The problem of communication among nodes in an is considered, where radio power decay and interference are limiting factors. It has been shown previously that, with simple multi-hopping, the achievable total communication rate in such a network is at most @math . In this work, we study the benefit of node cooperation in conjunction with multi-hopping on the network capacity. We propose a multi-phase communication scheme, combining distributed MIMO transmission with multi-hop forwarding among clusters of nodes. We derive the network throughput of this communication scheme and determine the optimal cluster size. This provides a constructive lower bound on the network capacity. We first show that in a rate of @math can be achieved with transmission power scaling of @math , where @math is the signal path-loss exponent. We further extend this result to , where we show a rate of @math can be achieved with transmission power scaling of @math in a random network with unit node density. In particular, as @math approaches 2, only constant transmission power is required. Finally, we study a random network with density @math and show that a rate of @math is achieved and the required power scales as @math .
@cite_4: We derive an information-theoretic upper bound on the rate per communication pair in a large ad hoc wireless network. We show that under minimal conditions on the attenuation due to the environment and for networks with a constant density of users, this rate tends to zero as the number of users gets large.
@cite_7: We derive upper bounds on the transport capacity of wireless networks. The bounds obtained are solely dependent on the geographic locations and power constraints of the nodes. As a result of this derivation, we are able to conclude the optimality, in the sense of scaling of transport capacity with the number of nodes, of a multihop communication strategy for a class of network topologies.
@cite_3: How much information can be carried over a wireless network with a multiplicity of nodes, and how should the nodes cooperate to transfer information? To study these questions, we formulate a model of wireless networks that particularly takes into account the distances between nodes, and the resulting attenuation of radio signals, and study a performance measure that weights information by the distance over which it is transported. Consider a network with the following features. I) n nodes located on a plane, with minimum separation distance spl rho sub min >0. II) A simplistic model of signal attenuation e sup - spl gamma spl rho spl rho sup spl delta over a distance spl rho , where spl gamma spl ges 0 is the absorption constant (usually positive, unless over a vacuum), and spl delta >0 is the path loss exponent. III) All receptions subject to additive Gaussian noise of variance spl sigma sup 2 . The performance measure we mainly, but not exclusively, study is the transport capacity C sub T :=sup spl Sigma on sub spl lscr =1 sup m R sub spl lscr spl middot spl rho sub spl lscr , where the supremum is taken over m, and vectors (R sub 1 ,R sub 2 ,...,R sub m ) of feasible rates for m source-destination pairs, and spl rho sub spl lscr is the distance between the spl lscr th source and its destination. It is the supremum distance-weighted sum of rates that the wireless network can deliver. We show that there is a dichotomy between the cases of relatively high and relatively low attenuation. When spl gamma >0 or spl delta >3, the relatively high attenuation case, the transport capacity is bounded by a constant multiple of the sum of the transmit powers of the nodes in the network. However, when spl gamma =0 and spl delta <3 2, the low-attenuation case, we show that there exist networks that can provide unbounded transport capacity for fixed total power, yielding zero energy priced communication. Examples show that nodes can profitably cooperate over large distances using coherence and multiuser estimation when the attenuation is low. These results are established by developing a coding scheme and an achievable rate for Gaussian multiple-relay channels, a result that may be of interest in its own right.
@cite_5: When n identical randomly located nodes, each capable of transmitting at W bits per second and using a fixed range, form a wireless network, the throughput spl lambda (n) obtainable by each node for a randomly chosen destination is spl Theta (W spl radic (nlogn)) bits per second under a noninterference protocol. If the nodes are optimally placed in a disk of unit area, traffic patterns are optimally assigned, and each transmission's range is optimally chosen, the bit-distance product that can be transported by the network per second is spl Theta (W spl radic An) bit-meters per second. Thus even under optimal circumstances, the throughput is only spl Theta (W spl radic n) bits per second for each node for a destination nonvanishingly far away. Similar results also hold under an alternate physical model where a required signal-to-interference ratio is specified for successful receptions. Fundamentally, it is the need for every node all over the domain to share whatever portion of the channel it is utilizing with nodes in its local neighborhood that is the reason for the constriction in capacity. Splitting the channel into several subchannels does not change any of the results. Some implications may be worth considering by designers. Since the throughput furnished to each user diminishes to zero as the number of users is increased, perhaps networks connecting smaller numbers of users, or featuring connections mostly with nearby neighbors, may be more likely to be find acceptance.
@cite_11: Wireless networks with a minimum inter-node separation distance are studied where the signal attenuation grows in magnitude as 1 ρδ with distance ρ. Two performance measures of wireless networks are analyzed. The transport capacity is the supremum of the total distance-rate products that can be supported by the network. The energy cost of information transport is the infimum of the ratio of the transmission energies used by all the nodes to the number of bit-meters of information thereby transported.If the phases of the attenuations between node pairs are uniformly and independently distributed, it is shown that the expected transport capacity is upper-bounded by a multiple of the total of the transmission powers of all the nodes, whenever δ > 2 for two-dimensional networks or δ > 5 4 for one-dimensional networks, even if all the nodes have full knowledge of all the phases, i.e., full channel state information. If all nodes have an individual power constraint, the expected transport capacity grows at most linearly in the number of nodes due to the linear growth of the total power. This establishes the best case order of expected transport capacity for these ranges of path-loss exponents since linear scaling is also feasible.If the phases of the attenuations are arbitrary, it is shown that the transport capacity is upper-bounded by a multiple of the total transmission power whenever δ > 5 2 for two-dimensional networks or δ > 3 2 for one-dimensional networks, even if all the nodes have full channel state information. This shows that there is indeed a positive energy cost which is no less than the reciprocal of the above multiplicative constant. It narrows the transition regime where the behavior is still open, since it is known that when δ < 3 2 for two-dimensional networks, or δ < 1 for one-dimensional networks, the transport capacity cannot generally be bounded by any multiple of the total transmit power.
: The seminal work @cite_5 provides both upper-bounds and constructive communication schemes to show that the point-to-point capacity of a dense wireless network is @math . In @cite_7 , @cite_4 , @cite_11 , and @cite_3 , the authors derive information theoretic upper-bounds on the network capacity. These works study the capacity scaling of extended networks and obtain upper-bounds on the capacity by using cut-set bounds. In particular, Xie prove that for environments with path-loss exponent @math and constant per node power, the expected transport capacity grows at most linearly in the number of nodes, so that for an extended network with uniformly distributed source-destination pairs, the network capacity is @math @cite_11 .