Adaptive modulation is widely employed to improve spectral efficiency. To date, square signal constellations have been used with adaptive modulation. In this paper, triangular constellations are considered for this purpose. Triangle quadrature amplitude modulation (TQAM) for both power-of-two and non-power-of-two modulation orders is examined. A technique for TQAM mapping is presented which is better than existing approaches. A new type of TQAM called semi-regular TQAM (S-TQAM) is introduced. Bit error rate expressions for TQAM are derived, and the detection complexity of S-TQAM is compared with that of regular TQAM (R-TQAM) and irregular TQAM (I-TQAM). The performance of S-TQAM over additive white Gaussian noise and Rayleigh fading channels is compared with that of R-TQAM and I-TQAM.

A novel reduced-complexity digital predistortion (DPD) solution is presented in this paper. The proposed DPD can suppress the unwanted distortions due to power amplifier (PA) nonlinearity and I/Q modulator impairments in direct conversion transmitters using reduced-bandwidth filtered basis functions. Moreover, the DPD parameter estimation is based on very simple decorrelation based closed-loop processing and reduced-bandwidth observation, thus further reducing the overall complexity. The proposed DPD can be used in large array or massive MIMO systems with large number of radio transceivers and PAs, where reducing the complexity of the DPD processing is very critical.

Digital predistortion (DPD) is an effective way of mitigating spurious emission violations without the need of a significant backoff in the transmitter, thus providing better power efficiency and network coverage. In this paper, the IM3 subband DPD, proposed earlier by the authors, is extended to more than two component carriers (CCs) through a sequential learning solution. The DPD learning is iterated over each spurious emission generated by each pair and trio of CCs. We train and apply the DPD coefficients for the intermodulation distortion (IMD) products until a satisfactory performance is achieved. The algorithm is tested in simulations using MATLAB and in a novel, real-time implementation on a CPU via a software version of the algorithm using GNURadio.

In this article, we focus on the RF and digital front-end design and implementation challenges associated with future 5G radio access systems with special emphasis on spectrally contained waveforms and small-cell system scenarios. In general, filter bank based multicarrier (FBMC) type of techniques have various potential benefits due to their excellent spectral containment compared to classical OFDM(A). However, these spectrally contained waveforms lose their intriguing spectral properties when power amplifier (PA) nonlinearities are considered. Flexible and efficient digital predistortion (DPD) algorithms are thus considered an interesting solution in order to restore the spectral containment of such advanced 5G waveforms. Noncontiguous spectrally-agile transmission is another key feature of future 5G systems for increasing data rates and spectral allocation flexibility. However, the PA nonlinearities impose even more severe challenges in such noncontiguous transmission scenarios due to the resulting spurious intermodulation emissions that can easily violate the emission limits or even desensitize the own receiver in frequency division duplexing based systems. Furthermore, at the network deployment level, different small-cell concepts are expected to play a major role in future 5G networks. Unlike the ordinary macro base-stations, the digital computing capabilities in small-cell base-stations are much more limited. Furthermore, they should also adopt lower-cost and small-size analog RF components, while still maintaining high energy-efficiency. The afore-mentioned constraints, along with advanced 5G waveforms, call for flexible and low-complexity DPD solutions, a challenge addressed in this article. We report novel DPD methods with built-in capability to direct the linearization performance to pre-defined frequencies or subbands in a flexible manner, and demonstrate their good performance and complexity benefits in the context of non-contiguous FBMC transmission.

Filter bank multicarrier (FBMC) is considered as one of the strong 5G waveform candidates due to better spectral containment compared to orthogonal frequency division multiplexing (OFDM). However, when considering the spectral leakage effects appearing in practical devices due to transmitter non-idealities, such as power amplifier (PA) nonlinearity and inphase-quadrature (I/Q) imbalance, the nice spectral properties of FBMC may be severely degraded, thus questioning the additional complexity needed to generate such waveforms in baseband. In this paper, digital pre-distortion (DPD) is investigated as a practical solution for both of these problems. We extend the study also to potential applications of spectrum sensing based cognitive radio in future wireless communications. We utilize the benefits of the analysis filter bank of FBMC receiver in wideband multichannel spectrum sensing. In this context, the power leakage due to transmitter non-idealities also makes it difficult to identify spectral gaps amongst relatively strong primary transmissions. It is demonstrated that effective DPD greatly facilitates both spectrum sensing and spectrum utilization functions and thus enhances the overall spectral efficiency in opportunistic dynamic spectrum use scenarios.

In this paper, we present a novel digital predistortion (DPD) solution based on a direct learning approach, which is capable of reducing the unwanted emissions resulting from the power amplifier (PA) at any prespecified frequency located in the transmitter's out-of-band or spurious domain. The proposed scheme is based on evaluating the power spectral density (PSD) of the PA output signal and optimizing the DPD coefficients iteratively in order to minimize the output PSD around the prespecified frequency. To highlight the feasibility of the proposed implementation, the predistortion processing is kept as simple as possible, deploying quasi-memoryless polynomial models. Efficient mitigation of unwanted emissions around the target frequency is demonstrated via simulations and actual RF measurements, in both single- and dual-carrier waveform scenarios, using memoryless and memory-based PAs. The proposed DPD solution could be potentially employed in applications such as mobile devices utilizing noncontiguous multicarrier transmission, where the intermodulation spurs may overlap with the device's own receiver band, or could be potentially violating the spurious emission limits. Another target application is cognitive radio, where the PA may produce unwanted emissions that are interfering with primary-user transmissions. To the best of the authors' knowledge, there does not exist a similar technique in the open literature, and thus, the purpose of this paper is to encourage scientific discussions and technological innovations toward the creation of relatively low-complexity frequency-optimized predistortion techniques employed against selected unwanted emissions produced by the transmitter.

Spurious intermodulation components have recently been identified as a major problem in carrier aggregation mobile transmitters with multi-band power amplifiers (PAs). This article presents novel adaptive digital predistortion (DPD) solutions with reduced complexity in both the predistortion processing and the feedback paths, to tackle this problem. Compared with conventional DPDs which aim to linearize the whole transmit bandwidth, the proposed technique aims at mitigating only those intermodulation components which are most problematic from the spurious emission limit perspective. The proposed technique is verified with extensive simulations in various 3GPP LTE-A carrier aggregation scenarios, showing that the intermodulation spurs can be efficiently mitigated below the spurious emission limit with relatively small back-offs.

Power-constrained mobile radio communication transmitters drive their transmit power amplifiers close to their saturation regions, which results in nonlinear intermodulation distortion that is especially harmful in multi-cluster and carrier aggregation transmission scenarios. Digital predistortion is a method for linearizing the transmitter and suppressing the most harmful spurious emissions at the transmitter power amplifier output. This paper describes a programmable implementation of a digital predistortion filter on a heterogeneous Transport Trigger Architecture (TTA) multiprocessor. The predistortion algorithm is based on a parallel Hammerstein polynomial model and the experimental results show that the proposed programmable architecture is capable of linearizing a 20 MHz LTE carrier in realtime with a power consumption that is suitable for mobile devices.