arXiv subjects

Zhi Ding

Publications and source records attributed to Zhi Ding.

At least 91 records · Page 5Linked to original sources

arXiv2018

Engineering Radio Map for Wireless Resource Management

Radio map in general refers to the geographical signal power spectrum density, formed by the superposition of concurrent wireless transmissions, as a function of location, frequency and time. It contains rich and useful information regarding the spectral activities and propagation channels in wireless networks. Such information can be leveraged to not only improve the performance of existing wireless services (e.g., via proactive resource provisioning) but also enable new applications, such as wireless spectrum surveillance. However, practical implementation of radio maps in wireless communication is often difficult due to the heterogeneity of modern wireless networks and the sheer amount of wireless data to be collected, stored, and processed. In this article, we provide an overview on the state-of-the-art techniques for constructing radio maps as well as applying them to achieve efficient and secure resource management in wireless networks. Some key research challenges are highlighted to motivate future investigation.

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arXiv2018

Beam Domain Massive MIMO for Optical Wireless Communications with Transmit Lens

This paper presents a novel massive multiple-input multiple-output (MIMO) transmission in beam domain for optical wireless communications. The optical base station equipped with massive optical transmitters communicates with a number of user terminals (UTs) through a transmit lens. Focusing on LED transmitters, we analyze light refraction of the lens and establish a channel model for optical massive MIMO transmissions. For a large number of LEDs, channel vectors of different UTs become asymptotically orthogonal. We investigate the maximum ratio transmission and regularized zero-forcing precoding in the optical massive MIMO system, and propose a linear precoding design to maximize the sum rate. We further design the precoding when the number of transmitters grows asymptotically large, and show that beam division multiple access (BDMA) transmission achieves the asymptotically optimal performance for sum rate maximization. Unlike optical MIMO without a transmit lens, BDMA can increase the sum rate proportionally to $2K$ and $K$ under the total and per transmitter power constraints, respectively, where $K$ is the number of UTs. In the non-asymptotic case, we prove the orthogonality conditions of the optimal power allocation in beam domain and propose efficient beam allocation algorithms. Numerical results confirm the significantly improved performance of our proposed beam domain optical massive MIMO communication approaches.

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arXiv2018

Blind Over-the-Air Computation and Data Fusion via Provable Wirtinger Flow

Over-the-air computation (AirComp) shows great promise to support fast data fusion in Internet-of-Things (IoT) networks. AirComp typically computes desired functions of distributed sensing data by exploiting superposed data transmission in multiple access channels. To overcome its reliance on channel station information (CSI), this work proposes a novel blind over-the-air computation (BlairComp) without requiring CSI access, particularly for low complexity and low latency IoT networks. To solve the resulting non-convex optimization problem without the initialization dependency exhibited by the solutions of a number of recently proposed efficient algorithms, we develop a Wirtinger flow solution to the BlairComp problem based on random initialization. To analyze the resulting efficiency, we prove its statistical optimality and global convergence guarantee. Specifically, in the first stage of the algorithm, the iteration of randomly initialized Wirtinger flow given sufficient data samples can enter a local region that enjoys strong convexity and strong smoothness within a few iterations. We also prove the estimation error of BlairComp in the local region to be sufficiently small. We show that, at the second stage of the algorithm, its estimation error decays exponentially at a linear convergence rate.

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arXiv2018

Generalized Low-Rank Optimization for Topological Cooperation in Ultra-Dense Networks

Network densification is a natural way to support dense mobile applications under stringent requirements, such as ultra-low latency, ultra-high data rate, and massive connecting devices. Severe interference in ultra-dense networks poses a key bottleneck. Sharing channel state information (CSI) and messages across transmitters can potentially alleviate interferences and improve system performance. Most existing works on interference coordination require significant CSI signaling overhead and are impractical in ultra-dense networks. This paper investigate topological cooperation to manage interferences in message sharing based only on network connectivity information. In particular, we propose a generalized low-rank optimization approach to maximize achievable degrees-of-freedom (DoFs). To tackle the challenges of poor structure and non-convex rank function, we develop Riemannian optimization algorithms to solve a sequence of complex fixed rank subproblems through a rank growth strategy. By exploiting the non-compact Stiefel manifold formed by the set of complex full column rank matrices, we develop Riemannian optimization algorithms to solve the complex fixed-rank optimization problem by applying the semidefinite lifting technique and Burer-Monteiro factorization approach. Numerical results demonstrate the computational efficiency and higher DoFs achieved by the proposed algorithms.

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arXiv2018

Data Shuffling in Wireless Distributed Computing via Low-Rank Optimization

Intelligent mobile platforms such as smart vehicles and drones have recently become the focus of attention for onboard deployment of machine learning mechanisms to enable low latency decisions with low risk of privacy breach. However, most such machine learning algorithms are both computation-and-memory intensive, which makes it highly difficult to implement the requisite computations on a single device of limited computation, memory, and energy resources. Wireless distributed computing presents new opportunities by pooling the computation and storage resources among devices. For low-latency applications, the key bottleneck lies in the exchange of intermediate results among mobile devices for data shuffling. To improve communication efficiency, we propose a co-channel communication model and design transceivers by exploiting the locally computed intermediate values as side information. A low-rank optimization model is proposed to maximize the achieved degrees-of-freedom (DoF) by establishing the interference alignment condition for data shuffling. Unfortunately, existing approaches to approximate the rank function fail to yield satisfactory performance due to the poor structure in the formulated low-rank optimization problem. In this paper, we develop an efficient DC algorithm to solve the presented low-rank optimization problem by proposing a novel DC representation for the rank function. Numerical experiments demonstrate that the proposed DC approach can significantly improve the communication efficiency whereas the achievable DoF almost remains unchanged when the number of mobile devices grows.

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arXiv2018

Iterative Turbo Receiver for LDPC-Coded MIMO Systems Based on Semi-definite Relaxation

In this work, we develop a new iterative turbo receiver for LDPC-coded multi-antenna systems based on semidefinite relaxation (SDR). For a classical turbo receiver, forward error correction (FEC) code is only used at decoder. Nonetheless, by taking advantage of FEC code in the detection stage, our proposed SDR detector can output extrinsic information with much improved reliability to the decoder. We also propose a simplified SDR turbo receiver that solves only one SDR problem per codeword instead of solving multiple SDR problems in the iterative turbo processing. This scheme significantly reduces the time complexity of SDR turbo receiver, while the error performance remains similar as before. In fact, our simplified scheme is generic in the sense that it is applicable to any list-based iterative receivers.

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arXiv2018

Non-iterative Joint Detection-Decoding Receiver for LDPC-Coded MIMO Systems Based on SDR

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arXiv2018

Integrated Semi-definite Relaxation Receiver for LDPC-Coded MIMO Systems

Semi-definite relaxation (SDR) detector has been demonstrated to be successful in approaching maximum likelihood (ML) performance while the time complexity is only polynomial. We propose a new receiver jointly utilizing the forward error correction (FEC) code information in the SDR detection process. Strengthened by code constraints, the joint SDR detector substantially improves the overall receiver performance. For further performance boost, the ML-SDR detector is adapted to MAP-SDR detector by incorporating a priori information in the cost function. Under turbo principle, MAP-SDR detector takes in soft information from decoder and outputs extrinsic information with much improved reliability. We also propose a simplified SDR turbo receiver that solves only one SDR per codeword instead of solving multiple SDRs in the iterative turbo processing. This scheme significantly reduces the time complexity of SDR turbo receiver, while the error performance remains similar as before. In fact, our simplified scheme is generic and it can be applied to any list-based iterative receivers.

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arXiv2018

Graph-Based Radio Resource Management for Vehicular Networks

This paper investigates the resource allocation problem in device-to-device (D2D)-based vehicular communications, based on slow fading statistics of channel state information (CSI), to alleviate signaling overhead for reporting rapidly varying accurate CSI of mobile links. We consider the case when each vehicle-to-infrastructure (V2I) link shares spectrum with multiple vehicle-to-vehicle (V2V) links. Leveraging the slow fading statistical CSI of mobile links, we maximize the sum V2I capacity while guaranteeing the reliability of all V2V links. We propose a graph-based algorithm that uses graph partitioning tools to divide highly interfering V2V links into different clusters before formulating the spectrum sharing problem as a weighted 3-dimensional matching problem, which is then solved through adapting a high-performance approximation algorithm.

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arXiv2017

A Survey on MIMO Transmission with Discrete Input Signals: Technical Challenges, Advances, and Future Trends

Multiple antennas have been exploited for spatial multiplexing and diversity transmission in a wide range of communication applications. However, most of the advances in the design of high speed wireless multiple-input multiple output (MIMO) systems are based on information-theoretic principles that demonstrate how to efficiently transmit signals conforming to Gaussian distribution. Although the Gaussian signal is capacity-achieving, signals conforming to discrete constellations are transmitted in practical communication systems. As a result, this paper is motivated to provide a comprehensive overview on MIMO transmission design with discrete input signals. We first summarize the existing fundamental results for MIMO systems with discrete input signals. Then, focusing on the basic point-to-point MIMO systems, we examine transmission schemes based on three most important criteria for communication systems: the mutual information driven designs, the mean square error driven designs, and the diversity driven designs. Particularly, a unified framework which designs low complexity transmission schemes applicable to massive MIMO systems in upcoming 5G wireless networks is provided in the first time. Moreover, adaptive transmission designs which switch among these criteria based on the channel conditions to formulate the best transmission strategy are discussed. Then, we provide a survey of the transmission designs with discrete input signals for multiuser MIMO scenarios, including MIMO uplink transmission, MIMO downlink transmission, MIMO interference channel, and MIMO wiretap channel. Additionally, we discuss the transmission designs with discrete input signals for other systems using MIMO technology. Finally, technical challenges which remain unresolved at the time of writing are summarized and the future trends of transmission designs with discrete input signals are addressed.

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arXiv2016

Low-Rank Matrix Completion for Mobile Edge Caching in Fog-RAN via Riemannian Optimization

The upcoming big data era is likely to demand tremendous computation and storage resources for communications. By pushing computation and storage to network edges, fog radio access networks (Fog-RAN) can effectively increase network throughput and reduce transmission latency. Furthermore, we can exploit the benefits of cache enabled architecture in Fog-RAN to deliver contents with low latency. Radio access units (RAUs) need content delivery from fog servers through wireline links whereas multiple mobile devices acquire contents from RAUs wirelessly. This work proposes a unified low-rank matrix completion (LRMC) approach to solving the content delivery problem in both wireline and wireless parts of Fog-RAN. To attain a low caching latency, we present a high precision approach with Riemannian trust-region method to solve the challenging LRMC problem by exploiting the quotient manifold geometry of fixed-rank matrices. Numerical results show that the new approach has a faster convergence rate, is able to achieve optimal results, and outperforms other state-of-art algorithms.

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arXiv2016

Embedding LTE-U within Wi-Fi Bands for Spectrum Efficiency Improvement

Driven by growing spectrum shortage, Long-term Evolution in unlicensed spectrum (LTE-U) has recently been proposed as a new paradigm to deliver better performance and experience for mobile users by extending the LTE protocol to unlicensed spectrum. In the paper, we first present a comprehensive overview of the LTE-U technology, and discuss the practical challenges it faces. We summarize the existing LTE-U operation modes and analyze several means for LTE-U coexistence with Wi-Fi medium access control protocols. We further propose a novel hyper access-point (HAP) that integrates the functionalities of LTE small cell base station and commercial Wi-Fi AP for deployment by cellular network operators. Our proposed LTE-U access embedding within the Wi-Fi protocol is non-disruptive to unlicensed Wi-Fi nodes and demonstrates performance benefits as a seamless and novel LTE and Wi-Fi coexistence technology in unlicensed band. We provide results to demonstrate the performances advantage of this novel LTE-U proposal.

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arXiv2016

A Convex Relaxation Approach to Higher-Order Statistical Approaches to Signal Recovery

In this work, we investigate an efficient numerical approach for solving higher order statistical methods for blind and semi-blind signal recovery from non-ideal channels. We develop numerical algorithms based on convex optimization relaxation for minimization of higher order statistical cost functions. The new formulation through convex relaxation overcomes the local convergence problem of existing gradient descent based algorithms and applies to several well-known cost functions for effective blind signal recovery including blind equalization and blind source separation in both single-input-single-output (SISO) and multi-input-multi-output (MIMO) systems. We also propose a fourth order pilot based cost function that benefits from this approach. The simulation results demonstrate that our approach is suitable for short-length packet data transmission using only a few pilot symbols.

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arXiv2016

Statistical Analysis of a Posteriori Channel and Noise Distribution Based on HARQ Feedback

In response to a comment on one of our manuscript, this work studies the posterior channel and noise distributions conditioned on the NACKs and ACKs of all previous transmissions in HARQ system with statistical approaches. Our main result is that, unless the coherence interval (time or frequency) is large as in block-fading assumption, the posterior distribution of the channel and noise either remains almost identical to the prior distribution, or it mostly follows the same class of distribution as the prior one. In the latter case, the difference between the posterior and prior distribution can be modeled as some parameter mismatch, which has little impact on certain type of applications.

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arXiv2015

Wireless Communications in the Era of Big Data

The rapidly growing wave of wireless data service is pushing against the boundary of our communication network's processing power. The pervasive and exponentially increasing data traffic present imminent challenges to all the aspects of the wireless system design, such as spectrum efficiency, computing capabilities and fronthaul/backhaul link capacity. In this article, we discuss the challenges and opportunities in the design of scalable wireless systems to embrace such a "bigdata" era. On one hand, we review the state-of-the-art networking architectures and signal processing techniques adaptable for managing the bigdata traffic in wireless networks. On the other hand, instead of viewing mobile bigdata as a unwanted burden, we introduce methods to capitalize from the vast data traffic, for building a bigdata-aware wireless network with better wireless service quality and new mobile applications. We highlight several promising future research directions for wireless communications in the mobile bigdata era.

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arXiv2014

Higher Rank Interference Effect on Weak Beamforming or OSTBC Terminals

User performance on a wireless network depends on whether a neighboring cochannel interferer applies a single (spatial) stream or a multi stream transmission. This work analyzes the impact of interference rank on a beamforming and orthogonal space-time block coded (OSTBC) user transmission. We generalize existing analytical results on signal-to-interference-plus-noise-ratio (SINR) distribution and outage probability under arbitrary number of unequal power interferers. We show that higher rank interference causes lower outage probability, and can support better outage threshold especially in the case of beamforming.

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arXiv2013

On Number of Almost Blank Subframes in Heterogeneous Cellular Networks

In heterogeneous cellular scenarios with macrocells, femtocells or picocells users may suffer from significant co-channel cross-tier interference. To manage this interference 3GPP introduced almost blank subframe (ABSF), a subframe in which the interferer tier is not allowed to transmit data. Vulnerable users thus get a chance to be scheduled in ABSFs with reduced cross-tier interference. We analyze downlink scenarios using stochastic geometry and formulate a condition for the required number of ABSFs based on base station placement statistics and user throughput requirement. The result is a semi-analytical formula that serves as a good initial estimate and offers an easy way to analyze impact of network parameters. We show that while in macro/femto scenario the residue ABSF interference can be well managed, in macro/pico scenario it affects the number of required ABSFs strongly. The effect of ABSFs is subsequently demonstrated via user throughput simulations. Especially in the macro/pico scenario, we find that using ABSFs is advantageous for the system since victim users no longer suffer from poor performance for the price of relatively small drop in higher throughput percentiles.

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arXiv2013

Linear Precoder Design for MIMO Interference Channels with Finite-Alphabet Signaling

This paper investigates the linear precoder design for $K$-user interference channels of multiple-input multiple-output (MIMO) transceivers under finite alphabet inputs. We first obtain general explicit expressions of the achievable rate for users in the MIMO interference channel systems. We study optimal transmission strategies in both low and high signal-to-noise ratio (SNR) regions. Given finite alphabet inputs, we show that a simple power allocation design achieves optimal performance at high SNR whereas the well-known interference alignment technique for Gaussian inputs only utilizes a partial interference-free signal space for transmission and leads to a constant rate loss when applied naively to finite-alphabet inputs. Moreover, we establish necessary conditions for the linear precoder design to achieve weighted sum-rate maximization. We also present an efficient iterative algorithm for determining precoding matrices of all the users. Our numerical results demonstrate that the proposed iterative algorithm achieves considerably higher sum-rate under practical QAM inputs than other known methods.

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