High-resolution Type II CSI feedback is a key differentiator between 4G (LTE) and 5G (NR). The large feedback overhead of Type II CSI reporting, however, is a bottleneck for UE implementations in real deployments. In this paper, a space-frequency compression based Type II CSI overhead reduction has been proposed. The system-level simulation results are provided to show that the performance similar to Type II CSI is achievable with significantly reduced CSI overhead.

This paper studies the haplotype assembly problem from an information-theoretic perspective. In the human genome, a haplotype is a sequence of nucleotide bases on a chromosome that differ from the bases in the corresponding positions on the other chromosome in a homologous pair. Haplotype sequences can conveniently be represented by binary strings, which enable us to transform the bioinformatics problem of haplotype assembly into an equivalent information-theoretic problem. Information about the order of bases in a genome is readily inferred using short reads provided by high-throughput DNA sequencing technologies. Performing haplotype assembly is challenging due to limited lengths of the reads and the presence of sequencing errors. In this paper, the recovery of the target pair of haplotype sequences using short reads is transformed into an equivalent joint source-channel coding problem. Two binary messages, representing haplotypes and chromosome memberships of reads, are encoded and transmitted over a channel with erasures and errors, where the channel model reflects salient features of high-throughput sequencing. The focus of this paper is on determining the required number of reads for reliable haplotype reconstruction. For the error-free reading case, erasure decoding is shown to be one of the optimal algorithms enabling reliable haplotype assembly. For the erroneous reading case, spectral partitioning is proved to be an efficient algorithm with orderwise optimal bounds.

This paper presents a polar coding scheme to achieve secrecy in block fading binary symmetric wiretap channels without the knowledge of instantaneous channel state information (CSI) at the transmitter. For this model, a coding scheme that hierarchically utilizes polar codes is presented. In particular, on the polarization of different binary symmetric channels over different fading blocks, each channel use is modeled as an appropriate binary erasure channel over fading blocks. Polar codes are constructed for both coding over channel uses for each fading block and coding over fading blocks for certain channel uses. In order to guarantee security, random bits are introduced at appropriate places to exhaust the observations of the eavesdropper. It is shown that this coding scheme, without instantaneous CSI at the transmitter, is secrecy capacity achieving for the simultaneous fading scenario. For the independent fading case, the capacity is achieved when the fading realizations for the eavesdropper channel are always degraded with respect to the receiver. For the remaining cases, the gap between lower and upper bounds is analyzed. Remarkably, for the scenarios where the secrecy capacity is achieved, the results imply that the instantaneous CSI does not increase the secrecy capacity.

The future wireless networks, such as Centralized Radio Access Network (C-RAN), will need to deliver data rate about 100 times to 1000 times the current 4G technology. For C-RAN based network architecture, there is a pressing need for tremendous enhancement of the effective data rate of the Common Public Radio Interface (CPRI). Compression of CPRI data is one of the potential enhancements. We introduce a vector quantization based compression algorithm for CPRI links, utilizing Lloyd algorithm. Methods to vectorize the I/Q samples and enhanced initialization of Lloyd algorithm for codebook training are investigated for improved performance. Multi-stage vector quantization is considered to reduce codebook search complexity. Simulation results show that our solution can achieve compression of 4 times for uplink and 4.5 times for downlink, within 2% Error Vector Magnitude (EVM) distortion. Remarkably, vector quantization codebook proves to be quite robust against data modulation mismatch, fading, signal-to-noise (SNR) and Doppler spread.

This paper presents a polar coding scheme for fading wiretap channels that achieves reliability as well as security without the knowledge of instantaneous channel state information at the transmitter. Specifically, a block fading model is considered for the wiretap channel that consists of a transmitter, a receiver, and an eavesdropper; and only the information regarding the statistics (i.e., distribution) of the channel state information is assumed at the transmitter. For this model, a coding scheme that hierarchically utilizes polar codes is presented in order to address channel state variation. In particular, on polarization of different binary symmetric channels over different fading blocks, each channel use (corresponding to a possibly different polarization) is modeled as an appropriate binary erasure channel over fading blocks. Polar codes are constructed for both coding over channel uses for each fading block and coding over fading blocks for certain channel uses. In order to guarantee security, message bits are transmitted such that they can be reliably decoded at the receiver, and random bits are introduced to exhaust the observations of the eavesdropper. It is shown that this coding scheme, without instantaneous channel state information at the transmitter, is secrecy capacity achieving for the corresponding fading binary symmetric wiretap channel.

A general method of source coding is proposed in this paper, which enables one to reduce the problem of compressing an analog (continuous-valued) source to a set of much simpler problems, compressing discrete sources. Specifically, the focus is on lossy compression of exponential and Laplace sources, which are represented as set of discrete variables through a finite alphabet expansion. Due to the decomposability property of such sources, the resulting random variables post expansion are independent and discrete. Thus, these variables can be considered as independent discrete source coding problems, and the original problem is reduced to coding over these sources with a total distortion constraint. Any feasible solution to this resulting optimization problem corresponds to an achievable rate distortion pair of the original continuous-valued source compression problem. Although finding the optimal solution for a given distortion is not a tractable task, we show that, via a heuristic choice, our expansion coding scheme still presents a good performance in the low distortion regime. Further, by adopting low-complexity codes designed for discrete source coding, the total coding complexity can be reduced for practical implementations.

This paper studies the haplotype assembly problem from an information-theoretic perspective. A haplotype is a sequence of nucleotide bases on a chromosome, often conveniently represented by a binary string, that differ from the bases in the corresponding positions on the other chromosome in a homologous pair. Information about the order of bases in a genome is readily inferred using short reads provided by high-throughput DNA sequencing technologies. Associating reads that cover variant positions with specific chromosomes in a homologous pairs, which enables haplotype assembly, is challenging due to limited lengths of the reads and presence of sequencing errors. In this paper, the recovery of the target pair of haplotype sequences using short reads is rephrased as a joint source-channel coding problem. Two messages, representing haplotypes and chromosome memberships of reads, are encoded and transmitted over a channel with erasures and errors, where the channel model reflects salient features of high-throughput sequencing. The focus of this paper is on determining the required number of reads for reliable haplotype reconstruction, and both the necessary and sufficient conditions are presented with order-wise optimal bounds.

This work presents a polar coding scheme for fading channels, focusing primarily on fading binary symmetric and additive exponential noise channels. For fading binary symmetric channels, a hierarchical coding scheme is presented, utilizing polar coding both over channel uses and over fading blocks. The receiver uses its channel state information (CSI) to distinguish states, thus constructing an overlay erasure channel over the underlying fading channels. By using this scheme, the capacity of a fading binary symmetric channel is achieved without CSI at the transmitter. Noting that a fading AWGN channel with BPSK modulation and demodulation corresponds to a fading binary symmetric channel, this result covers a fairly large set of practically relevant channel settings. For fading additive exponential noise channels, expansion coding is used in conjunction to polar codes. Expansion coding transforms the continuous-valued channel to multiple (independent) discrete-valued ones. For each level after expansion, the approach described previously for fading binary symmetric channels is used. Both theoretical analysis and numerical results are presented, showing that the proposed coding scheme approaches the capacity in the high SNR regime. Overall, utilizing polar codes in this (hierarchical) fashion enables coding without CSI at the transmitter, while approaching the capacity with low complexity.

A polar coding scheme for fading channels is proposed in this paper. More specifically, the focus is on the Gaussian fading channel with a BPSK modulation, where the equivalent channel is modeled as a binary symmetric channel with varying cross-over probabilities. To deal with variable channel states, a coding scheme of hierarchically utilizing polar codes is proposed. In particular, by observing the polarization of different binary symmetric channels over different fading blocks, each channel use corresponding to a different polarization is modeled as a binary erasure channel such that polar codes could be adopted to encode over blocks. It is shown that the proposed coding scheme, without instantaneous channel state information at the transmitter, achieves the capacity of the corresponding fading binary symmetric channel.

A general method of coding over expansions is proposed, which allows one to reduce the highly non-trivial problem of coding over continuous channels to a much simpler discrete ones. More specifically, the focus is on the additive exponential noise (AEN) channel, for which the (binary) expansion of the (exponential) noise random variable is considered. It is shown that each of the random variables in the expansion corresponds to independent Bernoulli random variables. Thus, each of the expansion levels (of the underlying channel) corresponds to a binary symmetric channel (BSC), and the coding problem is reduced to coding over these parallel channels while satisfying the channel input constraint. This optimization formulation is stated as the achievable rate result, for which a specific choice of input distribution is shown to achieve a rate which is arbitrarily close to the channel capacity in the high SNR regime. Remarkably, the scheme allows for low-complexity capacity-achieving codes for AEN channels, using the codes that are originally designed for BSCs. Extensions to different channel models and applications to other coding problems are discussed.

In next generation networks, mobile communication calls for service with higher quality, which brings new challenge for mobility management. Thereinto, utilization and improvement of mobility prediction helps for preserving resource and providing better performance. So this paper aims to propose a theoretical and factual method to perform mobility prediction in cellular network. By analyzing the demand and character of this kind of personal mobility prediction in large spacial and temporal scale, it is concluded that Hidden Markov Model fits for system modeling. However, classical HMM algorithm will meet with numerical calculation problem when adopted to practical communication system. An improved algorithm is put forward to overcome possible calculating defects. Three different scenarios are set to testify HMM's efficiency and accuracy, using factual measurement data in cellular network.

Understanding of urban mobility dynamics benefits both aggregate human mobility in wireless communications, and the planning and provision of urban facilities and services. Due to the high penetration of cell phones, the cellular networks provide information for urban dynamics with large spatial extent and continuous temporal coverage. In this paper, a novel approach is proposed to explore the space-time structure of urban dynamics, based on the original data collected by cellular networks in a southern city of China, recording population distribution by dividing the city into thousands of pixels. By applying principal component analysis, the intrinsic dimensionality is revealed. The structure of all the pixel population variations could be well captured by a small set of eigen pixel population variations. According to the classification of eigen pixel population variations, each pixel population variation can be decomposed into three constitutions: deterministic trends, short-lived spikes, and noise. Moreover, the most significant eigen pixel population variations are utilized in the applications of forecasting and anomaly detection.