Performance of DF Incremental Relaying with Energy Harvesting Relays in Underlay CRNs

In this paper, we analyze the throughput performance of incremental relaying using energy harvesting (EH) decode-and-forward (DF) relays in underlay cognitive radio networks (CRNs). The destination combines the direct and relayed signals when the direct link is in outage. From the derived closed-form expressions, we present an expression for the power-splitting parameter of the EH relay that optimizes the throughput performance. We demonstrate that relaying using EH DF relays results in better performance than direct signalling without a relay only when the destination combines the direct signal from the source with the relayed signal. Computer simulations demonstrate accuracy of the derived expressions

Quasi-Concavity for Gaussian Multicast Relay Channels

Standard upper and lower bounds on the capacity of relay channels are cut-set (CS), decode-forward (DF), and quantize-forward (QF) rates. For real additive white Gaussian noise (AWGN) multicast relay channels with one source node and one relay node, these bounds are shown to be quasi-concave in the receiver signal-to-noise ratios and the squared source-relay correlation coefficient. Furthermore, the CS rates are shown to be quasi-concave in the relay position for a fixed correlation coefficient, and the DF rates are shown to be quasi-concave in the relay position. The latter property characterizes the optimal relay position when using DF.Comment: Shortened version of a document that appeared as an open access paper at https://www.mdpi.com/1099-4300/21/2/10

Effect of Primary Interference on Cognitive Relay Network

Cognitive relay network is a method for optimizing frequency spectrum utilization. What’s important in these networks is to transmit data such that none of primary and secondary users cause destructive interference to other users. Although primary interference affect cognitive network performance, but is neglected in former researches. In this paper, we show cognitive network performance by calculating outage probability. We consider both primary and secondary interference links. Finally, our study is corroborated by representative numerical example. Simulation results demonstrate that increasing interference threshold increase outage probability and increasing data transmit rate cause outage probability increase

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

Software Defined Radio Design for OFDM Based Spectrum Exchange Information Using Arduino UNO and X-Bee

A cost expenditure of software defined radio software has limiting the development of cognitive radio in third countries. Moreover, a complexity of signal processing library in a SDR platform has contributed to the hard implementation in real applications. In this works, the development of SDR platform with low cost expenditure is proposed. Arduino UNO and X Bee uses for the OFDM based spectrum exchange information. In a case of spectrum sensing scenario, the objective of the local spectrum sensing is to detect the PU’s signal detection. The performance of SN ability to sense the PU’s signal is crucial. It was shown that from the previous works as the detected power is quantized into information bit is simulated.  In  order  to  implemented  the  spectrum exchange  information during  sensing,  Arduino  UNO  and  X  Bee  is implemented to sense the presence of PU activity channels of wifi terminals based on the energy of the signals. The detected power (RSSI) of wifi terminals is exchanged into an OFDM subcarrier tone signal such as orthogonal sub-channel that being equally divided from the licensed band.   The results shows that using proposed software defined radio (SDR) based on Arduino and X Bee, the cognitive radio spectrum sensing is applied. The received power from the PU’s channels such as wifi networks can be detected as well. The system could received and exchanged into OFDM-based subcarrier information bits