This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE Globecom 2010 proceedings.
Dynamic Spectrum Access with QoS Provisioning in Cognitive Radio Networks ∗ Key
Chunyan An∗ , Hong Ji∗ , Pengbo Si†
Laboratory of Universal Wireless Communication, Ministry of Education Beijing University of Posts and Telecommunications, Beijing, P.R. China † Beijing University of Technology, Beijing, China Email:
[email protected];
[email protected];
[email protected]
Abstract—Dynamic spectrum access is one of the most important premises of spectrum reuse based on cognitive radio technologies, which are considered to be the best way to alleviate the controversy on spectrum scarcity and low efficiency. However, most of previous work focuses on the increasing of system throughput, ignoring the QoS requirement of secondary users. In this paper, dynamic spectrum access with QoS provisioning is studied. We adopt discrete-time Markov chain to analyze and model the spectrum usage in time-slotted cognitive radio networks. Furthermore, three admission control schemes are proposed to minimize the forced termination probability of secondary users. Simulation results show that our proposed schemes can significantly improve the forced termination probability of secondary users, though slightly increase the blocking probability. Index Terms—dynamic spectrum access, discrete-time Markov chain, admission control, QoS provisioning
I. I NTRODUCTION Nowadays, the available spectrum bands have been running out of use due to the fixed spectrum allocation policy [1] and the proliferation of wireless services and devices. On the other hand, the utilization of some allocated spectrum bands is extremely low. This motivates the concept of spectrum reuse that allows secondary users to utilize the not-in-use spectrum bands allocated to primary users [2]. Dynamic spectrum access in cognitive radio networks is one of the key technologies to improve the efficiency of spectrum utilization. A lot of work has been done on dynamic spectrum access recently. Authors of [3] present an overview of challenges and recent developments of dynamic spectrum access from the aspect of technological and regulatory. In [4], spectrum access markets, which contains various centralized and decentralized networks, are designed based on stochastic game framework. The semi-Markov model is used to predict the channel’s behavior, and access strategies are derived as well, using Bluetooth/WLAN coexistence as an example in [5]. The work of [6] illustrates two dynamic spectrum access models primarily from the aspect of engineering. Continuous This paper is sponsored by the National Nature Science Foundation under Grand 60832009, Beijing National Sciences Foundation under Grand 4102044, the Fundamental Research Funds for the Central Universities under Grand BUPT2009RC0119, and New Generation of Broadband Wireless Mobile Communication Networks of National Major Projects for Science and Technology Development under Grand 2009ZX03003-003-01.
time Markov chain is used in [7] to model the spectrum usage and an access scheme with optimal channel reservation is proposed to improve the forced termination probability of secondary users. Although some work has been done for dynamic spectrum access in cognitive radio networks, the QoS requirement of secondary users has been largely ignored. However, if the QoS requirement of secondary users can not be satisfied, they will search for any other spectrum band on which better QoS could be provided. As a result, the number of secondary users in this spectrum band will decrease and the improvement of spectrum efficiency becomes un-achievable finally. In this paper, based on the analysis and modeling of the spectrum usage with discrete-time Markov chain, we propose three admission control schemes for secondary users, which can effectively improve the forced termination probability, in time-slotted cognitive radio networks. The motivations of our work are as follows. •
•
•
Secondary users usually work in time-slotted fashion to avoid the need for appropriate handoff scheme when primary user arrives. This prevents us to model the spectrum usage using continues models. The QoS provisioning of secondary users is also quite important. Not only the potential interference to primary users, but also the potential interference and forced termination to other secondary users should be considered when designing admission control scheme in secondary network. There is a tradeoff between the forced termination probability and the blocking probability of secondary users. We can sacrifice blocking probability for improving the forced termination probability.
Authors of [5], [7], [8] use continues-time Markov chain to model the spectrum usage in cognitive radio networks. However, to the best of our knowledge, the analysis and modeling of spectrum usage in slotted cognitive radio networks has not been addressed in previous work. The contributions of our work are as follows. •
We formulate the spectrum usage of primary users and secondary users as a discrete-time Markov chain, which is more suitable for the time-slotted cognitive radio networks. The spectrum access decision of secondary users
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could only be made at the beginning of each time slot. Based on the spectrum usage state in current time slot, three admission control schemes are proposed in this paper to dynamically adjust the number of secondary users in network with the aim of decreasing the forced termination probability of secondary users to a tolerable level. The rest of this paper is organized as follows. System model is presented in Section II. In Section III, the spectrum usage is analyzed and modeled by discrete-time Markov chain. Section IV presents three admission control schemes proposed in this paper. Some simulation results and discussions are given in Section V, and Section VI concludes this paper. •
II. S YSTEM M ODEL In this paper, we consider a cognitive radio system with secondary users, such as mobile users, and primary users, such as TV broadcast users [9]. Without loss of generality, the total spectrum owned by all primary networks in this area is divided into N primary channels with IDs from p1 to pN , and each primary channel pn , 1 ≤ n ≤ N consists of M secondary channels with IDs from sn1 to snM . This assumption is reasonable since that one TV channel is usually 6 MHZ in TV broadcast network, but one voice channel is only 200 kHZ in GSM cellular network. For the secondary network, the following assumptions are made. First, the secondary network is centralized. Spectrum access is controlled by a central controller, which plays the role of base station in cellular network. Second, as shown in Fig. 1, secondary users are operated in time-slotted fashion, where each time slot is divided into three parts: sensing, admission, and data transmission part. Moreover, the admission part consists of two subparts, the admission of keep-on users and the admission of new users. Even the duration of each time slot Tts is a constant, the length of each part is a variable. For example, if all the channels are occupied by primary users in one time slot, after spectrum sensing, secondary users will do nothing but wait for next time slot. Either, if there is but not enough channels for secondary users, admission part may only have keep-on users admission. For the sensing part, collaborative spectrum sensing [10], [11] is used in secondary network to enhance sensing performance, in terms of missing detection probability and false alarm probability. All the secondary users taking part in spectrum sensing will report their sensing results to the central controller. Besides, perfect spectrum sensing is assumed, since much work has been done on spectrum sensing, and the focus of this paper is on dynamic spectrum access. Finally, for analysis simplicity, secondary network can only support voice service with constant rate R. However, the modeling of spectrum usage and the admission control schemes proposed in this paper can be easily extended to multi-service cognitive radio networks too. The arrivals of secondary users and primary users are both assumed to be Poisson processes with average arrival rates λs and λp . The service times are exponentially distributed with average service rates μs and μp .
Fig. 1.
The framework of secondary users.
At the beginning of each time slot, the number of primary users j in network can be obtained by spectrum sensing. Moreover, the central controller has a record of users in secondary network. In other words, current state of spectrum usage is known by central controller. Based on the forced termination probability predicted by discrete-time Markov chain according to current state of spectrum usage, the goal of this paper is to improve the QoS of secondary users by dynamically controlling the number of users admitted in network in each time slot. III. A NALYSIS AND M ODELING OF S PECTRUM U SAGE Spectrum usage in time-slotted cognitive radio networks is modeled as discrete-time Markov chain in this paper. Each state is denoted as (i, j), where i means the number of secondary users while j means the number of primary users in cognitive radio networks in current time slot. Since the primary users and secondary users share the total N primary channels, each achievable state must satisfies i + jM ≤ N M . The number of achievable states can be expressed as: Ns =
N
[(N − j)M + 1] (1)
j=0
(N M + 2)(N + 1) = 2
A. States Transition Probability In this subsection, some formulas are first given to simplify the analysis of states transition probabilities of discrete-time Markov chain. For secondary users, since the arrival is assumed to be Poisson process with average rate λs , the probability that k users arriving in a period of time with length T can be expressed as: Pas (k) =
(λs T )k · e−λs T , k!
k = 0, 1, 2, · · · , +∞
(2)
As explained in section II, the service time of secondary users is assumed to be exponentially distributed with rate μs .
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According to [12], the departure of users is Poisson process with average rates μs . As a result, the probability that k users departing in a time slot with length T can be expressed as: (μs T )k · e−μs T , Pds (k) = k!
k = 0, 1, 2, · · · , +∞
(3)
•
(i,j)
γ(m,n) = Psd (i − m) · Ppa (n − j) •
Therefore, let Psa (k)(Psd (k)) denote the probability that the number of secondary users in network will increase(decrease) k. Psa (k)(Psd (k)) can be expressed as: Psa (k) =
∞
∞
State (m, n), 0 ≤ m ≤ i j < n < M and m + nM = N M . The corresponding transition probability is (i,j)
γ(m,n) =
Pds (k)Pas (k + l)
(4)
Pds (k + l)Pas (l)
(5)
l=0
•
(i,j)
γ(m,n) = Psa (m − i) · Ppa (n − j) •
−λp T
Pap (k) =
(λp T ) · e k!
,
k = 0, 1, 2, · · · , +∞
(6)
Pdp (k) =
(μp T )k · e−μp T , k!
k = 0, 1, 2, · · · , +∞
(7)
Ppa (k) =
∞
Pdp (k)Pap (k + l)
(8)
Pdp (k + l)Pap (l)
(9)
l=0
Ppd (k) =
∞ l=0
Let (i, j), i + jM ≤ N M denote the state of spectrum usage (i,j) in current time slot. γ(m,n) means the probability that current state (i, j) will transit to achievable state (m, n). By modeling the spectrum usage using discrete-time Markov chain, the (i,j) transition probability γ(m,n) can be divided into nine types: • State (m, n), 0 ≤ m < i and 0 ≤ n < j. The corresponding transition probability is (i,j)
γ(m,n) = Psd (i − m) · Ppd (j − n) •
State (i, n), 0 ≤ n < j. The corresponding transition probability is (i,j)
γ(m,n) = Psd (0) · Ppd (j − n) •
State (m, n), i + 1 ≤ m ≤ N M 0 ≤ n ≤ j and m + nM < M N . The corresponding transition probability is (i,j)
γ(m,n) = Psa (m − i) · Ppd (j − n) •
State (m, n), i + 1 ≤ m ≤ N M 0 ≤ n ≤ j and m + nM = M N . The corresponding transition probability is (i,j)
γ(m,n) =
∞
Psa (k) · Ppd (j − n)
k=m−i •
State (m, j), 0 ≤ m < i. The corresponding transition probability is (i,j)
Psd (k) · Ppa (n − j)
State (m, n), i + 1 ≤ m ≤ N M j < n < M and m + nM < N M . The corresponding transition probability is
State (0, M ). The corresponding transition probability is
Similarly, all the probabilities of primary users can be obtained too. k
i−m k=0
l=0
Psd (k) =
State (m, n), 0 ≤ m ≤ i j < n ≤ M and m + nM < N M . The corresponding transition probability is
γ(m,n) = Psd (i − m) · Ppd (0)
(i,j)
γ(m,n) =
∞
Ppa (k)
k=N −j
B. Forced Termination Probability Since spectrum handoff is permitted, the forced termination of secondary users will not occur when there are any secondary channels free. In other words, forced termination of secondary users only can happen in state ((N − k)M, k), 0 ≤ k ≤ N due to the appearance of primary users. Let PF (i, j) represent the predicted forced termination probability in next period of time with length T when current state is (i, j), and can be described as: PF (i, j) =
N k=j+1
(i,j)
Γ(i − m)γ(m,k)
i−m l=0
i−l−m Psd (l) i
where m = (N − k)M . In this formula, the total number of secondary users terminated in state (m, k), 0 ≤ k ≤ N should be i − l − m, 0 ≤ l ≤ i − m with probability Psd (l). This is because there may have some secondary users finishing data transmission and departing from system. IV. A DMISSION C ONTROL S CHEMES The transition probability between achievable states and the forced termination probability in a period time with length T are both analyzed in section III. Based on this, we will present three different admission control schemes to improve the QoS of secondary users in this section. For the simplicity of description, the following notations are used in this section. j is the number of primary users sensed at the beginning of current time slot. ip is the keep-on secondary users, while Nsa means the number of new secondary user request in last time slot. 1 × Nsa vector Tsn is the service time of all the new secondary users. The central controller has a service table, recording all the secondary users in system and their remaining service time. k denotes the number of secondary users may be admitted in network in current time slot, k can be less than 0 representing |k| secondary users terminated. i represents the number of secondary users in system after admission. PF th is the threshold of forced termination probability.
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A. Scheme 1: Predicted for one time slot In scheme 1, new secondary users will be admitted according to the predicted forced termination probability in next time slot, T = Tst . The transition probability between achievable states are constant, so a termination probability table can be made in central controller. Therefore, we can look up the termination probability table when need to calculate it. This scheme is described as: Step 1: Calculate the number of secondary users could be admitted in current time slot. If ip + jM > N M , not wanting to cause any interference to primary users, not only no new secondary users can be admitted in network, but also ip − (N −j)M keep-on secondary users should be terminated, k = −ip + (N − j)M . Otherwise, f or k = min(Nsa , (N − j)M − ip ) : 1 : 0 if PF (k + ip , j) < PF th break; end Step 2: Secondary users’ admission. If ip + jM > N M , random access (N −j)M keep-on secondary users. Otherwise first admit ip keep-on secondary users, and then the k new users. Step 3: Refresh the states of spectrum usage and secondary service table. The state of spectrum usage in this time slot will be updated to (ip + k, j). Add new secondary users and their service time to the secondary service table. B. Scheme 2: Predicted for lifetime Since the service time of each secondary user is usually more than one time slot, scheme 1 may not protect the QoS of secondary users sufficiently by making decisions only according to the predicted forced termination probability in a time slot. In this subsection, we propose another admission control scheme to further improve the QoS of secondary users by making admission decisions of new user according to the forced termination probability in its lifetime. This scheme can be explained as: Step 1: If ip + jM >= N M , similar to scheme 1, ip − (N − j)M secondary users should be terminated to release channels for primary users. The number of new users k should be admitted is −ip + (N − j)M . Step 2: If ip + jM < N M , first admit all the keep-on secondary users. Then, let k = min(Nsa , (N − j)M − ip ), if PF (k + ip , j) PF th break; end f or k = k − 1; and then admit k new secondary users. In this step, PF (i, j, Tsn (k)) means the predicted forced termination probability in the lifetime of the new secondary user. Since every
secondary user’s service time is different, even for the same state (i, j), the forced termination probability of accessing each user is different. Step 3: Refresh the states and secondary service table. The state of spectrum usage in this time slot will be updated to (ip + k, j). Add new secondary users and their service time to the secondary service table. C. Scheme 3: Predicted for the average lifetime Even scheme 2 can sufficiently protect the QoS of secondary users, the calculation complexity is very high, since it needs to calculate the predicted forced termination probability a lot of times due to the different duration of secondary user. In scheme 3, we use the average service time to instead the service time of each secondary user when calculating the predicted termination probability. As a result, the forced termination probability PF (i, j) is a constant as the service time of each secondary user changes, and we can make a termination probability table in the central controller as scheme 1. Scheme 3 is similar to scheme 2 except that the termination probability PF (i, j) is obtained by looking up the termination probability table in step 2. V. S IMULATION AND D ISCUSSIONS Extensive simulation results are presented in this section to evaluate the performance of three admission control schemes proposed in this paper and the existing scheme. Furthermore, the performance comparison of the proposed schemes and the existing scheme is shown. The existing scheme here denotes the one without considering the impact of admitting new secondary users on the potential forced termination of the existing ones. The following assumptions are adopted in the simulation. The number of primary channels in this area N is 3, each primary channel is divided into M = 5 secondary channels. All the primary and secondary channels are independent identically distributed. The secondary network operates in timeslotted fashion, in other words, secondary users can arrive at any time, but only can be accessed or blocked at the beginning of next time slot. Each time slot lasts 100 ms. The average arrival rate of secondary users is assumed to be 1, while the average service rates of secondary users and primary users are 0.83 and 0.5. The constant rate of secondary service is 100 kbps. The simulation data is recorded for 200000 times. Fig. 2 and 3 shows the blocking probability and forced termination probability when the average rate of primary users λp varies from 0.1 to 1.0. From these figures, the following things can be seen. Firstly, the existing scheme has the minimum blocking probability but the maximum forced termination probability. This is because in our proposed schemes, we sacrifice blocking probability for improving forced termination probability by dynamically controlling the number of secondary users in network. Secondly, by comparing the curves of scheme 1 and scheme 3, we can see that the longer time T in which the forced termination probability is predicted by
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0.8
Blocking probability
0.7
14
Scheme1, one time slot Scheme 2, lifetime Scheme 3, average lifetime Existing scheme
Scheme1, one time slot Scheme 2, lifetime Scheme 3, average lifetime Existing scheme
12
Average throughput (kbps)
0.9
0.6 0.5 0.4 0.3
10
8
6
4
0.2 2
0.1 0 0.1
0.2
0.3
0.4 0.5 0.6 0.7 Call arrival rate of primary users
0.8
0.9
1
Fig. 2. The blocking probability of secondary network when arrival rate of primary users varies from 0.1 to 1.0.
0.35
Forced termination probability
0.3
0 0.1
0.2
0.4 0.5 0.6 0.7 Call arrival rate of primary users
0.8
0.9
1
Fig. 4. The throughput of secondary network when arrival rate of primary users varies from 0.1 to 1.0.
the computational complexity is small since we can make a forced termination probability table in the central controller.
Scheme1, one time slot Scheme 2, lifetime Scheme 3, average lifetime Existing scheme
VI. C ONCLUSIONS
0.25
0.2
0.15
0.1
0.05
0 0.1
0.3
0.2
0.3
0.4 0.5 0.6 0.7 Call arrival rate of primary users
0.8
0.9
1
In this paper, the spectrum usage was analyzed and modeled by discrete-time Markov chain. Furthermore, three admission control schemes were proposed to protect the QoS of secondary users. The key step of these approaches is to predict the forced termination probability in a certain period of time by analyzing the states transition probabilities of discretetime Markov chain. Furthermore, the significant performance improvement of our admission control schemes compared with the existing scheme was shown by extensive simulation results.
Fig. 3. The forced termination probability of secondary network when arrival rate of primary users varies from 0.1 to 1.0.
admission control scheme, the higher the blocking probability and the lower the actual forced termination probability. Finally, comparing with scheme 3, scheme 2 has the lower blocking probability and forced termination probability as well, since it makes admission decisions according to each new user’s service time and will admit new secondary users with short service time when network is busy. Besides, when T is constant, the predicted forced termination probability of all achievable states will increase with the increment of λp . When there exists PF (s, t) > PF th as the increment of λp , state (s, t) becomes blocking state and the the blocking curve will shift up. This is why the curves of scheme 1 and scheme 3 in both figures are not smooth. As λp becomes larger, less and less secondary users will be admitted in network. This explains why the forced termination probability of scheme 2 slightly falls when λp is larger than 0.7. Fig. 4 shows that the system throughput performance of the four schemes. They all decrease with λp . In conclusion, scheme 2 has the best performance on forced termination probability, however, the computational complexity is very large. When the average arrival rate of primary users is lower, scheme 3 has similar performance with scheme 2, but
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