We consider settings in which T multi-antenna transmitters and K single-antenna receivers concurrently utilize the available communication resources. Each transmitter sends useful information only to its intended receivers and can degrade the performance of unintended systems. Here, we assume the performance measures associated with each receiver are monotonic with the received power gains. In general, the systems' joint operation is desired to be Pareto optimal. However, designing Pareto optimal resource allocation schemes is known to be difficult. In order to reduce the complexity of achieving efficient operating points, we show that it is sufficient to consider rank-1 transmit covariance matrices and propose a framework for determining the efficient beamforming vectors. These beamforming vectors are thereby also parameterized by T (K − 1) real-valued parameters each between zero and one. The framework is based on analyzing each transmitter's power gain-region which is composed of all jointly achievable power gains at the receivers. The efficient beamforming vectors are on a specific boundary section of the power gain-region, and in certain scenarios it is shown that it is necessary to perform additional power allocation on the beamforming vectors. Two examples which include broadcast and multicast data as well as a cognitive radio application scenario illustrate the results.

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We consider a set of secondary transmitter-receiver pairs in a cognitive radio setting. Based on channel sensing and access performances, we consider the problem of assigning channels orthogonally to secondary users through distributed coordination and cooperation algorithms. Two economic models are applied for this purpose: matching markets and competitive markets. In the matching market model, secondary users and channels build two agent sets. We implement a stable matching algorithm in which each secondary user, based on his achievable rate, proposes to the coordinator to be matched with desirable channels. The coordinator accepts or rejects the proposals based on the channel preferences which depend on interference from the secondary user. The coordination algorithm is of low complexity and can adapt to network dynamics. In the competitive market model, channels are associated with prices and secondary users are endowed with monetary budget. Each secondary user, based on his utility function and current channel prices, demands a set of channels. A Walrasian equilibrium maximizes the sum utility and equates the channel demand to their supply. We prove the existence of Walrasian equilibrium and propose a cooperative mechanism to reach it. The performance and complexity of the proposed solutions are illustrated by numerical simulations

We consider the uplink of a cellular massive MIMO network. Acquiring channel state information at the base stations (BSs) requires uplink pilot signaling. Since the number of orthogonal pilot sequences is limited by the channel coherence, pilot reuse across cells is necessary to achieve high spectral efficiency. However, finding efficient pilot reuse patterns is non-trivial especially in practical asymmetric BS deployments. We approach this problem using coalitional game theory. Each BS has a few unique pilots and can form coalitions with other BSs to gain access to more pilots. The BSs in a coalition thus benefit from serving more users in their cells, at the expense of higher pilot contamination and interference. Given that a cell's average spectral efficiency depends on the overall pilot reuse pattern, the suitable coalitional game model is in partition form. We develop a low-complexity distributed coalition formation based on individual stability. By incorporating a base station intercommunication budget constraint, we are able to control the overhead in message exchange between the base stations and ensure the algorithm's convergence to a solution of the game called individually stable coalition structure. Simulation results reveal fast algorithmic convergence and substantial performance gains over the baseline schemes with no pilot reuse, full pilot reuse, or random pilot reuse pattern.Comment: IEEE Transactions on Wireless Communications, 13 pages, 13 figures, 2 table

The optimal transmit strategies of single-user multiantenna systems with respect to average capacity maximization are well understood. However, the performance measure does neglect delay aspects which are important for higher layer design. Therefore, we consider the maximization of the effective capacity in a single-user multi-antenna system with covariance knowledge. The optimal transmit strategy is derived and the properties as a function of the decay-rate requirement of the buffer occupancy are analyzed. In particular, we show that the larger the decayrate requirement, the smaller the beamforming optimality range, i.e., the more spatial eigenmodes are activated. This behavior is illustrated by numerical simulations and explained by the channel hardening effect.This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE ICC 2009 proceedings 978-1-4244-3435-0/09/$25.00 ©2009 IEEE