Recent technological advancements are making the use of compact, low-cost, low-power mm-wave radars viable for providing environmental awareness in a number of applications, ranging from automotive to indoor mapping and radio resource optimisation. These emerging use-cases pave the road towards networks in which a large number of radar and broadband communications devices coexist, sharing a common spectrum band in a possibly uncoordinated fashion. Although a clear understanding of how mutual interference influences radar and communications performance is key to proper system design, the core tradeoffs that arise in such scenarios are still largely unexplored. In this paper, we provide results that help bridge this gap, obtained by means of an analytical model and extensive simulations. To capture the fundamental interactions between the two systems, we study mm-wave networks where pulsed radars coexist with communications devices that access the channel following an ALOHA policy. We investigate the effect of key parameters on the performance of the coexisting systems, including the network density, fraction of radar and communication nodes in the network, antenna directivity, and packet length. We quantify the effect of mutual interference in the coexistence scenario on radar detection and communication network throughput, highlighting some non-trivial interplays and deriving useful design tradeoffs.
The IEEE Task Group ay has recently defined new physical and medium access control specifications to design the next-generation 60 GHz wireless standard IEEE 802.11ay. Built upon the predecessor IEEE 802.11ad, IEEE 802.11ay introduces various technological advancements such as Multiple-Input and Multiple-Output (MIMO) communication, channel bonding/aggregation, and new beamforming techniques to offer unprecedented performance with 100 Gbit/s of throughput and ultra-low latency. Such performance paves the way for new emerging wireless applications such as millimeterwave distribution networks, data center inter-rack connectivity, mobile offloading, augmented reality/virtual reality, and 8K video streaming. Studying and analyzing these new use-cases is of paramount importance and demands high fidelity network-level simulator due to the scarcity and cost of real IEEE 802.11ay test-beds.In this paper, we present our implementation of the IEEE 802.11ay standard in the network simulator ns-3. Our implementation captures the specifics of IEEE 802.11ay operations such as the 802.11ay frame structure, channel bonding, new beamforming training procedures, quasi-deterministic MIMO channel support, and singleuser MIMO and multi-user MIMO beamforming training. We also validate and demonstrate the performance of the aforementioned techniques by simulations. The code for our simulation model is publicly available. CCS CONCEPTS• Networks → Network simulations; Wireless local area networks.
Millimeter-wave technology provides the necessary improvements in capacity and performance for the next generation of wireless networks. The new IEEE 802.11ay amendment extends IEEE 802.11ad to offer 100 Gbit/s connectivity in the unlicensed 60 GHz band through technical advancements such as Multiple-Input and Multiple-Output (MIMO), channel bonding and aggregation. Additionally, it offers improvements to the Beamforming Training (BFT) process in order to increase its efficiency and accuracy. One new technique defined by IEEE 802.11ay is Group Beamforming, which allows to simultaneously train all stations, and significantly reduces training overhead, especially in very dense networks. In this paper, we provide an implementation of IEEE 802.11ay in ns-3 and perform, to the best of our knowledge, the first detailed system-level evaluation of the performance of the novel IEEE 802.11ay protocol. We specifically study the performance of Group Beamforming and compare it against the legacy 802.11ad BFT. We explore how different BFT approaches scale in large networks, identify the possible problems and evaluate at how the BFT process influences the performance of the network overall. Our analysis shows that Group Beamforming can outperform the legacy approach, resulting in lower overhead and improved network performance. However, we also found that the Access Point (AP) training is quite vulnerable to interference in dense networks, introducing severe limitations to the performance, especially in large rooms where precise BFT is crucial to maintain the communication link. Therefore, we propose several improvements to Group Beamforming that improve performance and provide robust beamforming even in very dense scenarios.
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