We investigate cooperative wireless relay networks in which the nodes can help each other in data transmission. We study different coding strategies in the single-source single-destination network with many relay nodes. Given the myriad of ways in which nodes can cooperate, there is a natural routing problem, i.e., determining an ordered set of nodes to relay the data from the source to the destination. We find that for a given route, the decode- and-forward strategy, which is an information theoretic cooperative coding strategy, achieves rates significantly higher than that achievable by the usual multi-hop coding strategy, which is a point-to-point non-cooperative coding strategy. We construct an algorithm to find an optimal route (in terms of rate maximizing) for the decode-and- forward strategy. Since the algorithm runs in factorial time in the worst case, we propose a heuristic algorithm that runs in polynomial time. The heuristic algorithm outputs an optimal route when the nodes transmit independent codewords. We implement these coding strategies using practical low density parity check codes to compare the performance of the strategies on different routes.
@cite_26: In wireless networks there is strong coupling among the traditional layers of the architecture, and these interactions can not be ignored. One example is the interaction between routing in the network layer and access control in the MAC layer. Another one is the coupling between power control in the physical layer and the scheduling in the MAC layer. In this paper, we assume a TDMA-based wireless ad-hoc network; and provide a centralized algorithm of joint power control, scheduling, and routing. Simulation results are given to show the improvement of the network performance by using the joint algorithm. Energy efficiency is another very important topic for ad hoc networks, and is considered in our algorithm. Our simulation shows that there is a trade-off between the energy consumption and the network performance, such as throughput and delay. Algorithm for distributed implementation is also discussed.
@cite_14: Recent advances in multi-radio multi-channel transmission technology have the potential of substantially improving the system capacity in multi-hop wireless networks. While previous work has primarily focused on the link level protocol design, in this paper we investigate the achievable performance gain, by jointly optimizing routing and scheduling in a multi-radio multichannel multi-hop network. We formulate the optimization under a deterministic model, and we seek to minimize overall system activation time in use to satisfy given end-to-end traffic demands subjected to the multi-access interference among neighboring transmissions and the radio interface constraint at each node. The exact solution to such an optimization problem is prohibitively complex due to the combinatorial complexity, particularly with the deployment of multi-radio and multi-channel. We develop a column generation based approach to solve this problem, which decomposes the original problem into sub-problems and solves them iteratively. Numerical results are given, and discussions as well as insights into the performance aspects for multi-radio multichannel deployment are provided.
@cite_28: We investigate the problem of energy efficiency in TDMA link scheduling with transmission power control using a realistic SINR-based interference model, given packets of a set of links to be transmitted within a latency bound. First we formulate a fundamental optimization problem (TJSPC) that provides tunable tradeoffs between energy, throughput and latency through a single parameter β. We present both expo- nential and polynomial complexity solutions to this problem and evaluate their performance. Our results show that for moderate traffic loads, with appropriate tuning of parameters, major energy savings can be obtained without significantly sacrificing throughput. We then investigate the scheduling and power control problem with the objective of minimizing the total transmission energy cost under the constraint that all transmission requests are satisfied (JSPC-TR). We present an iterative approach to solve JSPC-TR that leverages the heuristics for TJSPC and converges rapidly to the setting of β which achieves energy efficiency while guaranteeing data delivery.
@cite_9: In this paper, we study the problem of joint routing, link scheduling and power control to support high data rates for broadband wireless multihop networks. We first address the problem of finding an optimal link scheduling and power control policy that minimizes the total average transmission power in the wireless multihop network, subject to given constraints regarding the minimum average data rate per link, as well as peak transmission power constraints per node. Multiaccess signal interference is explicitly modeled. We use a duality approach whereby, as a byproduct of finding the optimal policy, we find the sensitivity of the minimal total average power with respect to the average data rate for each link. Since the minimal total average power is a convex function of the required minimum average data rates, shortest path algorithms with the link weights set to the link sensitivities can be used to guide the search for a globally optimum routing. We present a few simple examples that show our algorithm can find policies that support data rates that are not possible with conventional approaches. Moreover, we find that optimum allocations do not necessarily route traffic over minimum energy paths.
@cite_24: Of late, there has been an avalanche of cross-layer design proposals for wireless networks. A number of researchers have looked at specific aspects of network performance and, approaching cross-layer design via their interpretation of what it implies, have presented several cross-layer design proposals. These proposals involve different layers of the protocol stack, and address both cellular and ad hoc networks. There has also been work relating to the implementation of cross-layer interactions. It is high time that these various individual efforts be put into perspective and a more holistic view be taken. In this article, we take a step in that direction by presenting a survey of the literature in the area of cross-layer design, and by taking stock of the ongoing work. We suggest a definition for cross-layer design, discuss the basic types of cross-layer design with examples drawn from the literature, and categorize the initial proposals on how cross-layer interactions may be implemented. We then highlight some open challenges and new opportunities for cross-layer design. Designers presenting cross-layer design proposals can start addressing these as they move ahead.
@cite_19: We consider n points (nodes), some or all pairs of which are connected by a branch; the length of each branch is given. We restrict ourselves to the case where at least one path exists between any two nodes. We now consider two problems. Problem 1. Constrnct the tree of minimum total length between the n nodes. (A tree is a graph with one and only one path between every two nodes.) In the course of the construction that we present here, the branches are subdivided into three sets: I. the branches definitely assignec to the tree under construction (they will form a subtree) ; II. the branches from which the next branch to be added to set I, will be selected ; III. the remaining branches (rejected or not yet considered). The nodes are subdivided into two sets: A. the nodes connected by the branches of set I, B. the remaining nodes (one and only one branch of set II will lead to each of these nodes), We start the construction by choosing an arbitrary node as the only member of set A, and by placing all branches that end in this node in set II. To start with, set I is empty. From then onwards we perform the following two steps repeatedly. Step 1. The shortest branch of set II is removed from this set and added to
@cite_13: 
In the past, link optimization (i.e., maximizing the transmission rate between node pairs) and route optimization were done separately. Routing was optimized after the links between the nodes had been established. Algorithms such as Bellman-Ford [Section 24.1] cormen01 @cite_13 and Dijkstra's algorithm @cite_19 that assign costs to all links were used to find a route with the lowest cost from source to the destination. These ways of separating routing and coding are not optimal for MH or DF as the rates of the links change depending on which route is chosen. Realizing the inter-dependency between links and routes, it has been suggested that links and routes be jointly optimized @cite_9 @cite_26 @cite_28 @cite_14 . This gives rise to cross-layering @cite_24 in the OSI model. However, in these joint routing and coding work, data transmission from the source to destination is still based on MH. Routing algorithms that are optimized for MH might not be suitable for DF.