To this end, we formulate an optimal scheduling problem over the RF and mmWave interfaces where the goal is to maximize the delay-constrained throughput of the mmWave interface. We prove using subadditivity analysis that the optimal scheduling policy is based on a single threshold that can be easily adopted despite high link variations. Index TermsMillimeter wave communication, 5G mobile systems, Out-of-band beamforming and communication arXiv:1701.06241v4 [cs.IT]
We experimentally investigate the benefits of multi-hop networking for intra-car data aggregation under the current state-of-the-art Collection Tree Protocol (CTP). We show how this protocol actively adjusts collection routes according to channel dynamics in various practical car environments, resulting in performance gains over single-hop aggregation. Throughout our experiments, we target traditional performance metrics such as delivery rate, number of transmissions per packet, and delay, and our results confirm, both qualitatively and quantitatively, that multi-hop communication can provide a reliable and robust approach for data collection within a car.
One key requirement for fountain (rateless) coding schemes is to achieve a high intermediate symbol recovery rate. Recent coding schemes have incorporated the use of a feedback channel to improve intermediate performance of traditional rateless codes; however, these codes with feedback are designed based on uniformly at random selection of input symbols. In this paper, on the other hand, we develop feedback-based fountain codes with dynamically-adjusted nonuniform symbol selection distributions, and show that this characteristic can enhance the intermediate decoding rate. We provide an analysis of our codes, including bounds on computational complexity and failure probability for a maximum likelihood decoder; the latter are tighter than bounds known for classical rateless codes.Through numerical simulations, we also show that feedback information paired with a nonuniform selection distribution can highly improve the symbol recovery rate, and that the amount of feedback sent can be tuned to the specific transmission properties of a given feedback channel.
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