Channel Assignment with Route Discovery (CARD)
using Cognitive Radio in Multi-channel Multi-radio
Wireless Mesh Networks
Chittabrata Ghosh and Dharma P. Agrawal
OBR Center for Distributed and Mobile Computing
Department of ECECS, University of Cincinnati
Cincinnati, Ohio - 45219.
Email: ghoshc,[email protected]
Abstract: For better spectrum utilization, efficient channel
allocation in multi-radio wireless mesh networks has become an
active research area. Our proposed CARD algorithm deals with
the application of cognitive mesh routers for fixed channel
assignments to mesh clients under each router’s domain. The
farthest channel assignment by the cognitive radio in mesh
routers ensures minimum inter-router and intra-router
interference. Initial fixed assignment of channels to clients
supporting k-connectivity (k = 3 and 5 sub-channels) shows
substantial increase with 15 concurrent transmissions when
compared to 4 in case of the CCA scheme with k=3 and 3
channels to choose, i.e., a factor of almost 4 . The improvement in
communication delay is about a factor of 80 when compared to
SC and a factor of 35 compared to CCA.
Keywords- Alpha-beta pruning, wireless mesh networks,
cognitive radio
I.
INTRODUCTION
Wireless Mesh Network (WMN) [1], a recent networking
technology, is envisioned to improve the performance of all the
existing networking technologies namely, adhoc networks,
Wireless Local Area Networks, Wireless Private Area
Networks and Wireless Metropolitan Area Networks. WMN is
similar to that of the adhoc networks in case of selfconfiguration and self-organization, which in turn demands
minimum network maintenance. The nodes have the capability
of forming a mesh network on an adhoc basis and forwarding
packets over multiple hops to the destination nodes not within
the communication region of the transmitted nodes. WMN is
expected to play a dominant role for future broadband home
networks.
Cognitive radio is a revolutionary technology that primarily
focuses on substantial spectrum efficiency with the aid of
advanced spectrum sensing and dynamic channel assignment in
licensed bands without actually obtaining a license [2, 3]. The
radio is built on a software defined radio and is capable of
taking decisions based on its surrounding environment.
Opportunistic spectrum sharing of the licensed spectrum [2] by
licensed users, hereafter referred to as secondary users (SUs) is
one of the key concepts discussed in our research work. For
improved spectrum utilization, a set of sub-channels in an
extended C-band (5.925-7.250 GHz) can be assigned to SUs
when the sub-channels are not utilized by the primiary users
(PUs). Since PUs have higher priority over the secondary
users, the former can use the entire C-band whenever
necessary. It is the job of the SU to detect the arrival of the
primary user also called spectrum sensing and to leave the
corresponding sub-channel in the C-band of the recently
arrived primary user. Otherwise, the power level of the primary
user’s signal, being much higher than that of the secondary
user, will entirely corrupt the signal of the latter resulting in
interference, called the Primary User Interference (PUI).
Again, when an SU uses a particular sub-channel, signals from
the adjacent SUs can interfere with this desired user. This
interference is called adjacent channel interference (ACI).
In case of 802.11 Wireless Local Area Network, the access
points generally use a single half-duplex radio with the MAC
protocol to support transmissions and receptions over a
common channel. Basically, the wireless users compete for the
channel using Carrier Sense Multiple Access/Collision
Avoidance (CSMA/CA) protocol. But the same strategy can
never be applied for wireless mesh networks because of its
topology and connectivity constraints. Communication over a
single channel can lead to all the mesh routers using the same
channel for restoring connectivity. The worst case scenario
may occur when mesh routers from neighboring hops of a
multi-hop path also use the same channel to ensure
connectivity.
Therefore, multiple channels are always
envisaged for wireless mesh networks.
Now we are convinced that multiple channels are essential to
ensure better connectivity in wireless mesh networks. But let us
walk through the situation of multiple channels being used by
wireless mesh routers over a single radio. The obvious problem
is to dynamically switch to different channels to initiate new
connections with other nodes while restoring existing
communication with an ongoing mesh node. Again, switching
between channels demands strong synchronization among
nodes.
Even, slow switching for channels to reduce
synchronization requirements will in turn, lead to end-to-end
delays. Therefore, to improve the performance of wireless
mesh networks in terms of spectrum utilization, multiple radios
may be implemented in each mesh router or a mesh client.
This paper deals with multi-radio multi-channel wireless mesh
networks and study the throughput and delay performance.
The central idea is to increase the capacity and additionally,
the throughput of the wireless mesh networks by using multiple
radios per node for simultaneous communications over
interfaces tuned to multiple free sub-channels, unlike the base
single channel assignment scheme.
Channel assignment is a major problem when discussing
multi-radio multi-channel WMN. One of the existing schemes
to solve this problem is the common channel assignment
scheme (CCA) [4] where the different radio interfaces are
tuned to the same set of channels. But this scheme is inefficient
for large number of channels being assigned to a small number
of radio interfaces. The inevitable reason behind this is the
pronounced intra-channel interference between the radio
interfaces. This leads us to a different channel assignment
strategy utilizing the relation between channel assignment and
channel interference.
While multiple radios operate on different channels, two
nodes can communicate with each other if they have a radio
interface tuned to a common channel. Again, if the nodes have
different radio interfaces assigned to same set of frequencies,
then it may provide better connectivity but can lead to severe
interference. Therefore, strategic channel assignment is vital in
overall performance evaluation of multi-radio wireless mesh
networks. The other way of looking into the problem is to
design a trade-off between connectivity and channel
interference. Our proposed CARD scheme has taken care of
these two parameters using opportunistic spectrum sharing and
updating the set of sub-channels to be used by mesh routers at a
particular time instant.
The rest of this paper is organized as follows: Section II
describes the underlying alpha-beta pruning scheme used in the
CARD algorithm. Section III describes about our proposed
CARD algorithm. Section IV evaluates the performance
measures using our proposed CARD algorithm. Finally, section
V draws the conclusions and future research directions.
II.
ALPHA-BETA PRUNING
Alpha–Beta technique is used on the game tree to compute the
best move by the player and in turn, ignore branches that do
not contribute further to the outcome. The advantage of this
scheme is that not all the outcomes need to be checked for the
best possible move. In other words, if the current outcome
results in a worse move when compared to our best possible
choice, then the first move that the opposition could make
would be our last possible choice. The few assumptions needed
for the Alpha-Beta pruning as follows: (i) all paths end up to
fixed depth limit, d and (ii) the opponent will always choose
the best move.
To get into the details of the game, each node has memory
enough to store four parameters namely, Alpha, Beta value for
itself, current score and, if not terminal nodes, must also store
the node address of its recently selected child node. The fourth
parameter will allow backtracking down to the terminal nodes.
To start with the game, Alpha and Beta values are assigned
initial values of –Infinity and +Infinity respectively. Then the
Figure 1. Parent node and its children with d=3.
following steps are followed for the best move to be taken by
the player/opponent.
(1) Track down to the depth of the game tree
(2) Calculate the utility of each terminal node based on certain
parameters that better describes its characteristics. The point to
be noted over here is that the same parameters should be used
to compute the utility function for all the nodes. in its parent
and also replace the score at the parent node with this new
score.
(3) Propagate the Alpha and Beta values along with the path
traced based on the following options:
•
If the opponent makes the move to be backtracked:
(i) If the current score obtained from its child node is less than
the stored score at its parent, store the path from the bottom and
the Beta value in its parent node and replace the stored score
with this new score.
(ii) If this newly stored score is less than the Alpha value stored
in its parent node, prune all the branches under this node. If
greater than the Alpha value in its parent node, replace the
Alpha value with this score and proceed with the next child
node and sending the Alpha and Beta values down. If no child
exists, these values are propagated up the tree and the Alpha
value becomes the MIN score as shown in Fig. 1.
•
If the player makes the move to be backtracked:
(i) If the current score of the child node is greater than the score
at its parent, replace the stored parent’s score with this new
score and store the path from the bottom and the Alpha value in
its parent.
(ii) If the newly stored score is greater than the Beta value in
the parent node, prune all the existing child nodes and
backtrack the parent’s Alpha and Beta values up the tree. If less
than the Beta value at the parent node, replace the Beta value
with this new value and proceed to the next child and sending
the Alpha and Beta values down. If no child exists, these
values are propagated up the tree and the Beta value becomes
the MAX score as shown in Fig. 1.
When the search is complete, the Alpha value at the top node
gives the minimum score which is guaranteed to attain if the
path stored in the parent node is backtracked. The entire
algorithm for the implementation of the Alpha-Beta pruning is
Figure 2. Algorithm to implement Alpha-Beta pruning
explained in Fig. 2. We have used this algorithm for our CARD
algorithm explained in the next section.
III.
PROPOSED CARD ALGORITHM
Our proposed CARD algorithm is based on the hierarchical
mesh networks with k-connectivity (node with k mutually
independent connected radio links). Each mesh router is
equipped with a cognitive radio which periodically scans and
detects the free channels [5] in five different sub-bands (0.265
GHz each) of the entire C band). These free channels are stored
in each mesh router’s free channel pools (FCP), each pool
having free channels from a single sub-band.
In our scheme, each channel selection from the FCP by a
mesh router is broadcasted to its one hop mesh routers and in
turn to its two hop routers. This assures that the same set of
channels is not assigned to mesh clients of adjacent mesh
routers, till two hops. On the other hand, this channel
assignment scheme can lead to primary user interference and
substantial adjacent channel interference.
Therefore, our proposed CARD scheme incorporates
cognitive routers with its two-fold strategy that helps in
reducing intra-hop interference to a great extent and as well as
the inter-hop interference: i) each channel request from the
same hop mesh clients is assigned from a different sub-band
and (ii) additional channel requests within the same hop
should be based on farthest channel assignment. The reason
for this assignment strategy is that usually all mesh nodes
request for one free sub-channel which, if assigned from
different sub-bands will not interfere with the transmissions
from the neighboring nodes. On the other hand for subsequent
channel requests, the cognitive radio must assign the farthest
channel with respect to the already assigned channel in the
same sub-band as that of the requesting node. The control
messages are sent over a common control channel but data
transfer takes place over assigned free sub-channels.
First, the one-hop mesh clients have their parent node (PN)
discovered as the mesh router with one radio interface
connected to the PN and channels assigned as per the above
mentioned strategy. The possible detection of the PN is based
on the received broadcast packet by the mesh node with hop
count =1. Now if two one hop members are within each
other’s communication range and have the same PN, then they
request for and share a common channel for their second radio
interface as shown by the dashed lines in Fig. 3. This common
channel is decided by the router and selected from the subband different from the ones used by the sharing nodes’ radio
interfaces. This helps in constructing the mesh with kconnectivity with k=2. Now, the two-hop clients under each of
these one-hop clients can request for channels. On request,
each one-hop members are discovered as PN for their
corresponding one-hop neighbors and channels assigned
similarly as explained above.
function is multiplied by its access rate, if MIN nodes are the
terminal nodes and divided by the access rate if MAX nodes
are the terminal nodes.
This manipulation is performed to avoid giving access to a
particular parent node having its terminal node with maximum
number of packets. The division minimizes the chances of the
same MIN node being selected by a MAX node and
multiplication increases the chances of a MAX node when
MIN node is the one to choose its move.
Figure 3. Initial channel assignment to mesh clients by the parent node
Thus, all data packets have to be routed through the
discovered PN. For future communication, one mesh client at
a time, the third radio interface of the PN requests for the same
channel as that of the desired member node interface. This
way, the k-connectivity (k=3) mesh network with fixed
channel assignment is created through our proposed scheme as
shown in Fig. 4. Additionally, the CARD algorithm also
discovers the route through intermediate PNs from each and
every mesh router till its two-hop neighbors.
The Alpha-Beta pruning algorithm has been modified for our
research in the following manner: the utility function is
calculated by the parent node for all the terminal nodes under
it. This function is a ratio of the total number of packets under
this parent node by the number of forwarding packets for the
concerned terminal node. The intention for this calculation is
to minimize the utility function for a node having maximum
number of packets to be forwarded. Again, when the utility
function is finally distributed to the terminal nodes, the utility
Another modification made in the Alpha-Beta pruning
algorithm is done to utilize the multi-radio facility. The parent
node checks for the destination address from the incoming
packets. If this address is related to its own hierarchical game
tree, it stores the packets and waits for its chances to be
selected as a MAX or a MIN player. If this address is not
within its own hierarchy, the parent node utilizes its different
radio interface tuning its radio interface to a different channel
and communicates to the different adjacent hierarchy. In cases
for different destination addresses other than its own hierarchy,
then the parent node does not forward the packets to its own
parent node within its own hierarchy. After storing the packets
within its buffer, it tunes to a different channel to a different
mesh node in a different hierarchy and forwards packets to that
node. The improvements achieved have been shown in our
simulation results.
IV.
SIMULATION RESULTS
In this section, we have studied our proposed CARD
scheme using network simulations. The single channel
assignment scheme has served as a baseline for comparison
purposes. We have also included CCA scheme [5] as the
comparative algorithm for channel assignment. The entire
simulation of the hierarchical wireless mesh networks has been
performed using GloMoSim [7]. As stated earlier, we have
used throughput and average delay parameters for evaluation
and comparison of CARD scheme with other mentioned
schemes. The entire simulation has been carried out with 50
randomly placed nodes in a 650m × 650m area. The
transmission range is assumed to be 200 m which in turn leads
us to an interference range of 525m.
Our simulation results are based on two distinct categories:
(i) Evaluation based on connectivity and topology and (ii)
Single hop performance for multi-radio mesh networks.
The topological performance in wireless mesh networks has
been studied in our research using the maximum concurrent
transmission as the deciding parameter. The reason for this
choice is to verify efficiency of the radio interfaces to switch
between different free sub-channels from the free channel pool.
The more the number of successful transmissions, the better is
the utilization of the spectrum and hence, throughput and also
the capacity of wireless mesh networks increases. Fig. 5 shows
the comparison between the CARD and the CCA algorithm
using three channels. The linear increase in the curve for
CARD algorithm shows the efficiency of our algorithm over
CCA with gradual increase in the number of radio interfaces
per node. As shown in Fig. 3, CARD algorithm has been
Figure 4 Assignment of sub-channels to ensure k-connectivity, k=3.
Figure 5 Comparison in number of concurrent transmissions for CARD and
CCA algorithms for 3 channels per node.
successful in 13 concurrent transmissions when compared to 5
in CCA.
Similarly, Fig. 6 indicates better spectrum utilization by CARD
algorithm over CCA when considering 12 channels to be
switched by progressively increasing number of radio
interfaces per node. The distinct feature to be observed in this
figure is that the linear increase in the number of concurrent
transmissions saturates for CARD algorithm after 4 radio
interfaces per node. This indicates that the adjacent channel
interference and intra-channel interference affects in concurrent
transmissions after adding more number of radio interfaces.
Therefore, as seen from Fig. 6, the trade-off for designing
multi-radio wireless mesh networks can be limited to 5 radio
interfaces per node when 12 free sub-channels can be utilized
for concurrent transmissions.
Figure 6. Comparison in number of concurrent transmissions for CARD and
CCA algorithms for 12 channels per node.
Figure 7 Throughput comparison of CARD algorithm with CCA and Single
channel base cases using 3 channels.
Fig. 7 gives a detailed comparison of our scheme when
compared to CCA and the single channel case. CARD
algorithm provide a substantial improvement in throughput
calculations – up to a factor of 4 with 3 free sub-channels and 3
radio interfaces per node.
Similar throughput improvements can also be achieved using
increasing number of channels with 3 radio interfaces as shown
in Fig. 8. The point to be noted over here is that the increase in
throughput is minimal with increase in the number of radios
per node when compared to that in Fig. 7 for 3 channels and 3
radio interfaces per node. The reason for this is accounted for
the same adjacent and intra-channel interference.
The average delay performance comparison has been
shown in Fig. 9. The curves show a distinct improvement in
average delay- about a factor of 2 when compared to single
channel and around a factor of 1.75 when compared to CCA
with 2 radios. The reason for this minimal delay is due to
minimal number of transmission needed and with increasing
switching technique used in CARD algorithm. The same can be
observed in Fig. 10 with 10 channels and 6 radios but better
results are obtained for average delay because of increasing
number of radios switching between 10 sub channels from the
free channel pool.
Figure 8 Throughput comparison of CARD algorithm with CCA and Single
channel base cases using 10 channels.
V.
CONLUSION
In this paper we have dealt with cognitive radio based fixed
channel assignment in multi-radio wireless mesh networks.
Extensive set of simulations emphasize the efficacy of the
CARD scheme in multi channel interference reduction with
multiple number of radio interfaces per node.
Our future work will concentrate on theoretical
performance characterization and evaluation of the CARD
scheme in a multi-radio mesh networks. Dynamic channel
assignment must also be considered in our research work.
The CARD algorithm has proved to be efficient when
compared to CCA or the single channel case when considering
the topological characteristics like maximum concurrent
transmissions. Again while considering the single hop multiradio wireless mesh networks, our algorithm proves to be
efficient when considering the average delay and throughput
parameters.
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