Data-Plane and Control-Plane Functions in Relay Stations

Multi-hop relay is an optional entity that may be deployed in conjunction with base stations to provide additional coverage or performance improvements in a radio access network. In relay-enabled networks, the BS may be replaced by a multi-hop relay BS (i.e., a BS that supports relay capability over the relay links) and one or more relay stations (RS). The traffic and signaling between the mobile station and relay-enabled BS are relayed by the RS, thus extending the coverage and performance of the system in areas where the relay stations are deployed. Each RS is under the control of a relay-enabled BS. 

In a multi-hop relay system, the traffic and signaling between an access RS and the BS may also be relayed through intermediate relay stations. The RS may either be fixed in location or it may be mobile. The mobile station may also communicate directly with the serving BS. The various relay-enabled BS features defined in the IEEE 802.16j-2009 standard allow a multi-hop relay system to be configured in several modes. The air interface protocols, including the mobility features on the access link (i.e., RS-MS link), remain unchanged.

The IEEE 802.16j-2009 standard specified a set of new functionalities on the relay link to support the RS–BS communication. Two different modes; i.e., centralized and distributed scheduling modes, were specified for controlling the allocation of bandwidths for an MS or an RS. In centralized scheduling mode the bandwidth allocation for subordinate mobile stations of an RS is determined at the serving BS. On the other hand, in distributed scheduling mode the bandwidth allocation of the subordinate stations is determined by the RS, in cooperation with the BS. Two different types of RS are defined, namely transparent and non-transparent. A non-transparent RS can operate in both centralized and distributed scheduling mode, while a transparent RS can only operate in centralized scheduling mode. A transparent RS communicates with the base station and subordinate mobile stations using the same carrier frequency. A non-transparent RS may communicate with the base station and the subordinate mobile stations via the same or different carrier frequencies. 

Relaying in the IEEE 802.16m system is performed using a decode-and-forward paradigm and supports TDD and FDD duplex modes. In TDD deployments, the relay stations operate in time-division transmit and receive (TTR) mode,xii whereby the access and relay link communications are multiplexed using time division multiplexing over a single RF carrier. In the IEEE 802.16m system, the relay stations operate in non-transparent mode, which essentially means that the relay stations compose and transmit the synchronization channels, system information, and the control channels for the subordinate stations. In any IEEE 802.16m deployment supporting relay functionality, a distributed scheduling model is used where each infrastructure station (BS or RS) schedules the radio resources on its subordinate links. In the case of a relay station, the scheduling of the resources is within the radio resources assigned by the BS. The BS notifies the relay and mobile stations of the frame structure configuration. The radio frame is divided into access and relay zones. In the access zone, the BS and the RS transmit to, or receive from, the mobile stations. In the relay zone, the BS transmits to the relay and the mobile stations, or receives from the relay and mobile stations. The start times of the frame structures of the BS and relay stations are aligned in time. The BS and relay stations transmit synchronization channels, system information, and the control channels to the mobile stations at the same time.

The MAC layer of a relay station includes signaling extensions to support functions such as network entry of an RS and of an MS through an RS, bandwidth request, forwarding of PDUs, connection management, and handover. Two different security modes are defined in the IEEE 802.16j-2009 standard: (1) a centralized security mode that is based on key management between the BS and an MS; and (2) a distributed security mode which incorporates authentication and key management between the BS and a non-transparent access RS, and between the access-RS and an MS. An RS may be configured to operate either in normal CID allocation mode, where the primary management, secondary, and basic CIDs are allocated by the BS, or in local CID allocation mode where the primary management and basic CID are allocated by the RS. 

The IEEE 802.16m RS uses the same security architecture and procedures as an MS to establish privacy, authentication, and confidentiality between itself and the BS on the relay link. The IEEE 802.16m relay stations use a distributed security model. The security association is established between an MS and an RS during the key exchange similar to a macro BS. The RS uses a set of active keys shared with the MS to perform encryption/decryption and integrity protection on the access link. The RS runs a secure encapsulation protocol with the BS based on the primary security association. The access RS uses a set of active keys shared with the BS to perform encryption/decryption and integrity protection on the relay link. The MAC PDUs are encapsulated within one relay MAC PDU and are encrypted or decrypted by primary security association, which is established between the RS and the BS. The security contexts used for the relay link (between a BS and an RS) and the access links (between an RS and an MS) are different and are maintained independently. The key management is the same as that performed by a macro BS.

IMPACT OF THE RATIO OF THE COST OF BS TO RS ON SOLUTION

It is also worth to notice how the ratio of the cost of BS and RS affects the site selection. Intuitively, as the ratio raising, the RS becomes relatively cheaper, so it should tend to select more RS compared to the BS and connections from TP to RS should increase. Figure 1 shows the trend. It shows number of connections between TP and BS and between TP and RS as the cost ratio varying from one to ten, i.e., from the cost of BS equals to the cost of RS to the cost of BS ten times the cost of RS. Figure 2 shows the corresponding average path loss between each TP and its communicating node. It can be seen that the path loss is decreasing which means the quality of the radio received becomes higher as the cost of RS becoming lower.

Figure 1: Average path loss between each TP and its communicating node as the ratio of the cost of BS and RS is varied.

Figures 2 and 3 show two extreme cases. In both cases, the number of candidate BS sites is 50, the number of candidate RS sites is 150, and the number of TP is 500. Figure 2 shows the plan of the cost of BS equals to the cost of RS. In this case, there are 38 BSs and 50 RSs being selected; 177 connections between TP and BS; 320 connections between TP and RS. Figure 3 shows the plan of the cost of BS ten times to the cost of RS. In this case, there are 28 BSs and 75 RSs being selected; 133 connections between TP and BS; 367 connections between TP and RS.

Figure 2: An output of the planning tool when the ratio of the cost of BS and RS is 1.

Figure 3: An output of the planning tool when the ratio of the cost of BS and RS is 10.

INTEGER PROGRAMMING MODEL | Network Planning for IEEE 802.16j Relay Networks

Four sets of tests were performed with the basic variant of the problem to determine its sensitivity to different parameters.

In the first experiment, all three parameters were scaled—the number of candidate BSs, candidate RSs, and TPs. The number of BSs was varied and the numbers of RSs and TPs were three times and ten times this figure, respectively. Figure 1 shows how the time required finding a solution scales up. As it can be seen, the problem can be solved for up to 80 candidate BSs and 240 RSs with ease. Further, the results show that the problem complexity is scaling up quite rapidly. Indeed, further experiments were performed in which the number of candidate BSs was increased to 120 and the resulting execution mean time was under 30 min. The system is exhibiting scaling properties which are quite nonlinear, although some basic curve fitting has shown that for the available data set, the scaling is considerably less than exponential.

 
Figure 1: Calculation time when three parameters are scaled at the same time.

Figure 2 shows the calculation time when only the number of BSs is scaling. The number of RSs is set to 90 and the number of TPs is set to 300 in all tests.

 
Figure 2: Calculation time when only the number of BS is scaled.

A similar experiment was performed in which the number of RSs was scaled up and the number of BSs and TPs remained constant. Again it is clear that the system is scaling up linearly in this parameter (Figure 3). The number of BSs is set to 30 and the number of TPs is set 300 in all tests.

 
Figure 3: Calculation time when only the number of RS is scaled.

Finally, in this set of experiments, the sensitivity to the number of TPs was considered. The same characteristic is again observed: the system scales linearly as can be seen from Figure 4. The number of BSs is set to 30 and the number of RSs is set to 90 in all tests.

 
Figure 4 Calculation time when only the number of TP is scaled.

From the figures, it can be seen that this algorithm should suit small size network planning problems since the time cost is very short for small number of BSs. The time varies almost linearly if individual parameter is varying. For the problem sizes studied—which are typical for small metropolitan scenarios—the solution can be found quickly on typical desktop computers, e.g., under two minutes for problems with 50 candidate BS sites, and approximately ten minutes for problems with 100 candidate BS sites. The time cost for the planning could increase to one day long or a few days to plan a larger network, e.g., around 500 candidate sites, but it is still practicable.

RESULTS AND DISCUSSION | IEEE 802.16j Relay Networks

The objective of these tests can be divided into two parts. One is to obtain an understanding of the scalability of the problem formulation—the basic and the state space reduction model. More specifically, the objective was to understand if this problem formulation can be used to solve problems of realistic size. Given that it is, in principle, an NP-hard problem, it is important to understand the range of problems for which standard solution techniques are appropriate and the range of problems which require the development of heuristics which employ domain knowledge.

The second is to determine how the clustering approach compares with the more rudimentary approaches. The comparison was performed based on both the time taken to obtain a solution and the quality of the resulting solution; naturally, the former relates directly to the scalability characteristics of the approach and its applicability for realistic scenarios.

A number of tests were performed in which the number of BSs, RSs, and TPs were varied. All tests were done using a standard desktop computer—Centrino Duo 2.0 GHz, 1 GB Memory, Windows Vista. Twelve tests were performed each time and the mean execution time taken. As there was some variation in the results, the minimum and maximum execution times were removed and the mean taken over the remaining ten results.

Problems were generated at random. The locations of each of the BSs, RSs, and TPs were chosen randomly from an area of size 3 × 3 km. The (x, y) coordinates of each node were chosen by selecting two random variable from the distribution U(0, 3000). For each of the problems the same set of weight parameters were used: λ₁ = 8, λ₂ = 8, and λ₃ = 20. However, it is worth noting that the values of these parameters have little impact on the time required to find solutions. In each of the problems, the BS cost was chosen at random and was three times the cost of the RS.

In all of the following tests, the branch and bound method found the optimal solution to the given problem. Figure 1 shows one possible result for planning a network with 20 candidate BSs, 60 candidate RSs, and 200 TPs. In the solution, 10 BSs are selected with 36 RSs

 
Figure 1: A typical output of the planning tool.