The UMTS radio network planning problem poses the challenge of designing a cost-effective network that provides users with sufficient coverage and capacity. We describe an optimization model for this problem that is based on comprehensive planning data of the EU project MOMENTUM. We present heuristic mathematical methods for this realistic model, including computational results.
Relaying is a protocol extension for cellular wireless computer networks; in order to utilize radio resources more efficiently, several hops are allowed within one cell. This paper investigates the principle potential of relaying by casting transmission scheduling as a mathematical optimization problem, namely, a linear program. We analyze the throughput gains showing that, irrespective of the concrete scheduling algorithm, performance gains of up to 30\% on average for concrete example networks are achievable.
Relaying - allowing multiple wireless hops - is a protocol extension
for cellular networks conceived to improve data throughput. Its benefits have only
been quantfied for small example networks. For assessing its general potential,
we define a complex resource allocation/scheduling problem. Several mathematical
models are presented for this problem; while a time-expanded MIP approach turns
out intractable, a sophisticated column generation scheme leads to good computational
results. We thereby show that for selected cases relaying can increase data
throughput by 30% on the average.
Wireless Local Area Network (WLAN) is currently among the most important technologies for wireless broadband
access. The IEEE 802.11 technology is attractive for its maturity and low equipment costs. The overall performance of a specific
WLAN installation is largely determined by the network layout and the radio channels used. Optimizing these design parameters
can greatly improve performance.
In this paper, access point (AP) placement and channel assignment is optimized using mathematical programming. Traditionally,
these decisions are taken sequentially; AP placement is often modeled as a facility location problem, channel assignment
as an (extended) graph coloring problem. Treating these key decisions separately may lead to suboptimal designs. We propose
an integrated model that addresses both aspects. The different optimization objectives and their tradeoff are taken into consideration simultaneously. Computational results show that indeed the integrated approach is superior to the sequential one.
Performance evaluation of UMTS radio networks is involved due to the specifics of W-CDMA technology. A common approach is Monte-Carlo simulation, which is computationally expensive. For purposes such as network
planning and optimization, it is vital to obtain estimates efficiently. While an entirely analytical and very efficient
method using expected coupling systems is known for cell powers, no equivalent for cell’s blocking rates is at hand. This work contributes a highly efficient scheme for estimating blocking rates. We introduce a simple knapsack model for the cell’s capacity, that relies on using fixed estimates for inter-cell interference. The event of blocking and the blocking rate can then be characterized and estimated. We demonstrate on the basis of realistic
planning data that the method works efficiently and yields estimates that are sufficiently accurate for applications
such as network optimization.
UMTS radio network evaluation and design are currently important issues for telecommunication operators.
We present a novel view on network evaluation. The recent dimension reduction approach is
generalized to an analytical approximation of the network's general performance based on average traffic.
The pivot is an average coupling matrix that captures the essential coverage and cell coupling properties of
the radio network. Based on this new evaluation method, we present new optimization methods, namely
a new optimization model based on designing the generalized average coupling matrix and an efficient
1-opt local search. We give preliminary computational results that show the potential of our methods on
realistic data.