Ranging in mobile WiMAX is an example of generalized allocation where the subcarriers reserved to the ranging process can take up any position in the available spectrum.
These algorithms can be divided into three groups, from lower to higher computational complexity: subband, interleaved, and generalized allocation of subcarriers.
CFO estimation schemes proposed in the literature can be classified based on sub-carrier allocation schemes, like[9 11] for grouped allocation,[12 17] for generalized allocation, and[18 23] for IAS.
As sub-carriers of different users as interleaved into each other, CFOs generate more MUI in IAS as compared to grouped or generalized allocation schemes.
We formulate the problem of quantitative service differentiation as a generalized resource allocation optimization towards the minimization of system delay, defined as the sum of weighted delay of client requests.
A CFO estimation scheme for generalized subcarrier allocation in OFDMA systems has been proposed in [14], in which CFO estimation is performed by assuming that there is a preamble at the beginning of the frame.
In this paper, we consider the generalized redundancy allocation problem (GRAP), which extends traditional RAP to a more realistic situation where the system under consideration has a generalized (typically complex) network structure; for example, the components are connected with each other neither in series nor in parallel but in some logical relationship.
It also deals with inventory management and decisions on strategical and tactical level (facility location, production rate, warehouse capacity, demand allocation between generalized nodes, inventory levels, product flows, suppliers' product availability and links between all facilities).
Therefore, to widen its application scope, current work focusses on extending the cross-layer unified approach to distributed scheduling and resource allocation in generalized MIMO-OFDMA multicellular wireless heterogeneous networks, possibly including more sophisticated MIMO techniques, one- and two-way relays, shared relays, femto-cells and/or clusters of coordinated BSs.
Risk estimates will be adjusted for cluster allocation via generalized estimating equations using a log link between the binary response variable (binomial family) and the linear predictor, and unstructured correlations between observations.
Continuous outcome measures will be analyzed using generalized linear models including treatment allocation, test of interaction term between allocation and the vignette used, as well as variables associated with attrition or baseline imbalances between the groups, if any.
