Aaqib Patel

Achievable Rates of Underlay-Based Cognitive Radio Operating Under Rate Limitation

A new information-theoretic model is proposed for underlay-based cognitive radio (CR), which imposes rate limitation on the secondary user (SU), whereas the traditional systems impose either interference or transmit power limitations. The channel is modeled as a twin-user interference channel constituted by the primary user (PU) and the SU. The achievable rate of the SU is derived based on the inner bound formulated by Han and Kobayashi, where the PU achieves the maximum attainable rate of the single-user point-to-point link. We show that it is necessary for the SU to allocate its full power for the “public” message that can be decoded both by the SU and by the PU. We also demonstrate that it is optimal for the PU to allocate its full power for the “private” message that can only be decoded by the PU if the level of interference imposed by the PU on the SU is “ergodically strong.” Similarly, it is optimal for the PU to allocate its full power for the public message that can be decoded both by the SU and PU if this interference is “ergodically weak.” These findings suggest that this power allocation is independent of the level of interference imposed by the SU on the PU. Furthermore, the achievable rate is analyzed as a function of the average level of interference. An interesting observation is that if the level of interference imposed by the SU on the PU is “ergodically weak,” the achievable rate becomes a monotonically increasing function of this interference, and it is independent of the level of interference imposed by the PU on the SU. Furthermore, we analyze the realistic imperfect channel estimation scenario and demonstrate that the channel estimation errors will not affect the optimal nature of the SUs power allocation.

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

Cognitive radio sensor networks (CRSNs) is the state-of-the-art communication paradigm for power constrained short range data communication. It is one of the potential technologies adopted for Internet of Things (IoT) and other futuristic machine-to-machine-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 OPS 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 reveal 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 s, while for KKT-based Algorithm-2, it is of the order of 5–10 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.

The Achievable Rate of Interweave Cognitive Radio in the Face of Sensing Errors

Cognitive radio (CR) systems are potentially capable of mitigating the spectrum shortage of contemporary wireless systems. In this paper, we provide a brief overview of CR systems and the important research milestones of their evolution, along with their standardization activities, as a result of their research. This is followed by the detailed analysis of the interweave policy-based CR network (CRN) and by a detailed comparison with the family of underlay-based CRNs. In the interweave-based CRN, sensing of the primary user’s (PU) spectrum by the secondary user’s (SU) has remained a challenge, because the sensing errors prevent us from fulfilling the significant throughput gains that the concept of CR promises. Since missed detection and false alarm errors in real-time spectrum sensing cannot be avoided, based on a new approach, we quantify the achievable rates of the interweave CR by explicitly incorporating the effect of sensing errors. The link between the PU transmitter and the SU transmitter is assumed to be fast fading. Explicitly, the achievable rate degradation imposed by the sensing errors is analyzed for two spectrum sensing techniques, namely, for energy detection and for magnitude squared coherence-based detection. It is demonstrated that when the channel is sparsely occupied by the PU, the reusing techniques that are capable of simultaneously providing low missed detection and false alarm probabilities cause only a minor degradation to the achievable rates. Furthermore, based on the achievable rates derived for underlay CRNs, we compare the interweave CR and the underlay CR paradigms from the perspective of their resilience against spectrum sensing errors. Interestingly, in many practical regimes, the interweave CR paradigm outperforms the underlay CR paradigm in the presence of sensing errors, especially when the SNR at the SU is below 10 dB and when the SNR at the PU is in the range of 10–40 dB. Furthermore, we also provide rules of thumb that identify regimes, where the interweave CR outperforms the underlay CR.

Post sensing optimal channel selection in cognitive radio networks

In cognitive radio networks parallel sensing the spectrum is not possible due to hardware limitations. This poses a challenge for good sequential sensing algorithms. We present a simple channel selection method for selecting the best channel amongst the sequentially scanned channels for the secondary user transmission taking the cost of scan per channel into consideration. We model the problem in the framework of the optimal stopping problem. Under practical assumptions, we show that this problem is a monotone problem and that the optimal policy is completely characterized by the one stage look ahead or 1 - sla set. We impose conditions on the states and analytically characterize the 1-sla set. We show that the policy from the finite horizon MDP and the analysis of the 1-sla set yield the same result for different distributions on the channel state.

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

The determination of optimal packet size (OPS) for a 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 OPS of data transmission under the simple point-to-point CRSN topology. This is contributed primarily to the peak transmit power constraint of the cognitive nodes. To address this specific challenge, this paper proposes a multiple-input multiple output-based CRSNs (MIMO-CRSNs) architecture for futuristic technologies, such as Internet of Things and machine-to-machine communications. A joint optimization problem is formulated, considering network constraints, such as 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 the generic exhaustive search technique to solve the optimization problem. Furthermore, a low complexity suboptimal Algorithm 2 based on solving classical Karush–Kuhn–Tucker 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-CRSN outperforms the conventional point-to-point CRSN in terms of overall energy consumption. Moreover, the proposed Algorithm 1 and Algorithm 2 show a perfect match, and the implementation complexity of Algorithm 2 is less than Algorithm 1. Algorithm 1 takes almost 680 ms to execute and provides OPS value for a given number of users, whereas Algorithm 2 takes 4–5 ms on average to find the OPS for the proposed MIMO-CRSN framework.

Optimal Spectrum Sensing for Cognitive Radio with Imperfect Detector

This paper investigates the sequential opportunistic channel sensing and access problem of an unlicensed or secondary user (SU) in presence of multiple licensed or primary users (PU). The SU senses one channel at a time for the presence of PU. The sensing is considered to be imperfect, i.e, the presence or absence of PU may not be detected correctly. The problem of selection of best channel amongst the sequentially scanned channels for the transmission of SU is modeled as an Optimal Stopping Problem (OSP) which also takes into account the cost of scan per channel. A simple threshold based policy is presented as a solution to OSP in which SU after sensing the channel to be free of PU activity checks the channel quality by comparing the channel gain with a threshold and if the channel gain is more than the threshold then SU selects this channel for transmission. The threshold is seen to be dependent on probabilities of channel occupancy and detection errors. The variation of threshold with respect to detection errors is analyzed and supported with plots. The plots for the variation are presented which match closely with the analytical results.

On bounds and capacity of Cognitive Multiple Access Z-Interference Channel

In this paper, we introduce the Cognitive Multiple Access Z Interference Channel (CMAZIC) where there are two secondary user (SU) transmitters and one SU receiver and one primary user (PU) transceiver pair. Both the SUs are aware of the PU message non causally. There is interference only from the PU transmitter to the SU receiver but not vice versa. We study the Discrete Memoryless CMAZIC (DM-CMAZIC) and provide achievable rate regions with and without superposition coding at the PU transmitter and with Gelfand Pinsker coding against the interference of the PU at both the SU transmitters. For the Gaussian case we show that the inner bound without superposition coding is capacity achieving when appropriate Dirty paper coding is performed at the SU transmitters.

Optimal channel stopping rule under constrained conditions for CRNs

Cognitive radio is an innovative technique for spectrum management which uses Dynamic Spectrum Access (DSA). DSA has the potential to mitigate the spectrum scarcity by letting the Secondary User (SU) access the idle spectrum unused by the Primary User (PU). DSA improves the spectrum efficiency via spectrum sensing and optimal spectrum access. Spectrum sensing should be perfect so that the SU does not cause interference to the PU. Moreover, the channel selected should be the best for maximising throughput of the SU. In this paper we consider a best channel selection problem in Cognitive Radio Networks (CRNs). The problem is modelled as optimal stopping problem where the controller at each state can stop or continue sensing based on the channel gains achieved. There is a Markovian relation given between the channel occupancies of PU. The stopping rule comes out to be simple threshold rule. The controller stops sensing when the channel gain exceeds the given threshold.

Secondary User Satisfying Primary User Rate Constraint for Cognitive Radio

In this paper, we propose a generalized model for underlay based cognitive radio (CR) that is based on the information theoretic interference channel model. Utilizing the achievable rate region for the two user interference channel proposed by Han and Kobayashi, we establish achievable rates for the unlicensed or the Secondary User (SU) that depends upon a given target rate for the licensed or the Primary User. Hence, the transmission of the SU is rate limited due to the PU achievable rate. Unlike the traditional approach for underlay, our results bring out the impact of SU transmission on the set of achievable rates for the PU and also of the effect of PU target rate (which may be called its QoS) on the set of achievable rate for the SU.