Mobile cloud computing relieves the tension between computation-intensive mobile applications and battery-constrained mobile devices by enabling the offloading of computing tasks from mobiles to a remote processors. This study considers a mobile cloud computing scenario in which the ‘cloudlet’ processor that provides offloading opportunities to mobile devices is mounted on unmanned aerial vehicles (UAVs) to enhance coverage. Focusing on a slotted communication system with frequency division multiplexing between mobile and UAV, the joint optimisation of the number of input bits transmitted in the uplink by the mobile to the UAV, the number of input bits processed by the cloudlet at the UAV and the number of output bits returned by the cloudlet to the mobile in the downlink in each slot is carried out by means of dual decomposition under maximum latency constraints with the aim of minimising the mobile energy consumption. Numerical results reveal the critical importance of an optimised bit allocation in order to enable significant energy savings as compared with local mobile execution for stringent latency constraints.

The performance of multicell massive multiple-input multiple-output (MIMO) systems is heavily affected by pilot contamination. This study considers the problem of pilot contamination and analytical expressions are presented on the normalised mean square error (NMSE) of the minimum mean square error channel estimation algorithm. Based on the NMSE of the massive MIMO systems, a pilot design criterion is proposed to design the optimal pilot sequences for mitigating the pilot contamination. Following this criterion, Chu sequence with perfect auto-correction and cross-correlation properties are employed to design the optimal pilot sequences. Then the performance of the proposed pilot design-based scheme is investigated, and the exact NMSE expressions are presented. The excellent performance of this pilot design scheme has been confirmed in the authors’ simulations.

A new multiple-ring star- M -ary quadrature amplitude modulation-aided spatial modulation ( N R-STAR- M QAM-SM) scheme is proposed. The aim is to minimise the average bit error probability (ABEP) of the system. Then in the interest of further minimising the ABEP, the authors formulate a low-complexity algorithm to achieve the optimum radii of N R-STAR- M QAM-SM. This algorithm is based on a specific multiple-input multiple-output configuration and the pairwise error probability function. To validate the results of the proposed scheme, a closed-form union-bound theoretical ABEP expression is derived. Finally, to improve the error performance of N R-STAR- M QAM-SM, the authors propose a low-complexity Euclidean distance-based antenna selection (EDAS) algorithm for N R-STAR- M QAM-SM (EDAS- N R-STAR- M QAM-SM). Since all amplitude/phase modulation schemes are considered to have rotational symmetry and symmetric constellation points have the same EDAS solution, the authors achieve EDAS- N R-STAR- M QAM-SM by searching only one constellation point per symmetric constellation set. The EDAS- N R-STAR- M QAM-SM solution exhibits a significant reduction in the computational complexity compared with the conventional EDAS solution.

Cooperative communications has recently been introduced into multiple-access networks to combat the signal attenuation caused by fading and therefore improve the network performance. This study proposes the outage probability and error analysis of cooperative interleave-division multiple-access (IDMA) networks with regenerative relays over Weibull fading channels. The main contribution of this study relies on deriving a moment-generating function (MGF) of the total signal-to-noise ratio with the help of Pade approximation for IDMA with regenerative relaying. The derived MGF expression is employed to evaluate the outage probability of the considered system. Moreover, a closed-form asymptotic expression for the error probability of the considered system is derived and then comprehensive computer simulations have been performed to validate the accuracy of the authors’ analytical results. It is shown that results obtained by the proposed MGF and asymptotic error expressions are in good agreement with their corresponding simulation results.

Spatial correlation is a decisive factor for pragmatic multiple-input multiple-output (MIMO) system, simultaneously bringing about some problems in the received signal modulation identification respect. In this study, the authors focus on blind digital modulation identification in the spatially correlated MIMO system and deliver a robust signal recognition algorithm based on extreme learning machine (ELM) and higher order statistical features for MIMO signal identification without a priori knowledge of the channel and signal parameters. The superiority of ELM lies in random selections of hidden nodes and ascertains output weights analytically, which result in lower computational complexity. Theoretically, this algorithm has a tendency to supply excellent generalisation performance at staggering learning rate. Further, the simulation results indicate that the ELM could reap a perfectly acceptable recognition performance and thus provides a solid ground structure for tackling MIMO modulation challenges in low signal-to-noise ratio.

In this study, performance analysis of variable-gain amplify-and-forward relaying orthogonal frequency division multiplexing-based systems over independently but not necessarily identically distributed Nakagami-m fading channels using non-linear power amplifier (PA) at the relay and maximal ratio combining scheme at destination. Specifically, novel closed-form expressions of outage probability and asymptotic outage probability are investigated for the considered system model. Further, an average symbol error rate (ASER) expression is derived for general-order rectangular quadrature amplitude modulation (RQAM) scheme using cumulative distribution function-based approach, which is also the novel contribution of the work. Since the transfer function of PA used at relay is nonlinear, this introduces non-linear distortion (NLD) in the considered system model. Thus, the impact of NLD and fading parameters on outage probability, diversity gain of the system and ASER performances are analysed. Further, the impact of NLD on constellation order for RQAM scheme is also discussed. The theoretical results are compared with computer simulations to verify the accuracy of the derivations. The derived expressions are generalised over variety of fading environments for integer-valued fading parameter.

This study addresses the achievable rate of single cell and sum rate of multi-cell orthogonal frequency division multiplexing (OFDM) index modulation (IM). The single-cell achievable rate of OFDM-IM with Gaussian input is calculated using a multi-ary symmetric channel. Then, the cumulative distribution function of multi-cell OFDM-IM is investigated by stochastic geometry. Furthermore, it is proved in this study that the probability density function of noise plus inter-cell interference in multi-cell OFDM-IM with quadrature-amplitude modulation follows a mixture of Gaussians (MoGs) distribution. Next, parameters of the MoG distribution are estimated using a simplified expectation maximisation algorithm. Upper bound of sum rates of multi-cell OFDM-IM is derived. Furthermore, analytic and simulated results are compared and discussed.

Cooperative transmission is an efficient method for wireless sensor networks (WSNs) to decrease power consumption of sensor nodes and combat the fast fading inherent to wireless multipath channels. During the course, a key issue is how a sensor relay handles the balance between assisting other nodes’ communication and accomplishing its own transmission tasks. To address this problem, this study investigates the potential bargaining for resource allocation in WSNs over Rayleigh fading environments by considering energy fairness in cooperative transmission. Specifically, based on the characteristics of Rayleigh fading channel, a relevant system model is built. Then, after analysing the system capacity with a given outage probability over the fading channels, a system utility function for transmit-time allocation is proposed to address the participants’ energy concern. An iterative algorithm is subsequently achieved by solving the Nash equilibrium. Furthermore, essential analysis and discussion on the pure equilibrium and convergence conditions of the iterative algorithm are provided. Finally, numerical results are presented to evaluate the feasibility and system performance of the proposed framework.

In this study, the authors present a method to derive sequential detector (SD) for a general class of composite hypothesis problems. The authors first explain how the SD is constructed by employing a ‘weight function’. Then, the authors employ this method in the cooperative spectrum sensing (SS) in cognitive radio networks where the primary user transmits a phase shift keying (PSK) signal with unknown complex amplitude in additive white Gaussian noise. The noise power is assumed known in the first scenario and unknown in the second one. To evaluate the performance of the resulting SDs, the authors obtain the required average sample number (ASN) function to meet the bounds of false alarm and missed detection probabilities through some numerical evaluations. The results illustrate that the average sensing delay of the proposed SDs are less than the required number of observations in the traditional fixed sample size detectors. In the proposed SDs, the authors also demonstrate that the increase of signal-to-noise ratio leads to decrease of ASN.

Energy harvesting (EH) is a promising technique for cognitive relaying transmission (CRT) where secondary users (SUs) and relaying nodes do not have a constant power supply each. Unlike conventional CRT where the end-to-end data rate is usually maximised without taking into account the energy consumption at the source and relay, in this study, the energy consumption is characterised by means of energy efficiency, defined as the achievable data rate per Joule. In particular, the energy states at each node (either at a SU or a relay) is modelled as a finite-state Markov chain and the transmit power at a node is optimally allocated by jointly accounting for the interference threshold prescribed by primary users (PUs), the maximum allowable transmit power and the harvested energy at the node. To maximise the energy efficiency, a best relay selection criterion is proposed and the subsequent optimal transmit power allocation is initially formulated as a non-linear fractional programming problem and, then, equivalently transformed into a parametric programming problem and, finally, solved analytically by using the classic Karush–Kuhn–Tucker conditions. With extensive Monte-Carlo simulation results, the effectiveness of the proposed relay selection algorithm and corresponding optimal power allocation strategy are corroborated.

Based on the criterion of maximising the end-to-end receiving signal-to-noise power ratio, a dual-antenna selection scheme combining with distributed space–time block code is proposed for two-way relaying networks with amplify-and-forward protocol in Rayleigh fading channels. To facilitate the performance analysis, the authors derive the lower bound and asymptotic expressions for the network outage probability performance. Moreover, a concise solution for optimal power allocation is also determined to minimise the outage probability under a total power constraint. From the asymptotic outage probability analysis, the authors demonstrate that the proposed dual-antenna selection scheme can achieve full outage diversity. Finally, numerical results verify the accuracy of the authors’ analysis. Notably, the proposed scheme achieves competitive performance improvement compared with the beamforming scheme and the existing antenna selection schemes in the state of the arts.

Relay selection in cooperative communication is a crucial task for achieving the spatial diversity since the improper relay selection can decrease the overall capacity of the network. In this study, the authors use a reinforcement learning technique, called as Q-learning (QL), to solve the relay selection problem. They propose a ‘QL-based relay selection algorithm’ (QL-RSA) for wireless cooperative networks that maximises the total capacity of the network. QL-RSA receives the reward (feedback) in terms of the capacity by learning a multi-node amplify-and-forward cooperative environment with time-varying Rayleigh fading channels. The advantages of QL-RSA are that it is less complex, requires less channel feedback information and it is distributed in a multiple-sources environment as it provides each source a self-learning capability to find the optimal relay without exchanging information with other source nodes.

Stochastic geometry tool is an accurate and tractable approach to analyse the performance characteristics of heterogeneous networks (HetNets) with unplanned deployment of base stations (BSs). In this study, the authors analyse the downlink coverage and capacity by taking into account the separation between the macro BS (MBS) and small BSs (SBSs) in a HetNet. MBSs, SBSs, and users are spatially distributed as independent Poisson point processes. The whole space is divided into inner and outer subspaces around the MBSs and a typical user is assumed to be in outer subspace. Analysis on the typical user in the outer subspace incorporates the effect of distance of SBSs from MBS. They derive the expressions for the coverage probability of an outer subspace typical user from SBS and MBS. They also derive the expressions for rate coverage of an outer subspace typical user from SBS and MBS. These metrics are analysed with varying inner subspace radius to incorporate the effect of BSs separation. Simulation results show that SBSs away from MBS in the coverage region of MBS provide good performance to their associated users in terms of coverage probability and rate coverage.

Spectrum scarcity and dramatically increasing demand for high data rate and high-quality video live streaming are of future cellular network design challenges. As a solution to this problem, cache-enabled cellular network architecture has been recently proposed. Device-to-device (D2D) communications can be exploited for distributed video content delivery, and devices can be used for caching of the video files. This can increase the capacity and reduce the end-to-end delay in cellular networks. In this study, the authors propose a new scheme for video distribution over cellular networks by exploiting full-duplex (FD) radios for D2D devices in two scenarios: (i) two nodes exchange their desired video files simultaneously and (ii) each node can concurrently transmit to and receive from two different nodes. In the latter case, an intermediate transceiver can serve one or multiple users’ file request(s) whilst capturing its desired file from another device in the vicinity. Mathematical expressions along with extensive simulations are used to compare their proposed scheme with a half-duplex scheme to show the achievable gains in terms of sum throughput, active links, and delay. They will also look into the energy cost for achieving the improvements provided by operation in FD mode.

Deployment in wireless sensor networks (WSN) addresses maximising the coverage of sensors and reducing the total cost of deployment. The area-priority concept for WSN deployment that the authors contributed to the literature recently allows environments with regions that have different importance or priority levels. In this study, the authors propose the first priority-estimation method for area-priority-based WSN deployments. First, a satellite image of the environment that will be used in the deployment of the sensors is clustered by a K-means algorithm using the colour features of the regions. In the sensor deployment phase, this cluster information is used to determine the priorities of the sensor coverage areas on positions of the image. Sensors are initially deployed quickly using a priority queue-based technique. Then, a simulated annealing algorithm is used to maximise the total covered area priority and to minimise the gaps between the sensors. Various experiments are performed for different scenarios (land, sea, and forest) on images captured from Google Maps using different parameter values. The experiments confirm that the proposed approach performs well and outperforms the random deployment of sensors.

Classifiers, such as artificial neural networks non-linear equaliser (ANN-NLE), Wiener–Hammerstein non-linear equaliser, Volterra non-linear equaliser (Volterra-NLE) and support vector machine non-linear equaliser (SVM-NLE), can play a significant role in compensating non-linear imperfections in the optical communications context. Using classifiers to mitigate the non-linear effects in coherent optical orthogonal frequency division multiplexing (CO-OFDM) systems is an interesting idea to be investigated. In this study, a novel support vector machine robust version, specifically adapted to a 100 Gb/s CO-OFDM data structure for long haul distance, is proposed. Firstly, the authors demonstrate that SVM-NLE upgrades the system performance by about 10⁻¹ in terms of bit-error rate compared to Volterra-NLE at optical signal-to-noise ratio equal to 14 dB. Then, they show that it can double the transmission distance up to 1600 km over single mode fibre channel. Furthermore, a performance comparison is performed using 16 quadrature amplitude modulation and 40 Gb/s bit rate for SVM-NLE, ANN-NLE and inverse Volterra series transfer function non-linear equaliser, respectively.

Terahertz band (0.1–10 THz) nanocommunications are expected to have a plethora of biomedical, industrial, and military applications. A pulse-based modulation scheme could enable communications among nano-devices. Nevertheless, synchronisation could be a significant performance limiting factor and an arduous task to accomplish considering the huge bandwidth of the transmitted pulse, and the simple design and energy constraint requirements of the nano-transceivers. This study proposes a novel synchronisation scheme for pulse-based nanocommunication based on a continuous-time moving average (CTMA) algorithm. Since CTMA uses an energy collection approach, it does not increase the energy and complexity requirements of nano-transceivers. This approach is based on continuously moving sub-integration windows and searching for the maximum energy over the entire preamble duration. The simulation results showed that using the proposed scheme, for a distance of up to 5 mm, a synchronisation probability of >0.7 was obtained for a signal energy to noise ratio (SNR) of 20 dB. The results showed good performance in terms of the tradeoff among the synchronisation probability, SNR, and distance between nanonodes. Moreover, synchronisation error was also evaluated to check the overall performance of the scheme. Altogether, the proposed scheme makes an appropriate step forward to enable nanocommunication among resource-constrained nanonetworks.

For large-scale wireless sensor networks (WSNs) with thousands of sensors, cooperative self-localisation is a key task and has caused extensive concerns. In this study, the authors propose a message passing algorithm for cooperative self-localisation of mobile WSNs by using belief propagation (BP) and variational message passing (VMP) on factor graphs. The sensors locate themselves through two steps: a prediction operation accounting for the sensors’ mobility and a correction operation accounting for ranging measurements between neighbouring sensors. All the messages for computing and transmitting are restricted to be Gaussian to reduce communication overhead and computational complexity. According to the linear state-transition model and the non-linear ranging model, BP and VMP methods are employed to perform prediction and correction, respectively. Simulation results show that when the standard deviations of the prior distributions is small, the positioning accuracy of the proposed algorithm is comparable with that of sum-product algorithm over a wireless network (SPAWN) with much low communication overhead and computational complexity.

In this study, the authors consider the α − η − μ fading channels and derive a new form for the cumulative distribution function of the signal-to-noise ratio (SNR) in terms of the bivariate Fox H-function. They also derive closed-form expressions for the moments and the amount of fading of the SNR. The capacity of α − η − μ fading channels is analysed assuming different adaptive transmission protocols. To this end, they provide analytical closed-form expressions for the average channel capacity with optimal rate adaptation (C _ORA), optimal power and rate adaptation (C _OPRA), channel inversion with fixed rate (CIFR) (C _CIFR), and truncated CIFR (C _TIFR). The derived expressions are valid for arbitrary values of the α, η, and μ fading parameters. The derived expressions can also be reduced to study the capacity of other fading distributions such as η − μ, α − μ, Weibull, and Nakagami. Numerical results are provided and compared with simulations to validate of the derived expressions.

In this study, the capacity problem in a two-way fading channel is considered. The authors propose amplify-and-forward with lattice codes (AF&LC), exploiting a modulo operation at the relay. Without destroying the construct of codebook, the modulo operation reduces the power of the received signals, and thus achieves a larger power-scaled gain than the amplified-and-forward with random codes (AF&RC). It is proved that AF&LC outperforms AF&RC in the high signal-to-noise ratio (SNR) regime by employing theoretical analyses. By simulating one-dimension lattice codes, AF&LC achieves about 1.5 dB SNR gain over AF&RC with error probability 10⁻². Compared with decode-and-forward with lattice codes (DF&LC) and fixed modulo-and-forward (FMF), AF&LC achieves larger rate region which is closer to upper bound in some scenarios. Moreover, AF&LC can work in fading two-way channels without feedback schemes, which is easier to be implemented than DF&LC, FMF and compress-and-forward.

Hidden-tag problem is one of the most important issues in the implementation of radio-frequency identification (RFID) systems. Due to effects of imperfect wireless channels, RFID tags can be hidden during the identification process by either another tag or an unsuccessful detection. The former is known as the capture effect (CE) while the latter is the detection error (DE). This study newly proposes two modified tree-based identification protocols, namely tweaked binary tree (TBT) and tweaked query tree (TQT), which are able to tackle the hidden-tag problem caused by both the CE and DE. The performance of the proposed TBT and TQT protocols, in terms of the average number of slots required to detect a tag, and the tag-loss rate, is evaluated in comparison with that of previously proposed ones. Computer simulations and numerical results confirm the effectiveness of the proposed protocols.

The computational complexity required in the channel estimation plays an important role in underwater acoustic communications (UAC) with orthogonal frequency duplex access (OFDM), especially when the channel is sparse. The authors develop an algorithm to carry out the orthogonal matching pursuit (OMP) for the sparse channel estimation based on the compressive sensing, where the goal is to obtain the minimum computational complexity. It is discovered that the inter-carrier interference (ICI) mainly depends on the adjacent subcarriers since the ICI interferences become more and more marginable with the increase of the distance from the other subcarriers to the current desired subcarrier in the frequency domain, which can be utilised to reduce the complexity of the design over the sparse channel estimator. By exploiting this property, the authors propose that the diagonal band of the ICI channel matrix is employed in the calculation of the objective function to minimise the required computational complexity, which develops an adaptive algorithm that is theoretically proved to be a faster algorithm. Numerical simulations are demonstrated for the typical UAC system that the proposed algorithm achieves the remarkable gain of the computational complexity compared to the existing algorithm.

To improve energy efficiency and spectral efficiency, massive multiple-input–multiple-output (MIMO) is proposed and becomes a promising technology in the next generation mobile communication. However, massive MIMO systems equip with scores of or hundreds of antennas which induce large-scale matrix computations with tremendous complexity, especially for matrix inversion in data detection. Thus, many detection methods have been proposed using approximate matrix inversion algorithms, which satisfy the demand of precision with low complexity. In this study, the authors focus on the approximate detection method based on Newton iteration (NI), and propose upgraded methods named NI method with iterative refinement (NIIR) and diagonal band NIIR (DBNIIR) which combine NI method and DBNI method with iterative refinement (IR). The results show that their proposals provide about 2 dB improvement on bit error rate (BER) for 16-quadrature amplitude modulation (QAM), and could even break the error floor existing in NI and DBNI methods for 64-QAM modulation. Furthermore, the BER of their proposals could provide almost the same performance as the exact method. Moreover, in contrast with NI and DBNI methods, NIIR and DBNIIR methods require quite few extra complexity cost and no extra hardware resource which is quite suitable for data detection in massive MIMO.

In large-scale computing centres, the advancement of knowledge in regard to the predicted power consumption (PC) and concerns of host servers that run virtual machines (VMs) could improve the capacity planning and networks’ energy efficiency. A parameterised power model is proposed to explore the individual components within the virtualisation-based cloud radio access network. The model evaluates the PC and trade-offs of a server undergoing virtualisation. After cooling and total PC for cloud radio access network architecture, with and without virtualisation have been compared using differentiated parameters, such as varying number of bare-metal base band units, VMs and system's resource blocks (RBs) share/bandwidth. The results show dramatic decrease in the total PC via virtualising the core network. In addition, the degraded performance of each virtualised server is demonstrated via modelling the execution time and overhead costs. These costs have been resulted from increasing the number of hosted VMs and utilised RBs by each VM.

This study presents the final implementation of a multiresolution acquisition structure, designed and tested for a long-distance ionospheric High Frequency link from Antarctica to Spain. It completes the receiver's physical layer design for this link of 12,760 km, improving the performance of the communications system. The goals of this proposal are to achieve a faster convergence, as well as a more robust and stable acquisition and demodulation. The multiresolution structure is based on least mean square adaptive filters. Acquisition is achieved when a filter converges to a decimated version of the Pseudo-Noise sequence. Convergence parameters have been adjusted for best performance in the long-haul time variant ionospheric channel. The information about channel's multipath dispersion is used to improve the direct sequence spread spectrum demodulation at the receiver. The results presented in this study are obtained from real data transmitted through the ionospheric Antarctic channel in the austral summer of 2008 and show the improvements in terms of stability, robustness and Bit Error Rate in the receiver. These improvements led us to choose this multiresolution acquisition structure for the receiver of the Antarctic HF system.