Software-defined routing protocol for mobile cognitive radio networks : a cross-layer perspective

The growing demand for wireless applications, combined with inefficient spectrum use, necessitates developing a new wireless communication paradigm that focuses on dynamic spectrum access rather than the fixed spectrum using cognitive radio technology. The unlicensed user, known as secondary user or cognitive user, uses cognitive radio technology to grow opportunistic communication over licensed spectrum bands and improve spectrum management performance. The routing protocol in Cognitive Radio Networks (CRNs) serves as a communication backbone, allowing data packets to transfer between cognitive user nodes through multiple paths and channels. However, the problem of routing in CRNs is to create a robust-stable route over higher channel availability. The previously developed protocols missed opportunities to exploit the time-variant channel estimation technique, which selects the best route using the cross-layer routing decision engine to track the adverse impact of cognitive user mobility and primary user activity. This study aims to construct a robust routing path while limiting interference with primary user activity, delaying routing, and maximizing routing throughput. Here, a new routing framework is created in this study to explore new extended routing functions and features from the lower layers (Physical layer and Data Link layer) feedback to improve routing performance. Then, the link-oriented channel availability and channel quality have been developed based on two reliable metrics, which are channel availability probability and channel quality, to estimate and select a channel that maximizes link-throughput. Furthermore, this study proposes a novel cross layer routing protocol, namely, the Software-Defined Routing Protocol. It is a cross-layer method to combine the lower layer (Physical layer and Data Link layer) sensing derived from the channel estimation model. It periodically updates the routing table for optimal route decision making. The output simulation of the channel estimation method has shown that it has produced a powerful channel selection strategy to maximize the average rate of link throughput and achieved a channel estimate under the time-variant effect. Extensive simulation experiments have been performed to evaluate the proposed protocol in compression with the existing benchmark protocols, namely, dual diversity cognitive Ad-hoc routing protocol and cognitive Ad-hoc on-demand distance vector. The proposed protocol outperforms the benchmarks, resulting in increasing the packet delivery ratio by around (11.89%-12.80%), reducing delay by around (2.74%-4.05%), reducing overhead by around (14.31%-18.36%), and increasing throughput by around (23.94%-28.35%). The software-defined routing protocol, however, lacks the ability to determine the better idle channel at high-speed node mobility. In conclusion, the cross-layer routing protocol successfully achieves high routing performance in finding a robust route, selecting high channel stability, and reducing the probability of interference with primary users for continued communication

Spectrum and transmission range aware clustering for cognitive radio ad hoc networks

Cognitive radio network (CRN) is a promising technology to overcome the problem of spectrum shortage by enabling the unlicensed users to access the underutilization spectrum bands in an opportunistic manner. On the other hand, the hardness of establishing a fixed infrastructure in specific situations such as disaster recovery, and battlefield communication imposes the network to have an ad hoc structure. Thus, the emerging of Cognitive Radio Ad Hoc Network (CRAHN) has accordingly become imperative. However, the practical implementation of CRAHN faced many challenges such as control channel establishment and the scalability problems. Clustering that divides the network into virtual groups is a reliable solution to handle these issues. However, previous clustering methods for CRAHNs seem to be impractical due to issues regarding the high number of constructed clusters and unfair load distribution among the clusters. Additionally, the homogeneous channel model was considered in the previous work despite channel heterogeneity is the CRN features. This thesis addressed these issues by proposing two clustering schemes, where the heterogeneous channel is considered in the clustering process. First, a distributed clustering algorithm called Spectrum and Transmission Range Aware Clustering (STRAC) which exploits the heterogeneous channel concept is proposed. Here, a novel cluster head selection function is formulated. An analytical model is derived to validate the STRAC outcomes. Second, in order to improve the bandwidth utilization, a Load Balanced Spectrum and Transmission Range Aware Clustering (LB-STRAC) is proposed. This algorithm jointly considers the channel heterogeneity and load balancing concepts. Simulation results show that on average, STRAC reduces the number of constructed clusters up to 51% compared to conventional clustering technique, Spectrum Opportunity based Clustering (SOC). In addition, STRAC significantly reduces the one-member cluster ratio and re-affiliation ratio in comparison to non-heterogeneity channel consideration schemes. LB-STRAC further improved the clustering performance by outperforming STRAC in terms of uniformity and equality of the traffic load distribution among all clusters with fair spectrum allocation. Moreover, LB-STRAC has been shown to be very effective in improving the bandwidth utilization. For equal traffic load scenario, LB-STRAC on average improves the bandwidth utilization by 24.3% compared to STRAC. Additionally, for varied traffic load scenario, LB-STRAC improves the bandwidth utilization by 31.9% and 25.4% on average compared with STRAC for non-uniform slot allocation and for uniform slot allocation respectively. Thus, LB-STRAC is highly recommended for multi-source scenarios such as continuous monitoring applications or situation awareness applications

Cooperative spectrum sensing using adaptive quantization mapping for mobile cognitive radio networks

Sparsity in spectrum is the result of spectrum underutilization. Cognitive radio (CR) technology has been proposed to address inefficiency of spectrum utilisation through dynamic spectrum access technique. CR in general allows secondary node (SN) users to access the licensed or primary users’ (PU) band without disrupting their activities. In CR cooperative spectrum sensing (CSS), a group of SNs share their spectrum sensing information to provide a better picture of the spectrum usage over the area where the SNs are located. In centralised CCS approach, all the SNs report their sensing information to a master node (MN) through a control reporting channel before the MN decides the spectrum bands that can be used by the SNs. To reduce unnecessary reporting information by the cooperating nodes, orthogonal frequency division multiplexing (OFDM) Subcarrier Mapping (SCM) spectrum exchange information was proposed. In this technique, the detection power level from each secondary SN user is quantized and mapped into a single OFDM subcarrier number before delivering it to the MN. Most researches in cooperative spectrum sensing often stated that the SNs are absolutely in stationary condition. So far, the mobility effect on OFDM based SCM spectrum exchange information has not been addressed before. In this thesis, the benchmarking of SCM in mobility environment is carried out. The results showed that during mobility, the performance of OFDM-based SCM spectrum exchange information degraded significantly. To alleviate the degradation, OFDM-based spectrum exchange information using adaptive quantization is proposed, which is known as Dynamic Subcarrier Mapping (DSM). The method is proposed to adapt to changes in detected power level during mobility. This new nonuniform subcarrier mapping considers the range of received power, threshold level and dynamic subcarrier width. The range of received power is first compressed or expanded depending on the intensity of the received power against a pre-determined threshold level before the OFDM subcarrier number is computed. The results showed that OFDM-based DSM spectrum exchange information is able to enhance the probability of detection for cooperative sensing by up to 43% and reduce false alarm by up to 28%. The DSM spectrum exchange information method has the potential to improve cooperative spectrum sensing for future CR mobile wireless networks

Performance Analysis of Secondary Users in Heterogeneous Cognitive Radio Network

Continuous increase in wireless subscriptions and static allocation of wireless frequency bands to the primary users (PUs) are fueling the radio frequency (RF) shortage problem. Cognitive radio network (CRN) is regarded as a solution to this problem as it utilizes the scarce RF in an opportunisticmanner to increase the spectrumefficiency. InCRN, secondary users (SUs) are allowed to access idle frequency bands opportunistically without causing harmful interference to the PUs. In CRN, the SUs determine the presence of PUs through spectrum sensing and access idle bands by means of dynamic spectrum access. Spectrum sensing techniques available in the literature do not consider mobility. One of the main objectives of this thesis is to include mobility of SUs in spectrum sensing. Furthermore, due to the physical characteristics of CRN where licensed RF bands can be dynamically accessed by various unknown wireless devices, security is a growing concern. This thesis also addresses the physical layer security issues in CRN. Performance of spectrum sensing is evaluated based on probability of misdetection and false alarm, and expected overlapping time, and performance of SUs in the presence of attackers is evaluated based on secrecy rates

Primary-user mobility impact on spectrum sensing in Cognitive Radio networks

In this paper, the effects of the primary-user (PU) mobility on spectrum sensing in Cognitive Radio (CR) networks are studied. To this aim, first, the spectrum sensing problem is reformulated to account for the PU mobility. Then, the effects of the PU mobility are studied with the objective to determine the parameters that affect the spectrum sensing functionality. For this, two performance metrics are analytically derived: i) the detection capability, which measures the PU mobility impact on the CR user detection probability; ii) the mobility-enabled sensing capacity, a new metric that measures the expected transmission capacity achievable by a CR user in the presence of PU mobility. The mathematical analysis is carried out in different scenarios, by using mobility and spectrum occupancy models. The results show that the detection capability is affected by five parameters: the PU protection range, the network region size, the PU mobility model, the CR spatial distribution, and the number of PUs that use the same spectrum band. Moreover, it is shown that the sensing capacity can significantly increase in the presence of PU mobility if the PU protection range is smaller than the network region size. The mathematical results are derived by considering the dynamic PU traffic, and validated through simulations