We consider a user communicating over a fading channel with perfect channel state information. Data is assumed to arrive from some higher layer application and is stored in a buffer until it is transmitted. We study adapting the user's transmission rate and power based on the channel state information as well as the buffer occupancy; the objectives are to regulate both the long-term average transmission power and the average buffer delay incurred by the traffic. Two models for this situation are discussed; one corresponding to fixed-length/variable-rate codewords and one corresponding to variable-length codewords. The trade-off between the average delay and the average transmission power required for reliable communication is analyzed. A dynamic programming formulation is given to find all Pareto optimal power/delay operating points. We then quantify the behavior of this tradeoff in the regime of asymptotically large delay. In this regime we characterize simple buffer control policies which exhibit optimal characteristics. Connections to the delay-limited capacity and the expected capacity of fading channels are also discussed.

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Abstract-We consider scheduling and resource allocation for the downlink of a cellular OFDM system, with various practical considerations including integer carrier allocations, different subchannelization schemes, a maximum SNR constraint per tone, and "self-noise" due to channel estimation errors and phase noise. During each time-slot a subset of users must be scheduled for transmission, and the available tones and transmission power must be allocated among the selected users. Employing a gradient-based scheduling scheme presented in earlier papers reduces this to an optimization problem to be solved in each time-slot. Using dual decomposition techniques, we give an optimal algorithm for this problem when multiple users can time-share each carrier. We then give several low complexity heuristics that enforce an integer constraint on the carrier allocation. Simulations show that the algorithms presented all achieve similar performance under a wide range of scenarios, and that the performance gap between the optimal and suboptimal algorithms widens when per user SNR constraints or channel estimation errors are considered.

Abstract-Multiuser diversity refers to a type of diversity present across different users in a fading environment. This diversity can be exploited by scheduling transmissions so that users transmit when their channel conditions are favorable. Using such an approach leads to a system capacity that increases with the number of users. However, such scheduling requires centralized control. In this paper, we consider a decentralized medium access control (MAC) protocol, where each user only has knowledge of its own channel gain. We consider a variation of the ALOHA protocol, channel-aware ALOHA; using this protocol we show that users can still exploit multi-user diversity gains. First we consider a backlogged model, where each user always has packets to send. In this case we show that the total system throughput increases at the same rate as in a system with a centralized scheduler. Asymptotically, the fraction of throughput lost due to the random access protocol is shown to be 1/e. We also consider a splitting algorithm, where the splitting sequence depends on the users' channel gains; this algorithm is shown to approach the throughput of an optimal centralized scheme. Next we consider a system with an infinite user population and random arrivals. In this case, it is proved that a variation of channel-aware ALOHA is stable for any total arrival rate in a memoryless channel, given that users can estimate the backlog. Extensions for channels with memory are also discussed.

Abstract-Orthogonal Frequency Division Multiplexing (OFDM) with dynamic scheduling and resource allocation is widely considered to be a key component of 4G cellular networks. However, scheduling and resource allocation in an OFDM system is complicated, especially in the uplink due to two reasons: (1) the discrete nature of channel assignments, and (2) the heterogeneity of the users' channel conditions, individual resource constraints and application requirements. We approach this problem using a gradient-based scheduling framework presented in previous work. Physical layer resources (bandwidth and power) are allocated to maximize the projection onto the gradient of a total system utility function which models application-layer Quality of Service (QoS). This is formulated as a convex optimization problem. We present an optimal solution using a dual decomposition. This solution has prohibitively high computational complexity but reveals guiding principles that we use to generate a family of lower complexity sub-optimal algorithms. We compare the performance of these algorithms via a realistic OFDM simulator.

Abstract-To achieve the full multiplexing gain of MIMO interference networks at high SNRs, the interference from different transmitters must be aligned in lower-dimensional subspaces at the receivers. Recently a distributed "max-SINR" algorithm for precoder optimization has been proposed that achieves interference alignment for sufficiently high SNRs. We show that this algorithm can be interpreted as a variation of an algorithm that minimizes the sum Mean Squared Error (MSE). To maximize sum utility, where the utility depends on rate or SINR, a weighted sum MSE objective is used to compute the beams, where the weights are updated according to the sum utility objective. We specify a class of utility functions for which convergence of the sum utility to a local optimum is guaranteed with asynchronous updates of beams, receiver filters, and utility weights. Numerical results are presented, which show that this method achieves interference alignment at high SNRs, and can achieve different points on the boundary of the achievable rate region by adjusting the MSE weights.

The subject of this paper is the long-standing open problem of developing a general capacity theory for wireless networks, particularly a theory capable of describing the fundamental performance limits of mobile ad hoc networks (MANETs). A MANET is a peer-to-peer network with no pre-existing infrastructure. MANETs are the most general wireless networks, with single-hop, relay, interference, mesh, and star networks comprising special cases. The lack of a MANET capacity theory has stunted the development and commercialization of many types of wireless networks, including emergency, military, sensor, and community mesh networks. Information theory, which has been vital for links and centralized networks, has not been successfully applied to decentralized wireless networks. Even if this was accomplished, for such a theory to truly characterize the limits of deployed MANETs it must overcome three key roadblocks. First, most current capacity results rely on the allowance of unbounded delay and reliability. Second, spatial and timescale decompositions have not yet been developed for optimally modeling the spatial and temporal dynamics of wireless networks. Third, a useful network capacity theory must integrate rather than ignore the important role of overhead messaging and feedback. This paper describes some of the shifts in thinking that may be needed to overcome these roadblocks and develop a more general theory that we refer to as non-equilibrium information theory.

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