Efficient Channelization Code Management in WCDMA




Introduction

In order to support variable rates of data multimedia in CDMA system, a set of orthogonal codes with
different lengths must be used, because the rate of information varies and the available bandwidth is
fixed [1, 2]. It is possible to support higher data rates in direct sequence CDMA (DS-CDMA) systems
by assigning multiple fixed-length orthogonal codes to a call [3]. In an alternative CDMA scheme
witch is known as OVSF-CDMA, a single Orthogonal Variable Spreading Factor (OVSF) code is
assigned to each user. In this case, a higher data rate can be accessed by using a lower spreading factor
[4, 5]. In this paper, we focus on the environment where one single OVSF code is available for each
call.
OVSF codes can be represented as a code tree. The data rates provided are always a power of
two with respect to the lowest-rate codes. OVSF codes assignment has significant impact on the code
utilization of the system. There are two types of code assignment schemes: Static and Dynamic. This
paper addressed both static and dynamic schemes in a WCDMA system where OVSF code tree are
used. The general objective is to make the OVSF code tree as compact as possible so as to support
more new calls by incurring less blocking probability and less reassignment costs.


Problem Statement

When a new call arrives requesting for a code of rate iR, where i is a power of two, we have to allocate
a free code of rate iR for it. In static schemes we address the allocation algorithm when multiple free
codes exist in the code tree. When no such free code exist but the remaining capacity of the code tree is
sufficient (i.e. summation of data rates of all free code is greater than iR), we can use dynamic
schemes. In dynamic schemes, relocate some codes in the code tree to find a free space for the new
call. We define OVSF code blocking as the condition that a new call cannot be supported although the
system has excess capacity to support the rate requirement of the call [6].


Optimal Scheme

The objective of this scheme is to keep the remaining assignable codes in the most compact state after
each code assignment without reassigning codes. To achieve this purpose into the existing busy codes,
new-code assignments are packed as tightly as possible into the existing busy codes, i.e. the assignable
code in the most congested position is found for the new call. As a result, the busy codes are also kept
as compact as possible after each code assignment. For a typical branch, say the branch under (k,m), let
C(k,m) be the assignable capacity of the branch, witch is defined as total capacity of the assignable leaf
codes in this branch. In other word:


Dynamic Code Assignment Schemes

Reassignment (Dynamic assignment) schemes are necessary when the capacity of the tree is enough to
carry the incoming call, but no code of required rate is available. In this case, a code of the required
rate can be become available, by transferring all the existing calls at the subbranch of the code to some
other branches of the tree.


Numerical Results
Our proposed schemes are evaluated on a 6-layer code tree. The call arrival process is modeled by a
Poisson distribution with mean arrival rate λ=1-16 calls/unit time, while the call duration is
exponentially distributed with a mean value of 1/μ=0.25 unit of time, according to [6]. The possible
rates for a new call are R, 2R, 4R, and 8R, each with a different probability of appearance. In our
simulation we use a uniform rate distribution R:2R:4R:8R=25:25:25:25 (calls have equal probability to
request for rate 1R, 2R, 4R, 8R) and a more realistic scenario with lower rate calls being more probable
(R:2R:4R:8R=40:40:10:10). To ensure stable results, each point on the figures has been produced by a
simulation run with at least 10000 incoming calls. Fig.4 shows the code blocking probability at
different traffic load λ/μ (average service rate×average call duration) and uniform rate distribution. We
can see that Dynamic Optimal (D-Optimal) strategy performs the best and which is followed by the
Dynamic Ordered (D-Ordered), Static Optimal (S-Optimal) and Static Ordered (S-Ordered),
respectively. According to this figure, at light load, the blocking probability is quiet insensitive to code
placement algorithm. Another important result is that code blocking rate of dynamic schemes is
obviously less than that of static schemes. But code reassignment strategy at dynamic schemes makes
the implementation of these schemes more complicated than static schemes.