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Cognitive Radio (CR) usually performs an analysis of the existing environment, enabling collection of all information on current spectrum use and available resources, to decide on its own transmission parameters to optimize communication performance. From practical point of view, the principal task of spectrum sensing is an efficient detection of d...
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... number 1 assumes that there are eight NcNs in the network radio operational area. NcNs are defined as FF Jammers, TETRA, PMR and CHIRP Jammer with parameters presented in Fig. 3: NcNs are activated on selected channels with different power levels, time and distances from CORASMA nodes. In this case created ranking list of monitored channels should change during the simulation time and channels used by NCNs should change their positions on the ranking list. Topology of the simulated network is presented in Fig. 1. Fig. 4 show the ranking list of channels with expected ...
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... in Fig. 10 and Fig. 11 show ranking list of channels for both clusters. Jammer1 (id = 1000) activation in 30s on channel with id = 6 causes CH1 used data channel This change is visible in CH1 (as a new used data channel -black color in Fig. 10) and CH2 (as a new neighbor's cluster channel -white color in Fig. 11) respectively. Similar situation occurred after Jammer2 (id = 1001) activation in 50s. Analysis of this event shall be performed analogically as described above. More interesting case one can see in 80s (both NcNs are active on new data channels). In this moment each cluster uses channel 2, as the best one in the ranking list. CH1 changed used data channel faster than CH2. This event is noticed by CH2, which modifies channels ranking list and selects new best channel with id = ...
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... in Fig. 10 and Fig. 11 show ranking list of channels for both clusters. Jammer1 (id = 1000) activation in 30s on channel with id = 6 causes CH1 used data channel This change is visible in CH1 (as a new used data channel -black color in Fig. 10) and CH2 (as a new neighbor's cluster channel -white color in Fig. 11) respectively. Similar situation occurred after Jammer2 (id = 1001) activation in 50s. Analysis of this event shall be performed analogically as described above. More interesting case one can see in 80s (both NcNs are active on new data channels). In this moment each cluster uses channel 2, as the best one in the ranking list. CH1 changed used data channel faster than CH2. This event is noticed by CH2, which modifies channels ranking list and selects new best channel with id = ...
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... in Fig. 10 and Fig. 11 show ranking list of channels for both clusters. Jammer1 (id = 1000) activation in 30s on channel with id = 6 causes CH1 used data channel This change is visible in CH1 (as a new used data channel -black color in Fig. 10) and CH2 (as a new neighbor's cluster channel -white color in Fig. 11) respectively. Similar situation occurred after Jammer2 (id = 1001) activation in 50s. Analysis of this event shall be performed analogically as described above. More interesting case one can see in 80s (both NcNs are active on new data channels). In this moment each cluster uses channel 2, as the best one in the ranking list. CH1 changed used data channel faster than CH2. This event is noticed by CH2, which modifies channels ranking list and selects new best channel with id = ...
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... in Fig. 10 and Fig. 11 show ranking list of channels for both clusters. Jammer1 (id = 1000) activation in 30s on channel with id = 6 causes CH1 used data channel This change is visible in CH1 (as a new used data channel -black color in Fig. 10) and CH2 (as a new neighbor's cluster channel -white color in Fig. 11) respectively. Similar situation occurred after Jammer2 (id = 1001) activation in 50s. Analysis of this event shall be performed analogically as described above. More interesting case one can see in 80s (both NcNs are active on new data channels). In this moment each cluster uses channel 2, as the best one in the ranking list. CH1 changed used data channel faster than CH2. This event is noticed by CH2, which modifies channels ranking list and selects new best channel with id = ...
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The current work investigates the performance of multi‐slot‐based dynamic clustering in a cognitive radio network. For the spectrum sensing, every secondary user utilizes the multi‐slots of sensing time frame and combines the decisions of every multi‐slot, using the OR logic scheme, to get the sub‐local decision. The channel state information of th...
Citations
... Knowledge of the noise power is required to properly set the decision threshold, which is the main disadvantage of the ED, because the channel conditions vary and change in each node location, so the sensing decision can also vary inside a cluster. To prevent this, cooperative sensing inside clusters is proposed by the authors [42]. Here, the individual measures from nodes are sent to the CH that performs algorithm data fusion, makes the channel ranking list, and selects the best channel as a backup. ...
... An example of a ranking list is shown in Figure 3. A proposed algorithm for ranking list creation is described in [42]. It is based on fitness parameter values, which are calculated for all channels according to the following formula: ...
... -is link quality measure weight (learning). A proposed algorithm for ranking list creation is described in [42]. It is based on fitness parameter values, which are calculated for all channels according to the following formula: ...
The problem of waveform construction for mobile ad hoc networks with cognitive radio (MANET-CR) is discussed. This is the main limitation to widely use this very attractive technique, which does not need the deployment of expensive communication infrastructure. Two main questions correspond to MANET-CR effectiveness: spectrum sensing and spectrum sharing. The paper presents the structure of CR nodes that enables Opportunistic Spectrum Sharing. Procedures for advanced Dynamic Spectrum Management together with the concept of policy-based radio and a sensing method are presented. In the proposed system, the basic policy is to avoid interference generated by other users or jammers. The experiments were performed in a real environment, using the elaborated testbed. The results show that the use of sensing and cognitive management mechanisms enable more efficient use of the spectrum while maintaining reasonable overhead values related to the management procedures.
... Localization techniques for this type of propagation environment are primarily based on the prediction and identification of LOS/NLOS conditions [32], followed by the application of appropriate correction, e.g., using the EKF [33]. The detection of LOS or NLOS conditions may be carried out using techniques of estimating a Rician K-factor [34,35] or an energy detector used in cognitive radio networks to assess channel occupancy [36,37]. From the viewpoint of location accuracy, the most crucial aspect is the correction mechanism used due to propagation conditions. ...
This paper focused on assessing the effectiveness of the signal Doppler frequency (SDF) method to locate a mobile emitter using a swarm of unmanned aerial vehicles (UAVs). Based on simulation results, we showed the impact of various factors such as the number of UAVs, the movement parameters of the emitter and the sensors on location effectiveness. The study results also showed the dependence of the accuracy and continuity of the emitter coordinate estimation on the type of propagation environment, which was determined by line-of-sight (LOS) or non-LOS (NLOS) conditions. The applied research methodology allowed the selection of parameters of the analyzed location system that would minimize the error and maximize the monitoring time of the emitter position.
... The designed framework maximized throughput while maintaining fairness. Similarly, an adaptive channel selection method, was developed in [31] for hierarchical cluster-based CRNs. In this work, channels with the highest ranking position based on a predefined sensing policy are selected as potential backup channels, in case of decreasing channel quality for legacy systems. ...
Cognitive radio has emerged as an enabling technology in the realization of a spectrum-efficient and delay-sensitive industrial wireless communication where nodes are capable of responding in real-time. However, particularly for time-critical industrial applications, because of the link-varying channel capacity, the random arrival of a primary user (PU), and the significant delay caused by spectrum handoff (SH), it is challenging to realize a seamless real-time response which results in a quality of service (QoS) degradation. Therefore, the objectives of this paper is to increase spectrum utilization efficiency by allocating channel based on the priority of a user QoS requirements, to reduce SH delay, to minimize latency by preventing avoidable SHs, and to provide real-time response. To achieve an effective spectrum utilization, we proposed an integrated preemptive/non-preemptive priority scheme to allocate channels according to the priority of user QoS requirements. On the other hand, to avoid significant SH delays and substantial latency resulting from random PU arrival, a unified spectrum sensing technique was developed by integrating proactive sensing and the likelihood estimation technique to differentiate between a hidden and a co-existence PU, and to estimate the mean value of the busy and the idle periods of a channel respectively. Similarly, to prevent poor quality channel selection, a channel selection technique that jointly combines a reward system that uses metrics, e.g. interference range, and availability of a common channel to ranks a set of potential target channels, and a cost function that optimizes the probability of selecting the channel with the best characteristics as candidate channels for opportunistic transmission and for handoffs was developed. The simulation results show a significant performance gain of the delay-PritSHS in terms of number of SHs, Latency, as well as throughput for time-critical industrial applications in comparison to other schemes.
... In their work, an L-CAQ channel selection scheme was design, which selects channel that jointly maximizes channel availability probability as well as channel quality. Similarly, in [33] an adaptive channel selection method was developed for hierarchical cluster-based CRNs. In their work, when a legacy system begins to experience decreasing channel quality, channels with the highest ranking position based on a predefined sensing policy are selected as possible backup channels. ...
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... Then, the data sets are sent to separate blocks of channel parameters estimation and prediction. With the knowledge of the transmitted signal power, and according to [34,[37][38][39][40], the system loss exponent, n, and the mean system loss values can be determined according to the following formula: ...
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... The use of these technologies allows for the spectrum sensing in operating frequency of the receivers. In addition, each node of the CR network performs spectrum sensing to search for free spectral resources, evaluate currently used, and available backup channels [6,7]. ...
... Detection of the used spectral analysis method depends on the received signal power in the channel used by the localized transmitter. For this aim, we use methods typical for the spectrum sensing, e.g., [6,7]. ...
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... CH collects detection results from ORD, identifies available bandwidth and sends information back to ORDs. This solution was selected to reduce influence of the radio channel fading and increase detection efficiency [11]. ...
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The purpose of the article is to present major goals, assumptions and results of European Defense Agency project MAENA, devoted to mobile ad hoc networks with dynamic spectrum access, which is realized locally by network nodes. They are also supported by additional spectrum management entities, acting in coordinated, centralized way to optimize use of assigned spectral resources and maximize network properties.
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