In this study a new approach to filter-and-forward (FF) relay design in an overlay cognitive radio (CR) network with frequency selective channels is proposed. In this system, to implement two-way relaying scheme, a secondary multi-antenna node acts as a relay and helps two primary users (PUs) communicate with each other and in return the secondary node superposes its signal to filtered signals of PUs. The authors intend to design secondary transceiver under two criteria, one from the viewpoint of improving the performance of secondary network and the other to guarantee quality of service (QoS) of primary network. The first criterion is to maximise the signal-to-interference-plus-noise ratio (SINR) of secondary receiver subject to constraints on QoS of PUs and the relay transmitted power. For the other one, the goal is to achieve max-min fairness among PUs through maximising the worse signal-to-interference-plus-noise ratio (SINR) of PUs subject to constraints on QoS of secondary receiver and transmit power of relay. The authors employ an alternating optimisation algorithm to find the beamforming matrices and the allocated power to secondary receiver's message in these non-convex problems. Simulation results show that the proposed FF approach has better performance compared with simple AF relaying strategy.

In this study, the authors propose an efficient peak-to-average power ratio (PAPR) reduction system for OFDM transmission based on tone reservation (TR). In this system, a novel cost function to jointly optimise the clipping level and the peak-cancelling signal is proposed. The obtained optimal clipping level can not only render the clipping noise to be well approximated by the reserved tones, but also be small enough to deeply suppress the peaks. In addition, a new cyclic method to optimise the peak-cancelling signal and the clipping level is presented with the insight that when optimising the reserved tone values, the time-domain signal to be thresholded itself depends on the peak-cancelling signal instead of being constant, as assumed by many other methods. Simulation results show that the proposed system significantly improves the PAPR reduction performance over the state-of-the-art clipping-based TR algorithms.

Iterative channel estimation and decoding over flat Rayleigh fading channels have been shown in literature to improve the performance of receivers in wireless communication systems. Turbo and low density parity check codes have mostly been used as the forward error correction (FEC) scheme in the iterative receiver structures. This study proposes a decision directed iterative channel estimation and decoding receiver using Reed–Solomon codes. In particular, the Koetter and Vardy, Reed–Solomon (KV–RS) soft decision decoder is adopted in the receiver structure as the FEC scheme. Two methods of deriving a priori information in joint iterative channel estimation and decoding receiver structures are also analysed in this study. The first method derives the a priori information employing the log likelihood ratio (LLR) method. A priori information derived using the LLR method is mostly used in iterative receiver structures in the literature. The second method derives the a priori information employing the distance metric (DM) method. The DM method is proposed in this study as an alternative approach to the LLR method of deriving a priori information in the iterative receivers. Hard and soft feedback information from the KV–RS decoder is considered to verify the performance of these methods. The performance of the two a priori information methods is documented through computer simulation assuming an M-ary quadrature amplitude modulation system. Simulation results verify the improvement in symbol error rate (SER) performance in the KV–RS iterative receiver structure in comparison with the KV–RS receiver without feedback. Also, the proposed DM method exhibits the same SER performance in comparison with the LLR method which is mostly used in the literature. More importantly, the proposed DM method has the advantage of less computational delay and time complexity compared with the LLR method.

Spectrum auction is an emerging economic scheme to stimulate both primary spectrum operators (POs) and secondary users (SUs) to be involved in spectrum sharing. Previous spectrum auction works mostly assume each PO can only have one type spectrum or each SU can only buy homogeneous spectrum bands from the same PO. However, in a ubiquitous network scenario, each PO possesses heterogeneous spectrum resources such as WiFi, 3G and each SU may request different types of spectrum bands from the same PO. Existing auction schemes cannot be used to effectively solve the problem. Therefore, the authors come out with a lightweight combinatorial double auction to tackle this challenge. Since spectrum combinatorial double auction problem is NP-hard, the authors develop a general greedy algorithm G-Greedy to solve the problem. Inspired by the recent group-buying discounts, they also invent an enhanced scheme E-Greedy to further optimise total utility. They theoretically prove the economy properties of the proposed schemes such as individual rationality, budget balance and truthfulness. Simulation results show that both of the two algorithms can yield higher utilities and are effective.

Fractional repetition (FR) codes are a family of repair-efficient storage codes that provide exact and uncoded node repair at the minimum bandwidth regenerating point. The advantageous repair properties are achieved by a tailor-made two-layer encoding scheme which concatenates an outer maximum-distance-separable (MDS) code and an inner repetition code. In this study, the authors generalise the application of FR codes and propose heterogeneous fractional repetition (HFR) code, which is adaptable to the scenario where the repetition degrees of coded packets are different. The authors provide explicit code constructions by utilising group divisible designs, which allow the design of HFR codes over a large range of parameters. The constructed codes achieve the system storage capacity under random access repair and have multiple repair alternatives for node failures. Further, the authors take advantage of the systematic feature of MDS codes and present a novel design framework of HFR codes, in which storage nodes can be wisely partitioned into clusters such that data reconstruction time can be reduced when contacting nodes in the same cluster.

In this study, the authors propose a polar coding (PC) scheme for the power line communication (PLC) system to cope with the impulsive noise and thereby promote the transmission performance. This new error-correcting coding scheme is essentially inspired on a novel conception of channel polarisation. To be specific, via recursively channel combing and splitting, a group of channels with ideal transmission conditions, that is having a capacity of 1, will be constructed to carry the useful information, while the other band sub-channels bear useless information. The decoding performance of PC under realistic impulsive noises is investigated under the condition that the impulsive noise is modelled by a well-known Middleton Class-A model. To mitigate the error propagation caused by sudden strong impulsive noises and further enhance the decoding performance, a matrix interleave operator is integrated. Simulations validate the suggested PC scheme in PLC systems. Compared with another commonly used low-density parity-check (LDPC) coding scheme, the suggested PC scheme, which has the low complexity, can significantly improve the bit error rate (BER) performance of PLC transmissions with impulsive interference. The PC scheme, as demonstrated by simulation results, can be of great importance to practical PLC systems.

In this study, the authors propose a joint iterative detection and decoding (JIDD) method for a turbo coded multiple-input multiple-output (MIMO) system, with a linear order of complexity. Accurate estimation of soft information should be conditioned for excellent performance of the JIDD, but it usually requires an exponential order of complexity. They propose a method which improves the performance of soft interference cancellation minimum mean-squared error (SIC-MMSE) method by increasing the reliability of the soft information utilised for interference cancellation. The proposed method utilises a posteriori probabilities from the MIMO detector as well as a priori probabilities from the turbo decoder, and perform soft minimum mean-squared error filtering for symbol level detection. With this approach, soft information is fully fed into the symbol detection process, and thus the reliability of soft symbol is increased. In addition, they can separate out the bit-level soft estimation process by using a simple linear method. Simulation results show that the proposed method provides substantial complexity reduction, with a bit error rate performance comparable to the conventional SIC-MMSE methods.

Low-density parity-check (LDPC) codes from affine permutation matrices, called APM-LDPC codes, are a class of LDPC codes whose parity-check matrices consist of zero matrices or APMs of the same orders. APM-LDPC codes are not quasi-cyclic (QC), in general. In this study, necessary and sufficient conditions are provided for an APM-LDPC code to have cycles of length 2l, l ≥ 2, and a deterministic algorithm is given to generate APM-LDPC codes with a given girth. Unlike Type-I conventional QC-LDPC codes, the constructed (J, L) APM-LDPC codes with the J × L all-one base matrix can achieve minimum distance greater than (J + 1)! and girth larger than 12. Moreover, the lengths of the constructed APM-LDPC codes, in some cases, are smaller than the best known lengths reported for QC-LDPC codes with the same base matrices. Another significant advantage of the constructed APM-LDPC codes is that they have remarkably fewer cycle multiplicities compared with QC-LDPC codes with the same base matrices and the same lengths. Simulation results show that the binary and non-binary constructed APM-LDPC codes with lower girth outperform QC-LDPC codes with larger girth.

In this study, the authors consider a cooperative communication framework based on one-way and two-way relays to provide secure communications for secondary users (SUs) within an orthogonal frequency-division multiple access-based underlay cognitive network. By proposing a radio resource allocation problem with the aim of maximising the secrecy sum-rate of SUs and solving it, they show that the deployment of relays is vital to achieve a non-zero secrecy sum-rate. Also, the impact of two-way relay in system performance improvement is clearly visible in comparison with one-way relay such that it can roughly double the resulting system secrecy sum-rate. The impact of different system parameters on the achievable secrecy sum-rate for both one-way and two-way relays is also investigated and compared through simulations.

In this study, the authors propose a cooperative transmission scheme with energy-harvesting (EH) relays under wiretapping of an eavesdropper. In this scheme, the relays harvest energy from radio-frequency (RF) signals of a source through power-splitting receivers, and process arrived signals by amplify-and-forward (EH-AF protocol) and decode-and-forward (EH-DF protocol) technologies. In the proposed protocols, a best relay is selected at the destination so that the end-to-end achievable secrecy rates are maximal. Exact and asymptotic secrecy outage probability expressions are used to evaluate the EH-AF and EH-DF protocols, respectively, and are confirmed by Monte Carlo simulations. The simulation results show that (i) the EH-DF protocol outperforms the EH-AF protocol, and both proposed protocols are improved when the number of cooperative relays and the distances of the relay–eavesdropper links increase and the relays move toward the destination, (ii) the secrecy performance of the EH-AF and EH-DF protocols is the best at optimal power-splitting ratios, which are obtained by the golden section search method, (iii) the energy conversion efficiency and the noise at RF-to-baseband conversion units seriously affect the proposed protocols, and finally, (iv) the theoretical analysis results fit those of the Monte Carlo simulations.

Single-frequency network (SFN) can provide multimedia broadcast multicast service (MBMS) over a large coverage area, so it receives more and more attention from both academia and industry. However, the application of SFN is still restricted by a large number of feedbacks. Therefore, the authors propose a novel multicast resource allocation algorithm based on limited feedback scheme. In the algorithm, they first design a user limited feedback scheme based on channel gain threshold to effectively reduce feedback load. The scheme determines to which base stations users should report channel state information. Next, to overcome the MBMS capacity limitation drawback, they encode the MBMS data into a base layer and multiple enhancement layers and develop a joint subcarrier and power allocation strategy to maximise the throughput of enhancement layers while guaranteeing the rate requirement of base layer. Simulation results show that the proposed algorithm significantly reduces 83% of the feedback overhead while achieving a comparable multicast throughput performance to the case of full feedback.

Aiming at the inefficiency of polynomial time algorithm of linear network coding (NC) for multicast applications, the authors propose an improved algorithm which combines polynomial time algorithm with subtree decomposition. The algorithm is composed of five steps, including line graph transforming, subtree decomposition, static subtree set generating, assigning of global coding vector, and calculation of local coding vector. Subtree decomposition decreases network scale and coding complexity steeply, so it is an efficient algorithm of linear NC for multicast. An example is given to illustrate the implementation of the algorithm. Detailed analysis to algorithm complexity and a group of numerical experiments are made to show the efficiency of the algorithm.

The high peak-to-average power ratio (PAPR) of the transmitted signal is a major drawback of multicarrier transmission such as orthogonal frequency-division multiplexing OFDM. A plethora of PAPR reduction techniques has been reported in the literature. Some of the techniques modify the phase and/or amplitude of symbols in the original symbol alphabet (SA) such as selected mapping and partial transmit sequences techniques. However, such methods have shortcomings of a heavy computational burden caused by required multiple inverse fast-Fourier transform (IFFT) operations and bit error rate performance degradation due to side information (SI). In this study, a low-complexity PAPR reduction framework is proposed to jointly modify phase and amplitude values of the original symbols in the alphabet. This framework utilises only one IFFT/FFT operator pair for transmultiplexing of symbols without any SI. The merit of the proposed method to design a SA modifier matrix (SAM) for PAPR reduction is shown through performance comparisons for the application scenarios presented in this study.

In this study, the authors propose simple methods to evaluate the achievable rates and outage probability of a cognitive radio (CR) link that takes into account the imperfectness of spectrum sensing. In the considered system, the CR transmitter and receiver correlatively sense and dynamically exploit the spectrum pool via dynamic frequency hopping. Under imperfect spectrum sensing, false-alarm and miss-detection occur which cause impulsive interference emerged from collisions due to the simultaneous spectrum access of primary and cognitive users. That makes it very challenging to evaluate the achievable rates. By first examining the static link where the channel is assumed to be constant over time, they show that the achievable rate using a Gaussian input can be calculated accurately through a simple series representation. In the second part of this study, they extend the calculation of the achievable rate to wireless fading environments. To take into account the effect of fading, they introduce a piece-wise linear curve fitting-based method to approximate the instantaneous achievable rate curve as a combination of linear segments. It is then demonstrated that the ergodic achievable rate in fast fading and the outage probability in slow fading can be calculated to achieve any given accuracy level.

Large-scale multiple-input multiple-output (MIMO) system is considered one of promising technologies to realise next-generation wireless communication system (5G). So far, channel estimation problem is a big obstacle to develop large-scale MIMO system design due to high computational complexity and curse of dimensionality, which are caused by the long delay spread as well as a large number of antennas. Hence, devising any low-complexity channel estimation method could promote the successful development of the large-scale MIMO system. Due to the fact that, large-scale MIMO channels often exhibit sparse or/and cluster-sparse structure, in this study, the authors propose an effective low-complexity large-scale MIMO channel estimation method by using affine combination of sparse adaptive filtering filters. First, problem formulation and standard affine combination of adaptive least mean square (LMS) filters are introduced. Then they propose an effective affine combination method with two sparse LMS filters and design an approximate optimum affine combiner according to stochastic gradient search method. Later, to verify the proposed algorithm for large-scale MIMO channel estimation, both theoretical analysis and numerical simulations are provided to confirm effectiveness of the proposed algorithm which can achieve better estimation performance than the traditional methods.