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Unlike conventional cellular networks where the evolved Node B (eNB) performs centralised scheduling, future relayenhanced cellular (REC) networks allow relay nodes (RNs) to schedule users independently. This decentralised nature of the REC networks brings about challenges to maintain fairness. In this study, we formulate the generalised proportional fair (GPF) resource allocation problem, where resource partition and routing are included as part of the overall radio resource management aiming to provide fairness across all users served by the eNB and its subordinate RNs. Although the traditional proportional fair scheduling algorithm is executed independently at the eNB and each RN to maintain local fairness, we propose efficient resource partition and routing algorithms to maintain global fairness by optimising the GPF objective for the whole relay-enhanced cell. Through system level simulations, the proposed algorithms are evaluated and compared with both non-relaying and relaying systems with benchmark resource partition and routing algorithms. The simulation results show that the proposed algorithms outperform the existing algorithms in providing a better trade-off between system throughput and fairness performance.

MIMO (Multiple Input Multiple Output) technique is one of the important means to enhance the system capacity. Diversity gain could be acquired by using traditional ±45° dual-polarized antenna, but in the scenario where multipath scattering is not strong, power-unbalance in two polarizations caused by polarization mismatching between transmitting and receiving antennas will reduce diversity gain. This problem can be effectively solved by using circular polarized antennas. In this paper, through theory analysis and test, the improvement of MIMO diversity gain using circular polarization antenna is analyzed.

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With the rapid growth of mobile communication technologies, the schemes of the mobile networks become more and more complex. Self-optimizing method can make the wireless network resolve the problems automatically, and attracts more attention. The traffic in wireless network has the characteristics of spatial and temporal distribution. The load of the hotspot areas is very heavy, while the neighbors have light load. For the same place, the traffic varies timely. Load balancing is an effective way to resolve the uneven traffic distribution. In this paper, a novel ant colony self-optimizing method of load balancing is proposed, which is based on stimulation intensity of all users in the cell. First the method estimates the load of all cells in the LTE network; then according the stimulation intensity of all users in the cell, some users are selected to be handover to the neighbor cells in order to achieve load balancing. The simulation results show that the proposed algorithm can reduce the number of unsatisfied users significantly compared with iterative algorithm and drop the HO failure rate.