- Research
- Open Access
Online auction-based relay selection for cooperative communication in CR networks
- Tao Jing^{1},
- Fan Zhang^{1},
- Wei Cheng^{2}Email author,
- Yan Huo^{1} and
- Xiuzhen Cheng^{3}
https://doi.org/10.1186/s13638-015-0258-9
© Jing et al.; licensee Springer. 2015
- Received: 4 September 2014
- Accepted: 7 January 2015
- Published: 3 February 2015
Abstract
Cognitive radio and cooperative communication are two new network technologies. So, the combination of these two new technologies is a novel solution to solve the problem of spectrum scarcity. Two main challenges exist in the integration of cognitive radio and cooperative communication. First, there is a lack of incentives for the participating wireless devices to serve as relay nodes. Second, there is not an effective relay selection policy. In this paper, we propose an online auction-based relay selection scheme for cooperative communication in cognitive radio (CR) networks. Specifically, we design an auction scheme through adopting stopping theory. The proposed scheme ensures that the primary user (PU) can effectively select a CR relay to transmit its packets in a given time bound. In addition, we have analytically proven the truthfulness and the individual rationality of our online auction scheme. Extensive simulations demonstrate that the proposed relay selection scheme can always successfully and efficiently select a proper relay for a PU and can achieve a higher cooperative communication throughput compared with the conventional schemes.
Keywords
- Online auction
- Cooperative relay selection
- Optimal multiple stopping theory
1 Introduction
Cognitive radio technology has been recognized to be able to improve the spectrum utilization [1] through opportunistic spectrum access without interfering with the primary communications. Cooperative communication is an emerging technology that has the great potential to increase the throughput between two wireless devices [2] without requiring the support of infrastructure. Intuitively, the integration of these two hot technologies is expected to be a solution for the problems of spectrum scarcity and capacity limitation. In this paper, we particularly target on the scenario where the primary CR users (PU) seek to find a suitable cognitive radio (CR) relay to help transmit data, so that its end-to-end throughput can be increased and its end-to-end delay can be reduced.
Certainly, this hybrid cognitive-cooperative communication architecture is facing both technical and practical challenges. For instances, the relays should get sufficient incentives for participating in the cooperative communications. In addition, if there are multiple candidate relays, it may be impossible/impractical for the primary user to collect information from all the candidate relays to make a decision of relay selection. The reason is twofold: (i) The time for collecting information from all the candidate relays may exceed the PU’s end-to-end delay tolerance, and (ii) the availability of candidate relays may change during the collecting time as CR links are generally not stable. Therefore, an idea cognitive-cooperative scheme should be able to well incentivize the CR users and select a proper relay in a given time.
To design such a scheme, we conservatively consider to collect a candidate relay information and make the relay selection decision for this candidate relay online so that the collected information is valid and end-to-end delay requirement can be satisfied. In other words, the candidate relays send their information (including its sealed bid for cooperative transmissions) to the PU one by one. After receiving the information from a candidate relay, the PU immediately decides whether to choose this relay for cooperative transmission. Once a relay has been selected, the PU stops considering the rest of the candidate relays and pays the chosen relay a compensation based on its bid. In the paper, we consider the candidate relays as bidders and the PU as buyer, respectively.
- 1.
We formulate the online relay selection problem as an optimal stopping problem and design an optimal stopping policy, which can guarantee the success of relay selection in a given time while control the cost of PU for utilizing cooperative communication. To the best of our knowledge, this is the first work to jointly adopt stopping theory and design auction scheme for relay selection in CR networks.
- 2.
We analytically prove the truthfulness of the proposed online auction scheme without requiring any information about bidders behavior. This result guarantees the bid truthfulness of the candidate relays and thereby eliminates the possibility of market manipulation.
- 3.
Our proposed online auction scheme is also individual rational. This feature indicates that both the PU and relay can be benefited from cooperative transmission, which guarantees the incentive of participation for both the PU and the relay.
- 4.
Extensive experiments are conducted to study the performance of our online auction scheme. We investigate the impact of several parameters and compare our scheme with the optimal relay selection (ORS) scheme, which will look at all the candidate relays for minimizing the PU cost. The results demonstrate that the PU can always select an appropriate relay in the given time, and our scheme achieves a higher throughput than ORS with a tiny cost increase.
The rest of the paper is organized as follows: The related work is presented in Section 3. Preliminary is illustrated in Section 3. In Section 3, we describe the proposed stopping policy and the online auction scheme for cooperative communication. The analysis of our auction scheme appears in Section 3. The simulation results are reported and analyzed in Section 3. We conclude our paper in Section 3.
2 Related work
In this section, we briefly review the most related works to our research in the area of auction-based cooperative communication.
In [3], Huang et al. treated relay nodes as sellers and source nodes as buyers. Two auction mechanisms were proposed: the SNR auction and the power auction. In each auction mechanism, each user iteratively updates its bid according to the others’ previous bids to maximize its own profit. The existence and the uniqueness of the Nash equilibrium in a single-relay network has been proven. In networks with multiple relays, the Nash equilibrium only exists under certain conditions. Zhang et al. considered the network scenario where there is a single relay and multiple user nodes in [4]. The authors formulated this problem as a sellers’ market competition. A distributed algorithm has been developed to search the Nash equilibrium. In [5], Wang et al. studied the situation of one source node and multiple relay nodes. The authors modeled it as a Stackelberg game which is divided into two levels of hierarchy. The benefits of the source node and relay nodes were jointly considered. In [6], the authors formed a cooperative cognitive radio network (CCRN) where each PU employs a set of appropriate second users (SUs) for relaying the PU’s information in an energy-efficiency solution. The relay selection and parameter optimization have been formulated as two Stackelberg games. A unique Nash equilibrium is achieved and proved for each game. In [7], the authors supposed that SUs can make rational choice between cooperation and band leasing. This novel CCRN model has been modeled as a Stackelberg game and analyzed. In [8], the impact of spectrum leasing was studied with a game-theoretic analysis. The authors first illustrated the significance of the problem with a two-secondary-user game. Subsequently, a n-secondary-user game was studied and analyzed given the occupancy model of primary users. However, none of the above work had considered the truthfulness. As a result, these mechanisms are vulnerable to market manipulation and may produce poor outcome [9].
In [10], the authors considered the problem of cooperative spectrum sensing scheduling in a CR network. The author assumed that each SU has the freedom to decide whether or not to participate in cooperative spectrum sensing; if not, the SU can wiretap the decision on channel status made by other SUs. This mechanism strived the balance between conserving the energy for spectrum sensing and bring down the spectrum sensing performance. In [11], Yang et al. designed an optimal relay assignment algorithm for cooperative communication. In [12], Klemperer designed a double auction mechanism for cooperative communication. These two works satisfied the truthfulness requirements. They both needed to collect the information from all the source nodes and all the relay nodes, and their auction mechanisms were performed in a single-round fashion. As a comparison, our approach does not require the information from all relay nodes, and the auction is performed in an online manner.
3 Preliminary
In this section, we first give a description of online cooperative communication. Then, we present our system model and depict the two common economic properties (truthfulness and individual rationality) of an auction.
3.1 Online cooperative communication
There exist two types of cooperative communication pattern: amplify-and-forward (AF) and decode-and-forward (DF) [2]. In AF, the relay amplifies the received data and transmits it to the destination. In DF, the relay fully decodes the data from the source node and then delivers it after re-encoding the data. Our proposed scheme can be applied to both of these two cooperative communication patterns.
3.2 System model
Notations
Notations | Definition |
---|---|
R | Number of candidate relays |
T | The delay tolerance for the PU |
β | The value of a packet |
α | The time needed for checking each relay |
M | The maximal number of relays that can be checked within T |
X _{ i } | The ith candidate relay |
b _{ i } | The bid of X _{ i } |
v _{ i } | The private true valuation of X _{ i } |
Y _{ n } | Real-valued reward function for checking X _{ n } |
\(V_{n}^{(M)}\) | Maximum reward at stage n |
\(E\left (V_{n+1}^{(M)}\right)\) | Maximum expected reward at stage n+1 |
Z _{ M−n } | A equal representation of \(E\left (V_{M-n+1}^{(M)}\right)\) |
3.3 Economic properties
3.3.1 Truthfulness
An auction is truthful if revealing the private true value is the dominate strategy for each bidder. This means that no bidder can raise its utility by bidding b _{ i }≠v _{ i }, no matter how others bids. This property can resist market manipulation and ensure the auction fairness and efficiency. In untruthful auctions, a selfish bidder can alter its bid to obtain extra outcome which will benefit itself but hurt the others.
3.3.2 Individual rationality
An auction is individual rational if a winner (the selected relay) is always paid by no less than its bid and the PU’s cost is no more than the value of the packet. This property can ensure that the utilities of both the PU and the selected relay are no less than 0. In other words, both the PU and the selected relay have incentive to participate in the cooperative communication.
4 Online auction scheme for relay selection
In this section, we formulate the relay selection process as an optimal stopping problem and present our proposed scheme in detail.
4.1 Problem formulation
In the following, we first give a brief introduction of the finite stopping problems and then formulate the relay selection process accordingly.
Definition 1.
Finite stopping problems: Given a sequence of random variables, X _{1},X _{2},…,X _{ M }, and a sequence of real-valued reward functions, y _{0}(),y _{1}(X _{1}),y _{2}(x _{1},X _{2}),…,y _{ M }(x _{1},x _{2},…,X _{ M }), the objective is to find a n, so that the reward function y _{ n }(x _{1},x _{2},…,X _{ n }) is the maximal. Note that the joint distribution of the random variables and the reward functions are known and that X denotes the random variable and x denotes the value of a random variable, respectively.
In order to maximize the PU’s utility, the PU makes each decision by comparing the instantaneous reward of selecting X _{ i } with the expected reward of checking X _{ i+1}. Accordingly, we formulate the relay selection process as a sequence of decision problem which is defined based on the theory of optimal stopping [14] below.
According to [15], the PU knows (or can learn) the distribution of the bidding price of the bidders (candidate relays). We assume that there is no collusion among the candidate relays, and the bidding price of the candidate relays is independently distributed. In this paper, we particularly assume the bidding price follows the normal distribution with the mean value μ and the standard deviation σ, i.e., \(f\left (b_{i}\right)=\dfrac {1} {\sqrt {2\pi }\sigma }e^{-\dfrac {\left (b_{i}-\mu \right)^{2}} {2\sigma ^{2}}}\), where f(b _{ i }) is the probability density function of b _{ i }. Note that, our proposed scheme can be generally applied to any bidding price distribution.
Note that, in order to ensure that the PU’s cost is no larger than β, we set s _{ J }=β and s _{ J+1}=∞.
We have proven that the Y _{ n } defined in (3) satisfies the requirements. Due to the page limit, we will report the proof in our technical report.
4.2 Online auction-based selection scheme
the PU stops at stage n (selects the nth candidate relay); otherwise, it continues to check the next candidate relay.
5 Rationality and truthfulness analysis
In this section, we justify that our proposed online auction-based relay selection scheme is both truthful and individual rational. Due to the page limit, we only present the main results. The detailed proof of the lemmas will be given in our technical report.
Lemma 1.
Z _{ u }≥0, for u∈[1,M].
Theorem 1.
The proposed online auction scheme is individual rational.
Proof.
We can get that utility=p _{ n }−b _{ n }≥0. If the winner is M, its utility=0. Therefore, we can conclude that, for any winner, its utility≥0.
According to Lemma 1, we have Z _{ u }≥0,u∈[1,M]. Thus, utility≥0. When n=M, the utility=β−b _{ M }. As the PU has to transmit the packet in this worst case, it is also rational.
Lemma 2.
The payment to winner n will be the same whatever it had bidden for b _{ n } or v _{ n }.
Lemma 3.
If bidder n wins the auction when bidding truthfully, the payment from the PU is no less than its evaluation, i.e., p _{ n }≥v _{ n }.
Lemma 4.
If bidder n cannot win the auction when bidding truthfully, the possible payment from the PU is less than its evaluation, i.e., p _{ n }≤v _{ n }.
Theorem 2.
The auction scheme is truthful.
Proof.
We use u _{ n } and u n′ to represent the utilities when bidder n bids with v _{ n } and b _{ n }, respectively.
- 1.
Bidder n wins when bidding with either b _{ n } or v _{ n }: according to Lemma 2 and Lemma 3, we have \(u_{n}^{\prime }=p_{n}-v_{n}=u_{n}\geq 0\);
- 2.
Bidder n loses with b _{ n } but wins with v _{ n }: according to Lemma 3, we can get \(u_{n}^{\prime }=0\leq p_{n}-v_{n}=u_{n}\);
- 3.
Bidder n wins with b _{ n } but loses with v _{ n }: this cannot happen in our auction scheme;
- 4.
Bidder n loses with both b _{ n } and v _{ n }: u n′=u _{ n }=0;
- 1.
Bidder n wins when bidding with either b _{ n } or v _{ n }: according to Lemma 2 and Lemma 3, we have \(u_{n}^{\prime }=p_{n}-v_{n}=u_{n}\geq 0\);
- 2.
Bidder n loses with b _{ n } but wins with v _{ n }: this cannot happen in our auction scheme;
- 3.
Bidder n wins with b _{ n } but loses with v _{ n }: according to Lemma 4, we can get \(u_{n}^{\prime }=p_{n}-v_{n}\leq 0=u_{n}\);
- 4.
Bidder n loses with both b _{ n } and v _{ n }: \(u_{n}^{\prime }=u_{n}=0\).
Through the analysis of all the possible cases, we can conclude that the utility of the bidder n cannot be increased if b _{ n }≠v _{ n }. Therefore, our auction scheme can ensure the bidding truthfulness.
6 Simulations
In this section, we conduct simulations to examine the performance of our proposed online auction mechanism. We assume that the bidding price of each candidate relay follows the normal distribution with the expected value μ=0.5 and the variance σ=0.2. We partition the bidding price into J=20 intervals. We set the time limit for the PU to select a relay is 90 time slots, i.e., T=90. In the following, we study the impact of the parameters such as α, C, and β on the number of observation steps and the reward of the PU, respectively. In order to justify the effectiveness of our scheme, we compare ours with the ORS scheme, which has the knowledge of who is the optimal relay that has the maximal PU reward among the M candidate relays. In the ORS scheme, the PU checks the candidate relay one by one randomly and stops after checking the optimal relay. The simulations are conducted under several network scales. Particularly, the M takes the value of 20, 30, and 40, separately. Each reported result is the average of 100 independent instances.
6.1 The impact of observation duration α
6.2 The impact of time efficiency C
6.3 The impact of private value β
In summary, we can conclude that the PU can always select a relay within six observations by our scheme, which is much less than the number of observation steps in OSR. Although the reward of transmitting one packet in our scheme may be slightly smaller than the corresponding one in OSR, the accumulated reward of our scheme is always much larger than the corresponding one of OSR for transmitting multiple packets in a given time period.
7 Conclusions
In this paper, we have designed an online auction-based relay selection scheme for cooperative communication in CR networks. Without checking the information from all the candidate relays, the PU can select a desirable relay within a given time limit. Our scheme achieves the property of truthfulness, which assures that all the bidders reveal their true valuations and eliminates the fear of market manipulation. Meanwhile, our scheme is individual rational, which guarantees that both the PU and relays can be benefited from cooperative communication. Extensive simulations show that our scheme enables the PU to select a proper relay in a very short time and achieves a higher accumulated reward in a given time period comparing to the OSRscheme.
Declarations
Acknowledgements
This work is partially supported by the National Natural Science Foundation of China (Grant Nos. 61272505, 61172074, and 61371069) and the National Science Foundation of the US (CNS-1265311).
Authors’ Affiliations
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