PROJECT TITLE :
Energy Efficiency Maximization Framework in Cognitive Downlink Two-Tier Networks
To support the surge in wireless data traffic, the spectrum and energy efficiencies of cellular networks ought to be largely increased. Heterogeneous two-tier design has been identified mutually key answer. However, small-cell deployment raises queries about the ensuing energy efficiency and interference mitigation. So, we propose an energy-economical and cognitive spectrum sharing theme between primary macrocell and secondary little cells. Specifically, the small cells allocate their transmission power to maximize their total energy potency while respecting some interference constraints imposed by macrocell users. We have a tendency to solve this centralized optimization in 2 steps. Initial, assuming that the small-cell transmissions are noninterfering, the answer of this nonconvex optimization is characterised employing a convex parametric approach. Using this characterization, we tend to derive an algorithm based mostly on Newton method, that converges to a world optimal resolution. Second, when the tiny-cell transmissions aren't necessarily orthogonal, we have a tendency to derive an algorithm, that converges at least to a native optimum, using the minorization-maximization principle and Newton technique. Through simulations, we have a tendency to validate the convergence of these algorithms and compare their performance with existing schemes. We have a tendency to also analyze the consequences of the interference and of the number of users on the energy efficiency.
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