6 research outputs found
Deploying Dynamic On-Board Signal Processing Schemes for Multibeam Satellite Systems
This paper designs dynamic on-board signal processing
schemes in a multiple gateway multibeam satellite system
where full frequency reuse pattern is considered among the beams
and feeds. In particular, we deploy on-board Joint Precoding,
Feed selection and Signal switching mechanism (JPFS) so that the
following advantages are realized, I) No need of Channel State
Information (CSI) exchange among the gateways and satellite,
since the performance of precoding is highly sensitive to the
quality of CSI, II) In case one gateway fails, rerouting signals
through other gateways can be applied without any extra signal
processing, III) Properly selecting on-board feed/s to serve each
user which generates maximum gain toward corresponding user,
IV) Flexibly switching the signals received from the gateways
to requested users where each user can dynamically request
traffic from any gateway, and V) Multiple user with multiple
traffic streams can be dynamically served at each beam. However,
deploying such JPFS architecture imposes high complexity to the
satellite payload. To tackle this issue, this study aims at deploying
JPFS that can provide affordable complexity at the payload. In
addition, while increasing the data demand imposes extensive
bandwidth resources requirement in the feeder link, the proposed
JPFS design works efficiently with available feeder link resources
even if the data demand increases. The proposed design is evaluated
with a close-to-real beam pattern and the latest broadband
communication standard for satellite communications
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
On Distributed Estimation for Resource Constrained Wireless Sensor Networks
We study Distributed Estimation (DES) problem, where several agents observe a noisy version of an underlying unknown physical phenomena (which is not directly observable), and transmit a compressed version of their observations to a Fusion Center (FC), where collective data is fused to reconstruct the unknown. One of the most important applications of Wireless Sensor Networks (WSNs) is performing DES in a field to estimate an unknown signal source. In a WSN battery powered geographically distributed tiny sensors are tasked with collecting data from the field. Each sensor locally processes its noisy observation (local processing can include compression, dimension reduction, quantization, etc) and transmits the processed observation over communication channels to the FC, where the received data is used to form a global estimate of the unknown source such that the Mean Square Error (MSE) of the DES is minimized. The accuracy of DES depends on many factors such as intensity of observation noises in sensors, quantization errors in sensors, available power and bandwidth of the network, quality of communication channels between sensors and the FC, and the choice of fusion rule in the FC. Taking into account all of these contributing factors and implementing a DES system which minimizes the MSE and satisfies all constraints is a challenging task. In order to probe into different aspects of this challenging task we identify and formulate the following three problems and address them accordingly: 1- Consider an inhomogeneous WSN where the sensors\u27 observations is modeled linear with additive Gaussian noise. The communication channels between sensors and FC are orthogonal power and bandwidth-constrained erroneous wireless fading channels. The unknown to be estimated is a Gaussian vector. Sensors employ uniform multi-bit quantizers and BPSK modulation. Given this setup, we ask: what is the best fusion rule in the FC? what is the best transmit power and quantization rate (measured in bits per sensor) allocation schemes that minimize the MSE? In order to answer these questions, we derive some upper bounds on global MSE and through minimizing those bounds, we propose various resource allocation schemes for the problem, through which we investigate the effect of contributing factors on the MSE. 2- Consider an inhomogeneous WSN with an FC which is tasked with estimating a scalar Gaussian unknown. The sensors are equipped with uniform multi-bit quantizers and the communication channels are modeled as Binary Symmetric Channels (BSC). In contrast to former problem the sensors experience independent multiplicative noises (in addition to additive noise). The natural question in this scenario is: how does multiplicative noise affect the DES system performance? how does it affect the resource allocation for sensors, with respect to the case where there is no multiplicative noise? We propose a linear fusion rule in the FC and derive the associated MSE in closed-form. We propose several rate allocation schemes with different levels of complexity which minimize the MSE. Implementing the proposed schemes lets us study the effect of multiplicative noise on DES system performance and its dynamics. We also derive Bayesian Cramer-Rao Lower Bound (BCRLB) and compare the MSE performance of our porposed methods against the bound. As a dual problem we also answer the question: what is the minimum required bandwidth of the network to satisfy a predetermined target MSE? 3- Assuming the framework of Bayesian DES of a Gaussian unknown with additive and multiplicative Gaussian noises involved, we answer the following question: Can multiplicative noise improve the DES performance in any case/scenario? the answer is yes, and we call the phenomena as \u27enhancement mode\u27 of multiplicative noise. Through deriving different lower bounds, such as BCRLB,Weiss-Weinstein Bound (WWB), Hybrid CRLB (HCRLB), Nayak Bound (NB), Yatarcos Bound (YB) on MSE, we identify and characterize the scenarios that the enhancement happens. We investigate two situations where variance of multiplicative noise is known and unknown. We also compare the performance of well-known estimators with the derived bounds, to ensure practicability of the mentioned enhancement modes