Anycast Backpressure Routing: Scalable Mobile Backhaul for Dense Small Cell Deployments

Abstract

Dense small cell (SC) deployments pose new architectural and transport level requirements. It is expected that they will require local multi-hop wireless backhauls in which some SCs with high capacity links towards the core act as aggregation gateways. To enable scalable deployments, there is the need of routing schemes able to handle dynamicity coming not only from increasing traffic demands and varying wireless link quality, but also from incremental gateway deployment. This letter proposes Anycast Backpressure (AB), a practical distributed anycast routing protocol designed to scale with the number of gateways and to exploit path and gateway diversity. The distinguishing feature of AB is that under increasing traffic demands, it opportunistically exploits uncongested gateways. To distribute traffic among the gateways, AB solely relies on 1-hop neighborhood queue backlog and geographic information. Ns-3 simulations show that, compared to state-of-the-art single-path and multi-path multi-gateway schemes, AB results in up to 40% gains in aggregated throughput and 99% reduction in latency for the evaluated scenarios.

Experimental evaluation of self-organized backpressure routing in a wireless mesh backhaul of small cells

“…Unlike routing families previously described, our scheme [6], referred to as self-organized Backpressure Routing (BP), is based on a decentralized flavor of the original centralized backpressure algorithm [4] that promises through-110 put optimality assisted by scalable geographic routing [19]. As observed in [5] the centralized backpressure algorithm limits its implementation to small centralized wireless networks.…”

“…In addition, it does not provide any guarantees in terms of latency and it forces the wireless network to operate on 115 a Time Division Multiple Access (TDMA) MAC, as it is originally proposed in theory. In contrast, as detailed in [6], BP relies on a single queue backlog per node and geolocation information to take per-packet routing decisions that dynamically grow or shrink path utilization according to traffic conditions. For each packet being routed in SC i , wireless link weights with all potential SC 120 neighbors j according to w ij = ∆Q ij − V × cost.…”

“…However, its 20 implementation under real-world conditions showed scalability problems with the number of flows and forces the wireless network to operate on a Time Division Multiple Access (TDMA) MAC [5]. To counteract these issues, in [6,7], we presented and evaluated through ns-3 simulations [8] a self-organized backpressure routing protocol (BP) for the TNL that dynamically grows or shrinks 25 SC resources according to network conditions (see section 2). Specifically [6] focuses on multi-gateway SC deployments, whereas in [7] the focus is on sparse wireless mesh backhaul deployments.…”

“…To counteract these issues, in [6,7], we presented and evaluated through ns-3 simulations [8] a self-organized backpressure routing protocol (BP) for the TNL that dynamically grows or shrinks 25 SC resources according to network conditions (see section 2). Specifically [6] focuses on multi-gateway SC deployments, whereas in [7] the focus is on sparse wireless mesh backhaul deployments. Additionally, in [9] we presented the integration details and preliminary experimental results of our scheme using ns-3 emulation features (see section 2).…”

“…Additionally, in [9] we presented the integration details and preliminary experimental results of our scheme using ns-3 emulation features (see section 2). However, the evaluation in [6] was merely 30 based on simulations in a single-radio single-channel backhaul deployments, and the experimental evaluation in [9] was scarce.…”