This thesis presents an investigation of cooperative transmission techniques and resource block scheduling for maximizing the bandwidth efficiency in the downlink transmission of the urban macro LTE environment. The simulation results show that in order to achieve the highest user bandwidth efficiency normalized by the number of cooperating base stations, over half of the users do not need cooperation, and most of the remaining users choose one of the 2-BS, 3-BS and full cooperation cases to obtain the optimal normalized user bandwidth efficiency. From the simulation results for the resource block scheduling in a small network layout, there are essentially three types of possible optimal cases: the full cooperation case, the full frequency reuse non-cooperative case and finally, the 2/3 reuse non-cooperative case. When shadowing is not included in a 2-cell network and a 3-cell network, over half of the users choose full frequency reuse non-cooperative case as their optimal cases; the handover versions of these three types occur as the fourth type of the optimal cases for a 3-cell network with shadowing effects; the full cooperation case is chosen by over 90% of users in the tri-sectored I-cell network layout. Three sub-optimal algorithms with low complexity are proposed. A location based algorithm can achieve nearly 99% of the optimal total bandwidth efficiency in a non-shadowing environment, and two power based algorithms can realize over 90% of the optimal total bandwidth efficiency in a shadowing environment. Moreover, the second of two power based algorithms can also get approximately 100% of the total bandwidth efficiency for a 3-cell network in a non-shadowing environment and for a tri-sectored 1-cell network layout. For the extended 3-cell network layout, the genetic algorithm is used to obtain a near-optimal solution but without consideration of user fairness. A sub-optimal algorithm with user capacity fairness is proposed. The proposed algorithm can achieve 85% of the total bandwidth efficiency from the genetic algorithm approach and also greatly improves the fairness of the user capacity for the genetic algorithm.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:691263 |
| Date | January 2015 |
| Creators | Luo, Zihan |
| Publisher | University of Bristol |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
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