Information flow in a telecommunication network is accomplished through the interaction of mechanisms at various design layers with the end goal of supporting the information exchange needs of the applications. In wireless networks in particular, the different layers interact in a nontrivial manner in order to support information transfer. In this text we will present abstract models that capture the cross-layer interaction from the physical to transport layer in wireless network architectures including cellular, ad-hoc and sensor networks as well as hybrid wireless-wireline. The model allows for arbitrary network topologies as well as traffic forwarding modes, including datagrams and virtual circuits. Furthermore the time varying nature of a wireless network, due either to fading channels or to changing connectivity due to mobility, is adequately captured in our model to allow for state dependent network control policies. Quantitative performance measures that capture the quality of service requirements in these systems depending on the supported applications are discussed, including throughput maximization, energy consumption minimization, rate utility function maximization as well as general performance functionals. Cross-layer control algorithms with optimal or suboptimal performance with respect to the above measures are presented and analyzed. A detailed exposition of the related analysis and design techniques is provided.

The multiple-access framework of ZigZag decoding (Gollakota and Katabi 2008) is a useful technique for combating interference via multiple repeated transmissions, and is known to be compatible with distributed random access protocols. However, in the presence of noise this type of decoding can magnify errors, particularly when packet sizes are large. We show that ZigZag decoding can be seen as an instance of belief propagation in the high signal-to-noise ratio (SNR) limit. Building on this observation, we present a simple soft-decoding version, called SigSag, that improves performance. We show that for two users, collisions result in a cycle-free factor graph that can be optimally decoded via belief propagation. For collisions between more than two users, we show that if a simple bit-permutation is used then the graph is locally tree-like with high probability, and hence belief propagation is near-optimal. Further, we introduce the joint channel-collision decoding which decodes the collided packets while the packets are coded by an LDPC code. Through simulations we show that our scheme performs better than coordinated collision-free time division multiple access (TDMA) and the ZigZag decoder. Furthermore, we investigate the performance of the joint channel-collision decoder in different scenarios and show that it performs better than TDMA and ZigZag decoder accompanied by sum-product decoding.

This paper focuses on energy-efficient packet transmission with individual packet delay constraints over a fading channel. The problem of optimal offline scheduling (vis-a-vis total transmission energy), assuming information of all packet arrivals and channel states before scheduling, is formulated as a convex optimization problem with linear constraints. The optimality conditions are analyzed. From the analysis, a recursive algorithm is developed to search for the optimal offline scheduling. The optimal offline scheduler tries to equalize the energy-rate derivative function as much as possible subject to the causality and delay constraints. The properties of the optimal transmission rates are analyzed, from which upper and lower bounds of the average packet delay are derived. In addition, a heuristic online scheduling algorithm, using causal traffic and channel information, is proposed and shown via simulations to achieve comparable energy and delay performance to the optimal offline scheduler in a wide range of scenarios.

This paper considers a base station that delivers packets to multiple receivers through a sequence of coded transmissions. All receivers overhear the same transmissions. Each receiver may already have some of the packets as side information, and requests another subset of the packets. This problem is known as the index coding problem and can be represented by a bipartite digraph. An integer linear program is developed that provides a lower bound on the minimum number of transmissions required for any coding algorithm. Conversely, its linear programming relaxation is shown to provide an upper bound that is achievable by a simple form of vector linear coding. Thus, the information theoretic optimum is bounded by the integrality gap between the integer program and its linear relaxation. In the special case, when the digraph has a planar structure, the integrality gap is shown to be zero, so that exact optimality is achieved. Finally, for nonplanar problems, an enhanced integer program is constructed that provides a smaller integrality gap. The dual of this problem corresponds to a more sophisticated partial clique coding strategy that time-shares between maximum distance separable codes. This paper illuminates the relationship between index coding, duality, and integrality gaps between integer programs and their linear relaxations.

We investigate optimal resource allocation for delay-limited cooperative communication in time varying wireless networks. Motivated by real-time applications that have stringent delay constraints, we develop a dynamic cooperation strategy that makes optimal use of network resources to achieve a target outage probability (reliability) for each user subject to average power constraints. Using the technique of Lyapunov optimization, we first present a general framework to solve this problem and then derive quasi-closed form solutions for several cooperative protocols proposed in the literature. Unlike earlier works, our scheme does not require prior knowledge of the statistical description of the packet arrival, channel state, and node mobility processes and can be implemented in an online fashion.