Direct extensions of distributed greedy interference avoidance (IA) techniques developed for centralized networks to networks with multiple distributed receivers (as in ad hoc networks) are not guaranteed to converge. Motivated by this fact, we develop a waveform adaptation (WA) algorithm framework for IA based on potential game theory. The potential game model ensures the convergence of the designed algorithms in distributed networks and leads to desirable network solutions. Properties of the game model are then exploited to design distributed implementations of the algorithm that involve limited feedback in the network. Finally, variations of IA algorithms including IA with respect to legacy systems and IA with combined transmit-power and WA adaptations are investigated.

Various iterative algorithms for interference avoidance (IA) in networks with co-located receivers, suitable for distributed implementation, have been proposed in the literature. In this paper, the IA problem is cast in a game-theoretic framework and is formulated as a potential game. This formulation accommodates previously proposed algorithms and, in addition, gives us a framework that enables the design of new distributed and convergent algorithms for IA including algorithms with nonidentical utility functions for the users. Two new convergence results for potential games are then derived. The first result establishes the convergence of a class of potential games to the global solution while following best response iterations and when noise is added. The second result establishes the convergence of potential games to the Nash equilibria of the game while following random better response iterations. The first result combined with the potential game formulation allows us to show that for a large class of network scenarios, arbitrarily small noise assures the convergence of best response IA algorithms, including the eigeniterations, to an arbitrarily small neighborhood of the globally optimal signature sequence set. The second result enables the design of reduced feedback mechanisms for IA that converge to desirable solutions.

The biggest challenge faced by dynamic spectrum sharing (SS) systems is the design of SS schemes that do not adversely impact existing legacy systems in the absence of perfect knowledge. We address this issue by developing a framework to evaluate the interference profile at a legacy receiver under different system scenarios which include the hidden node and imperfect-sensing problems. By analyzing the interference distributions and by comparing the two basic approaches to SS - interference-avoidance-based overlay and interference averaging-based underlay - we identify desirable characteristics for SS radio systems.We then leverage this knowledge to motivate the use of a hybrid SS approach that combines the benefits of the two basic approaches and substantially reduces the impact to the legacy system. The advantage provided by this approach is shown to increase with an increase in the bandwidth available to the SS system. In addition, the approach is more robust to imperfect information. The inclusion of log-normal shadowing is shown to further accentuate these performance trends.

This paper presents a joint power control and waveform adaptation algorithm for networks with non-colocated receivers, amenable to a distributed implementation. The proposed algorithm allows users to meet their target signal to interference plus noise ratio (SINK) requirements while reducing the transmit power-levels in the network. The performance of the algorithm is investigated via theoretical-analysis and simulations. It is shown that the joint algorithm results in better solutions than a pure power-control or a pure waveform adaptation algorithm.

A framework to construct convergent interference avoidance (IA) algorithms in networks with multiple distributed receivers (as in ad hoc networks) based on potential game theory is developed in this paper. This is motivated by the fact that direct extensions of distributed greedy IA techniques for centralized networks to these de-centralized networks do not always lead to convergence. Some channel conditions that lead to non-convergence are also identified in the paper. A waveform adaptation algorithm for IA, designed on the basis of the framework, is then proposed. It is shown that this algorithm leads to a reduction of the interference in the network and also incorporates fairness in the allocation of resources.

The set of input-output permutations that are routable through a multistage interconnection network without any conflict (known as the admissible set), plays an important role in determining the capability of the network. Recent works on the permutation admissibility problem of shuffle-exchange networks (SEN) of size N/spl times/N, deal with (n+k) stages, where n=log/sub 2/N, and k denotes the number of extra stages. For k=0 or 1, O(Nn) algorithms exist to check if any permutation is admissible, but for k/spl ges/2, a polynomial time solution is not yet known. The more general problem of finding the minimum number (m) of shuffle-exchange stages required to realize an arbitrary permutation, 1/spl les/m/spl les/2n-1, is also an open problem. In this paper, we present an O(Nn) algorithm that checks whether a given permutation P is admissible in an m stage SEN, 1/spl les/m/spl les/n, and determines in O(Nnlogn) time the minimum number of stages m of shuffle-exchange, required to realize P. Thus, a single-stage shuffle-exchange network will be able to realize such a permutation with m passes, by recirculating all the paths m times through a single-stage, i.e., with minimum transmission delay, which, otherwise cannot be achieved with a fixed-stage SEN. Furthermore, we present a necessary condition for permutation admissibility in an m stage SEN, where n