Enhanced Random Access and Beam Training for Millimeter Wave Wireless Local Networks With High User Density

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Abstract
As the low frequency band has become more and more crowded, millimeter-wave (mmWave) has attracted significant attention. The 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 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, the 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 the A-BFT slots into two regions by defining a new E-A-BFT Length field compared with the legacy 802.11ad A-BFT, thereby maintaining compatibility when 802.11ay devices are mixed with 802.11ad devices. It can also greatly reduce the collision probability in dense user scenarios. 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 3-D 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 is beneficial to the optimization design of dense user wireless networks based on the IEEE 802.11ay standard and mmWave technology.
Funding Information
  • NSFC (61471303, 61601380)
  • NSFC Guangdong Joint Foundation (U1501255)
  • EP7-PEOPLE-2013-IRSES Project (612652)
  • Huawei HIRP Flagship Project (YB2015070106)
  • U.S. National Science Foundation (CNS-1717736, CNS-1343356)
  • Huawei HIRP Flagship Project (YB2015070106)

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