HWN* RN Site Planning

Plans have already been proposed to select RN sites for in other hybrid wireless networks. The solutions depend on the varied network performance objectives. Among these the packing based RN placement plan is simple, effective and straight-forward but is only suitable for ideal HWN* deployment scenarios where the BS sites are also packed with similar discipline plus the geographical site availability issues are not considered for both BS and RN. It is well known from planar geometry that to cover a two dimensional district with equal sized circles, the best possible packing solution can be obtained by surrounding each circle by six circles. But to have connections between the RNs to form a virtual RN backbone, an overlap is needed between relay cells. The framework therefore considers a situation where the locations of the RNs are centered with maximum coverage.

As the research addresses radio resource management, routing and node mobility problems, the RN site location planning should not be designed too idealistic such as in the packing based RN placement or not only include one or two parameters. We therefore propose a novel heuristic RN placement algorithm considers both physical distance through multi hop distance and Signal to Interference Ratio (SIR) through channel availability. The algorithm is devised in three steps described as:

1. Identify ideal RN locations based on radio resource management and MT mobility behaviour, then generate a set of RN position candidates.
2. Further formulate the RN site positioning as a constrained optimisation problem, of which the goal is to maximise the overall network throughput, the potential gain of MANET RN based services and minimise the hop distance and delay, so that more MTs can be served with guaranteed QoS.
3. Test RNs positioning sites combination recursively and update each RN's position based on performance result.

These three procedures are executed recursively until the algorithm converges. By imposing such apian, in practice, one can expect the system performance at disadvantaged locations should be improved with enlarged network dimensioning. In order to provide fixed RN assistance for both cellular and MANET interfaces radio resource management and routing, initial RN placement site candidates and possible topologies are considered in the following scenarios as presented in Figure 1. The scenario I, II and III cover the mobility management problems, scenario IV discusses the routing problem and scenario V is concerned with the relay structure.

 

Figure 1: The RN node test scenarios I, II and III for the cellular network, the test scenario IV for the MANET and virtual RN backbone scenario V for RN positioning evaluation

When RNs participate in cellular resource sharing and traffic handover along with the BSs and MTs (BSONRN mode), the situation is a little more complex than the BSON mode as more hops are involved during resource transfer, although RN does not have the traffic admission functionality. The heuristic algorithm first proposes to place RNs in positions within the coverage of several BSs, such as the shaded area presented in the Scenario I where the RN is located within the coverage of both BS1 and BS2. The RN can assist advanced communication mechanisms such as cell breathing traffic balancing and TDMA based soft handover. For example, suppose that in Scenario I the RN is associated with both BS1 and BS2. If BS1 reduces signal coverage radius to improve interference and capacity in considered area, the RN will lose its association to BS1, and it can transmit data to BS2. The BS2 may at this stage reduce or increase its coverage radius based on the information and traffic condition. The software handover support is similar to load balancing since the RN also acts as data relayer. Suppose a MT is moving from BS1 to BS2 and it currently receives data from the RN. If the received signal from BS2 becomes larger than it from BS1, the MT performs an intra cellular network handover from one cell to another cell without changing the serving RN. Scenario II places together two RNs at the coverage edge of BS1 and BS2 to facilitate the communication between two cells (It is assumed each cell has only one BS). From the SIR values combining with Shannon's formula, if the bandwidth allocation λB ratio to BS transmissions and the bandwidth allocation ratio to RN transmissions λR together is 1, Scenario II can have a slightly better system capacity performance compared to Scenario I because the average received signal power strength in Scenario I is lower than in Scenario II. However, the sites deployment of Scenario II can introduce much larger latencies, service interruption time and equipment cost, while most cellular coverage overlaps. To conclude, the shaded area in Scenario I is considered as a better site candidate plan other than Scenario I. Scenario III is also considered as an ideal location candidate. The RN at this position extends the cellular coverage to places without fixed infrastructure support.

As the attractor point mobility model is used to model MT movement, the MTs at some stage converge to the attraction points, dwell for a certain period of time and move within a short radius. In order to mitigate resource contention in the "hot spot", reduce service interruption time and latencies for ad hoc communications, a RN should be positioned where more MTs can associate to the RN within one or two hop distance as illustrated in Scenario IV. It is also important to place RN in places based on MT traffic density prediction. Scenario V presents the RN candidate sites combination topologies, it is preferred that RNs can compose a mesh like service layer with virtual backbone connections using ad hoc frequencies thus Scenario V topology right is recommended other than Scenario V topology left. The next step of the heuristic algorithm is to decide the number of RNs needed to assist traffic relaying with guaranteed QoS. The strategy first updates system traffic load information and initialises the HWN* system. MTs then continue moving using attraction point mobility model. As BSs and attraction points sites are pre-defined by the system so only MTs trajectories are needed to be used for Scenario IV analysis. A system performance result for a RN site combination is recorded and compared with theoretical analysis. The RN's location will be changed at disadvantaged locations if the result is not satisfactory. The algorithm runs recursively until an optimal solution is found. Meanwhile, in the final sites selection, a hard distant limit δ is introduced to solve BS RN overlapping problem to regulate the distance between one RN candidate site and any BS.