arXiv subjects

Xiang-yang Li

Publications and source records attributed to Xiang-yang Li.

4 recordsLinked to original sources

arXiv2014

Towards Distribution-Free Multi-Armed Bandits with Combinatorial Strategies

In this paper we study a generalized version of classical multi-armed bandits (MABs) problem by allowing for arbitrary constraints on constituent bandits at each decision point. The motivation of this study comes from many situations that involve repeatedly making choices subject to arbitrary constraints in an uncertain environment: for instance, regularly deciding which advertisements to display online in order to gain high click-through-rate without knowing user preferences, or what route to drive home each day under uncertain weather and traffic conditions. Assume that there are $K$ unknown random variables (RVs), i.e., arms, each evolving as an \emph{i.i.d} stochastic process over time. At each decision epoch, we select a strategy, i.e., a subset of RVs, subject to arbitrary constraints on constituent RVs. We then gain a reward that is a linear combination of observations on selected RVs. The performance of prior results for this problem heavily depends on the distribution of strategies generated by corresponding learning policy. For example, if the reward-difference between the best and second best strategy approaches zero, prior result may lead to arbitrarily large regret. Meanwhile, when there are exponential number of possible strategies at each decision point, naive extension of a prior distribution-free policy would cause poor performance in terms of regret, computation and space complexity. To this end, we propose an efficient Distribution-Free Learning (DFL) policy that achieves zero regret, regardless of the probability distribution of the resultant strategies. Our learning policy has both $O(K)$ time complexity and $O(K)$ space complexity. In successive generations, we show that even if finding the optimal strategy at each decision point is NP-hard, our policy still allows for approximated solutions while retaining near zero-regret.

cs.LG↗

arXiv2013

Almost Optimal Channel Access in Multi-Hop Networks With Unknown Channel Variables

We consider distributed channel access in multi-hop cognitive radio networks. Previous works on opportunistic channel access using multi-armed bandits (MAB) mainly focus on single-hop networks that assume complete conflicts among all secondary users. In the multi-hop multi-channel network settings studied here, there is more general competition among different communication pairs. We formulate the problem as a linearly combinatorial MAB problem that involves a maximum weighted independent set (MWIS) problem with unknown weights which need to learn. Existing methods for MAB where each of $N$ nodes chooses from $M$ channels have exponential time and space complexity $O(M^N)$, and poor theoretical guarantee on throughput performance. We propose a distributed channel access algorithm that can achieve $1/ρ$ of the optimum averaged throughput where each node has communication complexity $O(r^2+D)$ and space complexity $O(m)$ in the learning process, and time complexity $O(D m^{ρ^r})$ in strategy decision process for an arbitrary wireless network. Here $ρ=1+ε$ is the approximation ratio to MWIS for a local $r$-hop network with $m<N$ nodes,and $D$ is the number of mini-rounds inside each round of strategy decision. For randomly located networks with an average degree $d$, the time complexity is $O(d^{ρ^r})$.

cs.NI↗

arXiv2012

Triggercast: Enabling Wireless Collisions Constructive

It is generally considered that concurrent transmissions should be avoided in order to reduce collisions in wireless sensor networks. Constructive interference (CI) envisions concurrent transmissions to positively interfere at the receiver. CI potentially allows orders of magnitude reductions in energy consumptions and improvements on link quality. In this paper, we theoretically introduce a sufficient condition to construct CI with IEEE 802.15.4 radio for the first time. Moreover, we propose Triggercast, a distributed middleware, and show it is feasible to generate CI in TMote Sky sensor nodes. To synchronize transmissions of multiple senders at the chip level, Triggercast effectively compensates propagation and radio processing delays, and has $95^{th}$ percentile synchronization errors of at most 250ns. Triggercast also intelligently decides which co-senders to participate in simultaneous transmissions, and aligns their transmission time to maximize the overall link PRR, under the condition of maximal system robustness. Extensive experiments in real testbeds reveal that Triggercast significantly improves PRR from 5% to 70% with 7 concurrent senders. We also demonstrate that Triggercast provides on average $1.3\times$ PRR performance gains, when integrated with existing data forwarding protocols.

cs.DC↗

arXiv2012

Truthful Auction Mechanism for Heterogeneous Spectrum Allocation in Wireless Networks

Secondary spectrum auction is widely applied in wireless networks for mitigating the spectrum scarcity. In a realistic spectrum trading market, the requests from secondary users often specify the usage of a fixed spectrum frequency band in a certain geographical region and require a duration time in a fixed available time interval. Considering the selfish behaviors of secondary users, it is imperative to design a truthful auction which matches the available spectrums and requests of secondary users optimally. Unfortunately, existing designs either do not consider spectrum heterogeneity or ignore the differences of required time among secondary users. In this paper, we address this problem by investigating how to use auction mechanisms to allocate and price spectrum resources so that the social efficiency can be maximized. We begin by classifying the spectrums and requests from secondary users into different local markets which ensures there is no interference between local markets, and then we can focus on the auction in a single local market. We first design an optimal auction based on the Vickrey-Clarke-Groves (VCG) mechanism to maximize the social efficiency while enforcing truthfulness. To reduce the computational complexity, we further propose a truthful sub-optimal auction with polynomial time complexity, which yields an approximation factor 6+4\surd2. Our extensive simulation results using real spectrum availability data show that the social efficiency ratio of the sub-optimal auction is always above 70% compared with the optimal auction.

cs.GT↗