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Register a new userEnhanced Random Access and Beam Training for mmWave Wireless Local Networks with High User Density
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Added by
Pei Zhou
Publication date
2017
fields
Informatics Engineering
and research's language is
English
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As low frequency band becomes more and more crowded, millimeter-wave (mmWave) has attracted significant attention recently. IEEE has released the 802.11ad standard to satisfy the demand of ultra-high-speed communication. It adopts beamforming technology that can generate directional beams to compensate for high path loss. In the Association Beamforming Training (A-BFT) phase of beamforming (BF) training, a station (STA) randomly selects an A-BFT slot to contend for training opportunity. Due to the limited number of A-BFT slots, A-BFT phase suffers high probability of collisions in dense user scenarios, resulting in inefficient training performance. Based on the evaluation of the IEEE 802.11ad standard and 802.11ay draft in dense user scenarios of mmWave wireless networks, we propose an enhanced A-BFT beam training and random access mechanism, including the Separated A-BFT (SA-BFT) and Secondary Backoff A-BFT (SBA-BFT). The SA-BFT can provide more A-BFT slots and divide A-BFT slots into two regions by defining a new `E-A-BFT Length field compared to the legacy 802.11ad A-BFT, thereby maintaining compatibility when 802.11ay devices are mixed with 802.11ad devices. It can also reduce the collision probability in dense user scenarios greatly. The SBA-BFT performs secondary backoff with very small overhead of transmission opportunities within one A-BFT slot, which not only further reduces collision probability, but also improves the A-BFT slots utilization. Furthermore, we propose a three-dimensional Markov model to analyze the performance of the SBA-BFT. The analytical and simulation results show that both the SA-BFT and the SBA-BFT can significantly improve BF training efficiency, which are beneficial to the optimization design of dense user wireless networks based on the IEEE 802.11ay standard and mmWave technology.
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With the increasing demand of ultra-high-speed wireless communications and the existing low frequency band (e.g., sub-6GHz) becomes more and more crowded, millimeter-wave (mmWave) with large spectra available is considered as the most promising frequency band for future wireless communications. Since the mmWave suffers a serious path-loss, beamforming techniques shall be adopted to concentrate the transmit power and receive region on a narrow beam for achieving long distance communications. However, the mobility of users will bring frequent beam handoff, which will decrease the quality of experience (QoE). Therefore, efficient beam tracking mechanism should be carefully researched. However, the existing beam tracking mechanisms concentrate on system throughput maximization without considering beam handoff and link robustness. This paper proposes a throughput and robustness guaranteed beam tracking mechanism for mobile mmWave communication systems which takes account of both system throughput and handoff probability. Simulation results show that the proposed throughput and robustness guaranteed beam tracking mechanism can provide better performance than the other beam tracking mechanisms.
In this paper, we investigate the random access problem for a delay-constrained heterogeneous wireless network. As a first attempt to study this new problem, we consider a network with two users who deliver delay-constrained traffic to an access point (AP) via a common unreliable collision wireless channel. We assume that one user (called user 1) adopts ALOHA and we optimize the random access scheme of the other user (called user 2). The most intriguing part of this problem is that user 2 does not know the information of user 1 but needs to maximize the system timely throughput. Such a paradigm of collaboratively sharing spectrum is envisioned by DARPA to better dynamically match the supply and demand in the future [1], [2]. We first propose a Markov Decision Process (MDP) formulation to derive a modelbased upper bound, which can quantify the performance gap of any designed schemes. We then utilize reinforcement learning (RL) to design an R-learning-based [3]-[5] random access scheme, called TSRA. We finally carry out extensive simulations to show that TSRA achieves close-to-upper-bound performance and better performance than the existing baseline DLMA [6], which is our counterpart scheme for delay-unconstrained heterogeneous wireless network. All source code is publicly available in https://github.com/DanzhouWu/TSRA.
This letter proposes a novel random medium access control (MAC) based on a transmission opportunity prediction, which can be measured in a form of a conditional success probability given transmitter-side interference. A transmission probability depends on the opportunity prediction, preventing indiscriminate transmissions and reducing excessive interference causing collisions. Using stochastic geometry, we derive a fixed-point equation to provide the optimal transmission probability maximizing a proportionally fair throughput. Its approximated solution is given in closed form. The proposed MAC is applicable to full-duplex networks, leading to significant throughput improvement by allowing more nodes to transmit.
Millimeter-wave (mmWave) communications rely on directional transmissions to overcome severe path loss. Nevertheless, the use of narrow beams complicates the initial access procedure and increase the latency as the transmitter and receiver beams should be aligned for a proper link establishment. In this paper, we investigate the feasibility of random beamforming for the cell-search phase of initial access. We develop a stochastic geometry framework to analyze the performance in terms of detection failure probability and expected latency of initial access as well as total data transmission. Meanwhile, we compare our scheme with the widely used exhaustive search and iterative search schemes, in both control plane and data plane. Our numerical results show that, compared to the other two schemes, random beamforming can substantially reduce the latency of initial access with comparable failure probability in dense networks. We show that the gain of the random beamforming is more prominent in light traffics and low-latency services. Our work demonstrates that developing complex cell-discovery algorithms may be unnecessary in dense mmWave networks and thus shed new lights on mmWave network design.
In intelligent transportation systems (ITS), vehicles are expected to feature with advanced applications and services which demand ultra-high data rates and low-latency communications. For that, the millimeter wave (mmWave) communication has been emerging as a very promising solution. However, incorporating the mmWave into ITS is particularly challenging due to the high mobility of vehicles and the inherent sensitivity of mmWave beams to dynamic blockages. This article addresses these problems by developing an optimal beam association framework for mmWave vehicular networks under high mobility. Specifically, we use the semi-Markov decision process to capture the dynamics and uncertainty of the environment. The Q-learning algorithm is then often used to find the optimal policy. However, Q-learning is notorious for its slow-convergence. Instead of adopting deep reinforcement learning structures (like most works in the literature), we leverage the fact that there are usually multiple vehicles on the road to speed up the learning process. To that end, we develop a lightweight yet very effective parallel Q-learning algorithm to quickly obtain the optimal policy by simultaneously learning from various vehicles. Extensive simulations demonstrate that our proposed solution can increase the data rate by 47% and reduce the disconnection probability by 29% compared to other solutions.
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