This paper presents an approach for solving the resource allocation problem in heterogeneous networks (HETnets). An optimization model is formulated; its main objective is to achieve the best Quality of Service (QoS) measured by the Carrier to Interference Noise Ratio (CINR). This model is a quadratically constrained optimization problem with continuous and boolean variables. It is solved by an approximation by a sequence of linear optimization problems and a judicious allocation of frequency blocks. It attempts to satisfy the CINR values required by users and demanded by modern standards in data transmission. Theoretically, the model can detect whenever the system cannot fulfill the CINR required, in which case, its solution offers the service provider an allocation of resources that yields a good index of performance. This is reported in numerical simulations that resemble real scenarios of HETnets. The approach configures, in a flexible way, the exploration of the search space, the strategy of frequency block allocation to users, and the solution considered as acceptable by the service provider.

We propose a methodology to deploy large scale IEEE 802.11b telecommunications infrastructures, which are being currently used to provide broadband access in Spanish rural areas, as an expansion of shared asymmetric DVB-S or DVB-T gateways. Wireless broadband access networks have become possible with the advent of technologies like IEEE 802.11. Our approach is based on a set of basic rules to generate mesh topologies with survivability capabilities. These rules help to mitigate cochannel interference and inter-channel interference. Two infrastructure deployment algorithms are given: a distributed version, executed by infrastructure nodes themselves, and a centralized version derived from a mixed integer linear optimization model. Numerical results show that the optimization model is more efficient but the distributed algorithm is competitive, since it can be executed by the wireless network itself and generates good quality results.

This paper formulates an incomplete projection algorithm that is applied to the image recovery problem. The algorithm allows an easy implementation of dynamic load balancing for parallel architectures. Furthermore, the local computation-communication load ratio can be adjusted, since each processor performs a finite number of iterations of any projection-type technique, and this number can be provided as a parameter of the algorithm. Numerical results compare favorably with those obtained by the extrapolated method of parallel subgradient projections.