19,866 research outputs found
Cooperative Precoding/Resource Allocation Games under Spectral Mask and Total Power Constraints
The use of orthogonal signaling schemes such as time-, frequency-, or
code-division multiplexing (T-, F-, CDM) in multi-user systems allows for
power-efficient simple receivers. It is shown in this paper that by using
orthogonal signaling on frequency selective fading channels, the cooperative
Nash bargaining (NB)-based precoding games for multi-user systems, which aim at
maximizing the information rates of all users, are simplified to the
corresponding cooperative resource allocation games. The latter provides
additional practically desired simplifications to transmitter design and
significantly reduces the overhead during user cooperation. The complexity of
the corresponding precoding/resource allocation games, however, depends on the
constraints imposed on the users. If only spectral mask constraints are
present, the corresponding cooperative NB problem can be formulated as a convex
optimization problem and solved efficiently in a distributed manner using dual
decomposition based algorithm. However, the NB problem is non-convex if total
power constraints are also imposed on the users. In this case, the complexity
associate with finding the NB solution is unacceptably high. Therefore, the
multi-user systems are categorized into bandwidth- and power-dominant based on
a bottleneck resource, and different manners of cooperation are developed for
each type of systems for the case of two-users. Such classification guarantees
that the solution obtained in each case is Pareto-optimal and actually can be
identical to the optimal solution, while the complexity is significantly
reduced. Simulation results demonstrate the efficiency of the proposed
cooperative precoding/resource allocation strategies and the reduced complexity
of the proposed algorithms.Comment: 33 pages, 8 figures, Submitted to the IEEE Trans. Signal Processing
in Oct. 200
Weighted Max-Min Resource Allocation for Frequency Selective Channels
In this paper, we discuss the computation of weighted max-min rate allocation
using joint TDM/FDM strategies under a PSD mask constraint. We show that the
weighted max-min solution allocates the rates according to a predetermined rate
ratio defined by the weights, a fact that is very valuable for
telecommunication service providers. Furthermore, we show that the problem can
be efficiently solved using linear programming. We also discuss the resource
allocation problem in the mixed services scenario where certain users have a
required rate, while the others have flexible rate requirements. The solution
is relevant to many communication systems that are limited by a power spectral
density mask constraint such as WiMax, Wi-Fi and UWB
Optimal Power Control for Multiuser CDMA Channels
In this paper, we define the power region as the set of power allocations for
K users such that everybody meets a minimum signal-to-interference ratio (SIR).
The SIR is modeled in a multiuser CDMA system with fixed linear receiver and
signature sequences. We show that the power region is convex in linear and
logarithmic scale. It furthermore has a componentwise minimal element. Power
constraints are included by the intersection with the set of all viable power
adjustments.
In this framework, we aim at minimizing the total expended power by
minimizing a componentwise monotone functional. If the feasible power region is
nonempty, the minimum is attained. Otherwise, as a solution to balance
conflicting interests, we suggest the projection of the minimum point in the
power region onto the set of viable power settings. Finally, with an
appropriate utility function, the problem of minimizing the total expended
power can be seen as finding the Nash bargaining solution, which sheds light on
power assignment from a game theoretic point of view. Convexity and
componentwise monotonicity are essential prerequisites for this result.Comment: To appear in the proceedings of the 2005 IEEE International Symposium
on Information Theory, Adelaide, Australia, September 4-9, 200
Distributed Game Theoretic Optimization and Management of Multichannel ALOHA Networks
The problem of distributed rate maximization in multi-channel ALOHA networks
is considered. First, we study the problem of constrained distributed rate
maximization, where user rates are subject to total transmission probability
constraints. We propose a best-response algorithm, where each user updates its
strategy to increase its rate according to the channel state information and
the current channel utilization. We prove the convergence of the algorithm to a
Nash equilibrium in both homogeneous and heterogeneous networks using the
theory of potential games. The performance of the best-response dynamic is
analyzed and compared to a simple transmission scheme, where users transmit
over the channel with the highest collision-free utility. Then, we consider the
case where users are not restricted by transmission probability constraints.
Distributed rate maximization under uncertainty is considered to achieve both
efficiency and fairness among users. We propose a distributed scheme where
users adjust their transmission probability to maximize their rates according
to the current network state, while maintaining the desired load on the
channels. We show that our approach plays an important role in achieving the
Nash bargaining solution among users. Sequential and parallel algorithms are
proposed to achieve the target solution in a distributed manner. The
efficiencies of the algorithms are demonstrated through both theoretical and
simulation results.Comment: 34 pages, 6 figures, accepted for publication in the IEEE/ACM
Transactions on Networking, part of this work was presented at IEEE CAMSAP
201
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