In 2019, 5G was introduced and it is being gradually deployed all over the world. 5G introduces new concepts, such as network slicing to better support various applications with different performance requirements on data rate and latency; and edge and cloud computing that will be responsible for the leverage of computational requirements. This study aims to describe the functions and features of the key 5G technologies and conduct a survey on the latest development of driving technologies for 5G. This survey focuses on health care applications that would benefit from the advantages brought by 5G.

To explore the potential of machine learning-based cooperative spectrum sensing (CSS) in training time, classification speed and classification performance, this study mainly focuses on studying the problem of the feature vectors selecting for machine learning-based CSS. First, a new machine learning-based CSS framework is presented, in which, energy vector forming module, feature vector conversion module, training module, classification module and training sample database are included. Second, a new two-dimensional distance vector is developed, and it is converted by an m-dimensional energy vector according to the distance measurement between vectors. Furthermore, six combination modes are obtained by combining three feature vectors (energy, probability and distance vectors) with two supervised machine learning methods, which are support vector machine (SVM) and weighted K-nearest-neighbour, respectively. From the proposed experimental simulations, the authors can find that the distance vector is obviously superior to the probability vector in computation time. Moreover, the probability vector and distance vector are superior to the energy vector in training time except for the case of poor signal and fewer users, and obviously superior to the energy vector in classification speed. At last, the probability vector and distance vector with SVM classifier show the best classification performance in six combination modes.

The authors present a method of utilisation diversity combining to improve the secrecy performance of an underlay cognitive radio network (CRN) with single or multiple primary users (PUs) over Rayleigh fading environment. They consider a practical scenario where a secondary transmitter , a secondary receiver , and an eavesdropper (Eve) receiver are equipped with multiple antennas while all PUs are employed with a single antenna. The confidential information is transmitted from to under the malicious attempt of an Eve. Generalised selection combining is applied at the , and maximal ratio combining is adopted at the Eve. Depending upon the availability of the global channel state information of the main and eavesdropper's channels at , the secrecy outage performances of optimal antenna selection and sub-optimal antenna selection schemes have been examined. They derive a closed-form expression for the exact secrecy outage probability, asymptotic secrecy outage probability and intercept probability in a Rayleigh fading environment. Numerical results show that secrecy performance of the network improves with increasing the transmitting antennas while it degrades with increasing the number of PUs.

Cost, performance, and penalties are the key factors to revenue generation and customer satisfaction. They have a complex correlation, that gets more complicated when missing a proper framework that unambiguously defines these factors. Service-level agreement (SLA) is the initial document discussing selected parameters as a precondition to business initialisation. The clear definition and application of the SLA is of paramount importance as for modern as a Service online businesses no direct communication between provider and consumer is expected. For the proper implementation of SLA, there should be a satisfactory approach for measuring and monitoring quality of service metrics. This study investigated these issues and proposed performance-based SLA (PerSLA) framework for cost, performance, penalties and revenue optimisation. PerSLA optimises these parameters and maximises both provider revenue and customers satisfaction. Simulation results confirm that the proposed framework is adequate in revenue generation and customers satisfaction. Customers and providers monitor the business with respect to agreed terms and conditions. On violation, the provider is penalised. This agreement increases the trust in relationship between provider and consumer.

One of the prime concerns regarding multi-tier architecture in heterogeneous networks (HetNets) is the interference between users in macrocells and femtocells. Interference mitigation (IM) in femtocells is of critical importance due to high power interfering signals from macrocells. However, such IM techniques generally demand intensive signalling overhead that adversely affect the benefits of multi-tier network architecture. A novel interference cancellation technique with very low signalling overhead that does not require global channel state information at the transmitter is proposed in this study for the uplink of HetNets. The proposed subspace nulling (SN)-based method projects the composite received signal onto the intersection space of the left null space of the interfering space and the row space of desired space to cancel the cross-tier interference. This is achieved both in the presence and absence of macrocell user channel state information at receiver (MU-CSIR). The authors also propose a low complexity subspace method that works with the knowledge of MU-CSIR. Exhaustive simulation studies show that the proposed schemes fully cancel the cross-tier interference in femtocell tier resulting in better bit error rate performance for the FU receiver.

Cognitive radio (CR) is an innovative technology that supports dynamic spectrum access to utilise the spectrum more efficiently in an opportunistic manner without interfering with the licensed users. Multi-carrier modulation techniques such as orthogonal frequency division multiplexing (OFDM) is identified as one of the suitable candidates for the design of CR systems. To provide high data rates while avoiding interference with licensed users, a variant of OFDM called non-contiguous OFDM (NC-OFDM) is used in CR. NC-OFDM is useful for dynamic shaping of spectrum based on the channel impairments, interference and jamming threats. In this work, the customised NC-OFDM transceiver link is simulated and compared with the customised OFDM link in the MATLAB environment. It is implemented in Zynq-7000 System-on-Chip (SoC) 7Z020 with radio-frequency front-end board, AD-FMCOMMS3-EBZ Software Defined Radio platform. Implementation of this link is carried out on Spartan-3A FPGA and compared the hardware resource utilisation with Zynq platform. It is observed that the Zynq platform utilises less hardware resources for the implementation. The transmitter spectrum, spectrum after de-activating unwanted sub-carriers, the spectrum shaping using raised cosine filtering, spectrum sensing using the correlation of known pattern and FFT-based spectrum sensing are also simulated in the MATLAB.

In this work, the authors have analysed the impact of correlation among interferers, and between signal and interferers. In particular, the impact of correlation on average rate performance in an interference-limited scenario has been studied. The user's desired channel link and interfering channel links are assumed to experience Nakagami-m fading. Under this assumption, they compared the average rate for the following cases: (a) interferers are independent and the user's signal-and-interferers are also independent, (b) interferers are correlated and the user's signal-and-interferers are independent, and (c) interferers are correlated and the user's signal and-interferers are also correlated. It has been analytically shown that the average rate in case (b) is higher than the average rate in case (a) when the interferers are positively correlated. In other words, the positive correlation among the interferers increases the average rate. Furthermore, the average rate in case (c) is lower than the average rate in case (b). In other words, the positive correlation between the signal and interferers decreases the average rate. Moreover, the average rate in case (c) can be lower or higher than the average rate in case (a), depending upon the amount of correlation. Simulation results have been shown to support analytical observations.

In this study, a construction analysis and design of a new spreading code set are presented. This code is designed to be used as a source code in optical wireless communication (OWC) system to achieve better performance and security enhancement. The new proposed code is called double length modified prime code (DLMPC), which is constructed based on the popular modified prime code that is commonly applied in the optical code division multiple access (OCDMA) networks. The correlation properties and characteristics of the DLMPC are investigated in this study. Based on the new code, the OWC system architecture and channel model are illustrated and further analysed. Additionally, the transmitted and received data bits encoding and decoding scenarios are discussed. Moreover, a low power consumption optical light source and photo-detector are utilised for a transmitter and receiver, respectively. The overall OWC performance in terms of the bit error rate (BER) has been evaluated considering the multi-user interference as the only source of noise. Finally, the results show that the proposed DLMPC outperforms the popular codes with respect to the channel capacity, security, and system BER performance.

This study presents a multiuser (MU) multiple-input multiple-output (MIMO) downlink transmission scheme based on the quadrature spatial modulation (QSM) concept, which uses the indices of the non-zero entries in its transmission vector to modulate and transmit an independent sequence of bits for each user in the system. The MU interference is removed by using a matrix precoding technique based on channel state information (CSI) known as block diagonalisation (BD). The performance of the proposed scheme is compared with the conventional MU–MIMO–BD system for uncorrelated Rayleigh fading channels and correlated fading channels with imperfect CSI in the reception. Additionally, a low-complexity near maximum likelihood (ML) detection algorithm for the MU–MIMO–QSM signal's detection is proposed. For the considered cases, the proposed MU–MIMO–QSM scheme exhibits gains up to 1 dB in bit error rate performance and a reduction in detection complexity up to 93% as compared to the conventional MU–MIMO–BD scheme for the optimal ML detection. The proposed algorithm performs very near to the optimal ML detector whilst achieving a complexity reduction of up to 58%.

The authors investigate physical layer security design, which employs a random linear network coding with opportunistic relaying and jamming to exploit the secrecy benefit of both source and relay transmissions. The proposed scheme requires the source to transmit artificial noise along with a confidential message. Moreover, in order to further improve the dynamical behaviour of the network against an eavesdropping attack, aggregated power controlled transmissions with optimal power allocation strategy is considered. The network security is accurately characterised by the probability that the eavesdropper will manage to intercept a sufficient number of coded packets to partially or fully recover the confidential message.

In this paper, we propose a novel resource allocation scheme for non-orthogonal multiple access (NOMA) based cooperative cellular networks where in each cell, two pairs of transmitter–receiver want to communicate via the base station (BS) which serves as a decode-and-forward (DF) relay. In the first hop, the transmitters simultaneously transmit their information and the BS applies joint decoding method. The BS transmits the received information towards the receivers based on NOMA technique. The receivers apply the successive interference cancellation (SIC) where the SIC ordering of receivers is determined based on the direct channel gain. We formulate the resource allocation problem as an optimization problem which is non-convex. To tackle the non-convexity, we adopt the successive convex approximation (SCA) with difference of two concave functions (D.C.) as the approximation technique. We also study the computational complexity of our proposed scheme. Finally, we study the performance of our scheme by simulations and compare it with existing transmission schemes like the orthogonal frequency multiple access (OFDMA) and the case where both of the first hop and the second hop adopt PD-NOMA as well as the case where a special sort of subcarrier pairing is performed between the first and second hops.

The design and performance analysis of detectors for weak signals in the symmetric α-stable () noise is considered. The locally optimum detector (LOD) has the optimal performance, but in the case of noise, it is hard to implement in practise since there exists no closed form for the probability density function (PDF) of the noise. To solve this problem, many parametric suboptimal detectors have been proposed such as the soft limiter, locally suboptimum detector and generalised Cauchy detector. In this study, the authors propose a new low-complexity parametric suboptimal detector, generalised Gaussian detector, which does not need the explicit expression of the noise PDF. In addition, the maximum deflection coefficient criterion is proposed to optimise all the parametric detectors. Furthermore, they compare the performance of the suboptimal detectors with the LOD based on the maximum deflection efficacy. Then, an optimal detection programme for weak signals detection in practise is obtained, and finally simulation results provide its superiority.

In this work, a comparative analysis is performed to study the bit error rate and error vector magnitude achieved with the least-squares (LS), the minimum mean squared error (MSE), and the Kalman filter (KF) channel estimators when these are applied to the maximum-ratio combining (MRC) and the regularised zero-forcing (RZF) receivers. The MSE achieved with the different channel estimators was also compared by varying the noise and interference power at the receiver. The proposed methodology relies on the characterisation of a massive multiple-input multiple-output (MIMO) channel with a quasi-deterministic radio channel generator and a cyclic prefix orthogonal frequency division multiplexing link-level radio simulation. The fifth-generation (5G) new radio (NR) frame structure was used to perform channel estimation and equalisation for operation frequencies below 6 GHz. Numerical results show that the MRC receiver achieves its maximum performance with the KF estimator, especially at low signal-to-noise ratio scenarios, while the RZF receiver achieves its maximum performance with the LS estimation even in high interference scenarios.

With the advent of computers and their widespread use, cloud computing has been identified as one of the major emerging components of computer technology. The benefits of cloud computing, in the form of processing power and computing resources connected via the internet, have not only bolstered business and personal operations of users but have also led to severe security and privacy threats that require adaptation into cloud computing systems. In light of this, the present study explores the various threats to cloud computing, in addition to outlining defence mechanisms against these threats. It was found that there is a major threat concerning data breaches because of the lack of management understanding of the use of cloud computing services and their defence mechanisms. Furthermore, there can be an abuse of cloud computing services that, in turn, affect not only sensitive data pertaining to the organisation but also the personal identity and information of the user.

Nowadays, detecting anomaly events in communication networks is highly under consideration by many researchers. In a large communication network, traffic is massive, which leads to a larger amount of data travelling and also the growth of noise. Therefore, to extract meaningful data for anomaly detection would be very challenging. Each attack has its own behaviour that determines the type of attack. However, some attacks may have similar behaviours and only differ in some features. Extracting such meaningful features is of special importance. In this study, an association rule mining algorithm, in particular, the Apriori algorithm is employed to extract appropriate features from the raw data including rules and repetitive patterns. The extracted features would be used then for classifying the data and detecting anomalies in communication networks. A hybrid of artificial neural network and AdaBoost classification algorithms are employed for classifying the detected events with normal behaviour and attack events. The proposed method is compared with previous methods reported in this field such as CART, CHAID, multiple linear regression and logistic regression on KDDCUP99 data set. The results showed that the proposed method outperformed other classifiers examined. The strategy of reinforcement learning is used to combine the classifier's results which is based on Max vote strategy.