Dealing with power control and time allocation problems in the CCRN, we propose an algorithm to solve the problem iteratively in two steps. First, we allocate fractions of time for sub time slots. Given the time allocation coefficients, we optimize SU’s power levels for relaying and accessing. Then, we repeat the two steps iteratively until we achieve the maximum utility. The algorithm can obtain the optimal power levels and time coefficients to achieve the solution in the core. Detailed description is introduced in the following section. On the other hand, special design of channel allocation ensures that each user can access multiple channels in a sub time slot. With the special channel allocation, it is more beneficial for SUs to stay in the CCRN. In the simulations, we compare proposed algorithm with other approaches and show that proposed algorithm converges to the core.
As a final remark, we emphasize that our work considers power control and time allocation two problems. Other works often consider one problem, e.g. [7] and [11] only consider time allocation. In the related works, we only see one work [5] considering time allocation and power control problems. However, [5] is modeled as a Stackelberg game. Our work is the first one applying a coalitional game on power control and time allocation problems. We also proof that the considered problem has a nonempty core,
which ensures the stability of the system. In the simulations, we also show that proposed algorithm converges to the core.
Chapter 2
Cognitive Radio and Game Theory Preliminary
2.1 Cognitive Radio
Cognitive radio (CR) was first proposed by Joseph Mitola in 2000’s doctoral disserta-tion [4]. It is a software-defined wireless communicadisserta-tion system that is capable of achiev-ing highly reliable communication by adjustachiev-ing its transmission parameters accordachiev-ing to the radio environment it senses. CR is called “cognitive”, because its structure supporting a cognition cycle consisting of Observe, Orient, Plan, Decide, and Act phases as shown in Fig. 2.11. The figure has been widely used to understand the cognitive radio or analyze the performance of cognitive networks. Recently, the unbalanced utilization of spectrum urges the need for intelligent spectrum management technique. For realistic implemen-tation, CR is built on software based radio and wide-band RF front end to achieve the functionality. There are some prototypes of CR already built, such as the first prototype CR1 by Mitola [4], and CR and networking by Virginia tech [13].
Although the original purpose of cognitive radio is not utilized to improve the spec-trum efficiency, now it is viewed as a novel technique to tackle the problem of specspec-trum under-utilization. CR can be used to detect the spectrum holes or actively negotiate with
1This figure is adapted from Mitola, ”Cognitive Radio: An Integrated Agent Architecture for Soft-ware Defined Radio”, Ph.D. dissertation, Royal Inst. Technol. (KTH), pp. 48, 2000
Observe
Infer on Context HierarchyUrgent Immediate
Normal
Register to Current Time
Prior
primary users to access the spectrum. In recent years, there are lots of researches on CR-related topics. These researches can be classified into three fundamental tasks [3]:
1. Radio-scene analysis, which includes estimation of interference of the radio envi-ronment and detection of spectrum holes.
2. Channel state information and predictive channel modeling, which encompasses estimation of channel-state information (CSI) and prediction of channel capacity for the use by the transmitter.
3. Transmitter power control and dynamic spectrum management.
Our work is based on the transmitter power control and dynamic spectrum manage-ment. We apply cooperative CR technique to tackle the problem.
2.1.1 Cooperative Cognitive Radio Network
The idea of cooperative communication was first proposed by [5] in 2008, which intro-duced a property-right model of cognitive radio, also called as spectrum leasing. PUs are
Figure 2.2: System model for cooperative cognitive radio networks
aware of the existence of SUs and actively lease the spectrum for a fraction of time to SUs by charging at a certain price. In the scenario, PUs can gain additional revenue by spectrum leasing. Motivated by the idea of spectrum leasing, some recent works [5] [7]
incorporated cooperative communication into CR networks, which is termed as coopera-tive cognicoopera-tive radio network (CCRN).
A typical CCRN scenario is shown in Fig. 2.22. In Fig. 2.2(a), PUs transmit signals to SUs through primary transmission. Then, in Fig. 2.2(b), both SUs and PUs transmit signals to primary access point (PAP). In this sub time slot, SUs are served as relays to assist primary transmission. In the last sub time slot, SUs can access the spectrum for its own traffic. Hence, under the cooperative scenario, PU’s rate can be improved by exploiting cooperative diversity. In return, SUs gain opportunities to access the spectrum for a fraction of time, in which SUs can transmit its own traffic to secondary access point (SAP). Hence, both PUs and SUs can increase their interests in the CCRN scenario, achieving a “win-win” situation.
The scenario of CCRN is a new cognitive radio paradigm. SUs are served as coop-erative relays for primary transmission, so PU’s transmission rate increases significantly
2This figure is adapted from Y. Yi, et al., ”Cooperative Communication-Aware Spectrum Leasing in Cognitive Radio Networks”, in IEEE Proc. DySPAN, pp. 1–11, 2010
by exploiting cooperative diversity. The received SNR terms can be summed up by the technique of maximum ratio combining (MRC). For secondary systems, SUs are licensed a fraction of time to access the spare spectrum. Hence, both PUs and SUs can ben-efit from the cooperative scenario. However, for some PUs, when the required traffic demands are satisfied, primary systems are not interested to increase their transmission rates. They want to gain some certain benefits, e.g. payment, which is more interesting to PUs. Hence, there are many researches discussing about cooperative interactions between PUs and SUs. We adopt a game theoretic approach to formulate the problem.