TED AND KARYN HUME CENTER FOR

NATIONAL SECURITY AND TECHNOLOGY

An Anti-Jamming Strategy for Channel Access in Cognitive Radio Networks

Shabnam Sodagari and T. Charles Clancy

shabnams@vt.edu

tcc@vt.edu

http://www.hume.ictas.vt.edu

Cognitive Radio (CR)

Motivation

Primary Base Station

Primary User

CR

CR Base Station

Like any communication network, cognitive radio networks (CRNs) are vulnerable to jamming attacks, to prevent them from utilizing spectrum opportunities.

Problem Modeling

Jammer can do spectrum sensing too

Jams j channels at random from the total channels released to the secondary

In each time slot t secondary user (SU) tries to avoid interference to the primary user (PU) by spectrum sensing

Problem Modeling Random jamming strategy helps the jammer against a smart SU. We assume SU can select one channel in each time slot and does not want to select a channel under attack. We present the best strategy for the SU to maximize its throughput over time. We assume the jammer does not jam channels while PU is active and only targets the SU’s access.

In case PU can recognize and punish

jammers, or attacker distant from the primary, or jammer has no incentive to disrupt PU

access

Solution

Formulate as multi-armed bandit process

given unknown rewards of multiple levers, the player (here the SU) tries to pull the most rewarding lever at each time slot

with the goal of maximizing its total reward over the time horizon.

levers associated with available sub-channels

reward proportional to the SNR of the SU over the chosen sub-channel.

Formulation

Total of C sub-channels, each sub-channel c can be in one of K states, corresponding to SNR levels

denotes sub-channel c is under attack

Set of time-variant transition probabilities for each state is

suppose sub-channel c in state and is selected next after rounds, reward

1 2{ , ,..., }K

c c c cp p p p

1 0cp

{1,2,..., }k K ( , , )cq k j t

( , , ) 1c

j k

q k j t

k

cp

1t k

cr

Goal of CR User

Finding a sub-channel selection strategy at each time-slot such that infinite time-horizon reward that is expressed in terms of selected channel’s SNR is maximized.

( ) if

0 else

cS c C

Nr

Solution: Solve Whittle’s linear Program

and denote probabilities in the optimal policy, that sub-channel c in state is selected or not selected t time steps after it was last accessed.

kc

c

p tx k

c

c

p ty

k

c cp p

Formulation

Core of semi-uniform strategies:

Pulling the best lever greedily, except when a uniformly random action is taken.

For example, in epsilon-first strategy, there are distinct exploitation and exploration phases, with exploration ε% of the time and exploitation the rest (1-ε)% of shots

Epsilon-greedy strategy: the best lever is selected for a proportion 1 − ε of the trials and another lever is randomly selected with uniform probability with proportion ε

State machine of semi-uniform multi-armed bandit strategies

Epsilon-Decreasing and Adaptive Epsilon-Greedy Strategies

When the value of ε in an epsilon-greedy strategy is decreasing as the number of experiments increases, the strategy is called epsilon-decreasing

A solution can be the adaptive epsilon-greedy strategy, which adjusts ε based on reinforcement learning by keeping track of the reward differences during experiments, i.e., high changes in the reward enforce a higher ε or more exploration than exploitation.

Numerical Results

Monte Carlo simulations in MATLAB

-For various number of idle channels provide by PU

-varying number of jammed sub-channels

Goal: to show our method leads to better overall SNR

-help SU avoid jammed sub-channels

-0 SNR on jammed channels

- 5 to 20 dB SNR on non-jammed channels

- To come up with best exploration vs. exploitation phase length

Comparison of average SNR obtained using ε-greedy method and the random scheme

More than 5 dB SNR improvement

Effect of exploration phase length on average SNR obtained using ε-greedy method

Takeaway: Exploration phase up to 5% is enough

Comparison of average SNR obtained using ε-first, random and ε-greedy methods

Takeaway: With 1000 instances of completed plays and 33% jammed sub-channels, ε-first and ε-greedy approach same performance as number of sub-channels grow

Effect of exploration phase length on average SNR obtained using ε-first method

Takeaway: exploration phase longer than 5% of rounds degrades overall results

Average SNR over selected channels in ε-greedy method vs. varying number of sub-channels and jammed sub-channels

Takeaway: Our method always selects sub-channels with more than 5 dB improvement in SNR

Conclusion Presented an anti-jamming strategy for secondary users for DSA Application to CR: against jammers manipulation of spectrum sensing phase

Thank You

TED AND KARYN HUME CENTER FOR

NATIONAL SECURITY AND TECHNOLOGY