We analyze the capacity scaling laws of clustered ad hoc networks in which nodes are distributed according to a doubly stochastic shot-noise Cox process. We identify five different operational regimes, and for each regime we devise a communication strategy that allows to achieve a throughput to within a poly-logarithmic factor (in the number of nodes) of the maximum theoretical capacity.
Third, our constructive lower bounds require to employ novel scheduling routing strategies in combination to existing cooperative communication schemes. Such strategies represent an important contribution in themselves, as they could be adopted to cope with the nodes spatial inhomogeneity in more general topologies which cannot be described by the SNCP model considered here. At last we emphasize that this work extends @cite_10 @cite_12 , where we have analyzed the capacity of networks in which nodes are distributed according to a SNCP model, but considering single-user communication schemes only (i.e., traditional point-to-point links).
@cite_10 @cite_12
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In this paper, we introduce a distributed dynamic routing algorithm in multi-hop cognitive radio (CR) networks, in which secondary users (SUs) want to minimize their interference to the primary users (PUs) while keeping the delay along the route low. We employ a cognitive pilot channel (CPC) for SUs to be able to access the information about PUs, including PUs' locations and channel conditions. Medial axis with a relaxation factor is used as a reference path for the routing, along which we develop a hierarchical structure for multiple sources to reach their destinations. We introduce a temporal and spatial dynamic non-cooperative game to model the interactions among the SUs as well as their influences on the PUs, and obtain by backward induction a set of mixed (behavioral) Nash equilibrium strategies. We also employ a multi-stage fictitious play learning algorithm for distributed routing, which minimizes the overall interference from the SUs to the PUs, as well as the average packet delay along the route from the SU nodes to their destinations. Simulation results show that our proposed algorithm can avoid congestion in the CR network and minimize delay while keeping the interference level low. In this paper, we address the following question: given a specific placement of wireless nodes in physical space and a specific traffic workload, what is the maximum throughput that can be supported by the resulting network? Unlike previous work that has focused on computing asymptotic performance bounds under assumptions of homogeneity or randomness in the network topology and or workload, we work with any given network and workload specified as inputs.A key issue impacting performance is wireless interference between neighboring nodes. We model such interference using a conflict graph, and present methods for computing upper and lower bounds on the optimal throughput for the given network and workload. To compute these bounds, we assume that packet transmissions at the individual nodes can be finely controlled and carefully scheduled by an omniscient and omnipotent central entity, which is unrealistic. Nevertheless, using ns-2 simulations, we show that the routes derived from our analysis often yield noticeably better throughput than the default shortest path routes even in the presence of uncoordinated packet transmissions and MAC contention. This suggests that there is opportunity for achieving throughput gains by employing an interference-aware routing protocol. In this paper, a low-complexity delay-aware cross-layer scheduling algorithm for two-hop relay communication systems is proposed. The complex interactions of the queues at the source node and the M relay nodes (RSs) are modeled as an infinite horizon average reward Markov decision process (MDP), whose state space involves the joint queue state information (QSI) of the queues at the source node and the M RSs as well as the joint channel state information (CSI) of all S-R and R-D links. To address the curse of dimensionality, an equivalent MDP formulation is first proposed, where the system state depends only on global QSI. Furthermore, using approximate MDP and stochastic learning, an auction-based distributed online learning algorithm is derived, where each node iteratively estimates a per-node value function based on real-time observations of the local CSI and local QSI as well as signaling between relays. The combined distributed learning converges almost surely to a global optimal solution for large arrivals. Finally, it is showed by simulations that the proposed scheme achieves significant gain compared with various baselines such as the conventional CSIT-only control and the throughput optimal control (in stability sense). A fundamental problem in large scale wireless networks is the energy efficient broadcast of source messages to the whole network. The energy consumption increases as the network size grows, and the optimization of broadcast efficiency becomes more important. In this paper, we study the optimal power allocation problem for cooperative broadcast in dense large-scale networks. In the considered cooperation protocol, a single source initiates the transmission and the rest of the nodes retransmit the source message if they have decoded it reliably. Each node is allocated an-orthogonal channel and the nodes improve their receive signal-to-noise ratio (SNR), hence the energy efficiency, by maximal-ratio combining the receptions of the same packet from different transmitters. We assume that the decoding of the source message is correct as long as the receive SNR exceeds a predetermined threshold. Under the optimal cooperative broadcasting, the transmission order (i.e., the schedule) and the transmission powers of the source and the relays are designed so that every node receives the source message reliably and the total power consumption is minimized. In general, finding the best scheduling in cooperative broadcast is known to be an NP-complete problem. In this paper, we show that the optimal scheduling problem can be solved for dense networks, which we approximate as a continuum of nodes. Under the continuum model, we derive the optimal scheduling and the optimal power density. Furthermore, we propose low-complexity, distributed and power efficient broadcasting schemes and compare their power consumptions with those-of-a traditional noncooperative multihop transmission.