Research On Key Techniques For Energy Efficiency Optimization In LTE System

Long Term Evolution (LTE), as the new generation radio access system, provides larger bandwidth, increased system capacity and lower service latency. However, with the increasing number of newly deployed base stations (includes Pico, Relay, home Node and etc.), the energy consumed by mobile networks is increasing greatly, and resulting in large contribution to the OPEX for operators and negative impact to the environment. Hence, the energy efficiency (EE) optimization becomes an urgent and valuable work. In this dissertation, EE optimization algorithms for LTE system are studied under different application scenarios. The main contribution of this dissertation can be summarized as follows:First, a low complexity, high efficiency algorithm considering the impact of switching off cell to the system is proposed to improve energy efficiency in LTE networks. Energy efficiency optimization problem is formulated as NP-hard problem which requires high computational overhead due to optimal exhaustive search (ES). Then a low complexity energy efficiency optimization algorithm (LCEEO) based on optimal switching-off cell selection is proposed. In LCEEO, cells will be pre-ordered in descending order according to load increments brought into network and the cell with lowest load increment is selected to switch off. Then users in selected cell will be handed over to other cells if quality of service (QoS) for users can be guaranteed. After that, selected cell is switched off if network energy can be saved. Rest cells will be evaluated with updated pre-ordering if any cell is switched off. Extensive simulations demonstrate that LCEEO can achieve the optimal performance compare to exhaustive search, and the algorithm complexity is significantly reduced.Second, a Dynamic Programming based cell switch off and resource allocation algorithm (DPCRA), which aims to improve the energy saving performance by cell cooperation after switching off lightly-loaded cells is proposed. In this chapter, a joint power and subcarrier optimization problem to minimize the network energy consumption with considerations of dynamic CoMP clustering, user quality of service (QoS) requirement and base station (BS) load is formulated for cell switch off in CoMP transmission scenario. Since it needs to slove problems on cell clustering and recouce allocation under interference environment, the complexity of such optimization is prohibitive in practical application. As a result, following the manner of dynamic programming (DP), DPCRA algorithm is proposed. Hence, the original problem is decomposed into a sequence of sub-problems on cell switch off with dynamic CoMP clustering and transmit power optimization. For each of them, three low complexity clustering schemes and a heuristic power allocation algorithm are proposed. The simulation results show DPCRA achieves competitive network EE performance compared with benchmark optimization scheme while at the same time save overall overhead effectively.Third, a CoMP mode selection based energy saving scheme is proposed. In the proposed scheme, user location, QoS requirement, a proposed CTSES has the flexibility to choose the transmission mode and adjust the transmission power, avoiding unnecessary waste of network energy consumption. We conclude transmission scheme selection suggestions through a large number of simulation experiments. The experimental results show that CTSES achieves higher EE than JT mode without any increasement of the computational complexity.Fourth, since geographical position of base stations and user traffic distribution is not considered in existing heterogeneous network energy saving scheme, spart of them also have high computational complexity, and cannot be used in the real network. Hence, an energy minimization algorithm based on dynamic clustering (DCEM) with lower complexity, higher performance, and better adaptability for heterogeneous network is proposed. A heterogeneous network could be divided into several clusters, which are defined as groups of network nodes and users served by those nodes. In each cluster, traffic load and location distribution is evaluated, and EE optimizing user association which can be dynamically altered based on EE evaluation, thus results in further energy saving. Then, the optimal sleeping relay is found as follows. Firstly, the sleeping probability cost of each relay node (RN) is computed and ranked based on the user traffic and the position distribution of each cluster, and the relay with minimum sleeping probability is selected to be switched off. Hence, the sleep node is selected taking into account the traffic load and location of the eNB and all the RSs. Simulation results show that the proposed DCEM strategy offers significant energy efficiency gain with low system complexity.Finally, in the actual network, once the eNBs are deployed, it is difficult to save the network energy consumption through removing the eNBs. A low-complexity practical energy saving algorithm by switching off/on some eNBs in a real dense urban scenario considering historical and real-time eNB load is studied. First, eNBs are ranked according to its loads in an ascending order, and the first eNB in the list with load decreasing and smaller than a threshold is pre-selected as target switching off cell. Then, the effect of the target switching off eNB on neighboring eNBs is evaluated. The target eNB switches-off while the load of neighbor eNBs assuming switching-off the eNB are lower than the traffic threshold. Since estimation of the additional load in the neighbor eNBs due to the switch-off eNB is of high complexity, a low complexity estimation scheme through the load impact factor is proposed. Third, the switching-off eNB is switched on inspiring by the active eNBs in a distributed way. Simulation results show that the proposed energy saving scheme has a good performance.