Impact of Power Allocation and Antenna Directivity in the Capacity of a Multiuser Cognitive Ad Hoc Network

This paper studies the benefits that power control and antenna directivity can bring to the capacity of a multiuser cognitive radio network. The main objective is to optimize the secondary network sum rate under the capacity constraint of the primary network. Exploiting location awareness, antenna directivity, and the power control capability, the cognitive radio ad hoc network can broaden its coverage and improve capacity. Computer simulations show that by employing the proposed method the system performance is significantly enhanced compared to conventional fixed power allocation

Cognitive Radio Networks: Realistic or Not?

A large volume of research has been conducted in the cognitive radio (CR) area the last decade. However, the deployment of a commercial CR network is yet to emerge. A large portion of the existing literature does not build on real world scenarios, hence, neglecting various important interactions of the research with commercial telecommunication networks. For instance, a lot of attention has been paid to spectrum sensing as the front line functionality that needs to be completed in an efficient and accurate manner to enable an opportunistic CR network architecture. This is necessary to detect the existence of spectrum holes without which no other procedure can be fulfilled. However, simply sensing (cooperatively or not) the energy received from a primary transmitter cannot enable correct dynamic spectrum access. For example, the low strength of a primary transmitter's signal does not assure that there will be no interference to a nearby primary receiver. In addition, the presence of a primary transmitter's signal does not mean that CR network users cannot access the spectrum since there might not be any primary receiver in the vicinity. Despite the existing elegant and clever solutions to the DSA problem no robust, implementable scheme has emerged. In this paper, we challenge the basic premises of the proposed schemes. We further argue that addressing the technical challenges we face in deploying robust CR networks can only be achieved if we radically change the way we design their basic functionalities. In support of our argument, we present a set of real-world scenarios, inspired by realistic settings in commercial telecommunications networks, focusing on spectrum sensing as a basic and critical functionality in the deployment of CRs. We use these scenarios to show why existing DSA paradigms are not amenable to realistic deployment in complex wireless environments.Comment: Work in progres

Dynamic Resource Allocation in Cognitive Radio Networks: A Convex Optimization Perspective

This article provides an overview of the state-of-art results on communication resource allocation over space, time, and frequency for emerging cognitive radio (CR) wireless networks. Focusing on the interference-power/interference-temperature (IT) constraint approach for CRs to protect primary radio transmissions, many new and challenging problems regarding the design of CR systems are formulated, and some of the corresponding solutions are shown to be obtainable by restructuring some classic results known for traditional (non-CR) wireless networks. It is demonstrated that convex optimization plays an essential role in solving these problems, in a both rigorous and efficient way. Promising research directions on interference management for CR and other related multiuser communication systems are discussed.Comment: to appear in IEEE Signal Processing Magazine, special issue on convex optimization for signal processin

Mixed Power Control Strategies for Cognitive Radio Networks under SINR and Interference Temperature Constraints

Without consideration of the minimum signal-to-interference-plus-noise ratio (SINR) and frequent information exchange, traditional power control algorithms can not always satisfy SINR requirements of secondary users (SUs) and primary users (PUs) in cognitive radio networks. In this paper, a distributed power control problem for maximizing total throughput of SUs is studied subject to the SINR constraints of SUs and the interference constraints of PUs. To reduce message exchange among SUs, two improved methods are obtained by dual decomposition approaches. For a large-scale network, an average interference constraint is presented at the cost of performance degradation. For a small-scale network, a weighted interference constraint with fairness consideration is proposed to obtain good performance. Simulation results demonstrate that the proposed algorithm is superior to ADCPC and TPCG algorithms

Packet Size Optimization for Cognitive Radio Sensor Networks Aided Internet of Things

Cognitive Radio Sensor Networks (CRSN) is state of the art communication paradigm for power constrained short range data communication. It is one of the potential technology adopted for Internet of Things (IoT) and other futuristic Machine to Machine (M2M) based applications. Many of these applications are power constrained and delay sensitive. Therefore, CRSN architecture must be coupled with different adaptive and robust communication schemes to take care of the delay and energy-efficiency at the same time. Considering the tradeoff that exists in terms of energy efficiency and overhead delay for a given data packet length, it is proposed to transmit the physical layer payload with an optimal packet size (OPS) depending on the network condition. Furthermore, due to the cognitive feature of CRSN architecture overhead energy consumption due to channel sensing and channel handoff plays a critical role. Based on the above premises, in this paper we propose a heuristic exhaustive search based Algorithm-1 and a computationally efficient suboptimal low complexity Karuh-Kuhn- Tucker (KKT) condition based Algorithm-2 to determine the optimal packet size in CRSN architecture using variable rate m-QAM modulation. The proposed algorithms are implemented along with two main cognitive radio assisted channel access strategies based on Distributed Time Slotted-Cognitive Medium Access Control (DTS-CMAC) and Centralized Common Control Channel based Cognitive Medium Access Control (CC-CMAC) and their performances are compared. The simulation results reveals that proposed Algorithm-2 outperforms Algorithm-1 by a significant margin in terms of its implementation time. For the exhaustive search based Algorithm-1 the average time consumed to determine OPS for a given number of cognitive users is 1.2 seconds while for KKT based Algorithm-2 it is of the order of 5 to 10 ms. CC-CMAC with OPS is most efficient in terms of overall energy consumption but incurs more delay as compared to DTS-CMAC with OPS scheme