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Showing papers by "Shaojie Tang published in 2008"


Proceedings ArticleDOI
26 May 2008
TL;DR: The multicast capacity of a random wireless network consisting of ordinary wireless nodes and base stations, known as a hybrid network, is studied and asymptotic upper bounds and lower bounds on multicast Capacity are derived.
Abstract: We study the multicast capacity of a random wireless network consisting of ordinary wireless nodes and base stations, known as a hybrid network. Assume that n ordinary wireless nodes are randomly deployed in a square region and all nodes have the uniform transmission range r and uniform interference range R>r. We further assume that each ordinary wireless node can transmit/receive at W bits/second over a common wireless channel. In addition, there are m additional base stations (neither source nodes nor receiver nodes) placed regularly in this square region and connected by a high-bandwidth wired network. For each ordinary node v, we randomly pick k-1 nodes from the other n-1 ordinary nodes as the receivers of the multicast session at node v. The aggregated multicast capacity is defined as the total data rate of all multicast sessions in this hybrid network.We derive asymptotic upper bounds and lower bounds on multicast capacity of the hybrid wireless networks. The total multicast capacity is O(√n /√log n · √m/k · W) when k = O(n / log n), k = O(m), k / √m → ∞ and m = o(a2 / r2); the total multicast capacity is Θ(√n / √log n · W / √k) when k = O(n/log n), k = Ω(m) and m/k → O. When k = O(n/log n) and k = O(√m), the upper bound for minimum multicast capacity is at most O(r·n/a · √m · W/k) and is at least Ω(W) respectively. When k =Ωα(n/log n), the multicast capacity is Θ(W).

64 citations


Proceedings ArticleDOI
28 Oct 2008
TL;DR: This paper derives analytical upper bounds and lower bounds on broadcast capacity of a wireless network when all nodes in the network has the same bounded transmission power P and all nodes are placed in a square of side-length a.
Abstract: The capacity of a wireless network has been widely studied in the literature, including the capacity for unicast and the capacity for broadcast. In this paper, we studied the capacity of a wireless network for broadcast. Previous studies on broadcast capacity either assume that all links in the wireless network has the same channel capacity, or assume that the transmission ranges of a wireless node can be arbitrarily large. In this paper we derive analytical upper bounds and lower bounds on broadcast capacity of a wireless network when all nodes in the network has the same bounded transmission power P and all nodes are placed in a square of side-length a. When the fixed data rate channel is used (each node can send W bits/second to nodes within its transmission range if no interference happened), we prove that the broadcast capacity is Theta(W) under the physical interference model. When the Gaussian channel capacity is used, we show that the total broadcast capacity is only Theta((alpharadic(log n/n))-beta) when alpharadic(log n/n) rarr infin. When a alpharadic(log n/n) rarr O(1), we show that the broadcast capacity is Theta(1). We also generalize our results to multicast capacity for physical interference model.

37 citations


Book ChapterDOI
27 Jun 2008
TL;DR: This paper proposes to combine the game theory with wireless modeling to improve opportunistic spectrum usage, and designs PTAS or efficient approximation algorithms for each of problems such that overall social benefit is maximized.
Abstract: In this paper, we study the spectrum assignment problem for wireless access networks. Opportunistic spectrum usage is a promising technology. However, it could suffer from the selfish behavior of secondary users. In order to improve opportunistic spectrum usage, we propose to combine the game theory with wireless modeling. Several versions of problems are formalized under different assumptions. We design PTAS or efficient approximation algorithms for each of these problems such that overall social benefit is maximized. Finally, we show how to design a truthful mechanism based on all these algorithms.

28 citations