1,890 research outputs found
Energy-Efficient Resource Allocation in Wireless Networks: An Overview of Game-Theoretic Approaches
An overview of game-theoretic approaches to energy-efficient resource
allocation in wireless networks is presented. Focusing on multiple-access
networks, it is demonstrated that game theory can be used as an effective tool
to study resource allocation in wireless networks with quality-of-service (QoS)
constraints. A family of non-cooperative (distributed) games is presented in
which each user seeks to choose a strategy that maximizes its own utility while
satisfying its QoS requirements. The utility function considered here measures
the number of reliable bits that are transmitted per joule of energy consumed
and, hence, is particulary suitable for energy-constrained networks. The
actions available to each user in trying to maximize its own utility are at
least the choice of the transmit power and, depending on the situation, the
user may also be able to choose its transmission rate, modulation, packet size,
multiuser receiver, multi-antenna processing algorithm, or carrier allocation
strategy. The best-response strategy and Nash equilibrium for each game is
presented. Using this game-theoretic framework, the effects of power control,
rate control, modulation, temporal and spatial signal processing, carrier
allocation strategy and delay QoS constraints on energy efficiency and network
capacity are quantified.Comment: To appear in the IEEE Signal Processing Magazine: Special Issue on
Resource-Constrained Signal Processing, Communications and Networking, May
200
How Much Multiuser Diversity is Required for Energy Limited Multiuser Systems?
Multiuser diversity (MUDiv) is one of the central concepts in multiuser (MU)
systems. In particular, MUDiv allows for scheduling among users in order to
eliminate the negative effects of unfavorable channel fading conditions of some
users on the system performance. Scheduling, however, consumes energy (e.g.,
for making users' channel state information available to the scheduler). This
extra usage of energy, which could potentially be used for data transmission,
can be very wasteful, especially if the number of users is large. In this
paper, we answer the question of how much MUDiv is required for energy limited
MU systems. Focusing on uplink MU wireless systems, we develop MU scheduling
algorithms which aim at maximizing the MUDiv gain. Toward this end, we
introduce a new realistic energy model which accounts for scheduling energy and
describes the distribution of the total energy between scheduling and data
transmission stages. Using the fact that such energy distribution can be
controlled by varying the number of active users, we optimize this number by
either (i) minimizing the overall system bit error rate (BER) for a fixed total
energy of all users in the system or (ii) minimizing the total energy of all
users for fixed BER requirements. We find that for a fixed number of available
users, the achievable MUDiv gain can be improved by activating only a subset of
users. Using asymptotic analysis and numerical simulations, we show that our
approach benefits from MUDiv gains higher than that achievable by generic
greedy access algorithm, which is the optimal scheduling method for energy
unlimited systems.Comment: 28 pages, 9 figures, submitted to IEEE Trans. Signal Processing in
Oct. 200
Opportunistic Relaying in Wireless Networks
Relay networks having source-to-destination pairs and half-duplex
relays, all operating in the same frequency band in the presence of block
fading, are analyzed. This setup has attracted significant attention and
several relaying protocols have been reported in the literature. However, most
of the proposed solutions require either centrally coordinated scheduling or
detailed channel state information (CSI) at the transmitter side. Here, an
opportunistic relaying scheme is proposed, which alleviates these limitations.
The scheme entails a two-hop communication protocol, in which sources
communicate with destinations only through half-duplex relays. The key idea is
to schedule at each hop only a subset of nodes that can benefit from
\emph{multiuser diversity}. To select the source and destination nodes for each
hop, it requires only CSI at receivers (relays for the first hop, and
destination nodes for the second hop) and an integer-value CSI feedback to the
transmitters. For the case when is large and is fixed, it is shown that
the proposed scheme achieves a system throughput of bits/s/Hz. In
contrast, the information-theoretic upper bound of bits/s/Hz
is achievable only with more demanding CSI assumptions and cooperation between
the relays. Furthermore, it is shown that, under the condition that the product
of block duration and system bandwidth scales faster than , the
achievable throughput of the proposed scheme scales as .
Notably, this is proven to be the optimal throughput scaling even if
centralized scheduling is allowed, thus proving the optimality of the proposed
scheme in the scaling law sense.Comment: 17 pages, 8 figures, To appear in IEEE Transactions on Information
Theor
Two-Layered Superposition of Broadcast/Multicast and Unicast Signals in Multiuser OFDMA Systems
We study optimal delivery strategies of one common and independent
messages from a source to multiple users in wireless environments. In
particular, two-layered superposition of broadcast/multicast and unicast
signals is considered in a downlink multiuser OFDMA system. In the literature
and industry, the two-layer superposition is often considered as a pragmatic
approach to make a compromise between the simple but suboptimal orthogonal
multiplexing (OM) and the optimal but complex fully-layered non-orthogonal
multiplexing. In this work, we show that only two-layers are necessary to
achieve the maximum sum-rate when the common message has higher priority than
the individual unicast messages, and OM cannot be sum-rate optimal in
general. We develop an algorithm that finds the optimal power allocation over
the two-layers and across the OFDMA radio resources in static channels and a
class of fading channels. Two main use-cases are considered: i) Multicast and
unicast multiplexing when users with uplink capabilities request both
common and independent messages, and ii) broadcast and unicast multiplexing
when the common message targets receive-only devices and users with uplink
capabilities additionally request independent messages. Finally, we develop a
transceiver design for broadcast/multicast and unicast superposition
transmission based on LTE-A-Pro physical layer and show with numerical
evaluations in mobile environments with multipath propagation that the capacity
improvements can be translated into significant practical performance gains
compared to the orthogonal schemes in the 3GPP specifications. We also analyze
the impact of real channel estimation and show that significant gains in terms
of spectral efficiency or coverage area are still available even with
estimation errors and imperfect interference cancellation for the two-layered
superposition system
Guest Editorial: Nonlinear Optimization of Communication Systems
Linear programming and other classical optimization techniques have found important applications in communication systems for many decades. Recently, there has been a surge in research activities that utilize the latest developments in nonlinear optimization to tackle a much wider scope of work in the analysis and design of communication systems. These activities involve every “layer” of the protocol stack and the principles of layered network architecture itself, and have made intellectual and practical impacts significantly beyond the established frameworks of optimization of communication systems in the early 1990s. These recent results are driven by new demands in the areas of communications and networking, as well as new tools emerging from optimization theory. Such tools include the powerful theories and highly efficient computational algorithms for nonlinear convex optimization, together with global solution methods and relaxation techniques for nonconvex optimization
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