High-Rate Space-Time Coded Large MIMO Systems: Low-Complexity Detection and Channel Estimation

In this paper, we present a low-complexity algorithm for detection in high-rate, non-orthogonal space-time block coded (STBC) large-MIMO systems that achieve high spectral efficiencies of the order of tens of bps/Hz. We also present a training-based iterative detection/channel estimation scheme for such large STBC MIMO systems. Our simulation results show that excellent bit error rate and nearness-to-capacity performance are achieved by the proposed multistage likelihood ascent search (M-LAS) detector in conjunction with the proposed iterative detection/channel estimation scheme at low complexities. The fact that we could show such good results for large STBCs like 16x16 and 32x32 STBCs from Cyclic Division Algebras (CDA) operating at spectral efficiencies in excess of 20 bps/Hz (even after accounting for the overheads meant for pilot based training for channel estimation and turbo coding) establishes the effectiveness of the proposed detector and channel estimator. We decode perfect codes of large dimensions using the proposed detector. With the feasibility of such a low-complexity detection/channel estimation scheme, large-MIMO systems with tens of antennas operating at several tens of bps/Hz spectral efficiencies can become practical, enabling interesting high data rate wireless applications.Comment: v3: Performance/complexity comparison of the proposed scheme with other large-MIMO architectures/detectors has been added (Sec. IV-D). The paper has been accepted for publication in IEEE Journal of Selected Topics in Signal Processing (JSTSP): Spl. Iss. on Managing Complexity in Multiuser MIMO Systems. v2: Section V on Channel Estimation is update

Hybrid solutions to instantaneous MIMO blind separation and decoding: narrowband, QAM and square cases

Future wireless communication systems are desired to support high data rates and high quality transmission when considering the growing multimedia applications. Increasing the channel throughput leads to the multiple input and multiple output and blind equalization techniques in recent years. Thereby blind MIMO equalization has attracted a great interest.Both system performance and computational complexities play important roles in real time communications. Reducing the computational load and providing accurate performances are the main challenges in present systems. In this thesis, a hybrid method which can provide an affordable complexity with good performance for Blind Equalization in large constellation MIMO systems is proposed first. Saving computational cost happens both in the signal sep- aration part and in signal detection part. First, based on Quadrature amplitude modulation signal characteristics, an efficient and simple nonlinear function for the Independent Compo- nent Analysis is introduced. Second, using the idea of the sphere decoding, we choose the soft information of channels in a sphere, and overcome the so- called curse of dimensionality of the Expectation Maximization (EM) algorithm and enhance the final results simultaneously. Mathematically, we demonstrate in the digital communication cases, the EM algorithm shows Newton -like convergence.Despite the widespread use of forward -error coding (FEC), most multiple input multiple output (MIMO) blind channel estimation techniques ignore its presence, and instead make the sim- plifying assumption that the transmitted symbols are uncoded. However, FEC induces code structure in the transmitted sequence that can be exploited to improve blind MIMO channel estimates. In final part of this work, we exploit the iterative channel estimation and decoding performance for blind MIMO equalization. Experiments show the improvements achievable by exploiting the existence of coding structures and that it can access the performance of a BCJR equalizer with perfect channel information in a reasonable SNR range. All results are confirmed experimentally for the example of blind equalization in block fading MIMO systems

Proceedings of the Second International Mobile Satellite Conference (IMSC 1990)

Presented here are the proceedings of the Second International Mobile Satellite Conference (IMSC), held June 17-20, 1990 in Ottawa, Canada. Topics covered include future mobile satellite communications concepts, aeronautical applications, modulation and coding, propagation and experimental systems, mobile terminal equipment, network architecture and control, regulatory and policy considerations, vehicle antennas, and speech compression

Machine Learning Applications in Spacecraft State and Environment Estimation

There are some problems in spacecraft systems engineering with highly non-linear characteristics and noise where traditional nonlinear estimation techniques fail to yield accurate results. In this thesis, we consider approaching two such problems using kernel methods in machine learning. First, we present a novel formulation and solution to orbit determination of spacecraft and spacecraft groups which can be applied with very weakly observable and highly noisy scenarios. We present a ground station network architecture that can perform orbit determination using Doppler-only observations over the network. Second, we present a machine learning solution to the spacecraft magnetic field interference cancellation problem using distributed magnetometers paving the way for space magnetometry with boom-less CubeSats. We present an approach to orbit determination under very broad conditions that are satisfied for n-body problems. We show that domain generalization and distribution regression techniques can learn to estimate orbits of a group of satellites and identify individual satellites especially with prior understanding of correlations between orbits and provide asymptotic convergence conditions. The approach presented requires only observability of the dynamical system and visibility of the spacecraft and is particularly useful for autonomous spacecraft operations using low-cost ground stations or sensors. With the absence of linear region constraints in the proposed method, we are able to identify orbits that are 800 km apart and reduce orbit uncertainty by 92.5% to under 60 km with noisy Doppler-only measurements. We present an architecture for collaborative orbit determination using networked ground stations. We focus on clusters of satellites deployed in low Earth orbit and measurements of their Doppler-shifted transmissions made by low-gain antenna systems in a software-defined federated ground station network. We develop a network architecture enabling scheduling and tracking with uncertain orbit information. For the proposed network, we also present scheduling and coordinated tracking algorithms for tracking with the purpose of generating measurements for orbit determination. We validate our algorithms and architecture with its application to high fidelity simulations of different networked orbit determination scenarios. We demonstrate how these low-cost ground stations can be used to provide accurate and timely orbital tracking information for large satellite deployments, which is something that remains a challenge for current tracking systems. Last, we present a novel approach and algorithm to the problem of magnetic field interference cancellation of time-varying interference using distributed magnetometers and spacecraft telemetry with particular emphasis on the computational and power requirements of CubeSats. The spacecraft magnetic field interference cancellation problem involves estimation of noise when the number of interfering sources far exceed the number of sensors required to decouple the noise from the signal. The proposed approach models this as a contextual bandit learning problem and the proposed algorithm learns to identify the optimal low-noise combination of distributed magnetometers based on indirect information gained on spacecraft currents through telemetry. Experimental results based on on-orbit spacecraft telemetry shows a 50% reduction in interference compared to the best magnetometer.PHDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147688/1/srinag_1.pd

Contributions On Theory And Practice For Multi-Mission Wireless Systems

The field of wireless systems has long been an active research area with various applications. Recently much attention has been given to multi-mission wireless systems that combine capabilities including information sensing, data processing, energy harvesting as well as the traditional data communication. This dissertation describes our endeavor in addressing some of the research challenges in multi-mission wireless systems, including the development of fundamental limits of such multi-mission wireless systems and effective technologies for improved performance. The first challenge addressed in this dissertation is how to handle interference, which is encountered in almost all wireless systems involving multiple nodes, an attribute shared by most multi-mission systems. To deepen our understanding on the impact of interference, we study a class of Gaussian interference channels (GICs) with mixed interference. A simple coding scheme is proposed based on Sato\u27s non-naive frequency division. The achievable region is shown to be equivalent to that of Costa\u27s noiseberg region for the one-sided Gaussian interference channel. This allows for an indirect proof that this simple achievable rate region is indeed equivalent to the Han-Kobayashi (HK) region with Gaussian input and with time sharing for this class of Gaussian interference channels with mixed interference. Optimal power management strategies are then investigated for a remote estimation system with an energy harvesting sensor. We first establish the asymptotic optimality of uncoded transmission for such a system under Gaussian assumption. With the aim of minimizing the mean squared error (MSE) at the receiver, optimal power allocation policies are proposed under various assumptions with regard to the knowledge at the transmitter and the receiver as well as battery storage capacity. For the case where non-causal side information (SI) of future harvested energy is available and battery storage is unlimited, it is shown that the optimal power allocation amounts to a simple \u27staircase-climbing\u27 procedure, where the power level follows a non-decreasing staircase function. For the case where battery storage has a finite capacity, the optimal power allocation policy can also be obtained via standard convex optimization techniques. Dynamic programming is used to optimize the allocation policy when causal SI is available. The issue of unknown transmit power at the receiver is also addressed. Finally, to make the proposed solutions practically more meaningful, two heuristic schemes are proposed to reduce computational complexity. Related to the above remote sensing problem, we provide an information theoretic formulation of a multi-functioning radio where communication between nodes involves transmission of both messages and source sequences. The objective is to study the optimal coding trade-off between the rate for message transmission and the distortion for source sequence estimation. For point-to-point systems, it is optimal to simply split total capacity into two components, one for message transmission and one for source transmission. For the multi-user case, we show that such separation-based scheme leads to a strictly suboptimal rate-distortion trade-off by examining the simple problem of sending a common source sequence and two independent messages through a Gaussian broadcast channel. Finally we study the design of a practical multi-mission wireless system - the dual-use of airborne radio frequency (RF) systems. Specifically, airborne multiple-input-multiple-output (MIMO) communication systems are leveraged for the detection of moving targets in a typical airborne environment that is characterized by the lack of scatterers. With uniform linear arrays (ULAs), angular domain decomposition of channel matrices is utilized and target detection can be accomplished by detection of change in the resolvable paths in the angular domain. For both linear and nonlinear arrays, Doppler frequency analysis can also be applied and the change in frequency components indicates the presence of potential airborne targets. Nonparametric detection of distribution changes is utilized in both approaches

Ship localization using AIS signals received by satellite

This paper addresses the problem of ship localization by using the messages received by satellites and transmitted by the automatic identification system (AIS). In particular, one considers the localization of ships that do not transmit their actual position in AIS signals. The proposed localization method is based on the least squares algorithm and uses the differences of times of arrival and the carrier frequencies of the messages received by satellite. A modification of this algorithm is proposed to take into account the displacement model of the ships as additional measurements. This modification shows a significant localization improvement

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig