{'fraction_non_alphanumeric': 0.018086625416468348, 'fraction_numerical': 0.0, 'mean_word_length': 5.293413173652695, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Energy-efficient dynamic resource allocation with energy harvesting nodes

In wireless communications, there is a trend to deploy shorter-distance networks to cope with the high demanding necessities of bit-rate that current applications require. In such networks, the power needed for transmission is considerably low, due to proximity between base station and mobile terminals. As a consequence, complex baseband algorithms for signal processing and radio frequency circuitry require an amount of power that is comparable or even higher than the power for transmission. Moreover, energy harvesting techniques, which allows user to collect energy from the environment, are being emerged as a potential solution for battery durability. In this master thesis, the allocation of radio resources in such a scenario is addressed. Nodes are considered to be battery-powered devices with an energy harvesting source that allows them to recharge their batteries. Nodes feed back their battery status information jointly with the channel state information to the scheduler, which makes a resource allocation based on all the energy constraints of the problem and not only taking the transmitted power and the channel state information as in classical approaches. The final objective is to carry out a design of scheduling algorithms able to provide a longer lifetime network, where lifetime is defined to be the period of time till the first node runs out of battery. In the first part of the thesis we consider some simplifications. Flat-fading channels are assumed and the classical rate-power Shannon’s formula is used. As a result, continuous bandwidth and power assignment as well as Gaussian constellations are considered. Resource allocation problems are reformulated as convex optimization problems, which are solved using powerful software packages, or algorithms based on Lagrange duality developed in this master thesis In the second part of the thesis, a more practical approach is carried out. Now, finitesize constellations, such as QAM constellations are considered. Therefore, a discrete rate-