Curtailed Digital Predistortion Model for Crosstalk in MIMO Transmitters

An adaptive digital predistortion (PD) technique is proposed for linearization of power amplifiers (PAs) in multiple-input multiple-output (MIMO) transmitters. Instead of constructing a separate feedback path at each PA, we propose a PD technique based on a combined feedback, which adds all the PAs' outputs to form a single feedback. We develop an algorithm for finding the multiple PA characteristics jointly from the combined feedback. The PD parameters are calculated from the identified PA characteristics. Although the proposed method requires more computations for PA identification than the conventional method, its hardware implementation is much simpler and can overcome the crosstalk problem of a radio frequency (RF) selector, which is necessary for multiple feedback selection. The simulation results show that, despite its simple structure, the linearization performance of the proposed method is almost the same as the separate multiple feedback method.

Optimization of digital predistortion models for RF power amplifiers using a modified differential evolution algorithm

This paper expounds a complexity-reduced generalized memory polynomial (CR-GMP) model for multi-band power amplifier (PA) digital predistortion (DPD). First, PA block diagrams characterizing the behavior of PAs under multi-band stimulus are proposed. Second, CR-GMP forward models are derived from the feedback block diagrams of the PA, driven with both dual- and tri-band signals, leading to a general formulation for PAs driven with multi-band signals. The resulting models are used to linearize two PAs driven with dual- and tri-band signals. The proposed CR-GMP models are compared to a dual-input digital predistortion (2D-DPD) model and a triple-input digital predistortion (3D-DPD) model and show similar linearization performance while requiring fewer coefficients. Due to the presence of cross terms in the dual-band CR-GMP formulation, the proposed model is robust against time-delay misalignment between dual-band signals, whereas the 2D-DPD is not. With a reduced number of coefficients and the presence of cross terms, the proposed CR-GMP models represent excellent candidates for the linearization of highly nonlinear PAs driven with multi-band signals.

Digital predistortion (DPD) is known as an efficient solution to suppress nonlinear distortion of power amplifiers (PAs) in wireless transmitters to achieve both high linearity and power efficiency. In multiple input and multiple output (MIMO) applications, the power gains of multiple transmitting channels are usually different. Meanwhile, the channel crosstalk cannot be ignored, given the isolations between the physical channels are limited. Accordingly, the linearization performances of the conventional DPD methods are usually degraded. In this article, we propose a novel DPD method for MIMO transmitters, where both the crosstalk and different power gains of multiple channels are considered. In this method, the expected predistorted signals, which would be used to train the DPD model, are firstly obtained by an iterative algorithm derived from the contraction mapping theorem. Then, the coefficients of a MIMO-DPD model are extracted by fitting the DPD model to the obtained predistorted signal. Experimental results verify that the extracted coefficients can achieve an improved linearization performance for the MIMO transmitter with crosstalk between the channels, especially in the case where the channel gains are different. Specifically, for a $2 \times 2$ MIMO transmitter with -20 dB crosstalk and 1.2 dB difference between the two channel gains, the proposed DPD improves the adjacent channel power ratio (ACPR) from -31.4 dBc to -47.1 dBc, which outperforms the conventional DPD by 6.8 dB.

In recent years, several approaches have been proposed aiming the optimal joint design of finite impulse response (FIR) multiple-input multiple-output (MIMO) transmitter and receiver. Assuming that the channel input is power constrained and that the channel model is a known FIR MIMO system, it is possible to design high performance transceivers in the presence of additive noise. Unfortunately, most optimal transceivers available so far, do not have fast implementation and simple design approaches. This work proposes a minimum mean square error (MMSE) design approach where constraints are incorporated into the structure of the FIR MIMO transmitter, in such a way that the resulting optimal MIMO transmitter, in the MMSE sense, has inherent fast algorithm implementation. In comparison with the general jointly optimized transceivers recently proposed, the new approach allows substantial reduction in the computational complexity at expense of mild performance degradation.

Multiple-input multiple-output (MIMO) techniques have been extensively used in modern wireless communications. The front-end nonlinearity coupled with crosstalk in a MIMO transmitter is much more complex than that in a single-input single-output (SISO) system. In this paper, we propose a nonlinear crosstalk calibration algorithm with the corresponding reference signal design. We start with the nonlinear crosstalk model of the MIMO transmitter and derive its corresponding inverse compensation model. The proposed model characterizes the nonlinearity and the crosstalk in separated blocks. We then propose specifically designed reference signals that utilize the model structure. The nonlinear crosstalk model can be reorganized and simplified with the reference signal. Thus, the complexity of the model coefficients estimation is greatly reduced without compromising the model accuracy. In addition, with the proposed algorithm, the compensation of the nonlinearity and crosstalk can be done separately. Simulations show a satisfactory calibration results with low computational complexity.

The inherent nonlinear behavior exhibited by power amplifiers (PAs) in saturation mode is a major impediment in wireless systems to achieve higher power efficiency, and higher spectral efficiency. Although PA behavioral modeling and digital predistortion (DPD) techniques are widely utilized at transmitter to characterize and linearize such distorted nonlinear output of PAs, the nonlinear distortions with memory effects have become more severe due to simultaneous strong crosstalk between multiple PA branches in multiple-input multiple-output (MIMO) arrays. Furthermore, current multi-input behavioral models suffer from a sharp increase in number of coefficients as the number of transmitter path increases in MIMO. In overcoming these challenges, we propose a decomposed cross-correlation based single-input-single-output (CC-SISO) behavioral modeling architecture. The proposed solution utilizes a low-cost, novel cross-correlation based method to estimate and cancel the simultaneous nonlinear and reverse crosstalk from multiple PA branches. Once the crosstalk is mitigated, the MIMO DPD can be implemented with single-input DPD blocks which significantly reduces the complexity. Furthermore, CC-SISO DPD eliminates the requirement for signal feedback paths before and after the PA, and thus reduces overall hardware implementation complexity. Through simulations, we demonstrate that the proposed CC-SISO architecture can reduce the overall complexity of state-of-art multi-input DPD models for MIMO systems.

A channel state information (CSI) feedback algorithm based on scalar quantization is proposed for a multiple-input multiple-output (MIMO) transmission. The performance of the proposed algorithm is evaluated using the effective transmission rate, which incorporates the effect of the CSI feedback overhead on transmission time. Besides that, the power consumed due to the computational complexity of the CSI feedback algorithm is taken into account together with the transmit power when evaluating the energy efficiency. We compare our proposed algorithm to other typical CSI feedback algorithms under correlated channel conditions. Simulation results show that the proposed algorithm enables practical MIMO transmission to achieve an relative transmission rate at 95% of the ideal rate.

This letter presents a bias-adjusted group digital predistortion (DPD) method to enhance linearization in multiple-input multiple-output (MIMO) transmitters with hybrid beamforming. By introducing a cross-normalized mean square error (CNMSE) metric, the nonlinearity consistency of multiple Doherty power amplifiers (DPAs) sharing radio frequency (RF) chains is quantitatively evaluated. A reference state is established using the worst-performing DPA, and the gate biases of other DPA are optimized to minimize CNMSE, aligning their nonlinear characteristics. This simplifies DPD training to a single-channel paradigm. Experiments with four DPAs under a 64-quadrature amplitude modulation (QAM) signal (200 MHz bandwidth) demonstrate a 6.77 dB adjacent channel power ratio (ACPR) improvement over the baseline and 4.51 dB gain versus conventional DPD, validating efficient multi-DPA linearization for resource-constrained MIMO systems.

In order to decrease system implementation cost of fiber-optic relaying systems for multiple-input multiple output (MIMO) mobile transmissions, we have proposed a novel relaying scheme using spatial modulation (SM) technology. The proposed scheme uses much fewer radio over fiber (RoF) links than the conventional fiber-optic relaying schemes. We studied the performance of the proposed scheme through computer simulations and confirmed that the proposed scheme can achieve much better transmission performance than the conventional ones. Computer simulations show that the optimum number of RoF links of the proposed scheme is around 2 or 3 for 4×4 MIMO transmissions, and around 3 or 4 for 8×8 MIMO transmissions.

Orbital Angular Momentum (OAM) has been considered as a new dimension in wireless transmission. In Multiple Input Multiple Output (MIMO) system, OAM can also be utilized as the new Degrees of Freedom (DoF) to manipulate the beamforming instead of the traditional Channel State Information (CSI) feedback, so that the computation complexity of the beamforming dramatically decreases, as well as the capacity of the transmission is maintained approaching the upper bound of the MIMO system. For example, this beamforming manipulation with OAM is extremely efficient in the Line of Sight (LoS) MIMO system. In this paper, the mathematical model of the DoF in beamforming manipulation in MIMO with OAM is established. The computation complexity is evaluated, and compared in LoS environment within MIMO, MIMO-OAM, and OAM. The quantitatively simulation confirms the fact that MIMO-OAM has the best trade-off between the capacity and the computation complexity, which gives a consolidated evidence that the MIMOOAM scheme can be adopted as an efficient beamforming scheme in LoS backhaul links between the base station and the self backhaul small cell station, or Near Field Communications (NFC) directly between mobile terminals. Due to these promising applications, MIMO-OAM may become one of the candidate technologies in Beyond 5G (B5G) mobile communications.

Two novel models with lower complexity are proposed for linearization of 2×2 MIMO transmitters with nonlinear crosstalk. Crossover modified memory polynomial (COMMP) model is introduced as an extension of the 2-D modified memory polynomial (2-D MMP) model and COMMP-CPWL model is derived from the COMMP model but without high-order terms which are replaced by the canonical piecewise linear function. Genetic Algorithm (GA) is used to achieve optimal digital predistortion (DPD) performance and least-square (LS) algorithm is adopted to extract the models coefficients. Experimental results show that both models can achieve nearly the same DPD performance and model accuracy as the 2×2 Parallel Hammerstein (PH) model while requiring fewer model coefficients.

Multiple-input multiple-output (MIMO) signaling is one of the key technologies of current mobile communication systems. However, the complex and expensive radio frequency (RF) chains have always limited the increase of MIMO scale. In this paper, we propose a MIMO transmission architecture based on a dual-polarized reconfigurable intelligent surface (RIS), which can directly achieve modulation and transmission of multichannel signals without the need for conventional RF chains. Compared with previous works, the proposed architecture can improve the integration of RIS-based transmission systems. A prototype of the dual-polarized RIS-based MIMO transmission system is built and the experimental results confirm the feasibility of the proposed architecture. The dual-polarized RIS-based MIMO transmission architecture provides a promising solution for realizing low-cost ultra-massive MIMO towards future networks.

Multiple-input multiple-output (MIMO) systems are threatened by smart attackers, who apply programmable radio devices such as software defined radios to perform multiple types of attacks such as eavesdropping, jamming and spoofing. In this paper, MIMO transmission in the presence of smart attacks is formulated as a noncooperative game, in which a MIMO transmitter chooses its transmit power level and a smart attacker determines its attack type accordingly. A Nash equilibrium of this secure MIMO transmission game is derived and conditions assuring its existence are provided to reveal the impact of the number of antennas and the costs of the attacker to launch each type of attack. A power control strategy based on Q-learning is proposed for the MIMO transmitter to suppress the attack motivation of smart attackers in a dynamic version of MIMO transmission game without being aware of the attack and the radio channel model. Simulation results show that our proposed scheme can reduce the attack rate of smart attackers and improve the secrecy capacity compared with the benchmark strategy.

Video monitoring applications for underground rely on wireless train-to-wayside communication systems which require high data rate as well as high Quality of Service (QoS) level. In order to satisfy both constraints we propose a combined source and channel coding approach in the context of MIMO (Multiple Input Multiple Output) video transmission. In the present case, MIMO transmission is based on the PHY layer of IEEE 802.11n Wi-Fi standard currently deployed in a railway tunnels. Two different strategies are studied: first, the association between Multiple Description Coding (MDC) and a STBC (Space Time Block Code) MIMO scheme is considered when no channel information is available at transmitter side. In the case when perfect channel information is available at transmitter side (CSIT), a Singular Value Decomposition of the MIMO channel is possible. This transmission scheme is then associated with scalable video coding, which consists here in the separation of the scene into different Regions Of Interest (ROI). The creation of the regions of interest is based on the Flexible Macroblock Ordering (FMO) technique introduced in the new H.264/AVC compression standard. The stream associated to the area with the maximal perceptual relevance is transmitted on the eigen-channel with the higher gain. Consequently, this strategy which provides unequal protection against channel errors, allows guaranteeing better robustness and acceptable reconstructed video quality at the control-centre. The two different strategies of transmission have been evaluated thanks to realistic simulations. Two antenna configurations representative of real cases encountered in railway tunnels are considered. The channel model is generated by using the correlation based Kronecker model obtained by computing the channel matrix with a 3D ray tracing tool. Simulation results show that the two proposed solutions allow enhancing the reconstructed video quality compared to conventional transmission schemes with no increase of the transmitted power and of the number of radio access points along the infrastructure, even in tunnels in presence of spatial correlation.Keywordstrain-to-wayside communicationsvideo communicationsMultiple Input Multiple Output (MIMO) systemMultiple Description Coding (MDC)Scalable Video Coding (SVC)Kronecker model