Mobile users' data rate and quality of service are limited by the fact that, within the duration of any given call, they experience severe variations in signal attenuation, thereby necessitating the use of some type of diversity. In this two-part paper, we propose a new form of spatial diversity, in which diversity gains are achieved via the cooperation of mobile users. Part I describes the user cooperation strategy while Part II focuses on implementation issues and performance analysis. Results show that, even though the inter-user channel is noisy, cooperation leads not only to an increase in capacity for both users but also to a more robust system, where users' achievable rates are less susceptible to channel variations.

We study the use of channel state information for random access in fading channels. Traditionally, random access protocols have been designed by assuming simple models for the physical layer where all users are symmetric and there is no notion of channel state. We introduce a reception model that takes into account the channel states of various users. Under the assumption that each user has access to his channel state information (CSI), we propose a variant of Slotted ALOHA protocol for medium access control, where the transmission probability is allowed to be a function of the CSL The function is called the transmission control scheme. Assuming the finite user infinite buffer model we derive expressions for the maximum stable throughput of the system. We introduce the notion of asymptotic stable throughput (AST) that is the maximum stable throughput as the number of users goes to infinity. We consider two types of transmission control namely population independent transmission control (PITC) where the transmission control is not a function of the size of the network and population dependent transmission control where the transmission control is a function of the size of the network. We obtain expressions for the AST achievable with PITC. For population dependent transmission control, we introduce a particular transmission control that can potentially lead to significant gains in AST. For both PITC and PDTC, we show that the effect of transmission control is equivalent to changing the probability distribution of the channel state. The theory is then applied to CDMA networks with Linear Minimum Mean Square Error (LMMSE) receivers and Matched Filters (MF) to illustrate the effectiveness of utilizing channel state. It is shown that through the use of channel state, with an...

We seek to maximize the data throughput of an energy and time constrained transmitter sending data over a fading channel. The transmitter has a fixed amount of energy and a limited amount of time to send data. Given that the channel fade state determines the throughput obtained per unit of energy expended, the goal is to obtain a policy for scheduling transmissions that maximizes the expected data throughput. We develop a dynamic programming formulation that leads to an optimal closed-form transmission schedule. We then extend our approach to the problem of minimizing the energy required to send a fixed amount of data over a fading channel given deadline constraints.

We give a development of the ODE method for the analysis of recursive algorithms described by a stochastic recursion. With variability modeled via an underlying Markov process, and under general assumptions, the following results are obtained: (i) Stability of an associated ODE implies that the stochastic recursion is stable in a strong sense when a gain parameter is small. (ii) The range of gain-values is quantified through a spectral analysis of an associated linear operator, providing a non-local theory, even for nonlinear systems. (iii) A second-order analysis shows precisely how variability leads to sensitivity of the algorithm with respect to the gain parameter. All results are obtained within the natural operator-theoretic framework of geometrically ergodic Markov processes.

We seek to maximize the data throughput of an energy and time constrained transmitter sending data over a fading channel. The transmitter has a fixed amount of energy and a limited amount of time to send data.

Abstract — In order to understand the key merits of multiuser diversity techniques in relay-assisted cellular multihop networks, this paper analyzes the spectral efficiency of opportunistic (i.e., channel-aware) scheduling algorithms over a fading multiuser relay channel with K users in the asymptotic regime of large (but finite) number of users. Using tools from extreme-value theory, we characterize the limiting distribution of spectral efficiency focusing on Type I convergence and utilize it in investigating the large system behavior of the multiuser relay channel as a function of the number of users and physical channel signalto-noise ratios (SNRs). Our analysis results in very accurate formulas in the large (but finite) K regime, provides insights on the potential performance enhancements from multihop routing and spectrum reuse policies in the presence of multiuser diversity gains from opportunistic scheduling and helps to identify the regimes and conditions in which relay-assisted multiuser communication provides a clear advantage over direct multiuser communication. I.

In a multiple access communication system that uses packet transmissions, the packets of one user might be subject to asynchronous interference from the packets of other users in the system. This paper analyses the influence of the placement of training symbols on the performance of the channel estimator in this scenario. The analysis of the mean square error (MSE) of the minimum mean square error estimator (MMSE) is shown to be equivalent to the analysis of the Fisher Information Matrix (FIM) for mixtures of Gaussian distributions. A complete solution to this problem is hard to find, but the bounds, asymptotics and simulations suggest that the best placement of training symbols is in two clusters of equal or quasi-equal size at the two ends of the data packet.

Abstract not found

Abstract—We consider a single server discrete-time system with K users where the server picks operating points from a compact, convex and co-ordinate convex set in ℜ K +. For this system we analyse the performance of a stablising policy that at any given time picks operating points from the allowed rate region that maximise a weighted sum of rate, where the weights depend upon the workloads of the users. Assuming a Large Deviations Principle (LDP) for the arrival processes in the Skorohod space of functions that are right-continuous with left-hand limits we establish an LDP for the workload process using a generalised version of the contraction principle to derive the corresponding rate function. With the LDP result available we then analyse the tail probabilities of the workloads under different buffering scenarios. I.

For a wide class of multi-user systems, a subset of capacity region which includes the corner points and the sum-capacity facet has a special structure known as polymatroid. Multiaccess channels with fixed input distributions and multiple-antenna broadcast channels are examples of such systems. Any interior point of the sum-capacity facet can be achieved by time-sharing among corner points or by an alternative method known as rate-splitting. The main purpose of this paper is to find a point on the sum-capacity facet which satisfies a notion of fairness among active users. This problem is addressed in two cases: (i) where the complexity of achieving interior points is not feasible, and (ii) where the complexity of achieving interior points is feasible. For the first case, the corner point for which the minimum rate of the active users is maximized (max-min corner point) is desired for signaling. A simple greedy algorithm is introduced to find the optimum max-min corner point. For the second case, the polymatroid properties are exploited to locate a rate-vector on the sum-capacity facet which is optimally fair in the sense that the minimum rate among all users is maximized (max-min rate). In the case that the rate of some users can not increase further (attain the max-min value), the algorithm recursively maximizes the minimum rate among the rest of the users. It is shown that the problems