Coordinated Multiple Access Point Multiuser Beamforming Training Protocol for Millimeter Wave WLANs

Abstract

Millimeter wave (mmWave) systems have attracted considerable attention as a benefit of their Gbps services. Therefore, wireless-gigabit (WiGig) transmission has been developed based on the IEEE 802.11ad and 802.11ay standards of wireless local area networks (WLANs) in the 60-GHz band. To overcome the high path loss of mmWave, beamforming and beam-sector based training protocols are specified in 802.11ad/ay. With the increase in service demand, more WiGig access points (APs) will be deployed, hence the transmission frame structure of beam control should be adjustable for supporting both multiple APs and multiple users. The conventional exhaustive beam search (EBS) scheme has a potentially excessive complexity for high number of beams. Furthermore, assigning fixed training slots for users will lead to high-latency beam training under EBS. Hence, we design an advanced protocol for multiple APs and multiple users. Based on this protocol, we propose coordinated multiple AP multiuser training (CMMBT) including variable length framing and adjustable beam training. Our simulations demonstrate that CMMBT provides flexible training control and achieves the lowest training latency and highest average throughput performance amongst the state-of-the-art solutions in the open literature. Index Terms-Millimeter wave communications, wireless local area networks, multiuser association, beamforming training, non-slotted frame structure. I. INTRODUCTION The increasing number of wireless devices and the rapid evolution of high-rate applications such as high definition video streaming have led to a considerable increase in the amount of wireless tele-traffic [1]. This issue is critical both for industry and academia, since the existing sub-6 GHz communication systems are simply unable to support the simultaneous high-rate service demands of a large number of connections. Hence, millimeter wave (mmWave) solutions having high spectral resources have attracted increasing attention in the context of next-generation systems [2]. Therefore, several international organizations are developing mmWave

User-Heterogeneous Cell-Free Massive MIMO Downlink and Uplink Beamforming via Tensor Decomposition

“…We remark that for simplicity of notation in the above, we avoided using the superscripts d and u when not necessary. In particular, the quantities y k , s k , s k and n k in equation ( 5), as well as η k in equation ( 6), all obviously refer to DL, while the quantities y, s k , n, ŝk and s k in equations ( 8) and ( 9), as well as η k in equation (10), all obviously refer to UL, such that the use of the superscripts on those quantities is unnecessary. In what follows, we shall also omit these superscripts when referring to the TX and RX beamformers whenever both cases can be considered interchangeably.…”

“…With that clarification, it is evident from equations (6) and (10) that the maximization of the system's DL and UL spectral efficiencies under the knowledge of the channel matrices H k requires the joint optimization of the transmit and receive beamformers V k and U k , respectively, for the corresponding DL and UL modes. And at this point, it will prove convenient to collect all K components of each these key system matrices into system-wide quantities, which with M k = Q k for different UEs, can be achieved via the introduction of tensor notation.…”

A deep neural network based MU-MIMO beamforming training protocol for IEEE 802.11ay

“…Fixed wireless access has been investigated in [13] and [14], which described key elements incorporated into IEEE 802.11ay, including beamforming and link maintenance. For IEEE 802.11ay dense environments, [15] and [16] designed advanced protocols for adjustable BFT with the aim of shortening the BFT time. Furthermore, a recent study [2] described efficient BFT algorithms formulated as a first-order Markov process that supports multiple beam pairs, exploiting the spatial-temporal consistency of the mmWave channel.…”

Network Capacity Optimization for Cellular‐Assisted Vehicular Systems by Online Learning‐Based mmWave Beam Selection

“…Given the severe interbeam interferences caused by mobility of users, the authors of [34] proposed a beam resource allocation and mobility-aware subband scheme for the mmWave subband-beam massive MIMO systems. The authors of [35] focused on beam training problem of wireless local area networks in the 60-GHz band and designed an advanced beamforming training protocol for multiple access points and multiple users. The authors of [36] applied perbeam synchronization for a massive MIMO system to alleviate the delay dispersion effects and channel Doppler due to mobility and blockage.…”