Dynamic ICIC

Centralised RBA

As can be seen in Fig. 5.1(a), the centralised D-ICIC, requires all CSI and user information to be fed from all BSs or eNodeBs (eNBs) in the network to the radio network controller (RNC) which is situated with a BS. The Resource Block Allocation Unit (RBAU) performs the RBA using the information gathered and then transmits the assigned RBs back to the serving BSs for each user’s data transmission. The major problem associated with the centralised approach is the high data load exchanged in the backhaul network. This puts a strain on the network and increases the RBA time and required overhead. The control function of the RNC is now embeded into the NodeB to form the eNodeB in LTE-A. This makes the centralised D-ICIC technique impractical for LTE-A as it has no RNC which is needed for the centralised RBA. However in [65, 66, 67], the authors proposed the mobility management entity (MME) to enable the centralised coordination and management of the radio resources.

Some authors have investigated this mode of resource allocation in [53, 54], where the RBA was carried out centrally. The main challenge for the authors was performing the RBA centrally based on the SINR of the users, so the RBA problem was approached and solved in two steps. First, the interference level was managed using graph theory to match users into clusters such that the interference seen by the users are minimised. Then the resource allocation is performed on the clusters to leverage the CSI quality based on the SNR values of the users in the clusters.

RBA Overhead RBA Overhead

End Start RBA

Obtain all CSI and user locations at the central RNC. Perform centralised RBA. Transmit assigned RBs to the macro cells.

RBA Time

Figure 5.2: Centralised RBA performed by the RNC.

In Fig. 5.2 the diagram shows the RBA overhead experienced in the backhaul in order to obtain the CSI at the central RNC and also when transmitting allocated RBs to the macro cell sectors. The RBA time is the time used to allocate resources and transmit the information back to the BSs/eNBs for data transmission.

Semi-Centralised or Partitioned RBA

The semi-centralised or partitioned RBA approach divides the macro-cells within the network into sub-groups or clusters as shown in Fig. 5.1(b). Each cluster is delegated a RBAU, which is geographically located with a serving cell-site. In Table 5.1, different possible partition types are proposed with different numbers and sizes of clusters, for W = 19 macro cell sites. The macro cell site index number in the box is where the RBAU is located, and other cell sites within the cluster transmit information to the RBAU for resource management. This method is similar to the centralised D-ICIC but with smaller groups of BSs. The smaller the number of partitions, the lower the interference and vice versa. This is because as the number of partitions increase, possible interference from less number of neighbouring cells are considered during RBA which results in an increased interference within the network. In the simulation results, it can be observed that as the number of partitions within the network increases, a corresponding decrease is observed in the backhaul overhead and RBA time required to assign RBs to users within the network.

However, since the other clusters are not taken into account during the resource allocation, an increase in the sum-interference power is expected within the network. Another form of semi-centralised and frequency partitioned based RBA requires the RBs to be assigned centrally to different macro cells, and then the macro cells assign RBs to their users based on the allocation of RBs received [68].

Table 5.1: Proposed cell partition types. Type Number of Partitions Partition Sets

Group A 2 {1, 2, 3 , 4, 8, 9, 10, 11, 12, 19}, {5, 6 , 7, 13, 14, 15, 16, 17, 18} Group B 3 {1, 2 , 3, 8, 9, 10},{4, 5, 11, 12, 13 , 14}, {6, 7, 15 , 16, 17, 18, 19} Group C 4 {1, 4 , 11, 12, 13},{2, 3, 8, 9 , 10}, {7, 17, 18 , 19}, {5, 6, 14, 15 , 16} Group D 5 { 1 , 4, 6},{2, 8 , 9, 19},{3, 10, 11 , 12}, {5, 13, 14 , 15}, {7, 16, 17 , 18} Group E 6 {1 2 , 8, 9},{3, 10, 11 },{4, 12, 13 }, {5, 14, 15 },{6, 16, 17 },{7, 18, 19 }

Intuitively, one can see that this approach is repetitive as the RBs need to be re-assigned or re-evaluated at the RNC to minimise the interference. This could result in large overhead, poor synchronisation, high latency and poor interference avoidance. These methods are hybrids of centralised and distributed strategies, with the aim of trading off performance with high data backhaul and complexity. The problem of high data overhead and latency associated with RBA for interference management within the network is still a huge challenge. The cell partition shown in “Group B” and “Group E” will be used later in this chapter with the proposed RBA metric and compared to the proposed distributed RBA strategy.

partition. The larger the number of partitions the lower the CSI overhead and RBA time. The trade-off for lower RBA time and overhead in the system is a higher interference level. Since the BSs in each cell partition does not communicate with the BSs in other partitions, the interference is partially mitigated.

RBA Overhead

RBA Overhead

End

Start RBA Perform _semi-

centralised RBA.

Transmit assigned RBs to the macro cells in

the sub-group.

RBA Time

. .

Obtain all CSI & UE locations

at RBAU in partition (P1).

Obtain all CSI & UE locations

at RBAU in partition (P2).

Obtain all CSI & UE locations

at RBAU in partition (Pi).

Figure 5.3: Semi-centralised or partitioned RBA performed by the RBAU in each partition.

Distributed or De-centralised RBA

The distributed or de-centralised RBA under D-ICIC, aims to reduce the backhaul overhead by allowing the resource management techniques to be performed independently by the macro cells for its users as shown in Fig. 5.1(c). This method is most suitable for the LTE-A standard, since there is no provision for a central control unit (CCU) in the LTE-A standards for 4G networks. Also this method is needed to ensure that current and future cellular wireless networks are self-organising networks (SON). However, this approach faces several challenges especially interference, since the allocation on each cell is expected to be done simultaneously, hence the BSs have no prior information of possible interference transmitted or received from neighbouring cells.

To exploit the achievable rates, it became necessary to develop radio resource management techniques that tend towards an adaptive and dynamic coordination, taking into account the channel and user diversity in the time spectrum, frequency spectrum or both. In [69], a non-cooperative distributed RB allocation strategy was proposed to minimise the total transmit power in each cell, in order to achieve an efficient network. The solution did not fully exploit the achievable throughput in each cell, since the effect of interference was not taken into account. In [70], each BS was made to assign transmit powers and RBs independently, while minimising the total transmit power with a given minimum QoS constraint. The allocated RBs for the cell-edge users are then exchanged so that the neighbouring BSs do not use high transmit powers on those selected RBs. In reducing the transmit powers on the selected RBs, the user’s previous attainable rates are reduced and may not meet the QoS requirements previously attained during the RBA. Also in [62], the proposed distributed RBA approach is based on a limited feedback of SNRs of the “best M-RBs” for each user, and the users are then assigned RBs on a first- come, first-serve basis based on the available RBs and information on the best M-RBs for that user. This method only reduces the feedback of information and is ineffective since the effect of interference from neighbouring cells are not considered. The distributed RBA approach proposed by other authors in the past, avoids using any RBA metric that requires computing the interference for any user as this is very complex to achieve especially for the distributed mode of RBA. This has resulted in methods that avoid the interference entirely. But since the HomoNet is limited by high interference, and the achievable rates are dependent on the SINR of the users, it is important to take the interference into consideration during RBA for interference avoidance. The proposed work in Section 5.4 shows that this can be achieved in a distributed approach.