Bandit Policies for Reliable Cellular Network Handovers in Extreme Mobility

The demand for seamless Internet access under extreme user mobility, such as on high-speed trains and vehicles, has become a norm rather than an exception. However, the 4G/5G mobile network is not always reliable to meet this demand, with non-negligible failures during the handover between base stations. A fundamental challenge of reliability is to balance the exploration of more measurements for satisfactory handover, and exploitation for timely handover (before the fast-moving user leaves the serving base station's radio coverage). This paper formulates this trade-off in extreme mobility as a composition of two distinct multi-armed bandit problems. We propose Bandit and Threshold Tuning (BATT) to minimize the regret of handover failures in extreme mobility. BATT uses $\epsilon$-binary-search to optimize the threshold of the serving cell's signal strength to initiate the handover procedure with $\mathcal{O}(\log J \log T)$ regret.It further devises opportunistic Thompson sampling, which optimizes the sequence of the target cells to measure for reliable handover with $\mathcal{O}(\log T)$ regret.Our experiment over a real LTE dataset from Chinese high-speed rails validates significant regret reduction and a 29.1% handover failure reduction

An Evaluation of BBR and its variants

The congestion control algorithm bring such importance that it avoids the network link into severe congestion and guarantees network normal operation. Since The loss based algorithms introduce high transmission delay, to design new algorithm simultaneously achieving high throughout and low buffer occupation is a new working direction. The bottleneck bandwidth and round trip time (BBR) belongs such kind, and it has drawn much attention since its release. There are other algorithms modified from BBR to gain better performance. And the implementation of BBR v2.0 is released recently. We implement a framework to compare the performance of these algorithms in simulated environment