Research on underwater acoustic sensor networks has become a compelling field in recent years since resources on land are being depleted. Therefore, robust and efficient routing protocols are needed to ensure the reliability of message gathering and transmitting in underwater sensor networks. In the process of biological foraging in nature, creatures such as insects can find paths while adapting to dynamic environmental conditions through group cooperation, which provides a new perspective for research on routing protocols. In this study, the authors proposed an ant colony algorithm-based routing protocol (ACAR). In ACAR, the pheromone with a novel physical meaning and concentration changing mechanism is utilised to guide ants (path establishing packets) to the sink node. In addition, node depth information is used to decrease redundancy in the underwater environment. The routing process can be summarised in three parts: pheromone list setup, routing decision and damaged path repair. Simulation results, carried out on an underwater simulator based on NS-2, showed that ACAR outperforms other schemes with regards to network lifetime with a relatively better delivery ratio and latency in the proposed network scenarios.

Mobile users are interested in utilising high network capabilities without time and place constraints. However, with a high level of interest in the usage of mobile phones and internet facilities, the limited capacity of terrestrial base stations (BSs) is unbalanced. As a potential alternative to BSs, unmanned aerial vehicles (UAVs) are emerging as a means of transmitting wireless data to ground mobile users. As an air-to-ground communication network, the real UAVs deployed and collected communication data from ground mobile users. The main objective of this study is to analyse and evaluate user throughput, interference, and power transmission when the UAVs are at different heights. The parameters used include the locations of the UAVs and users, the altitudes and elevation angles from the users to UAVs, signal-to-noise-ratio, throughput values, the categories of line-of-sight, and non-line-of-sight links. Furthermore, K-means used as a clustering method for class identification, long short-term memory (LSTM), and gated recurrent unit (GRU) to analyse and evaluate system performance. The system's performance was compared with a multi-layer perceptron approach. The evaluation results show that the proposed LSTM–GRU provides reliable and encouraging performance with low computational complexity, which is appropriate for heterogeneous networks.

A multi-cell network that uses orthogonal frequency division multiplexing access (OFDMA) is studied here. Unlike prior works, the proposed network consists of both (i) legacy data users that do not have energy harvesting capability and have a minimum data rate requirement and (ii) radio frequency (RF)-energy harvesting devices with a minimum energy requirement. It studies sub-band allocation to users and transmits power allocation at base stations. The authors formulate a mixed-integer non-linear program and also present two heuristics to assign sub-band to base stations. Numerical results show that RF energy harvesting devices will not affect network capacity if legacy data users require a high data rate. In addition, the results obtained from the two proposed heuristics are 95% of the optimal solution.

Radio sensing can be integrated with communication in what the authors call future perceptive mobile networks. Due to the complicated signal structure, it is challenging to estimate sensing parameters such as delay, angle of arrival, and Doppler when joint communication and radar/radio sensing is applied in perceptive mobile networks. Radio sensing with signals compatible with a fifth-generation (5G) new radio standard using one-dimension (1D) to 3D compressive sensing (CS) techniques under 5G channel conditions is studied. In the case of 1D–3D CS techniques, they formulate the parameter estimation as a sparse signal recovery problem. These algorithms demonstrate respective advantages, but also show shortcomings in dealing with clustered channels. To effectively exploit the cluster structure in multipath channels, they also propose a 2D cluster Kronecker CS algorithm for significantly improved sensing parameter estimation via introducing a prior probability distribution. Simulation results are provided and they focus the respective advantages and disadvantages of these techniques that validate the effectiveness of the proposed algorithms.

To achieve reliable and green communication in the internet of things (IoT) networks, the authors extend the traditional half-duplex quantise-and-forward (QF) cooperation into the simultaneous wireless information and power transfer (SWIPT) regime. This study investigates the achievable rate of the SWIPT-based half-duplex QF cooperation with the channel state information available only at the receivers. The source and destination are provided by the external power supply and the relay simultaneously implements the information transmission and the energy harvesting by SWIPT technology with the power-splitting protocol. The achievable rate of the proposed system over the additive white Gaussian noise channels is analysed and further maximised by optimising the power-splitting factor. Furthermore, the expected rate of the proposed system over the slow-fading channels is analysed. Theoretical analysis and simulation results show that the achievable rate or expected rate of the SWIPT-based QF cooperation is obviously superior to that of the SWIPT-based amplify-and-forward or SWIPT-based decode-and-forward cooperation. Meanwhile, the SWIPT-based QF cooperation can achieve similar performance to the traditional QF cooperation when the external power supply at the relay is not provided.

In this work, the authors proposed the iterative access point (AP) selection (APS), linear minimum mean-square error (MMSE) precoding and power allocation techniques for cell-free massive multiple-input multiple-output (MIMO) systems. They considered the downlink channel with single-antenna users and multiple-antenna APs. They derive sum-rate expressions for the proposed iterative APS techniques followed by MMSE precoding and optimal, adaptive, and uniform power allocation schemes. Simulations show that the proposed approach outperforms existing conjugate beamforming and zero-forcing schemes and that performance remains excellent with APS, in the presence of perfect and imperfect channel state information.

The existence of link correlation has been empirically validated, and different exemplary works exploit the link correlation in the designing of various network protocols. In this work, the authors investigated the impact of this link correlation in contention mechanism of medium access control (MAC). They illustrated negative link correlation could deteriorate the contention mechanism designed to handle hidden terminal problems and, consequently, increase the packet collision rate between the neighbours. They also showed that negative link correlation could increase the chance of an exposed terminal problem. Therefore, ignoring negative link correlation could lead to overestimate overall network throughput and underestimate packet delay. Next, instead of designing a new contention mechanism exploiting the link correlation, they proposed a new routing tree which mitigates the negative effect of negative link correlation without altering the underlying MAC layer. They evaluated this routing tree on Indriya testbed with TelosB nodes and compared it to the minimum spanning tree based on link quality only. The results show improvement in end-to-end throughput and packet reception ratio at each node.

This study presents the design analysis and correlation properties of a new spreading code for the incoherent synchronous pulse position modulation-optical code division multiple access (PPM-OCDMA) networks. The proposed code called optimised modified prime code (OMPC), which refers to the modified prime codes (MPCs) family. Additionally, this new code designed at a higher code length, optimised code weight and good correlation characteristics to enhance communication security and improve the bit error rate (BER) performance. In this study, the proposed code is used as source code for different kinds of multimedia services such as data, voice, and video. Furthermore, the characteristics and correlation properties of the OMPC in comparison with the other MPCs families that utilised in the PPM-OCDMA networks are presented. Moreover, for the PPM-OCDMA networks, the effect of OMPC auto- and cross-correlation properties on the multiple access interference (MAI) was investigated. Consequently, the MAI is considered in the BER analysis and calculations. Finally, the results show that the proposed OMPC is better than the other existing codes with respect to the channel capacity and system performance.

The vehicular ad hoc network (VANET) is a promising technology to improve the comfort and safety of passengers, roads and urban traffic. Applications applied to VANETs require efficient routing protocols. Urban environments include tunnels, subways, overpasses, and multilevel highways, which indicate the multilevel information environment and specific conditions of radio channel dissemination. In this paper proposed a three-dimensional (3D) evidence theory based, opportunistic routing protocol, called 3DEOR, which address the above issues using a new hybrid criterion called PAL. The PAL criterion includes three metrics of packet delivery probability to compensate for unreliable dissemination environments, packet advancement appropriated to 3D environments to reduce the number of hop counts and level to improve link connectivity. Prioritise the relay set members based on the new criteria to select the best relay node. In cases where there is no certainty, the evidence theory can be a good choice for combining the three metrics of the PAL criterion. The simulation results show the superiority of the proposed protocol over the 3D link state aware geographic opportunistic (3DLSGO) and 3D greedy perimeter stateless routing (3DGPSR) protocols in network performance indices such as packet delivery rate, end-to-end delay and average hop count.

Comparing with the cloud computing, mobile edge computing (MEC) can further decrease the latency and improve the stability of the networks. However, it is challenging for the edge servers to deal with the large computation task due to the limited computing capacity. In this study, we design a novel three-layer network architecture consisting of mobile devices, edge cloudlets, and helper cloudlets, where the computing data can be partially processed at the edge cloudlet and helper cloudlet. Based on this, a joint communication, offloading, and computation resource allocation problem is formulated to minimise the computation cost and energy consumption. Due to its difficulty to directly solve the formulated problem, we first propose an offloading scheme to obtain the closed-form solutions for the optimal offloading data size. Next, we decompose the optimisation problem into two subproblems: (i) for the cloud execution, we dynamically adjust the data transmission rate according to the stochastic channel condition, (ii) for the mobile execution, the energy consumption can be further reduced by applying the dynamic voltage and frequency scaling technique. Finally, the numerical results demonstrate the efficiency of the proposed scheme, and show the performance gains in terms of delay, computation cost and energy consumption.

Non-reciprocity limits the potential of time division duplexing (TDD) massive multiple-input and multiple-output (MIMO) systems. Due to the reciprocal channel, there is no need for downlink (DL) pilot transmission implies computational complexity reduction and saving precious time resources. In this study, the DL channel matrix is estimated using the uplink channel matrix by a novel reciprocity calibration technique. Non-reciprocity arises due to the hardware mismatch (HM) across the base station (BS) and the user terminal (UT)/both. Its detrimental effect causes amplitude and phase impairment in the downlink signal transmission. Since a massive MIMO network has a large number of antenna elements across the BS, hence the instantaneous HM coefficient cannot be determined accurately. For solving such a problem, the authors use the statistical response of the hardware unit. Considering a complex Gaussian hardware response across each antenna terminal, the probability density function (PDF) of amplitude and phase mismatch is derived. They also derive the joint PDF of amplitude and phase mismatch, considering the HM. Simulation results address the sensitivity and complexity for evaluating the sum-rate capacity under three linear precoders (matched filter, regularized zero-forcing, and zero-forcing), which validate their effectiveness in this scenario.

Stochastic geometry is emerging as tractable solution and attractive approach to evaluate the performance of 5G heterogeneous network (5G-Hetnet) such as coverage, outage and rate probability. The base stations (BSs) and fading distribution in 5G-Hetnet are an almost ubiquitous assumption for analytical study, requiring flexible and scalable approaches. However, the β-Ginibre point process (β-GPP) has been proposed as a new method to model wireless network. This study provides a general framework to analyse downlink mmWave cellular network with Nakagami-m fading and directional beamforming. First, cell association scheme in both line-of-site and non-line-of-site is considered based on the strongest average received power. After deriving cell association probability between user equipment and BS, the computationally coverage probability expression is obtained depending on signal-to-interference-plus-noise ratio. Furthermore, the impact of biasing factor on rate coverage probability is investigated. Finally, considering inter-cell-interference coordination, most performances metrics will be re-evaluated to determine their influence on the network. Recommended approaches produce satisfactory results when biasing factor and antenna beamforming are used.

This study deals with the performance analysis of adaptive combining-based hybrid free-space optics (FSO)/radio-frequency (RF) system in a terrestrial communication scenario with and without pointing errors. Here, outage probability and average symbol error rate (SER) are used as the performance metrics. Adaptive combining is a switching scheme in which the FSO link is active throughout, whereas the RF link is activated based on the transmission reliability of the FSO link and maximal-ratio-combining (MRC) of RF and FSO links is performed at the destination. The small-scale fading in RF and atmospheric turbulence induced fading in FSO links have been characterised by Nakagami-m and Gamma–Gamma distributions, respectively. In addition, the radial displacement between the beam centre and detector centre, which induces pointing errors, is modelled using Rayleigh distribution. The exact closed form expressions for the outage and average SER have been derived along with their corresponding asymptotic expressions. Diversity gain of the system has also been determined using the asymptotic expressions. Further, the variation in the performance of the system has been analysed with respect to various parameters including link distance, fading severity parameter, average signal-to-noise of the RF link, and the pointing errors parameter.

In this study, a new hybrid secondary transmitter (ST) mode is proposed for cognitive radio Internet of Things (CR-IoT) networks with energy harvesting (EH) and ambient backscatter (AmBack) communication capabilities. The authors firstly describe the proposed hybrid mode with wireless-powered ST. The ST, consisting of EH, Amback, and conventional active transmission modules, adapts itself under different primary user operation states, i.e. active or idle. The overall throughput of the secondary system is formulated and the system settings for achieving optimal performance are derived and validated through numerical simulations.

Fibre optics is the best solution for the deployment of 5G fronthaul, but it is expensive and time-consuming. The use of a common fibre access network for fixed and mobile services results in cost reduction, quality of experience improvement, and synergy and convergence of the resources. Accordingly, in this study, a practical dual-purpose techno-economic model (FTTx + 5G) was introduced to evaluate its Capital Expenditures (CAPEX). Some technical, financial, and geographical data were also used for computation and comparing the costs in different situations by the integer linear programming method.

Automatic modulation classification (AMC) has recently attracted widespread attention nowadays due to its desirable features of generalisability and requirement of little prior knowledge through artificial intelligence (AI) technology. The authors propose a stacked auto-encoder (SAE) based on various optimisation methods structure to intelligently process a feature space that includes spectral-based features and high-order cumulants. To unify the dimensionality of the features, they apply different normalisation methods to the feature space before training the SAE model to decide corresponding normalisations under different noise environments. Linear normalisation is superior when signal-to-noise ratio (SNR) is low, and standardisation is superior when SNR is between -1 and 4 dB. Regularisation works best when SNR is greater than 5 dB. To increase the recognition accuracy of the proposed model, they introduce the unconstrained optimisation theory to adjust the proposed SAE model, including Nelder-Mead method, Newton optimisation method, conjugate gradient method and quasi-Newton method. They observe that the quasi-Newton method offers desirable performance when optimising SAE model. It is the first time to compare these data normalisation methods and discuss unconstrained optimisation theory together to recognise modulation types. The recognition accuracy of this model for eight modulation types can reach 99.8% when SNR ranges from to 10 dB.

Software-defined network (SDN)-based vehicular ad hoc network (VANET) is an outstanding technology for smart transportation as it increases traffic safety, efficiency, comfort, and manageability. However, despite all its benefits and good performance, SDN-based VANET is vulnerable to attack threats such as distributed denial of service (DDoS). When SDN-based VANET systems are exposed to DDoS attacks, this may affect traffic safety, causing traffic accidents and deaths. Therefore, the relevant security threats need to be addressed before integrating the SDN-based VANETs into smart transportation systems. In this study, the stacked sparse autoencoder (SSAE) + Softmax classifier deep network model is proposed to detect DDoS attacks targeting SDN-based VANETs. The features in the dataset obtained from the SDN-based VANET were reduced dimensionally utilising SSAE, and the most significant features were obtained. Then, these features were used as input into the Softmax classifier. According to the experimental results, the best accuracy scores were calculated as 96.9% using the four-layer SSAE + Softmax classifier deep network model proposed. When compared, the results demonstrate the SSAE + Softmax classifier deep network model proposed can obtain better results in the classification of DDoS attacks and is more successful than the other machine learning classifiers.

In order to alleviate network congestion and reduce request delay, device-to-device caching technology will be an important part of modern communication networks. People always browse the Internet for things they are interested in. However, different people have different points of interest. Therefore, how to choose a suitable cache node when people share content is a challenge. In this study, an optimal cache node selection algorithm based on virtual delay is proposed, where the multi-armed bandit model is used to obtain optimised cache decision based on the interest differences of users. Each candidate user may become a cache node, and the algorithm selects the user who minimises the overall delay as the node. When multiple candidate users exist, it is necessary to cooperate between multiple candidate users whose cache space is limited in order to maximise the cache hit rate. However, since each candidate user acts as a cache node has a defferent service efficiency for different requests from surrounding users, it is necessary to effectively distinguish cooperative cache users. This study proposes a master–slave node cooperative cache model based on the optimal cache node selection. The experimental results confirm that the proposed schemes achieve lower overall delay performance.

Phase noise problem in oscillators can degrade the performance of high-speed communication systems. The author analysed the impact of phase noise problem on multi-input–multi-output (MIMO) systems under common and independent oscillators. The estimation of system parameters (i.e. phase noise and channel gains) is a challenging task. In this study, a data-aided least square estimator based compressed sensing Kalman filter (KF)-based compressed sensing (CS) scheme is proposed for tracking phase parameters. The signal model and estimation problem for the system are mathematically derived. Also, Bayesian Cramér–Rao lower bound (BCRLB) scheme is also derived. For joint estimation, the mean square error (MSE) and bit error rate (BER) performances of the BCRLBs and proposed scheme are compared. Results demonstrate that the proposed KF-based CS scheme gives low BER values and better performance compared to other estimation schemes. The utilisation of the proposed scheme helps in reducing the MSE of the MIMO system. Finally, the proposed scheme enhances the estimation of phase noise parameters for the MIMO system.

This work proposes a machine learning detection scheme for wireless orbital angular momentum (OAM) communication systems. The new scheme works for single-input–single-output (SISO) and multiple-input–single-output (MISO) wireless communication between the transmitter and the receiver. The transmitter encodes its data in OAM modes which are constructed using Laguerre–Gaussian beam form. The transmitted beams travel through a wireless channel with weak to medium turbulence strength and they arrive at random positions on the same receiver area. The authors proposed detection scheme allows the reception of multiple overlapping beams without prior knowledge of beams centre positions. The proposed scheme uses a novel technique of receiver segmentation and space filtering along with neural network to decode the received beams. Simulations show the detection efficiency and the enhanced performance of their proposed scheme for the SISO case and the MISO case with 2, 4, and 16 beams.

In order to address the problem that the direction-of-arrival (DOA) estimation is restricted by the number of array elements, this study comes up with a novel DOA estimation algorithm based on discrete-Fourier-transformation (DFT) and multiple regression, which focuses on the direct data domain from each array element rather than the traditional covariance domain of the whole array. In this algorithm, the authors regard each channel of the array signal model as an independent form of multiple regression and achieve the DOA by estimating the regression coefficient of the reconstructed source signal from each array element one by one iteratively. In addition, the DOAs estimated from the multiple array elements are averaged to improve the accuracy by exploiting the asymptotic normality. This algorithm is proposed, modelled, and developed theoretically at first. Then the capabilities that the authors' algorithm can estimate not only the very close DOAs since they distinguish the irrelevant sources by the DFT on temporal frequency but also the DOAs when the number of sources is larger than that of array elements are fully demonstrated by simulations. Furthermore, no requirement for the known number of sources brings the algorithm broader application foreground.

Mobile ad hoc networks (MANETs) represent a class of important network models for supporting various critical applications, while the security breach due to eavesdropping attacks has been a critical issue. This study investigates the security issue of MANETs from the perspective of physical layer security (PLS). In particular, by combining PLS techniques [e.g. artificial noise (AN) injection and Secrecy Guard zoNe (SGN)] and the conventional Aloha protocol, the authors first propose an AN-based Aloha protocol and a SGN-based Aloha protocol to ensure secure medium access for legitimate transmitters. In the AN-based Aloha protocol, all potential transmitters are allowed to be active and each active transmitter injects AN into its transmitted signals to confuse eavesdroppers. In the SGN-based protocol, each potential transmitter has an SGN, a circle centred at itself, and only the potential transmitters whose SGN contains no eavesdroppers are allowed to be active. To understand the security performances of the proposed security-aware Aloha protocols, the authors then apply tools from Stochastic Geometry to analyse the secrecy transmission capacity (STC) performances of MANETs under both protocols. Finally, the authors provide simulation/numerical results to corroborate the proposed theoretical analysis and also to show the impacts of network parameters on the STC performances.

Massive multiple-input multiple-output (M-MIMO) is one of the cutting edge technologies that provides significant improvement in throughput, coverage and spectral efficiency. The challenge with M-MIMO systems is to extract individual signals from the composite signal, thus making optimal detectors prohibitively complex. Recently, approximate message passing (AMP) and its variant detectors have gained substantial importance due to their decreased complexity and improved performance. However, AMP algorithm does not always converge. Non-linear detectors like vertical Bell-Labs layered space-iime improve the bit error rate (BER) but with high complexity, whereas, linear minimum mean square error (MMSE) detector offers low complexity while compromising on BER performance. Neumann series based MMSE detectors further reduce MMSE computational complexity, however, the BER performance remains the same. In this work, the authors propose a hybrid Neumann series based MMSE detector which decomposes the detected signal into its constituent components and apply a neighbourhood selection algorithm on the obtained components thus improving the overall performance. Another contribution of this work is derivation of an off-set value for optimised neighbourhood set selection that enables more accurate detection while further reducing computational complexity. Simulation results confirm that the proposed scheme outperforms aforementioned algorithms in terms of BER performance and computational complexity in a continuously changing Rayleigh channel.

MIMO systems employing sphere decoding (SD) algorithm are known to achieve near maximum likelihood (ML) performance at a reduced complexity by restricting the candidate search space to a sphere of a certain radius. The performance of SD depends on the precise estimation of its soft output. In this paper, a low complexity modified Likelihood Ascent Search (LAS) algorithm is proposed to be used within a SD receiver in order to precisely estimate the counter-hypothesis for its winner candidates. The LAS algorithm is modified to search for the best counter-hypothesis in only one-half of the signal lattice thereby improving the performance of MIMO receiver. Our results challenge the popular perception that for a SD receiver a large number of candidates within the search sphere is essential for good performance. Instead, it is shown that accurate estimation of the counter-hypothesis is equally important and in fact, the performance of the proposed augmented SD receiver with only single candidate approaches that of a classical SD with multiple candidates. Bit error rate performance of the proposed method when compared with the existing research works on soft output generation for the same number of candidates shows that our proposed method outperforms them by upto 3 dB.