In this paper, we study a relaying network employing a rateless coding scheme proposed in the literature. We consider the problem of an energy-efficient operation of the scheme and derive algorithms that maximize the achievable rate for specified energy-per-bit bounds at the relay nodes. For this, we identify the rate function as a standard interference function, which allows to design efficient algorithmic solutions for solving the problem.

In this paper, we use the linear deterministic approximation model to study a two user multiple access channel (MAC) mutually interfering with a point to point link, which represents a basic model for the situation of device-to-device communication inside a cell. We derive upper bounds on the achievable sum rate and construct coding schemes achieving the upper bounds. For a large parameter range, the sum capacity is identical to the sum capacity of the interference channel obtained by silencing the weaker user in the MAC. For other interference configurations, the sum rate can be increased using interference alignment, which exploits the channel gain difference of the users in the MAC. From these results, lower bounds on the generalized degrees of freedom for the Gaussian counterpart are derived.

In this paper, we study the uplink of a cellular system using the linear deterministic approximation model, where there are two users transmitting to a receiver, mutually interfering with a third transmitter communicating with a second receiver. We give an achievable coding scheme and prove its optimality, i.e. characterize the capacity region. This scheme is a form of interference alignment which exploits the channel gain difference of the two-user cell.

This paper deals with computation of the capacity region of the discrete memoryless multiple-access channel (MAC), which is equivalent to solving a difficult nonconvex optimization problem. In the literature, it is claimed that for elementary MACs, i.e., MACs for which the size of output alphabet is greater or equal to the sizes of all input alphabets, the Karush-Kuhn-Tucker conditions provide a necessary and sufficient condition for sum-rate optimality. In this paper, we demonstrate that this claim does not hold, even for two-user channels with binary input and binary output alphabets. Consequently, the capacity computation problem for the discrete MAC remains an interesting and mostly unsolved problem.

In this paper, we study duality relations for the fading broadcast channel (BC) under additive white Gaussian noise and a strict ordering of the fading distributions without channel state information at the transmitter and the natural corresponding dual fading multiple-access channel (MAC). We show that if the fading distribution for the weaker user is non-deterministic, the achievable rate region using superposition coding and successive decoding and Gaussian signaling, which is conjectured to be the capacity region, is different from the dual MAC region. Duality holds only in the case of one-sided fading, where the fading distribution for the weaker user is deterministic. Despite this lack of duality, we propose to use the dual MAC in order to approximatively solve a non-convex problem for the BC. Specifically, we consider the problem of weighted sum-rate optimization for the BC and give, under some assumptions, upper bounds on the error incurred using this procedure.

Duality between multiple-access (MAC) and broadcast channels (BC) is an interesting and useful concept in multi-user information theory. While duality relationships have been established for some Gaussian and deterministic channels, the question to what extent and under which conditions duality holds for the case of discrete memoryless channels still remains open. In this paper, we define the notion of weak duality, closely related to the existing duality definitions. We give a sufficient condition and a necessary condition for a BC to be weakly dual to a discrete MAC of a certain type. Exemplarily, we use these conditions to derive a weak duality relation between the binary symmetric BC and this class of discrete MACs.

This paper studies the problem of admission control and air interface (AI) selection in heterogeneous network environments. The statistics of random user arrivals, channel conditions and service durations are considered for the optimization of heterogeneous access management strategies with respect to minimizing the expected mean cost for blocking events. Based on state aggregation in a semi-Markov decision process formulation, an efficient approximation algorithm using state aggregation for policy optimization is proposed. Though this solution is suboptimal, it still offers considerable performance gains in comparison to simpler heuristic strategies, which is demonstrated by simulations in a heterogeneous GSM-EDGE (Global System for Mobile Communications Enhanced Data Rate)/UMTS (Universal Mobile Telecommunications System) scenario. Furthermore, structural properties of optimal user assignment policies are studied, proving certain monotonicity properties for a specific type of systems.

This paper deals with the problem of computing the boundary of the capacity region for the memoryless two-user binary-input binary-output multiple-access channel ((2, 2; 2)-MAC), or equivalently, the computation of input probability distributions maximizing weighted sum-rate. This is equivalent to solving a difficult nonconvex optimization problem. For a restricted class of (2, 2; 2)-MACs and weight vectors, it is shown that, depending on an ordering property of the channel matrix, the optimal solution is located on the boundary, or the objective function has at most one stationary point in the interior of the domain. For this, the problem is reduced to a pseudoconcave one-dimensional optimization and the single-user problem.

This paper studies the problem of admission control and air interface selection in heterogeneous network environments. The statistics of random user arrivals and random service durations are considered for the optimization of heterogeneous access management strategies with respect to minimizing the expected mean cost for denial-of-service events. Based on state aggregation in a semi-Markov decision process formulation, an efficient approximation algorithm for policy optimization is proposed. Though this solution is suboptimal, it still offers considerable performance gains in comparison to simpler heuristic strategies, which is demonstrated by simulations in a heterogeneous GSM-EDGE/UMTS scenario.

A single queue incorporating a retransmission protocol is investigated, assuming that the sequence of per effort success probabilities in the Automatic Retransmission reQuest (ARQ) chain is a priori defined and no channel state information at the transmitter is available. A Markov Decision Problem with an average cost criterion is formulated where the possible actions are to either continue the retransmission process of an erroneous packet at the next time slot or to drop the packet and move on to the next packet awaiting for transmission. The cost per slot is a linear combination of the current queue length and a penalty term in case dropping is chosen as action. The investigation seeks policies that provide the best possible average packet delay-dropping trade-off for Quality of Service guarantees. An optimal deterministic stationary policy is shown to exist, several structural properties of which are obtained. Based on that, a class of suboptimal-policies is introduced. These suggest that it is almost optimal to use a K-truncated ARQ protocol as long as the queue length is lower than L, else send all packets in one shot. The work concludes with an evaluation of the optimal delay-dropping tradeoff using dynamic programming and a comparison between the optimal and suboptimal policies.