223 research outputs found
Game-Theoretic Power Control in Impulse Radio UWB Wireless Networks
In this paper, a game-theoretic model for studying power control for wireless
data networks in frequency-selective multipath environments is analyzed. The
uplink of an impulse-radio ultrawideband system is considered. The effects of
self-interference and multiple-access interference on the performance of Rake
receivers are investigated for synchronous systems. Focusing on energy
efficiency, a noncooperative game is proposed in which users in the network are
allowed to choose their transmit powers to maximize their own utilities, and
the Nash equilibrium for the proposed game is derived. It is shown that, due to
the frequency selective multipath, the noncooperative solution is achieved at
different signal-to-interference-plus-noise ratios, respectively of the channel
realization. A large-system analysis is performed to derive explicit
expressions for the achieved utilities. The Pareto-optimal (cooperative)
solution is also discussed and compared with the noncooperative approach.Comment: Appeared in the Proceedings of the 13th European Wireless Conference,
Paris, France, April 1-4, 200
Energy Efficient User Association and Power Allocation in Millimeter Wave Based Ultra Dense Networks with Energy Harvesting Base Stations
Millimeter wave (mmWave) communication technologies have recently emerged as
an attractive solution to meet the exponentially increasing demand on mobile
data traffic. Moreover, ultra dense networks (UDNs) combined with mmWave
technology are expected to increase both energy efficiency and spectral
efficiency. In this paper, user association and power allocation in mmWave
based UDNs is considered with attention to load balance constraints, energy
harvesting by base stations, user quality of service requirements, energy
efficiency, and cross-tier interference limits. The joint user association and
power optimization problem is modeled as a mixed-integer programming problem,
which is then transformed into a convex optimization problem by relaxing the
user association indicator and solved by Lagrangian dual decomposition. An
iterative gradient user association and power allocation algorithm is proposed
and shown to converge rapidly to an optimal point. The complexity of the
proposed algorithm is analyzed and the effectiveness of the proposed scheme
compared with existing methods is verified by simulations.Comment: to appear, IEEE Journal on Selected Areas in Communications, 201
A Comprehensive Survey of Potential Game Approaches to Wireless Networks
Potential games form a class of non-cooperative games where unilateral
improvement dynamics are guaranteed to converge in many practical cases. The
potential game approach has been applied to a wide range of wireless network
problems, particularly to a variety of channel assignment problems. In this
paper, the properties of potential games are introduced, and games in wireless
networks that have been proven to be potential games are comprehensively
discussed.Comment: 44 pages, 6 figures, to appear in IEICE Transactions on
Communications, vol. E98-B, no. 9, Sept. 201
Low-Complexity Distributed Radio Access Network Slicing: Algorithms and Experimental Results
Radio access network (RAN) slicing is an effective methodology to dynamically
allocate networking resources in 5G networks. One of the main challenges of RAN
slicing is that it is provably an NP-Hard problem. For this reason, we design
near-optimal low-complexity distributed RAN slicing algorithms. First, we model
the slicing problem as a congestion game, and demonstrate that such game admits
a unique Nash equilibrium (NE). Then, we evaluate the Price of Anarchy (PoA) of
the NE, i.e., the efficiency of the NE as compared to the social optimum, and
demonstrate that the PoA is upper-bounded by 3/2. Next, we propose two
fully-distributed algorithms that provably converge to the unique NE without
revealing privacy-sensitive parameters from the slice tenants. Moreover, we
introduce an adaptive pricing mechanism of the wireless resources to improve
the network owner's profit. We evaluate the performance of our algorithms
through simulations and an experimental testbed deployed on the Amazon EC2
cloud, both based on a real-world dataset of base stations from the OpenCellID
project. Results conclude that our algorithms converge to the NE rapidly and
achieve near-optimal performance, while our pricing mechanism effectively
improves the profit of the network owner
Stable Nash equilibria of medium access games under symmetric, socially altruistic behavior
We consider the effects of altruistic behavior on random medium access
control (slotted ALOHA) for local area communication networks. For an
idealized, synchronously iterative, two-player game with asymmetric player
demands, we find a Hamiltonian governing the Jacobi dynamics under purely
altruistic behavior. Though the positions of the interior Nash equilibrium
points do not change in the presence of altruistic behavior, the nature of
their local asymptotic stability does. There is a region of partially
altruistic behavior for which neither interior Nash equilibrium point is
locally asymptotically stable. Also, for a power control game with a single
Nash equilibrium, we show how its stability changes as a function of the
altruism parameter. Variations of these altruistic game frameworks are
discussed considering power (instead of throughput) based costs and linear
utility functions
Signal Processing and Optimal Resource Allocation for the Interference Channel
In this article, we examine several design and complexity aspects of the
optimal physical layer resource allocation problem for a generic interference
channel (IC). The latter is a natural model for multi-user communication
networks. In particular, we characterize the computational complexity, the
convexity as well as the duality of the optimal resource allocation problem.
Moreover, we summarize various existing algorithms for resource allocation and
discuss their complexity and performance tradeoff. We also mention various open
research problems throughout the article.Comment: To appear in E-Reference Signal Processing, R. Chellapa and S.
Theodoridis, Eds., Elsevier, 201
Cost-Efficient Throughput Maximization in Multi-Carrier Cognitive Radio Systems
Cognitive radio (CR) systems allow opportunistic, secondary users (SUs) to
access portions of the spectrum that are unused by the network's licensed
primary users (PUs), provided that the induced interference does not compromise
the primary users' performance guarantees. To account for interference
constraints of this type, we consider a flexible spectrum access pricing scheme
that charges secondary users based on the interference that they cause to the
system's primary users (individually, globally, or both), and we examine how
secondary users can maximize their achievable transmission rate in this
setting. We show that the resulting non-cooperative game admits a unique Nash
equilibrium under very mild assumptions on the pricing mechanism employed by
the network operator, and under both static and ergodic (fast-fading) channel
conditions. In addition, we derive a dynamic power allocation policy that
converges to equilibrium within a few iterations (even for large numbers of
users), and which relies only on local signal-to-interference-and-noise
measurements; importantly, the proposed algorithm retains its convergence
properties even in the ergodic channel regime, despite the inherent
stochasticity thereof. Our theoretical analysis is complemented by extensive
numerical simulations which illustrate the performance and scalability
properties of the proposed pricing scheme under realistic network conditions.Comment: 24 pages, 9 figure
Resource allocation in realistic wireless cognitive radios networks
Cognitive radio networks provide an effective solution for improving spectrum usage for wireless users. In particular, secondary users can now compete with each other to access idle, unused spectrum from licensed primary users in an opportunistic fashion. This is typically done by using cognitive radios to sense the presence of primary users and tuning to unused spectrum bands to boost efficiency. Expectedly, resource allocation is a very crucial concern in such settings, i.e., power and rate control, and various studies have looked at this problem area. However, the existing body of work has mostly considered the interactions between secondary users and has ignored the impact of primary user behaviors. Along these lines, this dissertation addresses this crucial concern and proposes a novel primary-secondary game-theoretic solution which rewards primary users for sharing their spectrum with secondary users. In particular, a key focus is on precisely modeling the performance of realistic channel models with fading. This is of key importance as simple additive white Gaussian noise channels are generally not very realistic and tend to yield overly optimistic results. Hence the proposed solution develops a realistic non-cooperative power control game to optimize transmit power in wireless cognitive radios networks running code division multiple access up-links. This model is then analyzed for fast and slow flat fading channels. Namely, the fading coefficients are modeled using Rayleigh and Rician distributions, and closed-form expressions are derived for the average utility functions. Furthermore, it is also shown that the strategy spaces of the users under realistic conditions must be modified to guarantee the existence of a unique Nash Equilibrium point. Finally, linear pricing is introduced into the average utility functions for both Rayleigh and Rician fast-flat fading channels, i.e., to further improve the proposed models and minimize transmission power for all users. Detailed simulations are then presented to verify the performance of the schemes under the proposed realistic channel models. The results are also compared to those with more basic additive white Gaussian noise channels
On the Base Station Association Problem in HetSNets
The dense deployment of small-cell base stations in HetSNets requires
efficient resource allocation techniques. More precisely, the problem of
associating users to SBSs must be revised and carefully studied. This problem
is NP-hard and requires solving an integer optimization problem. In order to
efficiently solve this problem, we model it using non-cooperative game theory.
First, we design two non-cooperative games to solve the problem and show the
existence of pure Nash equilibria (PNE) in both games. These equilibria are
shown to be far from the social optimum. Hence, we propose a better game design
in order to approach this optimum. This new game is proved to have no PNE in
general. However, simulations show, for Rayleigh fading channels, that a PNE
always exists for all instances of the game. In addition, we show that its
prices of anarchy and stability are close to one. We propose a best response
dynamics (BRD) algorithm that converges to a PNE when it exists. Because of the
high information exchange of BRD, a completely distributed algorithm, based on
the theory of learning, is proposed. Simulations show that this algorithm has
tight-to-optimal performance and further it converges to a PNE (when existing)
with high probability
User-Base Station Association in HetSNets: Complexity and Efficient Algorithms
This work considers the problem of user association to small-cell base
stations (SBSs) in a heterogeneous and small-cell network (HetSNet). Two
optimization problems are investigated, which are maximizing the set of
associated users to the SBSs (the unweighted problem) and maximizing the set of
weighted associated users to the SBSs (the weighted problem), under
signal-to-interference-plus-noise ratio (SINR) constraints. Both problems are
formulated as linear integer programs. The weighted problem is known to be
NP-hard and, in this paper, the unweighted problem is proved to be NP-hard as
well. Therefore, this paper develops two heuristic polynomial-time algorithms
to solve both problems. The computational complexity of the proposed algorithms
is evaluated and is shown to be far more efficient than the complexity of the
optimal brute-force (BF) algorithm. Moreover, the paper benchmarks the
performance of the proposed algorithms against the BF algorithm, the
branch-and-bound (B\&B) algorithm and standard algorithms, through numerical
simulations. The results demonstrate the close-to-optimal performance of the
proposed algorithms. They also show that the weighted problem can be solved to
provide solutions that are fair between users or to balance the load among
SBSs
- …