Modulation and Coding Classification for Adaptive Power Control in 5G Cognitive Communications

Abstract

Abstract-A key concept suggested for 5G networks is spectrum sharing within the context of Cognitive Communications (CC). This efficient spectrum usage has been explored intensively the last years. In this paper, a mechanism is proposed to allow a cognitive user, also called Secondary User (SU), to access the frequency band of a Primary User (PU) operating based on an Adaptive Coding and Modulation (ACM) protocol. The Spectrum Sensing (SS) technique used considers Higher Order Statistical (HOS) features of the signal and log-likelihood ratios (LLRs) of the code syndromes in order to constantly monitor the modulation and coding scheme (MODCOD) of the PU respectively. Once the Modulation and Coding Classification (MCC) is completed, a Power Control (PC) scheme is enabled. The SU can attempt to access the frequency band of the PU and increase its transmitting power until it causes a change of the PU's transmission scheme due to interference. When the SU detects the change of the PU's MODCOD, then it reduces its transmitting power to a lower level so as to regulate the induced interference. The proposed blind Adaptive Power Control (APC) algorithm converges without any interference channel information to the aforementioned interference limit and guarantees the preservation of the PU link throughput.

Active interference constraint learning with uncertain feedback for Cognitive Radio Networks

“…Another kind of implicit PU CSI, originally introduced in our previous work [8][9][10], is the MCC feedback which contains more information than the ACK/NACK packet and is easier to be extracted. For these case studies, the PU link is considered to be changing its MCS based on an Adaptive Coding and Modulation (ACM) protocol known to the coexisting system and the CRN is equipped with an MCC module to identify the modulation scheme and the code rate of the PU.…”

“…This case is usually not realistic and therefore the obtained information is not always reliable. This imperfect MCS identification depending on the SNR of the sensed PU signal samples is demonstrated in [10] and expressed quantitatively by the probability of correct classification (P cc ). In contrast with likelihood based approaches, the MCC procedure developed in [10] also includes ML techniques which do not provide us a metric of how likely a group of PU signal samples corresponds to a specific MCS class.…”

“…This imperfect MCS identification depending on the SNR of the sensed PU signal samples is demonstrated in [10] and expressed quantitatively by the probability of correct classification (P cc ). In contrast with likelihood based approaches, the MCC procedure developed in [10] also includes ML techniques which do not provide us a metric of how likely a group of PU signal samples corresponds to a specific MCS class. Therefore, modelling explicitly the error or the reliability of the MCC decision is not possible and the only way to derive a likelihood metric of the MCC feedback is in retrospect.…”

Power Control in Cognitive Radio Networks Using Cooperative Modulation and Coding Classification

“…Specifically, each SU collects PU signal samples using a standard sensing period T S , estimates the current MCS and transmits it through a control channel to the CBS. The MCS observation of the SU i over the n th sensing period is expressed as M CS n i and a detailed description about its estimation can be found in [3], [4]. Furthermore, at system level a PU link and N SU links exist in the same frequency band as shown in Fig.…”

“…An interesting approach to address this problem could be the classification of the modulation and coding scheme (MCC) [3], [4]. As far as the modulation classification is concerned, features like the signal cumulants of 2nd, 3rd, 4th, 6th and 8th order which have distinctive theoretical values among different modulation schemes [5] are estimated and then fed into a powerful classification tool, the Support Vector Machine (SVM) [6].…”