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A connection-level call admission control using genetic algorithm for MultiClass multimedia services in wireless networks
Call admission control in a wireless cell in a personal communication system (PCS) can be modeled as an M/M/C/C queuing system with m classes of users. Semi-Markov Decision Process (SMDP) can be used to optimize channel utilization with upper bounds on handoff blocking probabilities as Quality of Service constraints. However, this method is too time-consuming and therefore it fails when state space and action space are large. In this paper, we apply a genetic algorithm approach to address the situation when the SMDP approach fails. We code call admission control decisions as binary strings, where a value of “1” in the position i (i=1,…m) of a decision string stands for the decision of accepting a call in class-i; a value of “0” in the position i of the decision string stands for the decision of rejecting a call in class-i. The coded binary strings are feed into the genetic algorithm, and the resulting binary strings are founded to be near optimal call admission control decisions. Simulation results from the genetic algorithm are compared with the optimal solutions obtained from linear programming for the SMDP approach. The results reveal that the genetic algorithm approximates the optimal approach very well with less complexity
Autonomous Algorithms for Centralized and Distributed Interference Coordination: A Virtual Layer Based Approach
Interference mitigation techniques are essential for improving the
performance of interference limited wireless networks. In this paper, we
introduce novel interference mitigation schemes for wireless cellular networks
with space division multiple access (SDMA). The schemes are based on a virtual
layer that captures and simplifies the complicated interference situation in
the network and that is used for power control. We show how optimization in
this virtual layer generates gradually adapting power control settings that
lead to autonomous interference minimization. Thereby, the granularity of
control ranges from controlling frequency sub-band power via controlling the
power on a per-beam basis, to a granularity of only enforcing average power
constraints per beam. In conjunction with suitable short-term scheduling, our
algorithms gradually steer the network towards a higher utility. We use
extensive system-level simulations to compare three distributed algorithms and
evaluate their applicability for different user mobility assumptions. In
particular, it turns out that larger gains can be achieved by imposing average
power constraints and allowing opportunistic scheduling instantaneously, rather
than controlling the power in a strict way. Furthermore, we introduce a
centralized algorithm, which directly solves the underlying optimization and
shows fast convergence, as a performance benchmark for the distributed
solutions. Moreover, we investigate the deviation from global optimality by
comparing to a branch-and-bound-based solution.Comment: revised versio
Sample Approximation-Based Deflation Approaches for Chance SINR Constrained Joint Power and Admission Control
Consider the joint power and admission control (JPAC) problem for a
multi-user single-input single-output (SISO) interference channel. Most
existing works on JPAC assume the perfect instantaneous channel state
information (CSI). In this paper, we consider the JPAC problem with the
imperfect CSI, that is, we assume that only the channel distribution
information (CDI) is available. We formulate the JPAC problem into a chance
(probabilistic) constrained program, where each link's SINR outage probability
is enforced to be less than or equal to a specified tolerance. To circumvent
the computational difficulty of the chance SINR constraints, we propose to use
the sample (scenario) approximation scheme to convert them into finitely many
simple linear constraints. Furthermore, we reformulate the sample approximation
of the chance SINR constrained JPAC problem as a composite group sparse
minimization problem and then approximate it by a second-order cone program
(SOCP). The solution of the SOCP approximation can be used to check the
simultaneous supportability of all links in the network and to guide an
iterative link removal procedure (the deflation approach). We exploit the
special structure of the SOCP approximation and custom-design an efficient
algorithm for solving it. Finally, we illustrate the effectiveness and
efficiency of the proposed sample approximation-based deflation approaches by
simulations.Comment: The paper has been accepted for publication in IEEE Transactions on
Wireless Communication
A survey of self organisation in future cellular networks
This article surveys the literature over the period of the last decade on the emerging field of self organisation as applied to wireless cellular communication networks. Self organisation has been extensively studied and applied in adhoc networks, wireless sensor networks and autonomic computer networks; however in the context of wireless cellular networks, this is the first attempt to put in perspective the various efforts in form of a tutorial/survey. We provide a comprehensive survey of the existing literature, projects and standards in self organising cellular networks. Additionally, we also aim to present a clear understanding of this active research area, identifying a clear taxonomy and guidelines for design of self organising mechanisms. We compare strength and weakness of existing solutions and highlight the key research areas for further development. This paper serves as a guide and a starting point for anyone willing to delve into research on self organisation in wireless cellular communication networks
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