Partially overlapped channel assignment for multi-channel multi-radio wireless mesh networks
- Jihong Wang^{1},
- Wenxiao Shi^{1}Email author,
- Keqiang Cui^{1},
- Feng Jin^{1} and
- Yuxin Li^{1}
https://doi.org/10.1186/s13638-015-0259-8
© Wang et al.; licensee Springer. 2015
Received: 28 August 2014
Accepted: 14 January 2015
Published: 10 February 2015
Abstract
Partially overlapped channels (POCs)-based design has been identified recently as an emerging technology to further eliminate interference and improve network capacity. However, there are only few studies of channel assignment algorithms for POCs. In this paper, we research on utilizing POCs to improve network capacity and propose a traffic-irrelevant channel assignment algorithm, which assigns channels for all links in the network while minimizing total network interference. Theoretical calculation approach is utilized to obtain the direct relationship between interference ranges and channel separations, which can be easily applied to mesh networks with various configurations without modification. As traffic between the Internet and clients is considered to be dominant, distance from the gateway, number of neighbors, and interference are used to determine the channel assignment order of links. Simulation results reveal that network throughput and end-to-end delay performance can be dramatically improved by fully exploiting POCs as well as orthogonal channels.
Keywords
1 Introduction
Wireless mesh networks (WMNs), which can extend the coverage of current wireless networks, draw close attention from academic community and industry in recent years [1]. WMNs are composed of three types of nodes: mesh clients, mesh routers, and gateway nodes [2,3]. Mesh clients are user equipment, such as PC and mobile phone. Mesh routers, with the access and relay function, form the mesh backbone and connect mesh clients with the gateway nodes. Gateway nodes are special kinds of mesh routers with the function of bridging, and they connect the whole mesh networks with external networks, such as the Internet.
Since the opinion that POCs utilization can lead to better utilization of the spectrum and throughput improvement was proposed by Arunesh Mishra [5], there have been growing interests in exploiting POCs to improve network performance, and the focus is mainly on exploiting partially overlapped channel assignment to reduce interference. Partially overlapped channel assignment can be divided into multicast partially overlapped channel assignment [6-11] and unicast partially overlapped channel assignment [12-19] according to service types. In this paper, we research on unicast partially overlapped channel assignment problem. The unicast partially overlapped channel assignment schemes published have at least one of the limitations listed as follows. (1) Most of them are traffic-relevant load-aware channel assignment schemes which only assign channels for links that carry data flows, when load changes in the network, channel assignment for links should update accordingly. Thus, they do not adapt to load changes. (2) Traffic between the Internet and mesh clients is considered only, and traffic between clients (peer-to-peer traffic) is omitted, or vice versa. At present, people want to access the Internet and get service from it, so the traffic between the Internet and mesh clients is dominant. As newly emerging applications get popular, there may be substantial random and unpredictable traffic caused by peer-to-peer traffic. As a result, these two traffic types will co-exist in WMNs. (3) Current partially overlapped channel assignment schemes obtain interference ranges through field measurement, but field measurement is usually conducted with specific network configuration; thus, there is no fixed relationship between interference ranges and channel separations, which leads to weak transportability of the measurement results [20].
- (1)
Traffic-irrelevant channel assignment scheme is utilized to assign channels for all links in the network before carrying data flows which can avoid the weakness of load-aware channel assignment.
- (2)
Traffic between the Internet and clients and peer-to-peer traffic are both considered as they will co-exist in WMNs in the future, where traffic between the Internet and clients is dominant.
- (3)
Theoretical calculation approach is used to obtain interference ranges which can avoid weak transportability of interference ranges obtained by field measurement.
2 Related work
In general, partially overlapped channel assignment schemes published can roughly be classified into two types: one is traffic-relevant load-aware channel assignment schemes [12-15], which assume a known traffic profile in the network or pre-determined route paths for flows, therefore load on each link is known before performing channel assignment. The task is to compute a channel assignment scheme, such that the load can be delivered in time. The other is traffic-irrelevant channel assignment schemes [16-18], which assume dynamic traffic in the network and assign channels for all links with the goal of minimizing total network interference. Ours belongs to the second type. Of course, there is also research on partially overlapped channel assignment for scenarios in the absence of information exchange. For example, a graphical game and uncoupled learning-based distributed partially overlapped channel selection is proposed in [19], which is different from our proposed algorithm as ours is centralized for easy implementation.
For load-aware channel assignment schemes, the assumptions made on traffic load actually determine which links should be assigned a channel, and more importantly, for channel assignment algorithms that utilize traffic load to sort links, it determines in which order the channel assignment should occur. However, load on each link is difficult to predict in practice, and the channel assignment may not be suitable when load changes and may need to update accordingly.
For traffic-irrelevant channel assignment schemes, they are operated before any data flow transmissions in the network and assign channels for all links in the network, so there is no load on each channel/link when operating the scheme, and no matter where the sources and destinations of flows transmitted later in the network, the channel assignment for links has no need to change. Traffic-irrelevant channel assignment scheme helps avoid inadaptation to load changes of traffic-relevant channel assignment schemes.
which is the first part of metric α(s).
The following problems may exist in the above greedy partially overlapped channel assignment algorithm: (1) Interference ranges are obtained by field measurement; (2) When deciding channel assignment order, the algorithm gives higher priority to the link that has minimum expected interference with other links, but if there are several links whose expected interference values are equivalent, how to break the tie is still unknown; (3) If several channels all satisfy the minimum interference requirement, random channel selection may not yield good performance; and (4) The algorithm assumes that WMNs have dynamic traffic, that is, the connection demands have random sources, destinations, and arrival times, i.e., peer-to-peer traffic is dominant. From the analysis above, we conclude that a traffic-irrelevant channel assignment scheme which takes two types of traffic into consideration and gets interference ranges without using field measurement is still in need. In the following, we present our partially overlapped channel assignment (POCA) algorithm.
3 Interference model
In this paper, we are targeting at infrastructure mesh networks which is the most commonly used form of WMNs. Mesh clients are connected to the nearest mesh routers within one-hop distance, and multi-hop transmissions are limited among mesh routers. As the performance of WMNs is mainly decided by its backbone network, clients are usually ignored and the corresponding access routers are considered instead [21,22]. We assume that all mesh routers are stationary, which is reasonable in WMNs. Our algorithm is applied to mesh backbone, and our target is optimizing links between mesh routers, i.e., relay links. We use node and mesh router interchangeably in this paper.
For a directed link, if its receiving endpoint wants to successfully receive a packet from the sending endpoint, it requires that no third node located within the interference range of the receiving endpoint is transmitting. In this case, interference is not symmetric. However, in this paper, our algorithm tries to find a traffic-irrelevant channel assignment for all links in the network, thus links between nodes are considered as undirected. Also, before a channel assignment is known, the actual interference of links is unknown, thus we use the symmetric interference model in Equation 3 to comply with IEEE 802.11-style MAC protocol and guarantee successful communication over an undirected link, i.e., the sending endpoint is also required to be free of interference as it needs to receive the link layer acknowledgement from the receiving endpoint. In a word, successful communications over a link require that any node which is within the interference range of these two endpoints of the link should not be transmitting.
where P _{ t } is the transmission power at the sender, G _{ t } and G _{ r } are the antenna gains of the sender and receiver, respectively, h _{ t } and h _{ r } are the height of both antennas, d is the distance between the sender and the receiver, and k is the path loss parameter whose value is typically between 2 and 4.
where f _{ c } is the center frequency.
Reduced interference range ratios for ideal spectrum mask
τ | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | ≥ 9 |
---|---|---|---|---|---|---|---|---|---|---|
Irrr(τ) | 1 | 0.9376 | 0.8596 | 0.7515 | 0.5505 | 0.1714 | 0.1588 | 0.1422 | 0.1161 | 0 |
Reduced interference range ratios for raised cosine filter with roll-off factor 1
k=2 | τ | 0 | 1 | 2 | 3 | 4 | ≥ 5 |
---|---|---|---|---|---|---|---|
Irrr(τ) | 1 | 0.7512 | 0.4800 | 0.2246 | 0.0354 | 0 | |
k=3 | τ | 0 | 1 | 2 | 3 | 4 | ≥ 5 |
Irrr(τ) | 1 | 0.8264 | 0.6131 | 0.3695 | 0.1079 | 0 | |
k=4 | τ | 0 | 1 | 2 | 3 | 4 | ≥ 5 |
Irrr(τ) | 1 | 0.8667 | 0.6928 | 0.4739 | 0.1882 | 0 |
Reduced interference range ratios for raised cosine filter with roll-off factor 0.5
k=2 | τ | 0 | 1 | 2 | 3 | ≥ 4 |
---|---|---|---|---|---|---|
Irrr(τ) | 1 | 0.7355 | 0.3741 | 0.0442 | 0 | |
k=3 | τ | 0 | 1 | 2 | 3 | ≥ 4 |
Irrr(τ) | 1 | 0.8148 | 0.5192 | 0.1250 | 0 | |
k=4 | τ | 0 | 1 | 2 | 3 | ≥ 4 |
Irrr(τ) | 1 | 0.8596 | 0.6116 | 0.2103 | 0 |
Reduced interference range ratios for raised cosine filter with roll-off factor 0.25
k=2 | τ | 0 | 1 | 2 | ≥ 3 |
---|---|---|---|---|---|
Irrr(τ) | 1 | 0.7339 | 0.3138 | 0 | |
k=3 | τ | 0 | 1 | 2 | ≥ 3 |
Irrr(τ) | 1 | 0.8136 | 0.4617 | 0 | |
k=4 | τ | 0 | 1 | 2 | ≥ 3 |
Irrr(τ) | 1 | 0.8567 | 0.5601 | 0 |
4 Partially overlapped channel assignment algorithm
The proposed POCA algorithm is composed of two steps: neighbor-to-interface binding and interface-to-channel binding. The neighbor-to-interface binding determines the connection relationship among nodes, that is, through which interface a node communicates with its neighbor; the interface-to-channel binding determines which channel an interface should use according to certain order with the goal of minimizing total network interference.
4.1 Neighbor-to-interface binding
In the neighbor-to-interface binding step, the node degree is computed based on the neighboring relationship in physical topology. Nodes with higher degree should avoid sharing interface with other neighbors as possible, as higher degree means more neighbors and more flows going through the node. Links that share the same interface should be treated as a whole when assigning channels.
4.2 Interface-to-channel binding
where i r(p,l) denotes the channel interference ratio between links p and l; A _{ l } denotes set of links that have already been assigned a channel; R ^{′′}(τ) denotes the reduced interference range observed on channel with a separation of τ, which can be obtained through theoretical calculation; d(p,l) denotes the distance between links p and l; and α is a constant used to quantify the interference degree between POCs of different interfaces on the same node, which is usually set to a large value, say 10, to avoid the utilization of POCs on the same node as possible.
4.3 Optimality evaluation of POCA
In this paper, we propose a simple but efficient partially overlapped channel assignment algorithm for MRMC WMNs. In order to demonstrate its optimality, we formulate the optimal partially overlapped channel assignment problem with the goal of minimizing total network interference and set it as the baseline to evaluate our algorithm.
When deciding channel assignment for links, two constraints need to be satisfied:
where 0<NorThr≤1, larger value of NorThr means better performance of POCA; and Thr_{opt} and Thr are the throughput that can be achieved by O-POCA and POCA, respectively.
4.4 Complexity analysis of POCA
- (1)
The running time of computing node degree for all nodes takes at most O(|V|^{2}) steps. When a node in V calculates its degree, the maximum number of neighbors it can have is |V|−1 (e.g., a complete graph), thus the time complexity required to compute degree for all nodes is O(|V|^{2}).
- (2)
The running time of neighbor-to-interface binding procedure according to node degree takes at most O(|V|^{2} log|V|) steps.
- (3)
The running time of computing EIL (including Rank) values for all links and choosing one to be assigned a channel take at most O(c|E|^{2}) steps, where c is the number of channels.
- (4)
The running time of assigning channel for a selected link takes at most O(c|E|) steps.
Overall, the time complexity of proposed POCA algorithm is bounded by O(|V||E|^{2}) because procedure in 3 will repeat O(|V|) steps, and the number of channels c is a constant.
5 Performance evaluation
We evaluate the proposed POCA algorithm by comparing it with channel assignment algorithm based on OCs (termed as OCA for short below) in different scenarios. Our experiments are carried out using network simulator (NS-3.19). We also modify NS to support multi-channel multi-radio and partially overlapped channels. We randomly select certain number of nodes as flow sources and set the gateway node as the destination for majority of flows, and for other flows, the destinations are randomly selected. All these can simulate situations in real WMNs, where traffic between the Internet and clients and peer-to-peer traffic coexist and traffic between the Internet and clients is dominant. The simulations are based on IEEE 802.11b standard which has 3 OCs out of 11 available channels, and the data transmission rate at the physical layer is 2 Mbps.
The following are our performance metrics, simulation results, and analysis.
5.1 Performance metrics
- (1)
Average end-to-end delay: the end-to-end delay is defined as the time it takes a packet to reach the destination after it leaves the source. The average taken over all the received packets is then computed, which is the average end-to-end delay.
- (2)
Network throughput: the network throughput is defined as the total amount of data bits actually received by receivers divided by the time between receiving the first packet and the last packet.
- (3)
Average packet loss ratio: the packet loss ratio is defined as the number of packets delivered unsuccessfully divided by the total number of packets supposed to be delivered. The average taken over all the receivers is the average packet loss ratio.
5.2 Simulation results and analysis
We compare the performance of POCA algorithm with OCA, which are executed on the following topologies and evaluate the performance of them on the estimation of metrics listed in the ‘Perfomance metrics’ section.
5.2.1 Simulation results under grid topology
Grid topology of N×N squared grids is used, that is, each vertex is deployed with a mesh router, and each edge denotes a wireless link. Mesh routers are equipped with radios of similar capability and configuration, which means that the communication and co-channel interference ranges are uniformly set to 250 and 550 m, respectively, for all radios. The grid step is set to 250 m, which is the distance between adjacent nodes. This means that a node can communicate with its neighbors except the diagonal nodes. The node positioned in the bottom right corner is assumed to be the gateway. Traffic is generated by the constant bit rate (CBR) source, and the packet size is set to 512 bytes. In our simulations, channels 1 to 11 are used as POCs and channels 1, 6 and 11 are used as OCs.
From the simulations above, we can also draw conclusions that packet loss ratio is complementary to network throughput. In view of their relationship, performance results about average packet loss ratio are omitted in the following simulations.
From Figure 5a, we can see that the performance of our POCA algorithm is comparable to O-POCA. When the network is small, even the optimal channel assignment cannot further eliminate interference, thus NorThr value is almost 1; as network grows larger, O-POCA has the ability to search the whole solution space to find better channel assignment than POCA, thus the throughput of POCA algorithm is a little lower than that of O-POCA, but the reduction in NorThr value never exceeds 12%. From Figure 5b, we can see that when the number of flows increases under fixed network size, the space for O-POCA to find better solutions gets smaller, thus NorThr value increases, which means that POCA can provide comparable performance as O-POCA. As O-POCA is NP complete, solving it is very time-consuming, which results in that it cannot be well applied in practice, while our POCA algorithm can be solved with polynominal time complexity and its performance is near optimal; it achieves good balance between performance and complexity.
5.2.2 Simulation results under random topology
The following are our observations: the network throughput has similar trend with that under grid topology. The only difference is that the improvement is not so dramatic. Still, POCA outperforms OCA because it fully exploits the whole spectrum to perform channel assignment, so interference among adjacent links can be further eliminated and more flows can perform parallel transmissions. When there are 14 concurrent flows, the network throughput can be increased by approximately 19% and 9%, respectively, in 30-node network and 60-node network if we fully exploit the spectrum. We also observe that network throughput in 60-node network is more than that in 30-node network with the same number of concurrent flows; the reason is that the distribution of flows is more sparse in 60-node network, interference between flows is less, which gives flows more space to perform parallel transmissions, thus more packets can be routed to destinations more accurately, more quickly. Average end-to-end delay can be dramatically decreased, for instance, in the 60-node network using POCA; when there are nine flows or less, no interference occurs among these flows and packets can reach destinations with almost no delay. When more flows are injected into the network, average end-to-end delay increases, but it is always less than that using OCA.
6 Conclusions
In this paper, we consider about the characteristic of network traffic and propose a POCs-based assignment algorithm which utilizes theoretical calculation to obtain reduced interference ranges and assigns channels for all links in the network with the goal of minimizing total network interference. Through simulations, we demonstrate the effectiveness of the proposed algorithm in improving network performance. We plan to evaluate the performance of our proposed POCA algorithm in real testbed.
- (1)
Each node computes its own degree according to physical topology.
- (2)
In MRMC WMNs, gateway node periodically broadcasts messages to notify its existence and related information. Mesh nodes that receive these messages can obtain their hop count distance from the gateway.
- (3)
Nodes obtain node degree of their one-hop neighbors through ‘Information Exchange’ messages broadcasted within H hops, and then neighbor-to-interface binding can be finished according to node degree information.
- (4)
Each node calculates EIL and Rank values of links originating from it and records EIL, Rank, channel assignment list, and other information of links originating from other nodes within H hops distance according to the ‘Information Exchange’ messages.
- (5)
When assigning channels for a selected link, the channel which can minimize the total interference between it and links that have been assigned channels is selected and assigned. After its channel assignment, this node will notify nodes within its H hop distance about its channel information. On receiving the information, each node updates EIL and channel assignment list, etc.
- (6)
Steps (4) and (5) are repeated until channel assignment for all nodes within H hop distance is completed.
The basic condition to perform the above distributed algorithm is to allow information exchange between nodes, when information exchange cannot be achieved for some reasons or in order to reduce overhead, game-theoretic approach [27,28] can be used to model partially overlapped channel assignment for MRMC WMNs with the objective of minimizing total network interference, and uncoupled learning algorithms should also be used to achieve stable solutions.
At present, routing metrics published are all proposed on the assumption that channels are orthogonal [29-34]. When using OCs, the interference range is a constant, which is usually twice the transmission range. As a result, the interference estimation is very simple, that is, if two links are within the interference range of each other, they will interfere if they operate on the same channel, and otherwise not. However, when POCs are applied, the interference range is no longer a constant. The interference relationship is related to the distance between links and the separation between channels used by links. Thus, the determination of interference relationship, the model of intra-flow interference, and inter-flow interference should be modified. As future direction, we plan to study routing metrics that can capture the characteristics of WMNs using POCs to provide route guidance for traffics.
Declarations
Acknowledgements
This work was supported by the National Natural Science Foundation of China under Grant No. 61373124.
Authors’ Affiliations
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