TCP-Aware Cross-Layer Design in Cognitive Radio ... - IEEE Xplore

TCP-Aware Cross-Layer Design in Cognitive Radio Networks Changqing Luo1,3, F. Richard Yu2, Hong Ji1, Victor C.M. Leung3 Key Laboratory of Universal Wireless Communication, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, P.R. China 2 Department of Systems and Computer Engineering, Carleton University, Ottawa, ON, Canada 3 Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada [email protected], [email protected], [email protected], [email protected]

1

Abstract—In this paper, we propose an optimal TCP throughput framework to optimize the TCP throughput in CR networks, which jointly considers the channel sensing decision and access policy as well as low layer design parameters. The TCP throughput optimization is achieved by a two step process. First, we select a channel to maximize the spectrum utilization by reducing the probability of collision with primary users. Secondly, for an available and accessed channel, a cross-layer approach is proposed to improve TCP throughput which adaptively adjusts the modulation and coding scheme/frame size based on the observed channel gain. Simulation results show that the TCP throughput can be improved substantially compared with the existing approach that maximizes physical layer throughput. Keywords-TCP, Cross_layer Design, Cognitive Radio Networks

I.

INTRODUCTION

Cognitive radio (CR) [1] is an enabling technology to allow the secondary users(i.e., unlicensed users) to operate on the vacant allocated spectrum, which is recently considered as a promising technology to deal with the spectrum underutilization problem caused by the current inflexible spectrum allocation policy. Recent research activities that have been conducted in CR networks are mainly focusing on the physical layer. A spectrum sharing policy is exploited in [2], where power/channel allocation is jointly considered to improve the throughput. Authors in [3] study the sensing-throughput tradeoff problem in CR networks. An optimal spectrum sensing strategy is proposed in [4] to maximize throughput. However, other performance parameters in the upper layers, such as transmission control protocol (TCP) throughput, are also very important factors that may directly affect the secondary user' quality of service (QoS). TCP is by far one of the most important transport protocols used by many of the Internet's popular applications, including WWW, FTP and some steaming media applications. In addition, apart from the performance degradation due to the unstable wireless channel, secondary users would have a strictly lower QoS than those who enjoy guaranteed spectrum access [5]. Obviously, if TCP This work was supported in part by the National Science Foundation of China under Grant 60832009, the Hi-Tech Research and Development Program (National 863 Program) under Grant 2007AA01Z221, 2009AA01Z246, 2009AA01Z211, and the Natural Sciences and Engineering Research Council of Canada (NSERC). 9781-4244-3941-6/09/$25.00 ©2009 IEEE

is not carefully considered in CR networks, the perceived TCP performance by secondary user may degrade dramatically. Therefore, there is a strong motivation to consider TCP performance in CR networks. Recent researches illustrate that wireless networks should be optimized for standard TCP, rather than modifying standard TCP to adapt to wireless networks. In [6], the authors explore the use of rate adaptation in cellular networks to maximize TCP throughput. Based on TCP congestion window dynamics and wireless channel conditions, link layer transmission modes are selected to maximize TCP throughput in [7]. In this paper, we propose a cross-layer design based TCP performance improvement scheme in CR networks without modifying standard TCP. The proposed scheme can optimally select the most likely best channel for TCP traffic taking into account of spectrum sensing, access decision, physical layer modulation and coding scheme, and data-link layer frame size in CR networks. Following the work in [4, 8], we formulate the CR system as a partially observable Markov decision process (POMDP) [9] due to channel miss detection and channel estimation deviation. The simulation results show that TCP performance and spectrum utilization can be significantly improved. The rest of the paper is organized as follows. Section II describes the problem of TCP throughput in CR networks. Section III presents the proposed scheme. Some simulation results are given in Section IV. Finally, conclusions are given in Section V. II.

TCP THROUGHPUT IN COGNITIVE RADIO NETWORKS

In this section, we describe the models used in this paper for TCP traffic in CR networks. A. TCP Throughput Model Transmission control protocol (TCP), a connection-oriented protocol, provides more facilities for applications than user datagram protocol (UDP), notably error recovery, flow control, and reliability. Until now, several TCP algorithms have been proposed to improve TCP performance. The steady-state performance of TCP flow may be characterized by throughput, which is one of important factors that indicate TCP performance. A simple analysis model for TCP throughput is developed in [10] as follow.

B ( RTT , T0 , b , p ) ≈

1 RTT

RTT = 2 ⋅ Twired + (

2 bp 2 bp + T0 ⋅ min(1,3 ) p (1+ 32 p 2 ) 3 3

L fr

L ⋅ N ave + ack ) ⋅ N fr , r r

(6)

(1)

where L fr and Lack are the length of data contained in a frame

where p is the packet loss probability, RTT is a round trip time, T0 is the time out, and b is the number of packets that

and ACK frame, respectively. N ave is the average number of retransmission for one frame, which is determined as follows. (N +1) (N +1) N ave = (1 − Fe retran ) / (1 − Fe ) − Fe retran . (7)

are acknowledged by a received ACK ( b is typically 2). In addition, since TCP throughput is also confined by the maximum congestion window cwnd , the TCP throughput B is cwnd B(cwnd , RTT , T0 , b, p ) = min( , B( RTT , T0 , b, p )). RTT

(2)

It is well known that the packet loss probability p seriously affects TCP performance. In addition, in wireless TCP networks, packets lose mainly caused by wireless fading channel, which is a common assumption in the literature [6]. Hence, the packet loss probability p is approximate to the packet error rate pe . To obtain an exact closed-form packet error rate under adaptive modulation and coding (AMC) with ARQ scheme, we adopt the following bit error rate (BER) expression [11]: k γ ptr (3) BER = k1 ⋅ exp( − 2ρ ), 2 −1 where γ is the instantaneous received SNR, ptr is the transmit-

power, ρ is the AMC rate, and constants k1 and k 2 are related to specific constellation and code. Furthermore, for a given TCP packet with length LTCP , it will be divided into several smaller frames in the data-link layer over CR networks. Assume that the length of a frame header is L fr , and the number of frames per TCP packet is N fr ; thus, we can obtain the length of a frame

L fr =

LT C P N fr

+ L frh

.

Hence, the probability of a frame error Fe can be derived.

Fe = 1 − (1 − BER )

L

fr

(4)

.

Under ARQ scheme, a frame transmission is successful only if the number of retransmission is less than the maximum. Hence, given the maximum of retransmission number N retran , the frame error rate with ARQ is Fe

N retran +1

. Similarly, for the

packet, the packet error probability Pe can be obtained by the following equation: Pe = 1 − (1 − Fe

( Nretran +1) N fr )

(5)

According to a basic ARQ protocol, each of ACK frame will be returned to the sender when a frame is received at the corresponding end. Hence, the round trip time for a TCP packet can be approximately stated as follows.

From the above equations, we can see that physical/datalink layer design parameters in CR networks will contribute to the TCP throughput. B. System Model We consider a CR network with primary users and secondary users. Primary users send their data to their corresponding destinations via a primary network. On the other hand, secondary users access server via a secondary network. Both primary users and secondary users share a block of spectrum consisting of L radio channels, each with bandwidth w(l ) , 1 ≤ l ≤ L . Time is divided into slots with equal length T , and slot k refers to the discrete time period [ kT , ( k + 1)T ]. In addition, we assume that the system transitions to a new state at the beginning of each slot. The wireless channel in CR networks doesn't only suffer from Rayleigh fading in idle state but also meet primary users' occupancy. A finite state Markov channel (FSMC) model has been widely accepted as an effective approach to characterize the structure of the fading process [12]. In general, an FSMC model is constructed by first partitioning the range of the channel gain into discrete levels. Then each level corresponds to a state in the Markov chain. Let i and γ denote the instantaneous channel state and channel gain, respectively. When a channel is in state i , the corresponding channel gain is γ i , where γ i < γ < γ i +1 , 1 ≤ i ≤ S . In addition, the busy

state also occurs stochastically, and modeled as one of the states in the Markov chain. In our model, we use i = 1 to present the busy state. The S -state Markov channel model is completely described by its stationary distribution of each channel state i , denoted by p (i ) . Given knowledge of the fading process and primary network usage, the stationary distribution p (i ) as well as channel state transition probabilities

pij can be derived. III.

SOLVING TCP PERFORMANCE IMPROVEMENT PROBLEM IN CR NETWORKS

In this section, we formulate the CR network as a POMDP system, where the optimal action policy is selected to improve spectrum utilization and maximize TCP throughput in CR networks.

A. State Space, Transition Probabilities and Observation Space In each time slot, the system state is characterized by the network usage of primary user and channel state information. Let X denote the channel state space for one channel, where X ={1,2,..., S −1, S } . Hence, the system with L channels is modeled as a discrete-time homogeneous Markov process L with S states, denoted by X L = {1, 2,..., S L }. In practice, the channel states always transition independently of action taken. This is a special POMDP model. The transition probabilities of the system state are given by the S L x S L matrix. The observation available to the secondary user is the sensed channel outcome, θ k ∈ Θ , where Θ = { γ 1 (Busy), γ 2 ,…, γ S } and γ i < γ j , ∀ i < j . Due to channel estimation error and some mistake of detection, the secondary user cannot a have full knowledge of channel state in each slot. Let b jθ = Pr { θ | j , a } denote the probability that the sensor observes θ when channel state is j at this slot and when the last action (at last time slot) of observing state θ given that the channel is in state j and composite action a was taken. Following the work in [13], we can obtain the probability that the estimated channel gain is closed to γ j , which is given by

j ≠1,2,S , (8)

We can obtain the conditional probability of observing θ , 1 − δ , if θ =γ1 , j =1, ⎧ ⎪ δ , if θ ≠γ1 , j =1, ⎪ a b jθ = ⎨ ε , if θ =γ1 , j ≠1, ⎪ ⎪⎩Pce ( j , v (θ ))(1 − ε ), otherwise,

access the channel aa ( k ) ∈ {0 ( no _ access ), 1 ( access )}, and which modulation and coding scheme m ( k ) in the physical layer and frame size fr ( k ) in the data-link layer to use. Therefore, the composite action in slot k is denoted by ak = { as ( k ) , ( ε , δ ), aa ( k ) , ( m ( k ) , fr ( k ) )}.

C. Information State Information state is an important concept in POMDP. Although the system state cannot be directly known, it can be inferred from its decision and observation history encapsulated by the information state. The information state is a probability k k k distribution over the state space. Let π k = { π 0 , π1 ,..., π L } S k denotes the information space where π i represents the probability that we are currently in state i at time slot k . At the end of each slot, the information state is updated using Bayes' rule:

k

πj =

k a a ∑ i π i pij b jθ

∑ i , j π ik pijabajθ

(10)

D. Reward and Policy In this paper, we model TCP throughput as the immediate reward in our formulation. The immediate reward in time slot k is defined as

Pce (i , j ) = ⎧1 γ j +γ j +1−2γ i γ j +γ j −1−2γ i ⎪ [ erf ( )−erf ( )],if 2 2σ 2 2σ ⎪2 ⎪ γ j +γ j +1−2γ i 1 ⎪⎪ [1+erf ( )],if j =2, 2 ⎨ 2 2σ ⎪ γ j +γ j +1−2γ i 1 ⎪ [1−erf ( )],if j = S , ⎪ 2 2 2σ ⎪ 0,if j =1, ⎪⎩

where Aεδ are valid points on the ROC curve, whether to

(9)

where ε is the probability of false alarm, δ is the probability of miss detection, v (θ ) = i , 1 < i ≤ S given θ = γ i . The

B. Action Space At the beginning of each slot k , the secondary user need sequentially decide whether or not to sense as ( k ) ∈ {0 ( no _ sense , 1( sense _ ch _ 1 ),..., L ( sense _ ch _ L )}, determine which sensor operating point on the Receiver Operating Curve (ROC) curve to use ( ε , δ ) ∈ Aεδ ,

Rk = B (cwnd , RTTk , T0,k , b, p (ik , ak )),

(11)

where Rk is the round trip time, T0,k is the time out, b is the number of packets that are acknowledged by a received ACK, and p (ik , ak ) denotes the packet loss probability when the system is in state ik and action ak is taken in time slot k . The expected total reward of the POMDP depicts the overall reward over K time slots and can be expressed as K J μ = E μ , μ ,μ ,μ , μ [ ∑ Rk ], s ε ,δ a m fr k =1

(12)

where μ s is a channel sensing policy which specifies the sensing decision as . με ,δ is a sensor operating policy that specifies a spectrum sensor design ( ε , δ ) ∈ Aε ,δ based on the system tolerable probability of collision ζ . μ a is an access policy that specifies the access decision aa . μ m and μ fr respectively denotes the AMC policy and frame size

policy which specify the modulation and coding scheme frame size a fr .

and

E. Solving the POMDP Problem by Value Function Iteration Let J k (π ) be the value function that represents the maximum expected reward that can be obtained starting from slot k (1 ≤ k ≤ K ), given information state π k at the beginning of slot k . Provided that the secondary user takes action ak and observes acknowledgment θ k , the reward that can be accumulated starting from slot k consists of the immediate reward Rk = B (cwnd , RTTk , T0,k , b, p (ik , ak )) and the maximum

expected

future

reward

J k +1 (π + 1)

.

Mbps, and the propagation delay is set to Twired = 15 ms. In the data-link layer, the basic ARQ protocol is used, and the maximum number of retransmissions is N fr = 10 for a frame. There are 4 modulation schemes from which a secondary user can choose, BPSK, QPSK, 8PSK and 16QAM. For simplicity of the presentation, we use 1 coding scheme, rate 3/4 turbo code, together with 4 modulation schemes in our simulations. A. Effects of Low Layer Design Parameters Fig. 1 illustrates the change of TCP throughput under the increasing of channel gain for the two schemes, our proposed scheme and an existing scheme. In an existing scheme, in order to maximize the physical layer throughput, the high modulation will be applied to maximize physical layer throughput once its −3 bit error ratio is less than 10 .

k +1 } L = U (π k | ak , θ k ) , which represents the i∈S updated knowledge of system state after incorporating the action ak and the observation θ k in the time slot k . The sensing

π k +1 = { pi

policy is then given by

j=S L a J k (π k ) = max ∑ ∑ π kj Ti ',i ∑ b jθk [ Rk + k a∈A i∈X L i '∈X L j =1

(13)

J k +1 (U (π k | ak , θ k ))],1 ≤ k ≤ K − 1, L

j =S a K J K (π K ) = max ∑ ∑ π j Ti ',i [ ∑ b jθk Rk ] k a∈A i∈X L i '∈X L j =1

(14)

This value function with finite action space can be solved using linear programming techniques [9]. By considering only a subset of the piecewise linear segments that characterize the value function and discarding the other segments, one can reduce the computational complexity. Due to the space limitation, please refer to Subsection V-D of [14] for details. Moreover, in real system, solving the POMDP can be done offline during system initialization. During the TCP traffic transmission, the secondary user just needs to find the value for specific information state according to (13) and update the information state according to (10), which introduces little computational complexity. IV.

SIMULATION RESULTS AND DISCUSSIONS

In this section, we explore L = 2 channel with S = 3 states per channel system to evaluate the performance of the channel selection policy. 10 users (including 5 primary users and 5 secondary users) are randomly distributed on a 1000m x 1000m field. After a warm-up time of 100 seconds, one TCP connection is established over which an FTP file transfer is conducted. Assume that the sender always has data to send. The TCP packet size is LTCP = 1500 bytes. The maximum number of retransmissions for a TCP packet is N retran = 5. The bandwidth between a base station and a server is set to 100

Fig. 1. Average TCP throughput vs SNR. As shown from the figure, the TCP throughput in our proposed scheme is increasing with the improvement of channel quality. However, for an existing scheme, the TCP throughput shall degrade dramatically after a growing and steady period. The reason is that a new high modulation is deployed to maximize physical throughput. Due to the high bit error ratio introduced by high modulation, the TCP performance degrades dramatically. The simulation result reveals that the design parameters at the wireless link layer have significant impact on the TCP throughput in wireless networks, and TCP performance should be improved by exploiting cross-layer approach in the CR networks. B. Effects of Primary User on the Spectrum Utilization and TCP Performance The transition matrix is constructed based on the probability that any available channel state stays in the same available state at the next time slot, Pr{ X k +1 = v | X k = v },

the probability of transitioning from an available state to the busy state, Pr{ X k +1 = z | X k = v }$, and the probability of dwelling in the busy state, Pr{ X k +1 = z | X k = v }, ∀ v ∈ {1, 2,

..., S -1}, z = 1, where v and z indicate available and busy

states, respectively. Both channels have equal observation 1 1 matrix parameters ε = 0.1 and δ = 0.5. Pr{ X k +1 = z | X k = v } = 1 1 0.05. Pr{ X k +1 = v | X k = v } = 0.9.

V.

CONCLUSIONS AND FUTURE WORK

In this paper, we have presented an information state based channel sensing and access policy and cross-layer design scheme for TCP performance improvement over CR networks. Spectrum sensing and access decision are used to increase the spectrum utilization. According to the observed channel gain, modulation and coding scheme/frame size pair in the lower layer is selected to improve the TCP throughput. Simulation results were shown that low layer design parameters have significantly impact on the TCP throughput in CR networks. Other parameters, such as energy consumption, will be considered in future work.

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Fig. 2. Spectrum Utilization vs Probability of Channel 1 Transitioning to the Busy State.

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[9]

Fig. 3. Average TCP Throughput vs Probability of Channel 1 Transitioning to the Busy State. In Fig. 2, the spectrum utilization of our proposed scheme is greater than the random selection scheme. For our scheme, a secondary user needs to sense the surrounding environment to learn and adapt channel selection. As a consequence, it may take several time slots for the user to learn the system state and deriving a most likely policy. Thus the performance of our scheme improves with slower transition dynamics. Fig. 3 demonstrates the affect of TCP throughput by varying transition matrix of channel 1. Fig. 3 shows that the probability increases, the available opportunity decreases; thus, the average TCP throughput decreases. However, our proposed scheme can also get better performance than the random selection scheme.

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