The entropy computation of Gaussian mixture distributions with a large number of components has a prohibitive computational complexity. In this paper, we propose a novel approach exploiting the sphere decoding concept to bound and approximate such entropy terms with reduced complexity and good accuracy. Moreover, we propose an SNR region-based enhancement of the approximation method to reduce the complexity even further. Using Monte-Carlo simulations, the proposed methods are numerically demonstrated for the computation of the mutual information including the entropy term of various channels with finite constellation modulations such as binary and quadratic amplitude modulation (QAM) inputs for communication applications.

In this paper, we propose a preamble (PA) collision resolution (PACR) scheme based on multiple timing advance (TA) values captured via tagged PAs. In the proposed PACR scheme, tags are embedded in random access (RA) PAs and multiple TA values are captured for a single detected PA during a tag detection procedure. The proposed PACR scheme significantly improves RA success probability for stationary machine nodes since the nodes using collided PAs can successfully complete the corresponding RAs using exclusive data resource blocks.

Positioning in currently deployed LTE networks is made via a combination of enhanced cell identity (E-CID), observed time difference of arrival (OTDOA) and global navigation satellite system (A-GNSS) information. Both simulations and field trials indicate acceptable performance for outdoor users. However, today the majority of the connections to cellular networks are established from terminals located indoors. This paper provides baseline positioning performance results based on the 3GPP 3D MIMO deployment and propagation model that has been adopted in the 3GPP Release (Rel.) 13 study item on indoor positioning enhancements. Horizontal and vertical accuracies are investigated for both outdoor-only and outdoor-indoor network deployments.

Cognitive radio is an intelligent radio network that has advancement over traditional radio. The difference between the traditional radio and the cognitive radio is that all the unused frequency spectrum can be utilized to the best of available resources in the cognitive radio unlike the traditional radio. The core technology of cognitive radio is spectrum sensing, in which secondary users (SUs) opportunistically access the spectrum while avoiding interference to primary user (PU) channels. Various aspects of the spectrum sensing have been studied from the perspective of cognitive radio. Cooperative spectrum sensing (CSS) technique provides a promising performance, compared with individual sensing techniques. However, the existence of malicious users (MUs) highly degrades the performance of cognitive radio network (CRN) by sending falsified results to a fusion center (FC). In this paper, we propose a machine learning algorithm based on support vector machine (SVM) to classify legitimate SUs and MUs in the CRN. The proposed SVM-based algorithm is used for both classification and regression. It clearly classifies legitimate SUs and MUs by drawing a hyperplane on the base of maximal margin. After successful classification, the sensing results from the legitimate SUs are combined at the FC by utilizing Dempster-Shafer (DS) evidence theory. The effectiveness of the proposed SVM-based classification algorithm is demonstrated through simulations, compared with existing schemes.

In emerging ubiquitous wireless environments, mobile devices can have multiple communication modules such as WLAN and ZigBee. Since IEEE802.15.4 ZigBee devices and IEEE802.11b WLAN devices share the same 2.4 GHz ISM band, a ZigBee network operating in a low power environment can be severely interfered by overlapped WLAN networks with much larger bandwidth due to higher transmission power. To overcome this inter-system interference problem and guarantee the ZigBee communications, we propose an interference mediation scheme in an overlaid network environment of WLAN and ZigBee devices by using an interference mediator, and evaluate the performance of ZigBee and WLAN networks for three different resource allocation schemes in terms of throughput and channel occupancy time.

In this letter, we propose a user scheduling scheme, which enables non-scheduled users to generate artificial noise in order to enhance wireless security in a multiuser wiretap network, where there exist N users, a single desired receiver, and K eavesdroppers. Our proposed scheme can achieve the optimal multiuser diversity gain (i.e., log log N) by adopting appropriate threshold values for scheduling feedback, which is also able to significantly reduce signaling overhead. We mathematically prove the achievability based on the derivation of a secure user-scaling law. Simulation results demonstrate that the proposed scheme achieves a better average secrecy rate than the conventional scheme in a finite N case, even if both schemes obtain the same secure user-scaling law as N → ∞.