Cognitive radio networks (CRNs) rely on sensing of the licensed spectrum of a primary network to dynamically ascertain underutilized portion of the spectrum, thus affording additional communication opportunities. In a CRN, a single homogeneous spectrum access, such as interweave only deprives the secondary users (SUs) of channel access during handoff, particularly at high primary network traffic. Therefore, providing quality-of-service (QoS) to multi-class SUs with diverse delay requirements during handoff becomes a challenging task. In this paper, we have evolved a Markov-based analytical model to ascertain the gain in non-switching spectrum handoff scheme for multi-class SUs employing hybrid interweave-underlay spectrum access strategy. To satisfy the QoS requirements of the delay-sensitive traffic, we have analyzed the impact of hybrid spectrum access scheme for prioritized multi-class SUs traffic. The results show substantial improvement in spectrum utilization, average system throughput and extended data delivery time compared to conventional CRN using interweave only spectrum access. This demonstrates the suitability of the proposed scheme towards meeting QoS requirements of the delay-sensitive SU traffic while improving the overall performance for delay-tolerant SU traffic as well.

Integration of Device-to-Device (D2D) communication into the cellular network can greatly enhance the spectrum utilization as well as creating additional communication opportunities. D2D can be combined with cognitive radio to further enhance spectrum utilization and cellular network performance. Unlike the traditional D2D communication approach which employs only cellular spectrum, cognitive D2D can enable sensing and utilizing non-cellular spectrum opportunistically as well, thus allowing to offload cellular base station traffic to non-cellular spectrum such as WiFi, Bluetooth, or TV white-spaces. Cognitive D2D users (cDUs) must vacate channel for primary users and hand-off to another secondary channel which renders it quite challenging to meet Quality of Service (QoS) requirements of multi-class cDUs in the presence of higher primary network load. In this work, an enhanced hybrid spectrum access scheme has been developed based on non-switching spectrum hand-off for multi-class DUs, utilizing both interweave and hybrid interweave underlay spectrum access strategies. Further, lower priority cDUs with non-real-time traffic remains in the system and wait for channels to become available, rather than being dropped from the system due to lack of secondary channels. A Continuous-Time Markov Chain (CTMC) has been developed to analyze the performance of the proposed scheme. For comparison, several cases, ranging from the simple cellular network to complex cellular-cognitive-D2D with hybrid-spectrum-access, have been analyzed. The main focus of the analysis is to compare the efficacy of enhanced hybrid spectrum access scheme with individual interweave and hybrid interweave underlay spectrum access strategies in terms of QoS provisioning for multi-class cDUs. The results depict improvement in throughput, spectrum utilization, and extended data delivery time for the proposed scheme and validate the suitability of the proposed scheme to meet QoS requirements for both delay-sensitive and delay-tolerant users of the multi-class cognitive D2D communication system.

The focus of research efforts in cognitive radio networks (CRNs) has primarily remained confined to maximizing the utilization of the discovered resources. However, it is also important to enhance the user satisfaction in CRNs by finding a suitable match between the secondary users and the idle channels available from the primary network while taking into consideration not only the quality of service (QoS) requirements of the secondary users but the quality of the channels as well. In this work, the Gale Shapley matching theory was applied to find the best match, so that the most suitable channels from the available pool were allocated that satisfy the QoS requirements of the secondary users. Before applying matching theory, two objective functions were defined from the secondary user’s perspective as well as from the channel’s perspective. The objective function of secondary users is the weighted sum of the data rate of the secondary users and the probability of reappearance of the primary user on the channel. Whereas, the objective function of the channel is the maximum utilization of the channel. The weight factors included in the objective functions allow for diverse service classes of secondary users (SUs) or varying channel quality characteristics. The objective functions were used in developing the preference lists for the secondary users and the idle channels. The preference lists were then used by the Gale Shapely matching algorithm to determine the most suitably matched SU-channel pairs. The performance of the proposed scheme was evaluated using Monte–Carlo simulations. The results show significant improvement in the overall satisfaction of the secondary users with the proposed scheme in comparison to other contemporary techniques. Further, the impact of changing the weight factors in the objective functions on the secondary user’s satisfaction and channel utilization was also analyzed.

Blind-spots, where wireless signals do not reach within the coverage range, often emerge in a dynamic environment due to obstacles, geographical location or mobility of cellular users (CUs). Thus greatly reducing the overall system performance in terms of coverage and throughput. Relay-aided cognitive Device to Device (cD2D) communication system underlying the 5G cellular network can help mitigate blind-spots. Cognitive capability helps D2D users to acquire the spectrum opportunistically for proximity communication and establish a semi-independent network underlying the 5G network, which not only offloads 5G-New Radio (NR) base station but also enhances the overall system performance. In this work, we have developed a relay-aided cognitive D2D network that helps CUs falling into the blind-spots to retain access to the 5G network and increase wireless coverage. Relay selection requires mutual consent between the relay and the device in the blind-spot. The in-coverage devices are tempted to act as relays through incentive-based mechanism. For enhanced system performance a suitable match among the devices in blind-spots and the relays is required. cD2D enabled relay selection algorithm (cDERSA) is proposed in this work, in which a cognitive D2D user (cDU), which is a CU falling in the blind-spot, establishes a relayed cD2D link to access 5G-NR gNodeB. All cDUs as well as the tempted relays, i.e. cognitive D2D relays (cDRs), first scan their surroundings for devices capable of D2D communication and based on multi-criteria objective functions, build a priority table. A stable marriage problem is formulated and solved using a unique, stable, distributed, and efficient matching algorithm based on the Gale-Shapley algorithm. A new incentive mechanism is also developed to keep relays motivated to share their resources. Simulation is performed and their results show improvement in throughput and average user satisfaction, which validates our proposed cDERSA.

The spectrum handoff is highly critical as well as challenging in a cognitive radio ad hoc network (CRAHN) due to lack of coordination among secondary users (SUs), which leads to collisions among the SUs and consequently affects the performance of the network in terms of spectrum utilization and throughput. The target channel selection mechanism as part of handoff process can play an enormously significant role in minimizing the collisions among the SUs and improving the performance of a cognitive radio network (CRN). In this paper, an enhanced target channel selection scheme based on imperfect channel state prediction is proposed for the spectrum handoff among the SUs in a CRAHN. The proposed scheme includes an improved frame structure that increases coordination among the SUs in the ad hoc environment and helps in organizing the SUs according to the shortest job first principle during channel access. Unlike the existing prediction-based spectrum handoff techniques, the proposed scheme takes into account the accuracy of channel state prediction; the SUs affected due to false prediction are compensated by allowing them to contend for channel access within the same transmission cycle and thus enabling them to achieve higher throughput. The proposed scheme has been compared with the contemporary spectrum handoff schemes and the results have demonstrated substantial improvement in throughput and extended data delivery time by virtue of the reduced number of collisions.

In this paper, we propose an innovative self‐organizing medium access control mechanism for a distributed cognitive radio network (CRN) in which utilization is maximized by minimizing the collisions and missed opportunities. This is achieved by organizing the users of the CRN in a queue through a timer and user ID and providing channel access in an orderly fashion. To efficiently organize the users in a distributed, ad hoc network with less overhead, we reduce the sensing period through parallel sensing wherein the users are divided into different groups and each group is assigned a different portion of the primary spectrum band. This consequently augments the number of discovered spectrum holes which then are maximally utilized through the self‐organizing access scheme. The combination of two schemes augments the effective utilization of primary holes to above 95%, even in impasse situations due to heavy primary network loading, thereby achieving higher network throughput than that achieved when each of the two approaches are used in isolation. By efficiently combining parallel sensing with the self‐organizing MAC (PSO‐MAC), a synergy has been achieved that affords the gains which are more than the sum of the gains achieved through each one of these techniques individually. In an experimental scenario with 50% primary load, the network throughput achieved with combined parallel sensing and self‐organizing MAC is 50% higher compared to that of parallel sensing and 37% better than that of self‐organizing MAC. These results clearly demonstrate the efficacy of the combined approach in achieving optimum performance in a CRN.