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We analyze the reverse link of a single wireless cell in which mobile phones simultaneously transmit to the base station using code division multiple access (CDMA). The mobiles are transmitting data which is delay intolerant, so that scheduling cannot be employed. There is a finite number of data classes, and the users transmit either data or a lower rate synchronizing signal. To overcome the near-far problem, received power control is used, which has a log-normal error. For each class there is an outage probability that the user's signal-to-noise ratio (SNR) will be met (when active and when idle). Refinements of the central limit theorem are used to determine the number of users of each class that can be supported, i.e., the capacity. The approximation can also be used to determine the minimal target powers necessary to meet the outage requirements. Comparison with simulation shows these approximations to be accurate. [ABSTRACT FROM AUTHOR]

Presents a study which proposed dynamic channel-sensitive scheduling algorithms for wireless data throughput optimization. Model description; Development of adaptive online algorithms for determining the optimal-revenue vector in an iterative fashion; Results and conclusion.

Presents information on a study which introduced a reward paradigm to derive novel bounds for the performance of dynamic channel assignment schemes. Approaches to increase efficiency in the use of wireless services; Illustration on how the revenue-based bounds may be used to improve upon the Erlang bound approach; Numerical results for scenarios with uniform and varying reuse; Investigation on the tradeoff between efficiency and fairness that arises in the case of varying use.

Consider several source nodes communicating across a wireless network to a destination node with the help of several layers of relay nodes. Recent work by Avestimehr has approximated the capacity of this network up to an additive gap. The communication scheme achieving this capacity approximation is based on compress-and-forward, resulting in noise accumulation as the messages traverse the network. As a consequence, the approximation gap increases linearly with the network depth. This paper develops a computation alignment strategy that can approach the capacity of a class of layered, time-varying wireless relay networks up to an approximation gap that is independent of the network depth. This strategy is based on the compute-and-forward framework, which enables relays to decode deterministic functions of the transmitted messages. Alone, compute-and-forward is insufficient to approach the capacity as it incurs a penalty for approximating the wireless channel with complex-valued coefficients by a channel with integer coefficients. Here, this penalty is circumvented by carefully matching channel realizations across time slots to create integer-valued effective channels that are well suited to compute-and-forward. Unlike prior constant gap results, the approximation gap obtained in this paper also depends closely on the fading statistics, which are assumed to be i.i.d. Rayleigh. [ABSTRACT FROM PUBLISHER]

Traditional approaches in the analysis of downlink systems decouple the precoding and the channel estimation problems. However, in cellular systems with mobile users, these two problems are, in fact, tightly coupled. In this paper, this coupling is explicitly studied by accounting for the channel training overhead and estimation error while determining the overall system throughput. This paper studies the problem of utilizing imperfect channel estimates for efficient linear precoding and user selection. It presents precoding methods that take into account the degree of channel estimation error. Information-theoretic lower and upper bounds are derived to evaluate the performance of these precoding methods. In typical scenarios, these bounds are close. [ABSTRACT FROM PUBLISHER]