Traditional compressive sensing (CS) methods assume the data sparsity to be constant over time, which holds well in many long-term scenarios. However, the authors’ recent study on electrocardiogram (ECG) monitoring reveals that data sparsity varies dramatically for real-time monitoring systems where the data latency must be bounded, due to limited data collected within the delay bound. The variation of data sparsity makes the reconstruction error (RE) unstable. Furthermore, the variation of wireless channel quality also impacts the reconstruction quality. To accommodate both variations, this study proposes a novel adaptive feedback architecture for real-time wireless ECG monitoring based on the CS technique, which can bound the REs in the presence of the variations of data sparsity and wireless channel. An experiment testbed has been built to evaluate the performance of the proposed system. The results show that the data latency can be limited to <300 ms and the RE can be controlled to 9%.

In this study, a problem of efficient spectrum sensing and power allocation is studied. A network of cognitive radio adopting the technique of two-way relay is considered. A relay node, sitting between two secondary users, carries out spectrum sensing and helps the two secondary users to realise bidirectional relay communications. For such a system, an optimisation problem which targets at maximising the average rate between the two secondary users while guaranteeing the detection probability above a predefined threshold, is formulated. Although the problem is shown to be non-convex which means the global optimal solution is hard to be obtained, the authors solve the formulated problem global optimally by using a combination of bilevel optimisation and monotonic programming. Numerical results are provided to present the effectiveness of the authors’ proposed algorithm.

In this study, a rate-varying space-time pre-coding scheme for a multiple-input multiple-output system in a fast-fading channel is proposed. In particular, unlike the traditional fixed rate space-time coding scheme, the transmitter which has no channel state information keeps choosing a pre-coding matrix randomly at each space-time symbol period to deliver the information until it is successfully decoded by the receiver. Since the number of received symbol vectors needed to successfully decode the information is determined by the channel realisation, the coding rate of the proposed scheme can adapt to the fast-fading channel. The exact expression of the equivalent signal-to-noise ratio gain after the post-combining and zero-forcing equalisation at the receiver side is derived, and a design criteria to achieve optimal performance in terms of average error probability and ergodic capacity under the mean power constraint is provided. Based on this, a minimal variance pre-coding (MVP) scheme is designed, which is compared with an intuitive Gaussian pre-coding scheme. It is shown that the MVP scheme has better performance and could adapt to different channel conditions.

Vehicular communication for accident avoidance is an important application defined in the IEEE 802.11p. One of the challenges of vehicular communications in the current IEEE 802.11p standard is the complex and rapidly changing channel conditions. In this study, the author focusses on the vehicle-to-vehicle channels under the IEEE 802.11p standard and point out that there exist two different kinds of fading. Then, a new dedicated short range communication (DSRC) system with two improved methods is proposed for the weak performance of anti-fading of current system in IEEE 802.11p environments. The author adopts matrix spectral transformation method to replace least squares algorithm in spectral temporal averaging (STA) scheme proposed by Fernandez to further mitigate the negative effects of DSRC system caused by fast time-varying fading. Meanwhile, the author also proposes a spectral keying method to further enhance its ability of anti-frequency-selective fading. The final simulation results show that compared with STA and other methods, the improved scheme shows better performance on mean square error, bit error rate and packet error rate, which is promising for practical applications.

This study investigates the downlink resource allocation problem in orthogonal frequency division multiple access heterogeneous networks consisting of macrocells and femtocells sharing the same frequency band. The focus is to devise optimised policies for femtocells' access to the shared spectrum, in terms of femtocell transmissions, in order to maximise femto-users (FUEs) sum data rate while ensuring that certain level of quality of service (QoS) for the macro-cell users in the vicinity of femtocells is provided. The optimal solution to this problem is obtained by employing the well-known dual Lagrangian method and the optimal femtocell transmit power and resource allocation solution is derived in detail. However, the optimal solution introduces high computational complexity. To this end, a heuristic solution to the problem is proposed. The algorithms to implement both optimal and efficient suboptimal schemes in a practical system are also given in detail while their complexity is compared. Simulation results show that proposed dynamic resource allocation scheme (a) ensures the macro-users QoS requirements compared with the Reuse-1 scheme, where femtocells are allowed to transmit at full power and bandwidth; (b) can maintain FUE data rates at high levels; (c) provides performance close to the optimal solution, while introducing much lower complexity.

An optimal multi-slot channel sensing schedule is proposed in this study that considers an opportunistic spectrum access with the awareness of channel switching delay and imperfect sensing. A practical case is considered where channel availability statistics are usually correlated in time slots and in frequency channels. The switching delays between channels, hardware constraints, and collision with other cognitive users are considered to find an optimal sensing order of the channels that maximises throughput of cognitive user. The optimal sensing order is obtained using the partially observable Markov decision process framework. Throughput of cognitive user, with and without channel sensing errors, is analytically derived and for each case an algorithm is developed. The proposed scheme mitigates the effect of channel sensing errors on the throughput. Performance of the proposed scheme is evaluated through simulations by comparing it with the existing schemes in the literature.

Cloud computing is a promising computing paradigm which has drawn extensive attention. Serious concerns over the reliability and confidentiality of the outsourced data are arising. Traditional encryption methods can, guarantee data confidentiality, however, it also limits the cloud's functionality as few operations are supported over encrypted data. In this study, the authors construct an enhanced cloud that not only provides secure and robust data storage, but also supports the functionality that the cipher data can be forwarded without being retrieved back. Specifically, they design an all-or-nothing-transform-based encryption and a variant of ElGamal-based proxy re-encryption algorithms, blending them with Reed–Solomon code, the authors’ scheme is quite more efficient compared with previous studies because it only needs to update partial data blocks instead of the whole file for data forwarding. Besides, the authors’ scheme also satisfies another practical property that the original data owner can no longer decrypt or forward the re-encrypted data to others after a complete forwarding instance, which is termed to be ‘original inaccessibility’ in this study. Analysis shows that the authors’ scheme is secure and satisfactory. Finally, the authors theoretically and experimentally evaluate its performance and the results indicate that their scheme is efficient during file dispersal, forward and retrieval.

A definition of least cyclostationary order (LCO) is proposed based on the inner cyclic period of single carrier and orthogonal frequency division multiplexing (OFDM) signals. The LCOs of single carrier and OFDM signals are derived. Since the LCO has a relationship with the carrier frequency offset (CFO), a blind CFO estimation scheme for single carrier and OFDM signals can be proposed. The novel scheme is entirely blind without any priori information and is proved to be the same as cumulants estimation when the LCO is known. However, the new scheme is superior to the estimation based on cumulant when the LCO is unknown. The mean-square error of M-phase shift keying signals approaches the Cramér–Rao bound in additive white Gaussian noise (AWGN) channel. Simulation results are given to verify the effectiveness of the proposed blind estimation scheme in Rician fading channel and in AWGN channel.

Underwater acoustic communications (UAC) feature frequency-dependent signal attenuation, long propagation delay and doubly-selective fading. Thus the design of reliable UAC protocols is challenging. On the other hand, cooperative relay communications, which have been extensively studied in terrestrial environments, are promising paradigms for reliable communications. However, their application to UAC has not been thoroughly explored. In this study, the authors will design an asynchronous relaying protocol to achieve reliable underwater communications. This new scheme accounts for and takes advantage of the unique characteristics of UAC channels. To avoid time synchronisation difficulty in UAC, and facilitate energy-efficient relay processing, asynchronous amplify-and-forward relaying is adopted in the protocol. In addition, precoded orthogonal frequency division multiplexing is chosen to address the frequency selectivity issue in UAC, while collecting ample multipath diversity provided by the channel and enabled by the asynchronous relaying design. To demonstrate the performance of the protocol, the end-to-end signal-to-noise ratio is derived, the average pair-wise error probability is evaluated and the maximum collectable diversity is also proven. Simulations and comparisons are presented to corroborate the analyses and design.

In practical multiple input multiple output (MIMO) systems, accurate knowledge of the channel state information (CSI) is a prerequisite to guarantee the system performance. The conventional CSI acquisition methods for MIMO system usually rely on the orthogonal (either time- or frequency-domain) training sequences (TSs) to estimate the channel associated with each transmit–receive antenna pair, which is not spectrally efficient. This study proposes a non-orthogonal time-domain training-based CSI acquisition approach for MIMO systems under the framework of structured compressive sensing. By exploiting the spatial–temporal correlations of the sparse MIMO channels, a spatially–temporally spARsity-adaptivE-simultaneous orthogonal matching pursuit algorithm is proposed, which could use the inter-block interference free region of very small dimension within the received TS to recover the multiple channels. Furthermore, the proposed algorithm could utilise the priori channel partial common support to improve the recovery probability and reduce the complexity. Simulation results show that the proposed scheme has better performance and higher spectral efficiency than the conventional MIMO schemes, which might be an appealing solution for the future wireless communications.

In this study, the authors consider simultaneous wireless information and power transfer (SWIPT) in a multiple-input-single-output (MISO) downlink system, where power splitting scheme is considered for each user of this system. Since channel state information of each user cannot be available at the transmitter, robust beamforming for SWIPT in the MISO downlink system is presented by incorporating with different types of channel uncertainty models. The authors first formulate the robust power minimisation problem subject to the signal-to-inference-plus-noise ratio (SINR) and energy harvesting (EH) constraints by incorporating two Gaussian channel uncertainties. The original problem is not convex in terms of channel uncertainties, and cannot be solved efficiently. The authors employ the well-known Bernstein-type inequality and Gaussian error function to make probability based constraints tractable, respectively, in order to recast the original problem as the convex form. Moreover, the robust power minimisation problem with the probability based SINR and EH constraints is formulated by incorporating random distribution with known error mean and covariance matrix. By exploiting conditional value-at-risk functional and semi-definite relaxation, this optimisation problem is relaxed as the convex form. Finally, numerical results are provided to validate the performance of these proposed robust schemes.

In this study, the authors conduct the performance evaluation and resource allocation for two-way multiple-input multiple-output-based device-to-device (D2D) communications overlaying a cellular network. The considered system model comprises of a base station, a mobile user, and a pair of D2D users, where each of them is equipped with multiple antennas and employs transmit/receive zero forcing. Based on overlaying approach, both D2D and cellular communications take place bidirectionally by employing analogue network coding with a best selected D2D user as two-way relay for the cellular links. The authors analyse the outage performance of cellular and D2D systems in a Rayleigh fading environment. They also deduce asymptotic outage behaviour of cellular system and highlight the achievable diversity order. In addition, they derive the expressions of ergodic sum rate for both the systems. Above all, they provide a selection algorithm for the best relaying D2D user and describe feasible range of power splitting factor to maximise the data rate of cellular system for a given data rate of D2D system. Numerical and simulation results validate the authors’ theoretical findings and illustrate the performance gains of the considered scheme under various antenna configurations.

Protocol message format extraction is a principal process of automatic network protocol reverse engineering when target protocol specifications are not available. However, binary protocol reverse engineering has been a new challenge in recent years for approaches that traditionally have dealt with text-based protocols rather than binary protocols. In this study, the authors propose a novel approach called PRE-Bin that automatically extracts binary-type fields of binary protocols based on fine-grained bits. First, a silhouette coefficient is introduced into the hierarchical clustering to confirm the optimal clustering number of binary frames. Second, a modified multiple sequence alignment algorithm, in which the matching process and back-tracing rules are redesigned, is also proposed to analyse binary field features. Finally, a Bayes decision model is invoked to describe field features and determine bit-oriented field boundaries. The maximum a posteriori criterion is leveraged to complete an optimal protocol format estimation of binary field boundaries. The authors implemented a prototype system of PRE-Bin to infer the specification of binary protocols from actual traffic traces. Experimental results indicate that PRE-Bin effectively extracts binary fields and outperforms the existing algorithms.

In received signal strength (RSS)-based indoor wireless localisation system, radio pathloss model or radio map must be readily obtainable. However, the unpredictability of wireless channel makes it difficult to achieve high accuracy localisation in practice. In this study, the authors employed a multi-layer neural network (MLNN) for RSS-based indoor localisation without using the radio pathloss model or comparing the radio map. The proposed MLNN localisation system integrate the RSS signals transforming section, the raw data denoising section and the node locating section into a deep architecture. Furthermore, a boosting method is designed to promote location accuracy of the MLNN effectively. Experiment results demonstrate the feasibility and suitability of the proposed algorithm.

Human body communication (HBC) is a new technology for mobile and wearable devices operating over body area networks. In HBC, the body is used as a medium for transmitting data between devices to replace wired and wireless technologies. This study examines the bit error rate (BER) degradation experienced by HBC systems under the presence of co-channel interference. A degradation model is proposed to estimate the change in BER, and a selective simulation method is proposed to obtain the BER performance with a small exceedance probability and a small number of BER samples.

In this study, a new approach is developed to facilitate faster decoding of a binary systematic (71, 36, 11) quadratic residue (QR) code. In this decoder, it simplifies the step of calculating the condition and avoids calculating the unknown syndrome, thereby yielding a fast algebraic decoder for correcting four possible errors. Moreover, while using the proposed algorithm, if uses the channel measurement information proposed by Chase to sequentially invert the bits of the received word until one of the errors is cancelled for the five-error case and apply the new algebraic decoding algorithm mentioned above to correct the remaining four errors, the algorithm has been verified through a software simulation in C-language. The simulation shows that the decoding scheme developed here is more efficient than the previous decoding algorithm developed for the (71, 36, 11) QR code and it is naturally suitable for software implementation.