51 research outputs found
On the Implementation Complexity of Digital Full-Duplex Self-Interference Cancellation
In-band full-duplex systems promise to further increase the throughput of
wireless systems, by simultaneously transmitting and receiving on the same
frequency band. However, concurrent transmission generates a strong
self-interference signal at the receiver, which requires the use of
cancellation techniques. A wide range of techniques for analog and digital
self-interference cancellation have already been presented in the literature.
However, their evaluation focuses on cases where the underlying physical
parameters of the full-duplex system do not vary significantly. In this paper,
we focus on adaptive digital cancellation, motivated by the fact that physical
systems change over time. We examine some of the different cancellation methods
in terms of their performance and implementation complexity, considering the
cost of both cancellation and training. We then present a comparative analysis
of all these methods to determine which perform better under different system
performance requirements. We demonstrate that with a neural network approach,
the reduction in arithmetic complexity for the same cancellation performance
relative to a state-of-the-art polynomial model is several orders of magnitude.Comment: Presented at the 2020 Asilomar Conference for Signals, Systems, and
Computer
Identification of Non-Linear RF Systems Using Backpropagation
In this work, we use deep unfolding to view cascaded non-linear RF systems as
model-based neural networks. This view enables the direct use of a wide range
of neural network tools and optimizers to efficiently identify such cascaded
models. We demonstrate the effectiveness of this approach through the example
of digital self-interference cancellation in full-duplex communications where
an IQ imbalance model and a non-linear PA model are cascaded in series. For a
self-interference cancellation performance of approximately 44.5 dB, the number
of model parameters can be reduced by 74% and the number of operations per
sample can be reduced by 79% compared to an expanded linear-in-parameters
polynomial model.Comment: To be presented at the 2020 IEEE International Conference on
Communications (Workshop on Full-Duplex Communications for Future Wireless
Networks
Hardware Implementation of Neural Self-Interference Cancellation
In-band full-duplex systems can transmit and receive information
simultaneously on the same frequency band. However, due to the strong
self-interference caused by the transmitter to its own receiver, the use of
non-linear digital self-interference cancellation is essential. In this work,
we describe a hardware architecture for a neural network-based non-linear
self-interference (SI) canceller and we compare it with our own hardware
implementation of a conventional polynomial based SI canceller. In particular,
we present implementation results for a shallow and a deep neural network SI
canceller as well as for a polynomial SI canceller. Our results show that the
deep neural network canceller achieves a hardware efficiency of up to
Msamples/s/mm and an energy efficiency of up to nJ/sample, which is
and better than the polynomial SI canceller,
respectively. These results show that NN-based methods applied to
communications are not only useful from a performance perspective, but can also
be a very effective means to reduce the implementation complexity.Comment: Accepted for publication in IEEE Journal on Emerging and Selected
Topics in Circuits and System
Machine learning techniques for self-interference cancellation in full-duplex systems
Full-duplex (FD), enabling remote parties to transfer information simultaneously in
both directions and in the same bandwidth, has been envisioned as an important
technology for the next-generation wireless networks. This is due to the ability to
leverage both time and frequency resources and theoretically double the spectral efficiency. Enabling the FD communications is, however, highly challenging due to the
self-interference (SI), a leakage signal from the FD transmitter (Tx) to its own receiver
(Rx). The power of the SI is significantly higher when compared with the signal of
interest (SoI) from a remote node due to the proximity of the Tx to its co-located Rx.
The SI signal is thus swamping the SoI and degrading the FD system's performance.
Traditional self-interference cancellation (SIC) approaches, spanning the propagation,
analog, and/or digital domains, have been explored to cancel the SI in FD
transceivers. Particularly, digital domain cancellation is typically performed using
model-driven approaches, which have proven to be effective for SIC; however, they
could impose additional cost, hardware, memory, and/or computational requirements.
Motivated by the aforementioned, this thesis aims to apply data-driven machine
learning (ML)-assisted SIC approaches to cancel the SI in FD transceivers|in the digital
domain|and address the extra requirements imposed by the traditional methods.
Specifically, in Chapter 2, two grid-based neural network (NN) structures, referred
to as ladder-wise grid structure and moving-window grid structure, are proposed to
model the SI in FD transceivers with lower memory and computational requirements
than the literature benchmarks. Further reduction in the computational complexity
is provided in Chapter 3, where two hybrid-layers NN structures, referred to as
hybrid-convolutional recurrent NN and hybrid-convolutional recurrent dense NN, are
proposed to model the FD SI. The proposed hybrid NN structures exhibit lower computational
requirements than the grid-based structures and without degradation in the
SIC performance. In Chapter 4, an output-feedback NN structure, referred to as the
dual neurons-` hidden layers NN, is designed to model the SI in FD transceivers with
less memory and computational requirements than the grid-based and hybrid-layers
NN structures and without any additional deterioration to the SIC performance.
In Chapter 5, support vector regressors (SVRs), variants of support vector machines,
are proposed to cancel the SI in FD transceivers. A case study to assess the
performance of SVR-based approaches compared to the classical and other ML-based
approaches, using different performance metrics and two different test setups, is also
provided in this chapter. The SVR-based SIC approaches are able to reduce the training
time compared to the NN-based approaches, which are, contrarily, shown to be
more efficient in terms of SIC, especially when high transmit power levels are utilized.
To further enhance the performance/complexity of the ML approaches provided
in Chapter 5, two learning techniques are investigated in Chapters 6 and 7. Specifically,
in Chapter 6, the concept of residual learning is exploited to develop an NN
structure, referred to as residual real-valued time-delay NN, to model the FD SI with
lower computational requirements than the benchmarks of Chapter 5. In Chapter 7,
a fast and accurate learning algorithm, namely extreme learning machine, is proposed
to suppress the SI in FD transceivers with a higher SIC performance and lower training
overhead than the benchmarks of Chapter 5. Finally, in Chapter 8, the thesis
conclusions are provided and the directions for future research are highlighted
Cognitive Resource Management In 5G Networks
The 4G LTE network offers a high speed connectivity that is predicated on the construction platform of the 3G network and relies on an Internet Protocol (IP) for data transmission and reception. This platform’s utility is quickly becoming exhausted as the frequency spectrum approaches maximum device connectivity capacity. To improve network capacity, we must expand the bandwidth that our devices operate on. To effectively carry out this task, a self-configurable network must be employed in the development of the 5g network. This article aims to explore the technologies which form the platform for the 5G network and the cognitive resource management devices which the network is contingent upon. Base Stations (BS); Artificial Intelligence (AI); Internet Protocol (IP)
Dirty RF Signal Processing for Mitigation of Receiver Front-end Non-linearity
Moderne drahtlose Kommunikationssysteme stellen hohe und teilweise
gegensätzliche Anforderungen an die Hardware der Funkmodule, wie z.B.
niedriger Energieverbrauch, große Bandbreite und hohe Linearität. Die
Gewährleistung einer ausreichenden Linearität ist, neben anderen analogen
Parametern, eine Herausforderung im praktischen Design der Funkmodule. Der
Fokus der Dissertation liegt auf breitbandigen HF-Frontends für
Software-konfigurierbare Funkmodule, die seit einigen Jahren kommerziell
verfügbar sind. Die praktischen Herausforderungen und Grenzen solcher
flexiblen Funkmodule offenbaren sich vor allem im realen Experiment. Eines
der Hauptprobleme ist die Sicherstellung einer ausreichenden analogen
Performanz über einen weiten Frequenzbereich. Aus einer Vielzahl an
analogen Störeffekten behandelt die Arbeit die Analyse und Minderung von
Nichtlinearitäten in Empfängern mit direkt-umsetzender Architektur. Im
Vordergrund stehen dabei Signalverarbeitungsstrategien zur Minderung
nichtlinear verursachter Interferenz - ein Algorithmus, der besser unter
"Dirty RF"-Techniken bekannt ist. Ein digitales Verfahren nach der
Vorwärtskopplung wird durch intensive Simulationen, Messungen und
Implementierung in realer Hardware verifiziert. Um die Lücken zwischen
Theorie und praktischer Anwendbarkeit zu schließen und das Verfahren in
reale Funkmodule zu integrieren, werden verschiedene Untersuchungen
durchgeführt. Hierzu wird ein erweitertes Verhaltensmodell entwickelt, das
die Struktur direkt-umsetzender Empfänger am besten nachbildet und damit
alle Verzerrungen im HF- und Basisband erfasst. Darüber hinaus wird die
Leistungsfähigkeit des Algorithmus unter realen Funkkanal-Bedingungen
untersucht. Zusätzlich folgt die Vorstellung einer ressourceneffizienten
Echtzeit-Implementierung des Verfahrens auf einem FPGA. Abschließend
diskutiert die Arbeit verschiedene Anwendungsfelder, darunter spektrales
Sensing, robuster GSM-Empfang und GSM-basiertes Passivradar. Es wird
gezeigt, dass nichtlineare Verzerrungen erfolgreich in der digitalen
Domäne gemindert werden können, wodurch die Bitfehlerrate gestörter
modulierter Signale sinkt und der Anteil nichtlinear verursachter
Interferenz minimiert wird. Schließlich kann durch das Verfahren die
effektive Linearität des HF-Frontends stark erhöht werden. Damit wird der
zuverlässige Betrieb eines einfachen Funkmoduls unter dem Einfluss der
Empfängernichtlinearität möglich. Aufgrund des flexiblen Designs ist der
Algorithmus für breitbandige Empfänger universal einsetzbar und ist nicht
auf Software-konfigurierbare Funkmodule beschränkt.Today's wireless communication systems place high requirements on the
radio's hardware that are largely mutually exclusive, such as low power
consumption, wide bandwidth, and high linearity. Achieving a sufficient
linearity, among other analogue characteristics, is a challenging issue in
practical transceiver design. The focus of this thesis is on wideband
receiver RF front-ends for software defined radio technology, which became
commercially available in the recent years. Practical challenges and
limitations are being revealed in real-world experiments with these radios.
One of the main problems is to ensure a sufficient RF performance of the
front-end over a wide bandwidth. The thesis covers the analysis and
mitigation of receiver non-linearity of typical direct-conversion receiver
architectures, among other RF impairments. The main focus is on DSP-based
algorithms for mitigating non-linearly induced interference, an approach
also known as "Dirty RF" signal processing techniques. The conceived
digital feedforward mitigation algorithm is verified through extensive
simulations, RF measurements, and implementation in real hardware. Various
studies are carried out that bridge the gap between theory and practical
applicability of this approach, especially with the aim of integrating that
technique into real devices. To this end, an advanced baseband behavioural
model is developed that matches to direct-conversion receiver architectures
as close as possible, and thus considers all generated distortions at RF
and baseband. In addition, the algorithm's performance is verified under
challenging fading conditions. Moreover, the thesis presents a
resource-efficient real-time implementation of the proposed solution on an
FPGA. Finally, different use cases are covered in the thesis that includes
spectrum monitoring or sensing, GSM downlink reception, and GSM-based
passive radar. It is shown that non-linear distortions can be successfully
mitigated at system level in the digital domain, thereby decreasing the bit
error rate of distorted modulated signals and reducing the amount of
non-linearly induced interference. Finally, the effective linearity of the
front-end is increased substantially. Thus, the proper operation of a
low-cost radio under presence of receiver non-linearity is possible. Due to
the flexible design, the algorithm is generally applicable for wideband
receivers and is not restricted to software defined radios
Modeling and Linearization of MIMO RF Transmitters
Multiple-input multiple-output (MIMO) technology will continue to play a vital
role in next-generation wireless systems, e.g., the fifth-generation wireless networks
(5G). Large-scale antenna arrays (also called massive MIMO) seem to be the most
promising physical layer solution for meeting the ever-growing demand for high
spectral efficiency. Large-scale MIMO arrays are typically deployed with high
integration and using low-cost components. Hence, they are prone to different
hardware impairments such as crosstalk between the transmit antennas and power
amplifier (PA) nonlinearities, which distort the transmitted signal. To avert the
performance degradation due to these impairments, it is essential to have mechanisms
for predicting the output of the MIMO arrays. Such prediction mechanisms are
mandatory for performance evaluation and, more importantly, for the adoption of
proper compensation techniques such as digital predistortion (DPD) schemes. This
has stirred a considerable amount of interest among researchers to develop new
hardware and signal processing solutions to address the requirements of large-scale
MIMO systems.
In the context of MIMO systems, one particular problem is that the hardware
cost and complexity scale up with the increase of the size of the MIMO system.
As a result, the MIMO systems tend to be implemented on a chip and are very
compact. Reduction of the cost by reducing the bill of material is possible when
several components are eliminated. The reuse of already existing hardware is an
alternative solution. As a result, such systems are prone to excessive sources of
distortion, such as crosstalk. Accordingly, crosstalk in MIMO systems in its simplest
form can affect the DPD coefficient estimation scheme. In this thesis, the effect of
crosstalk on two main DPD estimation techniques, know as direct learning algorithm
(DLA) and indirect learning algorithm (ILA), is studied.
The PA behavioral modeling and DPD scheme face several challenges that seek
cost-efficient and flexible solutions too. These techniques require constant capture
of the PA output feedback signal, which ultimately requires the implementation
of a complete transmitter observation receiver (TOR) chain for the individual
transmit path. In this thesis, a technique to reuse the receiver path of the MIMO
TDD transceiver as a TOR is developed, which is based on over-the-air (OTA)
measurements. With these techniques, individual PA behavioral modeling and DPD
can be done by utilizing a few receivers of the MIMO TDD system. To use OTA
measurements, an on-site antenna calibration scheme is developed to individually
estimate the coupling between the transmitter and the receiver antennas.
Furthermore, a digital predistortion technique for compensating the nonlinearity
of several PAs in phased arrays is presented. The phased array can be a subset of
massive MIMO systems, and it uses several antennas to steer the transmitted signal
in a particular direction by appropriately assigning the magnitude and the phase
of the transmitted signal from each antenna. The particular structure of phased
arrays requires the linearization of several PAs with a single DPD. By increasing the
number of RF branches and consequently increasing the number of PAs in the phased
array, the linearization task becomes challenging. The DPD must be optimized to
results in the best overall linear performance of the phased array in the field. The
problem of optimized DPD for phased array has not been addressed appropriately in
the literature.
In this thesis, a DPD technique is developed based on an optimization problem
to address the linearization of PAs with high variations. The technique continuously
optimizes the DPD coefficients through several iterations considering the effect of
each PA simultaneously. Therefore, it results in the best optimized DPD performance
for several PAs.
Extensive analysis, simulations, and measurement evaluation is carried out as
a proof of concept. The different proposed techniques are compared with conventional approaches, and the results are presented. The techniques proposed in this
thesis enable cost-efficient and flexible signal processing approaches to facilitate the
development of future wireless communication systems
Reduced-complexity Digital Predistortion in Flexible Radio Spectrum Access
Wireless communications is nowadays seen as one of the main foundations of technological advancements in, e.g., healthcare, education, agriculture, transportation, computing, personal communications, media, and entertainment. This requires major technological developments and advances at different levels of the wireless communication systems and networks. In particular, it is required to utilize the currently available frequency spectrum in a more and more efficient way, while also adopting new spectral bands. Moreover, it is required that cheaper and smaller electronic components are used to build future wireless communication systems to facilitate increasingly cost-effective solutions. Meanwhile, energy efficiency becomes extremely important in wide scale deployments of the networks both from a running cost point of view, and from an environmental impact point of view. This is the big picture, or the so called ‘bird’s eye view’ of the challenges that are yet to be met in this very interesting and fast developing field of science.The power amplifier (PA) is the most power-hungry component in most RF transmitters. Consequently, its energy efficiency significantly contributes to the overall energy efficiency of the transmitter, and in fact the whole wireless network. Unfortunately, energy efficiency enhancement implies operating the PA closer to its saturation region, which typically results in severe nonlinear distortion that can deteriorate the signal quality and cause interference to neighboring users, both of which negatively impact the system spectral efficiency. Moreover, in flexible spectrum access scenarios, which are essential for improving the spectral efficiency, particular in the form of non-contiguous radio spectrum access, the nonlinear distortion due to the PA becomes even more severe and can significantly impact the overall network performance. For example, in noncontiguous carrier aggregation (CA) in LTE-Advanced, it has been demonstrated that in addition to the classical in-band distortion and regrowth around the main carriers, harmful spurious emission components are generated which can easily violate the spurious emission limits even in the case of user equipment (UE) transmitters.Technological advances in the digital electronics domain have enabled us to approach this problem from a digital signal processing point of view in the form of widely-adopted and researched digital predistortion (DPD) technology. However, when the signal bandwidth gets larger, and flexible or non-contiguous spectrum access is introduced, the complexity of the DPD increases and the power consumed in the digital domain by the DPD itself becomes higher and higher, to the extent that it might be close to, or even surpass, the energy savings achieved from using a more efficient PA. The problem becomes even more challenging at the UE side which has relatively limited computational capabilities and lower transmit power. This dilemma can be resolved by developing novel reduced-complexity DPD solutions in such flexible spectrum access and/or wide bandwidth scenarios while not sacrificing the DPD performance, which is the main topic area that this thesis work contributes to.The first contribution of this thesis is the development of a spur-injection based sub-band DPD structure for spurious emission mitigation in noncontiguous transmission scenarios. A novel and effective learning algorithm is also introduced, for the proposed sub-band DPD, based on the decorrelation principle. Mathematical models of the unwanted emissions are formulated based on realistic PA models with memory, followed by developing an efficient DPD structure for mitigating these emissions with reducedcomplexity in both the DPD main processing and learning paths while providing excellent spurious emission suppression. In the special case when the spurious emissions overlap with the own RX band in frequency division duplexing (FDD) transceivers, a novel subband DPD solution is also developed that uses the main RX for DPD learning without requiring any additional observation RX, thus further reducing the DPD complexity.The second contribution is the development of a novel reduced-complexity concurrent DPD, with a single-feedback receiver path, for carrier aggregation-like scenarios. The proposed solution is based on a simple and flexible DPD structure with decorrelationbased parameter learning. Practical simulations and RF measurements demonstrate that the proposed concurrent DPD provides excellent linearization performance, in terms of in-band error vector magnitude (EVM) and adjacent channel leakage ratio (ACLR), when compared to state-of-the-art concurrent DPD solutions, despite its reduced computational complexity in both the DPD main path processing and parameter learning.The third contribution is the development of a new and novel frequency-optimized DPD solution which can tailor its linearization capabilities to any particular regions of the spectrum. Detailed mathematical expressions of the power spectrum at the PA output as a function of the DPD coefficients are formulated. A Newton-Raphson optimization routine is then utilized to optimize the suppression of unwanted emissions at arbitrary pre-specified frequencies at the PA output. From a complexity reduction perspective, this means that for a given linearization performance at a particular frequency range, an optimized and reduced-complexity DPD can be used.Detailed quantitative complexity analysis, of all the proposed DPD solutions, is performed in this thesis. The complexity and linearization performance are also compared to state-of-the-art DPD solutions in the literature to validate and demonstrate the complexity reduction aspect without sacrificing the linearization performance. Moreover, all the DPD solutions developed in this thesis are tested in practical RF environments using real cellular power amplifiers that are commercially used in the latest wireless communication systems, both at the base station side and at the mobile terminal side. These experiments, along with the strong theoretical foundation of the developed DPD solutions prove that they can be commercially used as such to enhance the performance, energy efficiency, and cost effectiveness of next generation wireless transmitters
- …