Adaptive resources assignment in OFDM-based cognitive radio systems

Channel coding is commonly based on protecting information to be communicated across an unreliable medium, by adding patterns of redundancy into the transmission path. Also referred to as forward error control coding (FECC), the technique is widely used to enable correcting or at least detecting bit errors in digital communication systems. In this paper we study an original FECC known as polar coding which has proven to meet the typical use cases of the next generation mobile standard. This work is motivated by the suitability of polar codes for the new coming wireless era. Hence, we investigate the performance of polar codes in terms of bit error rate (BER) for several codeword lengths and code rates. We first perform a discrete search to find the best operating signal-to-noise ratio (SNR) at two different code rates, while varying the blocklength. We find in our extensive simulations that the BER becomes more sensitive to operating SNR (OSNR) as long as we increase the blocklength and code rate. Finally, we note that increasing blocklength achieves an SNR gain, while increasing code rate changes the OSNR domain. This trade-off sorted out must be taken into consideration while designing polar codes for high-throughput application.

Section: Introductionmentioning

confidence: 99%

Study of the operational SNR while constructing polar codes

Benkhouya¹,

Chana²,

Hadi³

2020

“…On the other hand, the applications supported by the IoT devices are very diverse, requiring heterogeneous uncertain bandwidth and rate demands. When applying CR technology in IoT networks (CRIoT networks), these peculiar characteristics call for new stochastic channel access mechanism that can efficiently utilize the available spectrum to maximize the number of simultaneous CRIoT transmissions with minimum total transmission power, and improved network throughput [2][3][4][5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Dynamic resource allocation for opportunistic software-defined IoT networks: stochastic optimization framework

Alnabelsi

Albataineh

2020

Several wireless technologies have recently emerged to enable efficient and scalable internet-of-things (IoT) networking. Cognitive radio (CR) technology, enabled by software-defined radios, is considered one of the main IoT-enabling technologies that can provide opportunistic wireless access to a large number of connected IoT devices. An important challenge in this domain is how to dynamically enable IoT transmissions while achieving efficient spectrum usage with a minimum total power consumption under interference and traffic demand uncertainty. Toward this end, we propose a dynamic bandwidth/channel/power allocation algorithm that aims at maximizing the overall network’s throughput while selecting the set of power resulting in the minimum total transmission power. This problem can be formulated as a two-stage binary linear stochastic programming. Because the interference over different channels is a continuous random variable and noting that the interference statistics are highly correlated, a suboptimal sampling solution is proposed. Our proposed algorithm is an adaptive algorithm that is to be periodically conducted over time to consider the changes of the channel and interference conditions. Numerical results indicate that our proposed algorithm significantly increases the number of simultaneous IoT transmissions compared to a typical algorithm, and hence, the achieved throughput is improved.

“…Thus, the COMPSS technique involves decreasing the sampling rate lower than the Nyquist rate. This paper develops a hybrid COMPSS scheme in details by proposing a constant local and global false alarm ratios (CLFAR) and (CGFAR) expressions, and their effects on detection performance, compression ratio, SNR wall, and computational complexity trade-offs in sensing the OFDM signal [11][12][13][14]. The rest of this paper is outlined as follows: The algorithm of the considered COMPSS scheme and its formulated problem are described in section 2.…”

Section: Introductionmentioning

confidence: 99%

Compressive spectrum sensing using two-stage scheme for cognitive radio networks

Taher

Ahmed

Salman

2020

The modern applications of communications that use wideband signals suffer the lacking since the resources of this kind of signals are limited especially for fifth generation (5G). The Compressive Spectrum Sensing (COMPSS) techniques address such issues to reuse the detected signals in the networks and applications of 5G. However, the raw techniques of COMPSS have low compression ratio and high computational complexity rather than high level of noise variance. In this paper, a hybrid COMPSS scheme has been developed for both non-cooperative and cooperative cognitive radio networks. The proposed scheme compiles on discrete wavelet transform – single resolution (DWT-SR) cascaded with discrete cosine transform (DCT). The first is constructed according to the pyramid algorithm to achieve 50% while the second performed 30% compression ratios. The simulation and analytic results reveal the significant detection performance of the proposed technique is better than that of the raw COMPSS techniques.