Wireless Channel Induced Coding

Feature point matching aims to automatically establish point-to-point correspondences between two images acquired from the same scene but through two different viewpoints. This matching process is essential to many image processing and computer vision tasks, such as image registration, object detection and tracking, to name a few. However, the well-known SIFT feature descriptors together with the use of the nearest neighbor matching criterion oftentimes lead to many incorrect point-to-point correspondences. This would be incurred especially when the two images undergo a large viewpoint variation and/or contain a severely cluttered background. To improve the performance of feature point matching process, three contributions are made in this thesis: 1) mismatch removal; 2) common visual pattern discovery; and 3) feature histogram equalization (FHE). The first two are graph model-based approaches, and the last one is a novel technique on the enhancement of feature descriptors. More details are provided as follows. For certain image processing applications such as matching two images captured from disparate views, or the so-called wide-baseline image matching as demonstrated in this thesis, a sufficiently large number of point-to-point correspondences are needed in the first place. However, these corresponding pairs often contain mismatched ones. Consequently, the accuracy of the spatial transformation estimation between the two images under matching will be reduced to a certain degree. Therefore, how to identify and remove the mismatched corresponding pairs from the established ones is the main goal of our first contribution. For that, a bipartite graph model is exploited to the segmented image regions to establish all possible region-to-region correspondences. After that one-to-one region correspondences, called the coherent region pairs (CRPs), can be further identified by using the Hungarian method and the proposed region-to-region similarity measurement metric. The established CRPs will be utilized as the reference information in order to identify and remove those mismatched point-to-point correspondences. Extensive experimental results have demonstrated that our proposed mismatch removal method could reduce incorrect SIFT-based point-to-point correspondences for the application of wide-baseline image matching that normally requires a large targeted number of matching pairs. For the second contribution on common visual pattern discovery, the entire work still begins with the SIFT feature descriptors. However, the main objective is to yield the correct point-to-point matching pairs in the first place, while the actual number of such established matching pairs tends to be small. The key novelty of our approach lies in the use of the directed graph (or diagraph) model that has two link weights on each link, rather than one link weight inherited in the conventional graph model. The principle of pairwise spatial consistency is first exploited to generate the initial link weight for each link. Based on this link weight value, two relative link weights are then generated by considering the relativeness of neighboring vertices at each vertex of the link. For that, the n-ranking process and a novel link weight enhancement technique are proposed. Consequently, the resulted relative link weights are more robust to combat various adverse scenarios such as large viewpoint variations and indiscriminative feature descriptors. Based on the relative link weights generated at each assumed scale change factor, the strongly-associated subgraph can be extracted from the digraph by applying the non-cooperative game theory for handling non-symmetric adjacency matrix issue. All the vertices (i.e., point-to-point correspondences) belonging to the strongly-associated subgraph extracted from the diagraph established at an assumed scale change factor are collectively treated as one common visual pattern; hopefully, this set of vertices corresponds to one visual object. If this is not the case, our proposed topological splitting algorithm might be able to further discriminate them. Extensive experiments have been conducted on the simulated SIFT feature points for standing out the technical challenges, followed by performing evaluations on six thoughtfully chosen natural image pairs and Columbia dataset to demonstrate the efficacy and robustness of the proposed method. For the third contribution, feature contrast is first introduced as a measurement of the degree of self-similarity contained in an image pair. Note that, for an image pair with strong self-similarity, the established SIFT feature descriptors would be quite similar to each other, therefore the feature contrast is low. The FHE is then proposed to equalize SIFT feature descriptors by independently modifying the vector-component values of feature descriptors at each vector dimension; consequently, the feature contrast is effectively enhanced for the purpose of better discrimination of the feature descriptors. Extensive simulation results have clearly shown that our proposed FHE method could effectively improve the precision of SIFT-based point-to-point correspondences, especially to those image pairs containing a large amount of self-similar regions. As expected, if the image pairs contain only a few or even no self-similar regions, the performance gain would be marginal in this case.

In this paper, carrier multiplexing generalized space shift keying (CM-GSSK) transmission scheme with low decoding complexity is proposed for multiple-input multiple-output (MIMO) systems, using quadrature carriers to increase the spectral efficiency. The CM-GSSK transmission scheme allows two antennas to be active during the same time slot, where the information is conveyed only by the transmit-antenna index. In CM-GSSK transmission scheme, each antenna transmits quadrature carriers, whereas conventional generalized space shift keying (GSSK) transmits the same radio frequency carrier pulse. Unlike a traditional linear decorrelator which is designed for MIMO receiver, a low complexity linear decorrelator based decoder for CM-GSSK scheme is presented and its performance is analyzed. At the receiver, the linear decorrelator decouples the received signal, which in turn, it significantly reduces the decoding complexity. The bit error rate (BER) performance results and the complexity of the proposed low complexity decoding scheme is compared with the baseline SSK schemes to demonstrate its efficiency.

For ADSL applications, very large constellation sizes are used, which can be as large as 32768QAM. It is a big challenge to design turbo TCM for such a large constellation size while still maintaining low decoding complexity. We propose a new turbo coded QAM scheme with a unique concatenated Gray for an ADSL system. This new mapping can not only provide good performance for turbo coded bits but can also efficiently maximize the minimum Euclidean distance of uncoded bits. This new turbo coded QAM scheme not only has as good error correcting performance as the conventional bit-interleaved turbo coded QAM and symbol-interleaved turbo TCM schemes, but also has very low decoding complexity which is only about 17% of the conventional bit-interleaved turbo coded QAM.

In this paper, we construct binary spatially coupled (SC) low-density parity-check (LDPC) codes based on Euclidean geometry (EG) LDPC codes for storage applications, where high error correction capability, extremely low uncorrectable bit error rate (UBER), and low decoding complexity are required. We propose a systematic way to construct the families of SC LDPC codes from $(m,2^{s})$ EG LDPC codes, which are termed EG-SC LDPC codes. In the construction method, we propose a 2-D edge-spreading process to construct the base matrix of EG-SC LDPC codes, which consists of matrix unwrapping and periodically time-varying of a protograph. A lower bound on the rank of the parity-check matrix of an EG-SC LDPC code is derived. We evaluate the error rate performance of the constructed EG-SC LDPC codes by using a weighted bit-flipping decoding algorithm for its low decoding complexity. Numerical results show that the UBER performance of the constructed EG-SC LDPC codes is superior to that of their EG LDPC code counterparts, and show no error floor compared with the constructed protograph SC LDPC codes and regular LDPC codes.

In this paper, we focus on mobile wireless networks comprising of a powerful communication center and a multitude of mobile users. We investigate the propagation of deadline-based content in the wireless network characterized by heterogeneous (time-varying and user-dependent) wireless channel conditions, heterogeneous user mobility, and where communication could occur in a hybrid format (e.g., directly from the central controller or by exchange with other mobiles in a peer-to-peer manner). We show that exploiting double opportunities, i.e., both time-varying channel conditions and mobility, can result in substantial performance gains. We develop a class of double opportunistic multicast schedulers and prove their optimality in terms of both utility and fairness under heterogeneous channel conditions and user mobility. Extensive simulation results are provided to demonstrate that these algorithms can not only substantially boost the throughput of all users (e.g., by 50% to 150%), but also achieve different consideration of fairness among individual users and groups of users.

We propose a novel class of provably good codes which are a serial concatenation of a single-parity-check (SPC)-based product code, an interleaver, and a rate-1 recursive convolutional code. The proposed codes, termed product accumulate (PA) codes, are linear time encodable and linear time decodable. We show that the product code by itself does not have a positive threshold, but a PA code can provide arbitrarily low bit-error rate (BER) under both maximum-likelihood (ML) decoding and iterative decoding. Two message-passing decoding algorithms are proposed and it is shown that a particular update schedule for these message-passing algorithms is equivalent to conventional turbo decoding of the serial concatenated code, but with significantly lower complexity. Tight upper bounds on the ML performance using Divsalar's (1999) simple bound and thresholds under density evolution (DE) show that these codes are capable of performance within a few tenths of a decibel away from the Shannon limit. Simulation results confirm these claims and show that these codes provide performance similar to turbo codes but with significantly less decoding complexity and with a lower error floor. Hence, we propose PA codes as a class of prospective codes with good performance, low decoding complexity, regular structure, and flexible rate adaptivity for all rates above 1/2.

A new class of quasi-orthogonal space-time block code (QO-STBC) namely minimum-decoding-complexity QO-STBC (MDC-QOSTBC) has recently been proposed in the literature. In this paper, we analyze some of its essential code parameters and code properties. Specifically we derive its maximum achievable code rate expression for any number of transmit antennas. We also derive the closed form expression of its diversity product and use it to obtain the optimum constellation rotation in order to achieve optimum decoding performance. Other performance benefits of MDC-QOSTBC, such as low decoding complexity, good coding gain and power distribution property, flexibility in supporting any constellation and number of transmit antennas, are also presented and discussed.

In the present paper, we propose a broadcast ARQ protocol based on the concept of index coding. In the proposed scenario, a server wishes to transmit a finite sequence of packets to multiple receivers via a broadcast channel with packet erasures until all of the receivers successfully receive all of the packets. In the retransmission phase, the server produces a coded packet as a retransmitted packet based on the side-information sent from the receivers via feedback channels. A notable feature of the proposed protocol is that the decoding process at the receiver side has low decoding complexity because only a small number of addition operations are needed in order to recover an intended packet. This feature may be preferable for reducing the power consumption of receivers. The throughput performance of the proposed protocol is close to that of the ideal FEC throughput performance when the erasure probability is less than 0.1. This implies that the proposed protocol provides almost optimal throughput performance in such a regime.

Long Term Evolution (LTE) network offers a high throughput with low latency that makes it the best choice for Evolved Multimedia Broadcast Multicast Service (E-MBMS). E-MBMS transmits data to all users simultaneously using the same channel and same Modulation and Coding Scheme (MCS) level. Each user experiences different channel condition, so an MCS level should be selected properly to satisfy all users. Typically, MCS level is selected on the basis of users' worst channel condition which results in wasting network resources. Since the E-MBMS performance directly depends on the selected MCS level, an efficient Adaptive Modulation and Coding (AMC) scheme is essentially needed. This paper introduces a new mechanism that uses the Standard Deviation (SD) of MCS level of E-MBMS user group operated on single cell mode to efficiently select its optimal MCS level. Simulation results show that the proposed mechanism increase the spectrum efficiency, throughput, and lower Packet Loss Ratio compared to the Worst User Channel Condition mechanism and Fixed Predefined MCS mechanism.

In this paper, the performance limits and the computational complexity of the lattice sequential decoder are analyzed for the unconstrained additive white Gaussian noise channel. The performance analysis available in the literature for such a channel has been studied only under the use of the minimum Euclidean distance decoder that is commonly referred to as the lattice decoder. Lattice decoders based on solutions to the NP-hard closest vector problem are very complex to implement, and the search for low complexity receivers for the detection of lattice codes is considered a challenging problem. However, the low computational complexity advantage that sequential decoding promises, makes it an alternative solution to the lattice decoder. In this work, we characterize the performance and complexity tradeoff via the error exponent and the decoding complexity, respectively, of such a decoder as a function of the decoding parameter - the bias term. For the above channel, we derive the cut-off volume-to-noise ratio that is required to achieve a good error performance with low decoding complexity.

In this paper, the performance limits and the computational complexity of the lattice sequential decoder are analyzed for the unconstrained additive white Gaussian noise channel. The performance analysis available in the literature for such a channel has been studied only under the use of the minimum Euclidean distance decoder that is commonly referred to as the \\textit{lattice decoder}. Lattice decoders based on solutions to the NP-hard closest vector problem are very complex to implement, and the search for low complexity receivers for the detection of lattice codes is considered a challenging problem. However, the low computational complexity advantage that sequential decoding promises, makes it an alternative solution to the lattice decoder. In this work, we characterize the performance and complexity tradeoff via the error exponent and the decoding complexity, respectively, of such a decoder as a function of the decoding parameter --- the bias term. For the above channel, we derive the cut-off volume-to-noise ratio that is required to achieve a good error performance with low decoding complexity.

Communication-based train control (CBTC) systems use wireless local area networks (WLANs) for train-ground transmission. Transmission delays and packet drops may cause unnecessary tractions and brakes of trains, degradation of riding comfortability, rising of energy consumption and loss of the line's capacity. In this paper, We model the system to control a group of trains in CBTC as a networked control system (NCS) with transmission delays and packet drops. We study the delays and packet drops in CBTC, analyze their effects on train's control, and propose a method to improve the performance of CBTC. Extensive simulation results are presented. We show that our proposed method has a strong tolerance for transmission delays and packet drops, and outperforms the currently adopted scheme in different channel conditions.

Visible light communications integrated with Internet-of-Things has numerous applications in indoor wireless communications and it requires high data rates and flicker mitigation. Flicker mitigation is an important consideration since light emitting diodes are simultaneously used for illumination and communications. Several run-length limited (RLL) codes have been proposed in the literature for flicker mitigation with trade-offs between code rate, bit error performance, encoding and decoding complexities. However, there are no generalized algorithms for high rate codes generation with low complexity encoding and decoding. Hence, in this paper, we propose generalized algorithm for generating high rate RLL codes with low complexity encoding and decoding. The proposed codes can be used for flicker mitigation conditioned on maximum flickering time period (MFTP). The performance of the proposed codes is compared with existing codes in terms of various metrics like code rate, minimum Hamming distance, peak to average power ratio, run-length, and bit error rate. We show that the proposed codes mitigate flicker conditioned on MFTP and provide high data rates with low complexity encoding and decoding compared to the codes in the literature.

Single Spectrum Bipartite Graph (SSBG) model is developed to forecast thunderstorms over Kolkata(22∘32′N,88∘20′E)during the premonsoon season (April-May). The statistical distribution of normal probability is observed for temperature, relative humidity, convective available potential energy (CAPE), and convective inhibition energy (CIN) to quantify the threshold values of the parameters for the prevalence of thunderstorms. Method of conditional probability is implemented to ascertain the possibilities of the occurrence of thunderstorms within the ranges of the threshold values. The single spectrum bipartite graph connectivity model developed in this study consists of two sets of vertices; one set includes two time vertices (00UTC, 12UTC) and the other includes four meteorological parameters: temperature, relative humidity, CAPE, and CIN. Three distinct ranges of maximal eigen values are obtained for the three categories of thunderstorms. Maximal eigenvalues for severe, ordinary, and no thunderstorm events are observed to be(2.6±0.12),(1.88±0.09), and(1.26±.03), respectively. The ranges of the threshold values obtained using ten year data (1997–2006) are considered as the reference range and the result is validated with the IMD (India Meteorological Department) observation, Doppler Weather Radar (DWR) Products, and satellite images of 2007. The result reveals that the model provides 12- to 6-hour forecast (nowcasting) of thunderstorms with 96% to 98% accuracy.

Given limitations with current technology, nodes in a sensor network have stringent energy and complexity constraints. This paper presents a scheme for cooperative error-control coding, using rateless and low-density generator-matrix codes, for sensor networks. Assuming knowledge of the source-relay channel quality, we show that the proposed scheme achieves good performance and a good energy tradeoff despite low computational complexity. The scheme exploits the flexibility of rateless and LDGM codes to permit, depending on the channel conditions, independent, relay and cooperative modes of operation. As a motivating example, we analyze networks of two cooperating nodes communicating with a more sophisticated receiver. We also discuss the generalization of our framework to a multi-node system.