This paper describes a multiuser power allocation strategy for fixed constellation over parallel Gaussian channels (e.g. OFDM systems). The criterion under consideration is mutual information, given arbitrary input distributions over users and over subcarriers. The algorithm achieves with very low complexity the multi-user aggregate sum mutual information upper bound. The algorithm is based on an iterative Mercury/waterfilling procedure. Moreover, we extend the framework to a decentralized scenario using a linear approximation of the MMSE function. We show, in particular that each user can, under certain assumptions, independently determine the power allocation without knowing the channel information of other users. Simulation results validate the theoretical claims.

The paper deals with design and performance analysis of orthogonal frequency-division multiple-access (OFDMA)-based downlink cellular wireless communications. Due to a high degree of user mobility, the base station is assumed to have only a statistical knowledge of the users' channels. Relying on the ergodic capacities connected to the user rates, a subcarrier and power allocation that minimizes the total transmitted power is proposed. The allocation strategy requires only the knowledge of the channel statistics and the rate requirements for all users. An extension and a performance analysis of this allocation algorithm in a multicell environment working with a frequency reuse factor equal to one is also conducted. A condition for the multicell network to be able to satisfy all rate requirements is derived

This contribution presents key functionalities and design approaches of a distributed system architecture as it is studied in the framework of the European E2R II project. An emphasis is laid on policy based self-governance, distributed reconfiguration concepts and corresponding cognitive support functionalities; this support is necessary to assure context awareness in the equipment in order to facilitate (enable) distributed decision making. The idea is to distribute decision making functionalities among network and user equipment elements in order to (i) limit the required calculation complexity on the network and user side for the determination of the optimum resource selection strategy, (ii) increase the reactivity of the equipment to any context change minimizing the required amount of human interaction and (iii) minimize the signaling overhead by broadcasting generic policy rules applicable to all users instead of targeting a user-by-user based reconfiguration approach. A simple use case illustrates how Game Theoretic tools can be used in order to derive suitable policies and how to perform decisions.

We derive the Cramer-Rao bound (CRB) on the joint estimates of the sampling clock offset and the channel impulse response when a training sequence is available. Simple closed form expressions are obtained for the CRB in the case where the observation window is large, and furthermore in the case where the channel degree is large. Our derivations are suited for single-carrier as well as for multi-carrier orthogonal frequency division multiplexing (OFDM) schemes. Data-aided maximum-likelihood (ML) estimates are also carried out.