Shan Zhou

Publications (4)0 Total impact

• Source

  Available from: ArXiv

  Article: Distributed Power Control and Coding-Modulation Adaptation in Wireless Networks using Annealed Gibbs Sampling

  Shan Zhou, Xinzhou Wu, Lei Ying
  [show abstract] [hide abstract]
  ABSTRACT: In wireless networks, the transmission rate of a link is determined by received signal strength, interference from simultaneous transmissions, and available coding-modulation schemes. Rate allocation is a key problem in wireless network design, but a very challenging problem because: (i) wireless interference is global, i.e., a transmission interferes all other simultaneous transmissions, and (ii) the rate-power relation is non-convex and non-continuous, where the discontinuity is due to limited number of coding-modulation choices in practical systems. In this paper, we propose a distributed power control and coding-modulation adaptation algorithm using annealed Gibbs sampling, which achieves throughput optimality in an arbitrary network topology. We consider a realistic Signal-to-Interference-and-Noise-Ratio (SINR) based interference model, and assume continuous power space and finite rate options (coding-modulation choices). Our algorithm first decomposes network-wide interference to local interference by properly choosing a "neighborhood" for each transmitter and bounding the interference from non-neighbor nodes. The power update policy is then carefully designed to emulate a Gibbs sampler over a Markov chain with a continuous state space. We further exploit the technique of simulated annealing to speed up the convergence of the algorithm to the optimal power and coding-modulation configuration. Finally, simulation results demonstrate the superior performance of the proposed algorithm.
  08/2011;
• Conference Proceeding: On Delay Constrained Multicast Capacity of Large-Scale Mobile Ad-Hoc Networks

  Shan Zhou, Lei Ying
  [show abstract] [hide abstract]
  ABSTRACT: This paper studies the delay constrained multicast capacity of large scale mobile ad hoc networks (MANETs). We consider a MANET that consists of n_s multicast sessions. Each multicast session has one source and p destinations. Each source sends identical information to the p destinations in its multicast session, and the information is required to be delivered to all the p destinations within D time-slots. Assuming the wireless mobiles move according to a two-dimensional i.i.d. mobility model, we first prove that the capacity per multicast session is O(min{1, (log p)(log (n_sp)) ¿(D/n_s)}). We then propose a joint coding/scheduling algorithm achieving a throughput of ¿ (min {1, ¿(D/n_s)}). Our simulation results suggest that the same scaling law also holds under random walk and random waypoint models.
  INFOCOM, 2010 Proceedings IEEE; 04/2010
• Conference Proceeding: Delay, cost and infrastructure tradeoff of epidemic routing in mobile sensor networks.

  Shan Zhou, Lei Ying, Srikanta Tirthapura
  Proceedings of the 6th International Wireless Communications and Mobile Computing Conference, IWCMC 2010, Caen, France, June 28 - July 2, 2010; 01/2010
• Source

  Available from: ArXiv

  Article: On Delay Constrained Multicast Capacity of Large-Scale Mobile Ad-Hoc Networks

  Shan Zhou, Lei Ying
  [show abstract] [hide abstract]
  ABSTRACT: This paper studies the delay constrained multicast capacity of large scale mobile ad hoc networks (MANETs). We consider a MANET consists of $n_s$ multicast sessions. Each multicast session has one source and $p$ destinations. The wireless mobiles move according to a two-dimensional i.i.d. mobility model. Each source sends identical information to the $p$ destinations in its multicast session, and the information is required to be delivered to all the $p$ destinations within $D$ time-slots. Given the delay constraint $D,$ we first prove that the capacity per multicast session is $O(\min\{1, (\log p)(\log (n_sp)) \sqrt{\frac{D}{n_s}}\}).$ Given non-negative functions $f(n)$ and $g(n)$: $f(n)=O(g(n))$ means there exist positive constants $c$ and $m$ such that $f(n) \leq cg(n)$ for all $ n\geq m;$ $f(n)=\Omega(g(n))$ means there exist positive constants $c$ and $m$ such that $f(n)\geq cg(n)$ for all $n\geq m;$ $f(n)=\Theta(g(n))$ means that both $f(n)=\Omega(g(n))$ and $f(n)=O(g(n))$ hold; $f(n)=o(g(n))$ means that $\lim_{n\to \infty} f(n)/g(n)=0;$ and $f(n)=\omega(g(n))$ means that $\lim_{n\to \infty} g(n)/f(n)=0.$ We then propose a joint coding/scheduling algorithm achieving a throughput of $\Theta(\min\{1,\sqrt{\frac{D}{n_s}}\}).$ Our simulations show that the joint coding/scheduling algorithm achieves a throughput of the same order ($\Theta(\min\{1, \sqrt{\frac{D}{n_s}}\})$) under random walk model and random waypoint model. Comment: 12 pages,8 figures, conference
  07/2009;