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Cooperative and Constrained MIMO Communications in Wireless Ad Hoc
#1

Cooperative and Constrained MIMO Communications in Wireless Ad Hoc


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INTRODUCTION
IN modern wireless communications, enhanced spectral
efficiency can be achieved by the use of multiple-inputmultiple-
output (MIMO) systems. Recently, MIMO has attracted
extensive attention and various techniques have been
proposed for both cellular systems and ad hoc networks [1,
2] to achieve improved system performance. However, in
wireless ad hoc/sensor networks, direct employment of MIMO
to each node might not be feasible, since MIMO might require
complex transceiver and signal processing modules, which
result in high power consumption. Furthermore, nodes in
wireless ad hoc networks/sensor networks are often powered
by batteries with limited energy.



SYSTEM DESCRIPTION
A. System and Channel Models
We assume that the source node can form a virtual MIMO
system by cooperating with its neighbors, where all such
nodes, including the source node, have a single antenna. However,
the destination node is assumed to be large enough so that

multiple receiver antennas can be implemented. For example,
this scenario might correspond to one where multiple soldiers
with small carry-on communication units want to transmit to a
destination node mounted on a vehicle.


IV. LOCAL DISTRIBUTION ANALYSIS
During the local distribution stage, the source node sends
the data information to the selected nodes. The energy for
the local distribution to each cooperative node consists of the
transmission energy which ensures reliable communications
from the source node to that particular cooperative node, and
the circuit energy consumption, which is the sum of the energy
consumptions of all the circuit blocks [3, 14].



III. OPTIMIZATION PROBLEM FORMULATION
In order to consider the local distribution and long haul
transmission together, we need to include the energy consumptions
and the delays of the two stages in the optimization
problem. That is, given possible candidates, the optimization
should look for the optimal subset of cooperative nodes,
labeled as ∗, with nodes, as well as the corresponding
power/bit allocations for each of them, and , = 1, , .
We denote by and the total end-to-end energy and the
total end-to-end delay, respectively, with maximum allowable
values and , respectively.
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#2
Given energy delay and data rate constraints, a source node dynamically selects its cooperating nodes from its neighbors to form a virtual MIMO system with the destination node (assumed to have multiple antennas), as well as adaptively assigns the power level and adjust the constellation size for each of the selected cooperative nodes. In order to optimize the performance of the system, we consider together the optimization of all these parameters, given the aforementioned system limitations.

Modern wireless communications technology has increased spectral efficiency can be achieved by using multiple input multiple output (MIMO) systems. Recently, MIMO has attracted extensive attention and several techniques have been proposed for cellular systems and ad hoc networks to achieve improved system performance. However, in ad hoc / sensor wireless networks, direct use of MIMO at each node may not be feasible, since MIMO may require complex signal processing and transceiver modules, resulting in high power consumption. In addition, nodes in ad hoc wireless networks / sensor networks are often powered by batteries with limited power. Wireless networks, such as ad hoc networks and sensors, are composed of a set of nodes that share a common wireless medium. Such networks have been attracting intense attention to their enormous potential in application. However, there are still many problems open today, making it difficult to effectively design high quality wireless networks.
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