Permutation based load balancing technique for long term evolution advanced heterogeneous networks

Traffic congestion has been one of the major performance limiting factors of heterogeneous networks (HetNets). There have been several load balancing schemes put up to solve this by balancing load among base stations (BSs), but they appear to be unfeasible due to the complexity required and other unsatisfactory performance aspects. Cell range extension (CRE) has been a promising technique to overcome this challenge. In this paper, a permutation based CRE technique is proposed to find the best possible formation of bias for BSs to achieve load balance among BSs. In comparison to the baseline scheme, results depict that the suggested method attains superior performance in terms of network load balancing and average throughput. The complexity of the suggested algorithm is considerably reduced in comparison to the proposed permutation based CRE method it is further modified from

Cell association with user behaviour awareness in heterogeneous cellular networks

In heterogeneous cellular networks (HetNets) with macro base station (BS) and multiple small BSs (SBSs), cell association of user equipment (UE) affects UE transmission rate and network throughput. Conventional cell association rules are usually based on UE received signal-to-interference-and-noise-ratio (SINR) without being aware of other UE statistical characteristics, such as user movement and distribution. User behaviors can indeed be exploited for improving long-term network performance. In this paper, we investigate UE cell association in HetNets by exploiting both individual and clustering user behaviors with the aim to maximize long-term system throughput. We model the problem as a stochastic optimization problem, and prove that it is PSPACE-hard. For mathematical tractability, we solve the problem in two steps. In the first step, we investigate UE association for a specific SBS. We use a restless multiarmed bandit model to derive an association priority index for the SBS. In the second step, we develop an index enabled association (IDEA) policy for making the cell association decisions in general HetNets based on the indices derived in the first step. IDEA determines a set of admissible BSs for a UE based on SINR, and then associates the UE with the BS that has the smallest index in the set. We conduct simulation experiments to compare IDEA with other three cell association policies. Numerical results demonstrate the significant advantages of IDEA in typical scenarios

Cloud Based Small Cell Networks: System Model, Performance Analysis and Resource Allocation

In cloud-based small cell networks (C-SCNs), radio resource allocation at the base station (BS) is moved to a cloud data centre for centralised optimisation. In the centre, multiple processors referred to as the cloud computational unit (CCU), is used for the optimisation. As the cell size and networks become respectively smaller and denser, the number of BSs to be optimised grows exponentially, resulting in high computational complexity and latency at CCUs. This thesis propose belief propagation (BP) based power allocation schemes for C-SCNs that can be used for any network optimisation objectives such as energy efficiency at the centre and BSs; and spectral efficiency (SE). The computation for the schemes is done in parallel, leading to very low latency and computational complexity with increasing number of BSs. The transmission-latency depends on the number of bits used to quantise the received signal from terminals at the remote radio head (RRH). The computational-latency depend on the speed of resource allocation procedure at the CCU. BP based joint SE and latency optimisation scheme that compute the optimum terminal’s uplink power and number of quantisation bits for each RRHs. The results indicate a significant reduction in transmission and computational-latencies compared to other schemes. This thesis further investigates a user association (UA) to the BS and subcarrier allocation (SCA) where a BS allocates different number of SC to different users associated to it. In jointly optimising the UA and SCA, the Sharpe Ratio (SR) is used as the utility function, which is defined as the ratio between the mean of user achievable rates to its standard deviation. Thus, the achieved user rates will be closer to each other, leading to a fair network access. By using binary BP algorithm, the results show that the achievable user rates are doubled in comparison with other schemes