Overlapping coverage of multiple radio access technologies provides new multiple degrees of freedom for tuning the fairnessthroughput tradeoff in heterogeneous communication systems through proper resource allocation. This paper treats the problem of resource allocation in terms of optimum air interface and cell selection in cellular multi-air interface scenarios. We find a close to optimum allocation for a given set of voice users with minimum QoS requirements and a set of best-effort users which guarantees service for the voice users and maximizes the sum utility of the best-effort users. Our model applies to arbitrary heterogeneous scenarios where the air interfaces belong to the class of interference limited systems like UMTS or to a class with orthogonal resource assignment such as TDMA-based GSM or WLAN. We present a convex formulation of the problem and by using structural properties thereof deduce two algorithms for static and dynamic scenarios, respectively. Both procedures rely on simple information exchange protocols and can be operated in a completely decentralized way. The performance of the dynamic algorithm is then evaluated for a heterogeneous UMTS/GSM scenario showing high-performance gains in comparison to standard load-balancing solutions.

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In this paper we cover the problem of how users of different service classes should be assigned to a set of radio access technologies (RAT). All RAT have overlapping coverage and the aim is to maximize a weighted sum of assignable users. Under the constraint that users cannot be split between multiple air-interfaces the problem is identified as NP-complete. In the first part of the paper we derive upper and lower bounds of polynomial assignment algorithms. Using Lagrangian theory and continuous relaxation we show for polynomial assignments that in scenarios with M air-interfaces there are at most M users less assigned than in the optimum solution. In the second part we present an algorithm and compare its performance to standard load-balancing strategies.

In this paper we cover the problem of resource allocation in terms of optimum air-interface and cell selection in cellular, heterogeneous multi-air-interface scenarios. For a given set of voice users with minimum quality of service requirements and a set of best effort users we find the optimum allocation that guarantees service for the voice users and maximizes the sum utility of the best effort users. Our model applies for arbitrary heterogeneous scenarios where the air-interfaces belong to the class of interference limited systems like UMTS or to a class with orthogonal resource assignment such as TDMA based GSM or WLAN. We achieve convexity of the problem by a transformation into the domain of mean square errors. Using a dual problem formulation we derive straight forward assignment rules and develop a decentralized algorithm, which solves the optimization problem. Simulation results for a heterogeneous UMTS/GSM scenario show high performance gains of the proposed a lgorithm compared to a Load Balancing strategy

In order to design efficient online resource allocation algorithms, convexity of the underlying optimization problem is an important prerequisite. This paper covers two resource allocation problems: the sum-power constrained utility maximization and the sum-power minimization for minimum utility requirements for parallel broadcast channels. We derive a new class of utility functions for which both optimization problems can be transformed into convex representations and give necessary and sufficient conditions for the optimum solution of the original non-convex problems with regard to power. We thereby extend the known log-convexity class for which convex representations can be found and, by introducing the square-root criteria, present a straight forward test to check whether arbitrary utility functions belong to our class. For the new class of utility functions we present simple algorithms which operate in the non-convex domain, prove convergence to the global optimum and evaluate their performance by simulations. Besides, the paper reveals some insights on the general structure of the mean square error region and thereby disproves a former result