In cellular networks, cells are grouped more densely around highly populated areas to provide more capacity. Antennas are pointed in accordance with local terrain and clutter to reduce signal shadows and interference. Hardware parameters are easily set during installation but difficult to change thereafter. In a dynamic environment of population migration, there is need to continuously tune network parameters to adapt the network performance. Modern mobile equipment logs network usage patterns and statistics over time. This information can be used to tune soft parameters of the network. These parameters may include frequency channel assignment or reuse, and transmitter radiation power assignment to provide more capacity on demand. The paper proposes that by combining the frequency and power assignments, further optimisation in resource allocation can be achieved over a traditional frequency assignment. The solution considers the interference, traffic intensity and use of priority flags to bias some edges. An Edge Weight Power and Frequency Assignment Algorithm is presented to solve the resource allocation problem in cellular networks. The paper also analyses the performance improvements obtained over that of the Edge Weight Frequency Assignment Algorithm. The results show that the proposed algorithm improves the performance of the Edge Weight Frequency Assignment Algorithm depending on the initial structure of the graph.
Energy and spectral efficiency are the main challenges in 5th generation of mobile cellular networks. In this paper, we propose an optimization algorithm to optimize the energy efficiency by maximizing the spectral efficiency. Our simulation results show a significant increase in terms of spectral efficiency as well as energy efficiency whenever the mobile user is connected to a low power indoor base station. By applying the proposed algorithm, we show the network performance improvements up to 9 bit/s/Hz in spectral efficiency and 20 Gbit/Joule increase in energy efficiency for the mobile user served by the indoor base station rather than by the outdoor base station.
A large amount of electric vehicles (EVs) charging load will bring significant impact to the power system. An appropriate resource allocation strategy is required for securing the power system safety and satisfying EVs charging demand. This paper proposed a power coordination allocation strategy of EVs’ in distribution systems. The strategy divides the allocation into two stages. The first stage is based on scores assigned to EVs through an entropy method, whereas the second stage allocates energy according to EV’s state of charge. The charging power is delivered in order to maximize EV users’ satisfaction and fairness without violation of grid constraints. Simulation on a typical power-limited residential distribution network proves the effectiveness of the strategy. The analysis re- sults indicate that compared with traditional methods, EVs, which have higher charging requirement and shorter available time will get more energy delivered than others. The root- mean-square-error (RMSE) and standard-deviation (SD) results prove the effectiveness of the methodology for improving the balance of power delivery.
A novel method to improve the performance of the frequency band is cognitive radio that was introduced in 1999. Due to a lot of advantages of the OFDM, adaptive OFDM method, this technique is used in cognitive radio (CR) systems, widely. In adaptive OFDM, transmission rate and power of subcarriers are allocated based on the channel variations to improve the system performance. This paper investigates adaptive resource allocation in the CR systems that are used OFDM technique to transmit data. The aim of this paper is to maximize the achievable transmission rate for the CR system by considering the interference constraint. Although secondary users can be aware form channel information between each other, but in some wireless standards, it is impossible for secondary user to be aware from channel information between itself and a primary user. Therefore, due to practical limitation, statistical interference channel is considered in this paper. This paper introduces a novel suboptimal power allocation algorithm. Also, this paper introduces a novel bit loading algorithm. In the numerical results sections, the performance of our algorithm is compared by optimal and conventional algorithms. Numerical results indicate our algorithm has better performance than conventional algorithms while its complexity is less than optimal algorithm.
In this study, the concepts of simultaneous user association and resource allocation in non-orthogonal multiple access systems have been investigated. Subscribers are randomly distributed in them. In the paper, a novel cooperative energy harvesting model is introduced so that user equipment near to the base stations acts as relay for further subscribers. In order to consider the local limitations of alternative energy resources, it was assumed that alternative energy would be shared among the base stations by means of the dynamic grid network. In this architecture, non-orthogonal resource allocation and user association frameworks should be reconfigured because conventional schemes use orthogonal multiple access. Hence, this paper suggests a novel approach to joint optimum cooperative power allocation and user association techniques to achieve a maximum degree of energy efficiency for the whole system in which the quality of experience parameters are assumed to be bounded during multi-cell multicast sessions. The model was also modified to develop joint multi-layered resource control and user association that can distinguish the service pattern in cooperative energy heterogeneous systems with non-orthogonal multiple access to obtain more resource optimality than in the current approaches. The effectiveness of the suggested approach is confirmed by numerical results. Also, the results reveal that non-orthogonal multiple access can provide greater energy efficiency than the conventional orthogonal multiple access approaches such as e.g. the MAX-SINR scheme.