Cooperative Cognitive Relaying with Ordered Cognitive Multiple Access

Cooperative Cognitive Relaying with Ordered Cognitive Multiple Access


We investigate a cognitive radio system with two secondary users who can cooperate with the primary user in relaying its packets to the primary receiver. In addition to its own queue, each secondary user has a queue to keep the primary packets that are not received correctly by the primary receiver. The secondary users accept the unreceived primary packets with a certain probability and transmit randomly from either of their queues if both are nonempty. These probabilities are optimized to expand the maximum stable throughput region of the system. Moreover, we suggest a secondary multiple access scheme in which one secondary user senses the channel for seconds from the beginning of the time slot and transmits if the channel is found to be free. The other secondary user senses the channel over the period to detect the possible activity of the primary user and the first-ranked secondary user. It transmits, if possible, starting after seconds from the beginning of the time slot. It compensates for the delayed transmission by increasing its transmission rate so that it still transmits one packet during the time slot. We show the potential advantage of this ordered system over the conventional random access system. We also show the benefit of cooperation in enhancing the network’s throughput.


Cognitive radio, multiple access, stable-throughput

1 Introduction

In a wireless communication network with many source-destination pairs, cooperative transmission by relay nodes has the potential to improve the overall network performance. Cognitive relaying is an integration between cognitive radios and cooperative transmission that could considerably improve the performance tradeoffs for all users. The problem of cognitive relaying has been considered in many papers, for instance [1, 2, 3, 4, 5, 6, 7, 8]. Note that cooperation requires changes in the primary network. Although this may seem to go against one conception of the secondary network operating with little or no modification at all at the primary terminals, the proposed cooperative cognitive schemes show clearly that the primary networks can benefit from secondary cooperation. The realized benefits may provide a strong incentive for implementing some changes in the primary network. In addition, there can be additional monetary compensation from the secondary network, but this issue is outside the scope of this paper.

In [1, 2], the main idea of a cooperative cognitive user was introduced, where the secondary user is used as a relay for the undelivered packets of the higher-priority primary user (PU). To accomplish this feat, the secondary user has an additional relaying queue to store primary packets. An acceptance factor controls the fraction of undelivered primary packets that gets accepted into the relaying queue at the secondary terminal.

Two secondary and one primary nodes are investigated in [3] under a random access scheme. All nodes communicate with the same access point. Priority in secondary transmission is given to the relaying queue holding the primary packets. In addition to conventional spectrum sensing, opportunistic sensing is employed where the secondary users sense the channel at different times depending on the quality of the channel to the access point. Multiple secondary users are considered in [4] together with one PU. The secondary users construct a cluster with a common relaying queue in order to relay the undelivered packets of the PU. The packet is added to the common relaying queue, which is accessible from all the nodes of the cluster. In [7], the authors characterize the stable-throughput region in a two-user cognitive shared channel with multipacket reception (MPR) capability added to the physical layer of the nodes.

In this paper, we analyze a system with one PU and multiple secondary users operating in a time-slotted fashion. We present here results for two cognitive users and leave the general case for an extended version of this work. In contrast with [4], we assume that every secondary user has an additional queue that can be used to store primary packets and help in relaying them to the primary receiver. When a transmission opportunity is available, a cognitive user randomly selects to transmit from its queue or the relaying queue. We propose an ordered access protocol where the secondary users are ordered in terms of accessing the channel. The users are also ordered in terms of their attempts to decode the received primary packets. The ordering is probabilistic, unlike [3] where a deterministic sensing order is proposed. The probability of each possible permutation of users indicates the fraction of time slots in which the permutation is employed. The ordering probabilities are optimized given all the system parameters and do not just depend on the quality of the channel to the receiver as in [3].

We summarize our contributions as follows. For two secondary users, we characterize outer and inner bounds for the maximum stable throughput of an ordered access/decoding scheme. We obtain the optimal queue selection probabilities, acceptance fractions, and probabilistic orders to achieve the bounds. We compare our scheme with conventional random access and show its potential benefit. Note that in [3] only inner bounds are provided and the authors do not consider the impact of delayed access on the outage probability.

The rest of the paper is organized as follows. We provide a detailed account of the system model in Section 2. We introduce the ordered access scheme and its stability analysis in Section 3. The random access system is discussed in Section 4. We provide some numerical results in Section 5 and conclude the paper in Section 6.

2 System Model

We consider a time-slotted synchronized cognitive relaying system, as depicted in Fig. Cooperative Cognitive Relaying with Ordered Cognitive Multiple Access, with one primary and two secondary users: and . All nodes have buffers with infinite capacity. The PU has one queue, , whereas secondary user , , has two queues: to store its own packets, and to possibly store some of the primary packets until they are relayed to the primary receiver. The arrivals at queues , and are independent Bernoulli random variables with means , and , respectively. Spectrum sensing is assumed to be perfect in this work.

We assume that and are ordered in terms of sensing and accessing the primary channel such that the probability of being ranked first is . This means that in a large number of time slots, user attempts to access the channel first in a fraction of them. Moreover, and are ordered in terms of decoding a correctly received primary packet near the end of the time slot such that the probability of being ranked first is . This means that over a large number of time slots, user attempts to decode the primary packet first in a fraction of them.

The PU transmits the packet at the head of its queue at the beginning of the time slot. The secondary terminals operate their receivers/spectrum sensors from the beginning of the time slots. The first-ranked secondary transmitter, which is with probability or with probability , utilizes the received signal over the first seconds of the slot to determine the state of primary activity. If the channel is sensed to be free and the secondary user has a packet to send, it will transmit over the remaining slot duration. Note that should be large enough to validate the perfect sensing assumption.

The second-ranked user gathers samples from primary transmission over the first seconds of the time slot to detect the possible activity of the PU, and over the interval to detect the activity of the first-ranked secondary user. If the channel is sensed to be free and if it has a packet to send, the second-ranked user switches to the transmission mode starting from time . The user still sends one full packet by adapting its transmission rate at the price of an increased probability of link outage as shown later.

If the channel is sensed to be busy due to the primary activity, the secondary users continue receiving the primary transmission till near the end of the time slot. If the primary receiver acknowledges the correct reception of the primary transmitted packet by sending an acknowledgment (ACK) message, the secondary terminals discard what they have received from the PU. If the primary receiver declares its failure to decode the primary packet correctly by generating a negative-acknowledgment (NACK) message, the secondary terminals attempt to decode the primary packet. We assume that the overhead for transmitting the ACK and NACK messages is very small compared to packet sizes. In addition, we assume the perfect decoding of the ACK and NACK messages at the primary and secondary users.


  1. We assume that the conventional ACK/NACK protocol of the primary network is modified such that the primary transmitter is notified about successful reception by either its respective receiver or any secondary transmitter. As mentioned in the Introduction, the higher throughput gains by the primary network may provide an incentive for implementing such protocol modifications.
  2. In a few cases, the dominant system method produces an exact result for the stability region, e.g., [12].
  3. Specifically, we use Matlab’s fmincon. Since the problems are nonconvex, fmincon produces a locally optimum solution. To enhance the solution and increase the likelihood of obtaining the global optimum, the program can be run many times with different initializations of the optimization variables.
  4. The analysis of the case of noncooperation is similar to the two-user ALOHA random access system for which the stability region is exact [12].


  1. J. Gambini, O. Simeone, U. Spagnolini, and Y. Bar-Ness, “Cooperative cognitive radios with optimal primary detection and packet acceptance control,” in IEEE 8th Workshop on Signal Processing Advances in Wireless Communications (SPAWC) 2007, June 2007, pp. 1 –5.
  2. O. Simeone, Y. Bar-Ness, and U. Spagnolini, “Stable throughput of cognitive radios with and without relaying capability,” IEEE Transactions on Communications, vol. 55, no. 12, pp. 2351–2360, Dec. 2007.
  3. J. Gambini, O. Simeone, and U. Spagnolini, “Cognitive relaying and opportunistic spectrum sensing in unlicensed multiple access channels,” in IEEE 10th International Symposium on Spread Spectrum Techniques and Applications, ISSSTA ’08., Aug. 2008, pp. 371–375.
  4. I. Krikidis, J. Laneman, J. Thompson, and S. Mclaughlin, “Protocol design and throughput analysis for multi-user cognitive cooperative systems,” IEEE Transactions on Wireless Communications, vol. 8, no. 9, pp. 4740–4751, Sept. 2009.
  5. I. Krikidis, N. Devroye, and J. Thompson, “Stability analysis for cognitive radio with multi-access primary transmission,” IEEE Transactions on Wireless Communications, vol. 9, no. 1, pp. 72–77, Jan. 2010.
  6. M. Elsaadany, M. Abdallah, T. Khattab, M. Khairy, and M. Hasna, “Cognitive relaying in wireless sensor networks: Performance analysis and optimization,” in IEEE Global Telecommunications Conference GLOBECOM, Dec. 2010, pp. 1–6.
  7. S. Kompella, G. Nguyen, J. Wieselthier, and A. Ephremides, “Stable throughput tradeoffs in cognitive shared channels with cooperative relaying,” in Proceedings IEEE INFOCOM, Apr. 2011, pp. 1961–1969.
  8. X. Bao, P. Martins, T. Song, and L. Shen, “Stable throughput and delay performance in cognitive cooperative systems,” IET Communications, vol. 5, no. 2, pp. 190–198, 2011.
  9. A. Sadek, K. Liu, and A. Ephremides, “Cognitive multiple access via cooperation: protocol design and performance analysis,” IEEE Transactions on Information Theory, vol. 53, no. 10, pp. 3677–3696, Oct. 2007.
  10. R. Loynes, “The stability of a queue with non-independent inter-arrival and service times,” in Proc. Cambridge Philos. Soc, vol. 58, no. 3.    Cambridge University Press, 1962, pp. 497–520.
  11. A. El Shafie and A. Sultan, “Stability analysis of an ordered cognitive multiple access protocol,” submitted to IEEE Journal on Selected Areas in Communications.
  12. R. Rao and A. Ephremides, “On the stability of interacting queues in a multiple-access system,” IEEE Transactions on Information Theory, vol. 34, no. 5, pp. 918–930, Sep. 1988.
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