In this study, the authors explore a generalised scheme for the synchronous code division multiple access (CDMA). In this scheme, unlike the standard CDMA systems, each user has different codewords for communicating different messages. Two main problems are investigated. The first problem concerns whether uniquely detectable overloaded matrices (an injective matrix, i.e. the inputs and outputs are in one-to-one correspondence depending on the input alphabets) exist in the absence of additive noise, and if so, whether there are any practical optimum detectors for such input codewords. The second problem is about finding tight bounds for the sum channel capacity. In response to the first problem, the authors have constructed uniquely detectable matrices for the generalised scheme and the authors have developed practical maximum likelihood detection algorithms for such codes. In response to the second problem, lower bounds and conjectured upper bounds are derived. The results of this study are superior to other standard overloaded CDMA codes since the generalisation can support more users than the previous schemes.

Based on the asymptotic analysis in spatially correlated Rician fading channel, the precoders for space-time block-coded multiple-input multiple-output system at high and low signal-to-noise ratios (SNRs) are, respectively, designed to minimise the system bit error rate (BER). The precoding for high SNR provides closed-form positive power allocation by using the Langrage optimisation method, and has the BER performance close to that of the optimal scheme, especially at high SNR. The Langrage multiplier does exist and is unique, and practical Newton's method is proposed to find its optimal value. Moreover, it has a closed-form solution for some special cases. Thus, it avoids the iterative calculation of some existing precoding schemes. The precoding for low SNR has a closed-form solution similar to onedirectional (1-D) beamforming, and can obtain the performance very close to that of the optimal scheme at low SNR. By combining the above two schemes, a simple and effective suboptimal precoding scheme can be achieved for different SNRs. Simulation results show that the proposed scheme derived from the integration of high and low SNR can provide BER lower than the equal power precoding, and obtain the BER performance close to that of the existing optimal scheme with low complexity.

The authors consider a linear relaying communication system using multiple antennas at a source, relay and destination. They maximise the end-to-end signal-to-noise-ratio (SNR) to find the relay transformation (RT) and the source transmit covariance (STC) matrices. For any given STC matrix, to maximise the SNR, they prove that the relay has to assign all its power in the direction of the dominant eigenmodes of the source–relay and the relay–destination channels. They also find the joint optimal rank-one matrices for the STC and RT. They prove that this solution also maximises the source–destination mutual information among all rank-one matrices. Furthermore, they find the optimal power budgets allocated to the source and relay that maximise the SNR under a constraint on the total transmit power of the system. This is a practical solution as only three positive quantities need to be communicated among the nodes to calculate these optimal power budgets. Interestingly, the authors computer simulations for multiple users reveal that the sum-rate significantly increases if users selfishly maximise their own SNRs using the proposed method, instead of maximising their own capacity. Thus, they conclude that the SNR-maximisation is spectrally more efficient as it consumes only the best subspaces and leaves the other subspaces free.

Recent studies have revealed that spectrum congestion is due to the inefficient usage of spectrum against the availability. Cognitive radio is viewed as an approach for improving the utilisation of radio spectrum. The spectrally encoded (spread-time) code-division multiple access (SE-CDMA) technique (which is regarded as the dual of spread spectrum CDMA) is considered and its performance in cognitive radio networks is studied. In cognitive network spectrum, overlay and underlay techniques are employed to enable the primary and secondary users to coexist while improving the overall spectrum efficiency. It is shown that SE-CDMA provides considerable flexibility to design overlay and underlay waveforms that are scenario-dependent. The performance of SE-CDMA method is evaluated in additive white Gaussian noise and fading channels in terms of both primary and secondary users. Moreover, its performance is compared with cognitive radio using soft-decision spectrally modulated spectrally encoded waveforms. The results indicate the efficiency of SE-CDMA.

An efficient method is proposed to estimate the carrier frequency offsets (CFOs) in the orthogonal frequency-division multiple access (OFDMA) uplink. The conventional alternating projection method is accelerated by utilising the inherited properties of the matrices involved. The multiplication of large sparse projection matrices can be elegantly transformed to a series of products involving small dense matrices, and the inverse operation of these large matrices can be substituted by direct computations. Hence, the computational cost is significantly reduced without compromising the accuracy of the CFO estimation.

Channel shortening is a necessary equalisation for mitigation of inter-symbol and inter-carrier interferences in multicarrier transceivers. Its purpose is to reduce the length of the impulse response of the channel. In this study, the authors present a new algorithm with low complexity for updating the coefficients of time-domain equalisation (TEQ) to shorten the effective channel in multicarrier transmission systems. Based on the orthogonality between adjacent subcarriers and cyclic prefix, a new cost function is defined to create a blind and adaptive TEQ. Simulation results are provided to demonstrate the performance of the algorithm.

Optimal iterative log-MAP decoding of turbo codes requires accurate knowledge of the operating signal-to-noise ratio (SNR). However, the SNR information, available at practical decoders for bit-interleaved coded modulation systems, such as the third generation partnership project high-speed packet access and long-term evolution wireless cellular systems, may be inaccurate. In this study, two decoder architectures for improved turbo decoding in the presence of SNR mismatch are proposed. The SNR-mismatch aware turbo decoder selects the decoder which is estimated to have the best performance at the current mismatch, according to the test criterion. The SNR-mismatch compensated turbo decoder provides a more accurate estimation of the noise variance and concurrently scales the channel and the decoder log-likelihood ratios (LLRs) to continue decoding. Two different methods are proposed to find the optimal scaling factors online, one on the symbol level and the other on the bit level. This study shows that online LLR scaling, without prior knowledge about the noise mismatch statistics, can result in near-optimal turbo decoding regardless of the initial SNR mismatch.

This study carries out the first-ever investigation of the analysis of a cooperative decode-and-forward (DF) relay network with chaos shift keying (CSK) modulation. The performance analysis of DF-CSK in this study takes into account the dynamical nature of chaotic signal, which is not similar to a conventional binary modulation performance computation methodology. The expression of a lower bound bit error rate (BER) is derived in order to investigate the performance of the cooperative system under independently and identically distributed Gaussian fading wireless environments. The effect of the non-periodic nature of chaotic sequence leading to a non-constant bit energy of the considered modulation is also investigated. A computation approach of the BER expression based on the probability density function of the bit energy of the chaotic sequence, channel distribution and number of relays is presented. Simulation results prove the accuracy of the authors BER computation methodology.

In this study, the authors evaluate the outage probability performance of an amplify-and-forward cooperative relay network where the relays are equipped with cognitive radios. When the number of available relays is more than four the authors use the channel conditions in order to select the best four cognitive relays from a set of M cognitive relay nodes and then they are used for cooperation between the source and the destination nodes. Expressions for outage probability are determined for a frequency flat Rayleigh-fading environment from the received signal-to-noise ratio with perfect and imperfect spectrum acquisition. In addition, a modified distributed quasi-orthogonal space–time block coding scheme with increased code gain distance is considered for use within the proposed cognitive relay network. To utilise the available spectrum opportunities with the modified quasi-orthogonal space–time block code, the code matrix can be adapted to the number of available relays. Simulation results show that the four relay selection improves the system performance. This is confirmed by the outage probability analysis. The simulations also show that the modified code can significantly enhance the performance of the system and improve the reliability of the link as compared with the conventional distributed quasi-orthogonal space–time block coding.

With the development of the high-speed communication channel or high-capacity storage device, the intersymbol interference (ISI) occurs more frequently in the condensed data. The traditional Bahl – Cocke – Jelineu – Raviv (BCJR) method is capable of solving the two-dimensional (2D) symbol influence problem with extremely high cost. By analysing the complexity in terms of branch, state and path in the 2D trellis-based ISI detection, the authors verify that the system complexity is mainly dominated by the window size. To reduce the detection complexity, the conventional simplifications are applied in the 2D strip-wise ISI detection. In addition, a two-phase strip-wise detection is proposed to achieve a lower complexity than that of the conventional simplifications. In this system, the first detector can be realised by a low-complexity method (like the hard detection or Viterbi algorithm) to provide the mask shrinking with predicted data. After reducing the mask size, the second detector can detect the main portion of ISI by the trellis-based detection (like BCJR algorithm) with a small mask. Under the similar detection performance, the proposed scheme can achieve more than 91.76% saving in the metric computation compared with the 5 × 3-window IRCSDFA (iterative row–column soft-decision feedback algorithm) simplified by the M-algorithm with M = 8.

There are two important factors which impact the performance of phase-based frequency estimation algorithms remarkably: the approximations of noise phase model and imperfections in phase unwrapping process. Support vector regression (SVR) exits excellent capabilities for learning unknown data and forecasting the future ones, especially under the small sample condition. Therefore, the authors introduce it to predict the variation trend of instantaneous phase and unwrap phases efficiently. Even though with that being the case, errors still exist in phase unwrapping process because of its ambiguous phase characteristic. Furthermore, a SVR-based frequency estimation algorithm is proposed and makes it immune to these error phases by means of setting the SVR's parameters properly. The results show that, compared with other phased-based algorithms, not only does the proposed one maintain a wide estimation range and quality capabilities at low frequencies, but also improves accuracy at high frequencies and decreases the impact with the initial phase. The proposed algorithm is fit for not only linear phase signal but also polynomial phase signal, under both the Gaussian and non-Gaussian condition.

Power amplifiers are one of the costly devices in wireless communication systems and exhibit non-linearity. The problem of non-linearity in the power amplifier categorises in two disturbance impacts, output distortion and in-band distortion which lead to adjacent channel interference and constellation deviation, respectively. In order to gain maximum efficiency of the power amplifier and reduce interference, the authors propose a new digital pre-distortion scheme called complex gain convergence (CGC). The proposed scheme is compared with indirect learning architecture and validated against an laterally diffused metal oxide semiconductor (LDMOS) power amplifier. The outcome of applying CGC scheme shows enhancement in adjacent channel leakage ratio and error vector magnitude performance while the complexity is even lower. An experimental demonstration with the actual power amplifier is also presented.