Due to their superior performance and moderate computational complexity, quasi-orthogonal space–frequency block codes (QOSFBCs) and quasi-orthogonal space–time–frequency block codes (QOSTFBCs) are among prime candidates for implementation in multiple-input multiple-output communications systems. Originally, these codes were introduced for two transmit antennas and it is proved that they are full-diversity within channels with equal-power profile and integer delays. In this study, first, the authors generalise the construction of QOSFBCs and QOSTFBCs for arbitrary number of transmit antennas. Then, they provide the theoretical proof that these codes are full-diversity for any arbitrary number of transmit antennas and any arbitrary delay and power profiles. Furthermore, they prove that the coding advantage of these codes is decomposed into two distinct parts, one of which represents the effect of the channel and the other represents the effect of the precoder. By optimising these two parts, they propose a modified version of QOSFBCs and QOSTFBCs which outperforms the latest space–frequency block codes and space–time–frequency block codes in the literature according to the analytical results and support of simulation results. Finally, they propose a new suboptimum linear decoder for the QOSFBCs and the QOSTFBCs which could achieve almost the same performance as the maximum-likelihood decoder for large number of subcarriers.

In this study, the authors focus on energy efficiency (EE) optimisation via the cooperation of small cell base stations (SBSs) in dense small cell networks (DSCNs) where the control plane (C-plane) and data plane (D-plane) are decoupled. They propose a coalition-based sleep mode and power allocation (CSMPA) scheme to improve the DSCN EE while guaranteeing the target rates of users and maintaining the system capacity. In the CSMPA scheme, the cooperation of SBSs is formulated as a coalitional game in partition form and a centralised heuristic coalition formation algorithm without power cost is developed to achieve the final stable coalition structure. Each SBS can serve users in the active subframes and sleep in those subframes without data transmission. Afterwards, since the interference between coalitions affects EE, a distributed price-based power allocation algorithm is presented to optimise the transmit power of active SBSs per subframe. System-level simulation results show that the proposed CSMPA scheme can yield less number of outage users and significant EE performance gain without jeopardising system capacity.

In this study, the authors investigate the resource allocation for orthogonal frequency-division multiple access (OFDMA) wireless networks, where the base station is powered by renewable energy and electric grid. To fully exploit the renewable energy, the authors propose an energy-aware resource allocation (EARA) algorithm to maximise the network utility, which captures the tradeoff between the system throughput and the grid energy consumption. Specifically, the EARA algorithm only has to track the current system states (e.g. channel and queueing conditions) without requiring a relevant priori distribution knowledge, making it applicable for practical OFDMA wireless networks with unpredictable channel dynamics, renewable energy arrivals and stochastic traffics. Moreover, the performance achieved by the EARA algorithm is theoretically characterised. Most importantly, the authors develop an implementation architecture to take the EARA algorithm into practice, and also analyse the low implementation costs (e.g. low computational complexity, trivial signalling overhead etc.). Finally, simulation results verify the theoretical analysis and also demonstrate the advantages of the EARA algorithm.

In this study, the authors apply the multi-objective optimisation (MOO) methods to the challenge posed by joint rate maximisation and total transmission power (TTP) minimisation in cooperative cognitive radio networks. The proposed MOO methods which are based on amplify and forward relaying strategy optimise the two conflicting objectives and, at same time, they maximise the rate quality and minimise the TTP allocated to the network relays simultaneously. The MOO problem under investigation is a non-convex non-linear combinatorial optimisation one that three MOO methods are presented for a desired solution. The explored methods are: weighted sum algorithm (which is based on a simplistic model), MOO fractional programming method (which has much in common with their MOO problem) and lexicographic algorithm (which is suitably adapted to complex combinatorial optimisation environments due to its robustness in avoiding trapping in local optima). Their simulation results confirm the proposed methods’ effectiveness for simultaneous rate maximisation and TTP minimisation as well as their superiority over counterpart algorithms.

Orthogonal frequency code division multiple access (OFCDMA) system is one of the most promising multi-user wireless communications systems. It outperforms orthogonal frequency division multiplexing (OFDM) and multi-carrier CDMA (MC-CDMA) through utilisation of two-dimensional spreading. This study proposes a downlink air interface that targets data rate increase and bit error rate (BER) performance enhancement with low complexity receiver. The proposed system is an integration of space-time spreading (STS) and OFCDMA that exploits transmit diversity needed for BER enhancing and data rate boosting with low complexity advantage. Further BER improvement was achieved using an effective iterative interference cancellation (IIC) algorithm at the receiver. Analytical and complexity analyses for the proposed system performance are presented in addition to simulation results. The proposed system attains better performance with lower complexity compared to the STS-aided direct sequence MC-CDMA that uses beamforming as a closed-loop transmit diversity. Moreover, the effect of frequency domain spreading factor with different number of iteration loops is investigated. The achieved BER performance was very close to maximal ratio receive combining system with 1Tx and 4Rx. A considerable improvement was obtained by increasing the number of IIC iteration loops. The system performance was enhanced significantly with the frequency domain spreading factor increase.

Cognitive femtocell has emerged as a promising technique for indoor users in wireless communication systems since they opt the cognitive and self-configuration capabilities. Spectrum sensing is a non-trivial approach in cognitive radio networks. A number of methods have been used for spectrum sensing in femtocells with a cognitive engine. In this work, the authors have proposed a near field source localisation technique through which the cognitive femtocell will be able to detect the active femtocells in the licensed band and will get additional information including the range, amplitude, angle and operating frequency of a certain femtocell.

In compressed sensing, an exact reconstruction depends on the restricted isometry property with a small restricted isometry constant (RIC). Based on singular value decomposition (SVD), the authors improve the RICs of measurement matrices. The proposed weighted measurement matrix method breaks the limit to attain a small RIC and has potential applications in imaging systems. They derive an improvement sufficient condition for the RIC for exact reconstruction using the orthogonal matching pursuit algorithm. The numerical stability of the SVD-based weighted equation is analysed. Simulation results show that the reconstruction results obtained by the proposed SVD-based weighted approach are obviously better than those obtained by the direct reconstruction. In the simulation, they also successfully apply the proposed weighted equation for a computed tomography reconstruction.

A coprime array has a larger array aperture as well as increased degrees-of-freedom (DOFs), compared with a uniform linear array with the same number of physical sensors. Therefore, in a practical wireless communication system, it is capable to provide desirable performance with a low-computational complexity. In this study, the authors focus on the problem of efficient direction-of-arrival (DOA) estimation, where a coprime array is incorporated with the idea of compressive sensing. Specifically, the authors first generate a random compressive sensing kernel to compress the received signals of coprime array to lower-dimensional measurements, which can be viewed as a sketch of the original received signals. The compressed measurements are subsequently utilised to perform high-resolution DOA estimation, where the large array aperture of the coprime array is maintained. Moreover, the authors also utilise the derived equivalent virtual array signal of the compressed measurements for DOA estimation, where the superiority of coprime array in achieving a higher number of DOFs can be retained. Theoretical analyses and simulation results verify the effectiveness of the proposed methods in terms of computational complexity, resolution, and the number of DOFs.

The non-equilibrium of network traffic data brings about the non-equilibrium of classification. Feature extraction is an effective method to reduce data dimensions, while it can intensify the influence of non-equilibrium further. A secondary feature extraction algorithm of multidimensional assessment is proposed in this study. The features of network traffic are evaluated in different dimensions to provide the basis for feature extraction. Furthermore, a model dealing with imbalanced data is proposed based on secondary feature extraction and sampling. The model combines the benefits of dimension reduction and redistribution. The experiment results show that the proposed model can not only increase classification accuracy and decrease non-equilibrium, but also enhance the performance of different classification algorithms.

Millimetre-wave propagation is a promising broadband transmission technology for future fifth generation mobile communication systems. For a vector orthogonal frequency division multiplexing system, the authors investigate the millimetre-wave propagation through a sparse multipath channel in a sense that it has a large time delay spread but with only a few non-zero taps. By exploiting the sparse nature of millimetre-wave channel, any sparse multipath channel can be characterised by the multipath delays and their corresponding channel coefficients. They first study an ideal case that the pilot signals are transmitted through a sparse channel without noise, and an exactly sparse inverse fast Fourier transform (SIFFT) algorithm is performed to estimate the non-zero channel taps with reduced complexity. Then, they consider a more practical scenario that the pilot signals through a sparse channel with noise interference, and an approximately SIFFT algorithm is employed to estimate the effective channel taps, while the remaining small coefficients interfered by noise can be wiped out. Through numerical analysis, they demonstrate that the proposed SIFFT algorithms can reduce the computational complexity while keeping the root mean squared error of channel estimation at a low level.

Device-to-device (D2D) communications are widely seen as an efficient network capacity scaling technology. The co-existence of D2D with conventional cellular (CC) transmissions causes unwanted interference. Existing techniques have focused on improving the throughput of D2D communications by optimising the radio-resource management and power allocation. However, very little is understood about the impact of the route selection of the users and how optimal routing can reduce interference and improve the overall network capacity. In fact, traditional wisdom indicates that minimising the number of hops or the total path distance is preferable. Yet, when interference is considered, the authors show that this is not the case. In this study, they show that by understanding the location of the user an interference-aware-routing (IAR) algorithm can be devised. They propose an adaptive IAR algorithm that on average achieves a 30% increase in hop distance, but can improve the overall network capacity by 50% whilst only incurring a minor 2% degradation to the CC capacity. The analysis framework and the results open up new avenues of research in location-dependent optimisation in wireless systems, which is particularly important for increasingly dense and semantic-aware deployments.

Machine learning (ML) techniques have been widely applied in recent traffic classification. However, the flow-level statistics are prone to improve the accuracies for some applications; however, to reduce the accuracies for others. To address the problem, the authors propose a cascaded traffic classifier that is composed of both several binary sub-classifiers and a multiclass sub-classifier. The authors first present theorems that show how to make an optimal cascade of sub-classifiers, and then design a cascaded classification algorithm for improving the accuracy of flow-level traffic classification. In addition, to improve the classification speed, the authors propose a parallel scheme for the cascaded classifier. The authors evaluate their approaches on the traces captured from entirely different networks. Compared with the previous multiclass traffic classifiers built in one-time training process, the cascaded classifier is superior in terms of the overall accuracy and the accuracy for each application.

In this study, a new cooperative spectrum sharing protocol based on overlay system paradigm is proposed for different relaying strategies. In the considered model, a single-antenna primary transmitter (PT) communicates with a primary receiver (PR) by the assistance of a secondary transmitter (ST) which seeks an opportunity to realise spectrum sharing by adopting spatial modulation (SM). The proposed protocol consists of two time slots. In the first time slot, primary M-ary modulated signal is transmitted from PT to ST and PR. ST processes the received signal according to the relaying strategy it adopts and transmits the processed signal by SM to PR and secondary receiver (SR) in the second time slot. Thanks to SM at ST, primary information is carried by conventional M-ary modulation whereas secondary information is conveyed by antenna indices. Therefore, the need for superposition of different users' information and power allocation at ST are avoided. Upper bounds on bit error probability according to three different relaying strategies: fixed and incremental amplify-and-forward, and selective decode-and-forward, are derived for both users. The theoretical results are supported via computer simulations and error performance comparisons with two existing reference systems are performed to show the superiority of the proposed protocol.

Non-orthogonal multiple access (NOMA), which uses the power domain for multiple users, has been considered as an emerging candidate for fifth generation downlink transmission. Energy harvesting (EH), a promising technology to prolong the lifetime of energy-constrained wireless networks, has attracted significant attention. In this study, the authors first combine NOMA with EH (NOMA–EH) to enhance system spectral efficiency and user fairness. An NOMA-based relaying network is analysed, where the relay node is equipped with EH device, and functions as the bridge between the base station and the multiple users. The outage performance of the NOMA–EH scheme-based relaying network is studied, where the transmitting antenna selection is applied at the base station and maximal ratio combining is applied at the multiple users. Furthermore, closed-form expression for the system outage probability is also derived. On the basis of the computer simulations, they show the correctness of the numerical analysis and confirm the superiority of the proposed NOMA–EH scheme.

Minimum mean square error (MMSE)-based techniques are often used for the joint iterative detection and decoding that is for coded multi-input–multi-output (MIMO) system due to a sound complexity and the performance trade-off. This study proposes an enhanced MMSE-based soft MIMO-detection scheme by using three main ideas. The first idea is an efficient complexity-reduced soft-bit estimation technique, the second one is a performance improvement method utilised inside the MMSE detection process, and the third one is a complexity-reduced soft-symbol estimation method for quadrature amplitude modulation. The proposed ideas enable the interference-cancellation processes to be activated in parallel on each symbol layer, thereby reducing the processing time. The simulation results show that the proposed method efficiently contributes to the improvement of the performance in addition to its reduction of the linear-order complexity.

This study presents cross-correlation of building density in outdoor environments for fifth generation mobile communications at 781 MHz. To avoid interference from Korean digital television (DTV) broadcasting, the authors measured the radio characteristics on Jeju Island using a channel sounder and four-by-four antennas. They derived basic large-scale parameters (LSPs) such as path loss, delay spread, angular spread, and Rician K-factor from eight different sites. The real building standing area was estimated with a geographic information system database. They propose an empirical model from analysis of the correlations between LSPs and building density. This model indicates that building density is not determined by measurement scenario such as urban, suburban, and rural. The proposed model only reflects change in building density for an outdoor environment. Finally, they verify their model through ray-tracing simulation similar to a real environment. This model can be useful in the design of future mobile communications systems according to changes in building density.

This study proposes a novel approach to joint channel estimation and detection of orthogonal frequency division multiplexing transmission over underwater acoustic (UWA) multipath channels exhibiting cluster sparsity. Unlike most sparse channel estimations, the authors exploit the cluster-sparsity characteristic of UWA channels without additional prior information. They adopt a modified spike-and-slab prior model in their non-parametric Bayesian learning framework. To avoid the need for a closed-form Bayesian estimate, they apply the Markov chain Monte Carlo technique to joint achieve channel estimation and signal detection. The proposed solution is amenable to being integrated with soft-input soft-output decoding to improve the performance through turbo iteration. Simulation results demonstrate improved bit error rate of the proposed algorithm over existing algorithms.

The third generation partnership project (3GPP) long-term evolution (LTE) cellular system offers high data rate capabilities by leveraging several techniques including link adaptation and frequency selective scheduling. These techniques rely on accurate channel quality indicator (CQI) feedback reports that are sent by the user equipment (UE) to the evolved node B, a process which results in high signalling overhead. In this study, the authors propose a UE-assisted sub-band feedback compression scheme based on a predictive filtering technique to reduce this signalling overhead. Four schemes based on adaptive filters have been designed, implemented and tested in an LTE system level simulator. Simulation results indicate that the proposed compression scheme has shown efficacy with an overall CQI feedback signalling reduction of up to 92.5% whilst maintaining stable sector throughput, when compared with the standard 3GPP CQI feedback mechanism. Although the proposed scheme exhibits low computational and memory complexity, a reduced-complexity scheme achieving an average computational load reduction of up to 35% is also presented.

Many approaches to build a wiretap channel (WTC) by multi-input multi-output system have been introduced. Different from those approaches, the authors propose a feedback method combined with the low-density parity-check (LDPC) codes for building the WTC I (WTC-I) to achieve unconditional security under single-input single-output system by the soft decision decoding. The novel approach establishes the WTC-I on both the binary symmetric channel and binary input additive white Gaussian noise channel. In order to keep the eavesdropper be fully ignorant about the secret information, randomness is added to the feedback signals from the destination by taking advantage of feedback. In addition, the message to be sent is encoded by the LDPC codes such that it can be correctly decoded by a legitimate receiver. Furthermore, the secret information transmission capacity can be improved by the soft decision decoding.

The Internet continues to flourish, while an increasing number of network applications are found deploying Bloom filters. However, the heterogeneity of the Bloom filter realisations complicates the utilisation of relevant applications. Moreover, when applying Bloom filter to traffic that usually has a gigabit capacity, even insignificant delays will accumulate and restrict the effectiveness of the real-time protocols. In this study, the authors present a Bloom filter construction that can be easily and consistently adopted at network nodes, with also considerable processing speed. Specifically, the authors show that AES-based hashes are adequate to create Bloom filters correctly. Then they illustrate how AES new instructions (AES-NI) can be leveraged to accelerate the Bloom filter realisation. According to the authors' experimental results, the proposed Bloom filter enables the best speed performance compared to the competing approaches.