Communication Primitives in Cognitive Radio Networks

Cognitive radio networks are a new type of multi-channel wireless network in which different nodes can have access to different sets of channels. By providing multiple channels, they improve the efficiency and reliability of wireless communication. However, the heterogeneous nature of cognitive radio networks also brings new challenges to the design and analysis of distributed algorithms. In this paper, we focus on two fundamental problems in cognitive radio networks: neighbor discovery, and global broadcast. We consider a network containing $n$ nodes, each of which has access to $c$ channels. We assume the network has diameter $D$, and each pair of neighbors have at least $k\geq 1$, and at most $k_{max}\leq c$, shared channels. We also assume each node has at most $\Delta$ neighbors. For the neighbor discovery problem, we design a randomized algorithm CSeek which has time complexity $\tilde{O}((c^2/k)+(k_{max}/k)\cdot\Delta)$. CSeek is flexible and robust, which allows us to use it as a generic "filter" to find "well-connected" neighbors with an even shorter running time. We then move on to the global broadcast problem, and propose CGCast, a randomized algorithm which takes $\tilde{O}((c^2/k)+(k_{max}/k)\cdot\Delta+D\cdot\Delta)$ time. CGCast uses CSeek to achieve communication among neighbors, and uses edge coloring to establish an efficient schedule for fast message dissemination. Towards the end of the paper, we give lower bounds for solving the two problems. These lower bounds demonstrate that in many situations, CSeek and CGCast are near optimal

Packet Size Optimization for Multiple Input Multiple Output Cognitive Radio Sensor Networks aided Internet of Things

The determination of Optimal Packet Size (OPS) for Cognitive Radio assisted Sensor Networks (CRSNs) architecture is non-trivial. State of the art in this area describes various complex techniques to determine OPS for CRSNs. However, it is observed that under high interference from the surrounding users, it is not possible to determine a feasible optimal packet size of data transmission under the simple point-to-point CRSN network topology. This is contributed primarily due to the peak transmit power constraint of the cognitive nodes. To address this specific challenge, this paper proposes a Multiple Input Multiple Output based Cognitive Radio Sensor Networks (MIMO-CRSNs) architecture for futuristic technologies like Internet of Things (IoT) and machine-to-machine (M2M) communications. A joint optimization problem is formulated taking into account network constraints like the overall end to end latency, interference duration caused to the non-cognitive users, average BER and transmit power.We propose our Algorithm-1 based on generic exhaustive search technique blue to solve the optimization problem. Furthermore, a low complexity suboptimal Algorithm-2 based on solving classical Karush-Kuhn-Tucker (KKT) conditions is proposed. These algorithms for MIMO-CRSNs are implemented in conjunction with two different channel access schemes. These channel access schemes are Time Slotted Distributed Cognitive Medium Access Control denoted as MIMO-DTS-CMAC and CSMA/CA assisted Centralized Common Control Channel based Cognitive Medium Access Control denoted as MIMO-CC-CMAC. Simulations reveal that the proposed MIMO based CRSN network outperforms the conventional point-to-point CRSN network in terms of overall energy consumption. Moreover, the proposed Algorithm-1 and Algorithm2 shows perfect match and the implementation complexity of Algorithm-2 is much lesser than Algorithm-1. Algorithm-1 takes almost 680 ms to execute and provides OPS value for a given number of users while Algorithm- 2 takes 4 to 5 ms on an average to find the optimal packet size for the proposed MIMO-CRSN framework

Competing Mobile Network Game: Embracing antijamming and jamming strategies with reinforcement learning

We introduce Competing Mobile Network Game (CMNG), a stochastic game played by cognitive radio networks that compete for dominating an open spectrum access. Differentiated from existing approaches, we incorporate both communicator and jamming nodes to form a network for friendly coalition, integrate antijamming and jamming subgames into a stochastic framework, and apply Q-learning techniques to solve for an optimal channel access strategy. We empirically evaluate our Q-learning based strategies and find that Minimax-Q learning is more suitable for an aggressive environment than Nash-Q while Friend-or-foe Q-learning can provide the best solution under distributed mobile ad hoc networking scenarios in which the centralized control can hardly be available.Engineering and Applied Science

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

Transmitter Optimization in Multiuser Wireless Systems with Quality of Service Constraints

In this dissertation, transmitter adaptation for optimal resource allocation in wireless communication systems are investigated. First, a multiple access channel model is considered where many transmitters communicate with a single receiver. This scenario is a basic component of a. wireless network in which multiple users simultaneously access the resources of a wireless service provider. Adaptive algorithms for transmitter optimization to meet Quality-of-Service (QoS) requirements in a distributed manner are studied. Second, an interference channel model is considered where multiple interfering transmitter-receiver pairs co-exist such that a given transmitter communicates with its intended receiver in the presence of interference from other transmitters. This scenario models a wireless network in which several wireless service providers share the spectrum to offer their services by using dynamic spectrum access and cognitive radio (CR) technologies. The primary objective of dynamic spectrum access in the CR approach is to enable use of the frequency band dynamically and opportunistically without creating harmful interference to licensed incumbent users. Specifically, CR users are envisioned to be able to provide high bandwidth and efficient utilization of the spectrum via dynamic spectrum access in heterogeneous networks. In this scenario, a distributed method is investigated for combined precoder and power adaptation of CR transmitters for dynamic spectrum sharing in cognitive radio systems. Finally, the effect of limited feedback for transmitter optimization is analyzed where precoder adaptation uses the quantized version of interference information or the predictive vector quantization for incremental updates. The performance of the transmitter adaptation algorithms is also studied in the context of fading channels

A Distributed Approach to Interference Alignment in OFDM-based Two-tiered Networks

In this contribution, we consider a two-tiered network and focus on the coexistence between the two tiers at physical layer. We target our efforts on a long term evolution advanced (LTE-A) orthogonal frequency division multiple access (OFDMA) macro-cell sharing the spectrum with a randomly deployed second tier of small-cells. In such networks, high levels of co-channel interference between the macro and small base stations (MBS/SBS) may largely limit the potential spectral efficiency gains provided by the frequency reuse 1. To address this issue, we propose a novel cognitive interference alignment based scheme to protect the macro-cell from the cross-tier interference, while mitigating the co-tier interference in the second tier. Remarkably, only local channel state information (CSI) and autonomous operations are required in the second tier, resulting in a completely self-organizing approach for the SBSs. The optimal precoder that maximizes the spectral efficiency of the link between each SBS and its served user equipment is found by means of a distributed one-shot strategy. Numerical findings reveal non-negligible spectral efficiency enhancements with respect to traditional time division multiple access approaches at any signal to noise (SNR) regime. Additionally, the proposed technique exhibits significant robustness to channel estimation errors, achieving remarkable results for the imperfect CSI case and yielding consistent performance enhancements to the network.Comment: 15 pages, 10 figures, accepted and to appear in IEEE Transactions on Vehicular Technology Special Section: Self-Organizing Radio Networks, 2013. Authors' final version. Copyright transferred to IEE