arXiv subjects

Kai-Kit Wong

Publications and source records attributed to Kai-Kit Wong.

At least 325 records · Page 18Linked to original sources

arXiv2016

Wireless Power Transfer in Massive MIMO Aided HetNets with User Association

This paper explores the potential of wireless power transfer (WPT) in massive multiple input multiple output (MIMO) aided heterogeneous networks (HetNets), where massive MIMO is applied in the macrocells, and users aim to harvest as much energy as possible and reduce the uplink path loss for enhancing their information transfer. By addressing the impact of massive MIMO on the user association, we compare and analyze two user association schemes. We adopt the linear maximal ratio transmission beam-forming for massive MIMO power transfer to recharge users. By deriving new statistical properties, we obtain the exact and asymptotic expressions for the average harvested energy. Then we derive the average uplink achievable rate under the harvested energy constraint.

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arXiv2016

Wireless Information and Power Transfer Design for Energy Cooperation Distributed Antenna Systems

Distributed antenna systems (DAS) have been widely implemented in state-of-the-art cellular communication systems to cover dead spots. Recent studies have also indicated that DAS have advantages in wireless energy transfer (WET). In this paper, we study simultaneous wireless information and power transfer (SWIPT) for a multiple-input single-output (MISO) DAS in the downlink which consists of arbitrarily distributed remote antenna units (RAUs). In order to save the energy cost, we adopt energy cooperation of energy harvesting (EH) and two-way energy flows to let the RAUs trade their harvested energy through the smart grid network. Under individual EH constraints, per-RAU power constraints and various smart grid considerations, we investigate a power management strategy that determines how to utilize the stochastically spatially distributed harvested energy at the RAUs and how to trade the energy with the smart grid simultaneously to supply maximum wireless information transfer (WIT) with a minimum WET constraint for a receiver adopting power splitting (PS). Our analysis shows that the optimal design can be achieved in two steps. The first step is to maximize a new objective that can simultaneously maximize both WET and WIT, considering both the smart grid profitable and smart grid neutral cases. For the grid-profitable case, we derive the optimal full power strategy and provide a closed-form result to see under what condition this strategy is used. On the other hand, for the grid-neutral case, we illustrate that the optimal power policy has a double-threshold structure and present an optimal allocation strategy. The second step is then to solve the whole problem by obtaining the splitting power ratio based on the minimum WET constraint. Simulation results are provided to evaluate the performance under various settings and characterize the double-threshold structure.

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arXiv2016

Wireless Information and Power Transfer in Full-Duplex Communication Systems

This paper considers the problem of maximizing the sum-rate for simultaneous wireless information and power transfer (SWIPT) in a full-duplex bi-directional communication system subject to energy harvesting and transmit power constraints at both nodes. We investigate the optimum design of the receive power splitters and transmit powers for SWIPT in full-duplex mode. Exploiting rate-split method, an iterative algorithm is derived to solve the non-convex problem. The effectiveness of the proposed algorithm is justified through numerical simulations.

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arXiv2016

User Association in 5G Networks: A Survey and an Outlook

The fifth generation (5G) mobile networks are envisioned to support the deluge of data traffic with reduced energy consumption and improved quality of service (QoS) provision. To this end, the key enabling technologies, such as heterogeneous networks (HetNets), massive multiple-input multiple-output (MIMO) and millimeter wave (mmWave) techniques, are identified to bring 5G to fruition. Regardless of the technology adopted, a user association mechanism is needed to determine whether a user is associated with a particular base station (BS) before the data transmission commences. User association plays a pivotal role in enhancing the load balancing, the spectrum efficiency and the energy efficiency of networks. The emerging 5G networks introduce numerous challenges and opportunities for the design of sophisticated user association mechanisms. Hence, substantial research efforts are dedicated to the issues of user association in HetNets, massive MIMO networks, mmWave networks and energy harvesting networks. We introduce a taxonomy as a framework for systematically studying the existing user association algorithms. Based on the proposed taxonomy, we then proceed to present an extensive overview of the state-of-the-art in user association conceived for HetNets, massive MIMO, mmWave and energy harvesting networks. Finally, we summarize the challenges as well as opportunities of user association in 5G and provide design guidelines and potential solutions for sophisticated user association mechanisms.

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arXiv2015

Millimeter Wave Power Transfer and Information Transmission

Compared to the existing lower frequency wireless power transfer, millimeter wave (mmWave) power transfer takes advantage of the high-dimensional multi-antenna and narrow beam transmission. In this paper we introduce wireless power transfer for mmWave cellular networks. Here, we consider users with large energy storage that are recharged by the mmWave base stations prior to uplink information transmission, and analyze the average harvested energy and average achievable rate. Numerical results corroborate our analysis and show that the serving base station plays a dominant role in wireless power transfer, and the contribution of the interference power from the interfering base stations is negligible, even when the interfering base stations are dense. By examining the average achievable rate in the uplink, when increasing the base station density, a transition from a noise-limited regime to an interference-limited regime is observed.

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arXiv2015

Bayes-Optimal Joint Channel-and-Data Estimation for Massive MIMO with Low-Precision ADCs

This paper considers a multiple-input multiple-output (MIMO) receiver with very low-precision analog-to-digital convertors (ADCs) with the goal of developing massive MIMO antenna systems that require minimal cost and power. Previous studies demonstrated that the training duration should be {\em relatively long} to obtain acceptable channel state information. To address this requirement, we adopt a joint channel-and-data (JCD) estimation method based on Bayes-optimal inference. This method yields minimal mean square errors with respect to the channels and payload data. We develop a Bayes-optimal JCD estimator using a recent technique based on approximate message passing. We then present an analytical framework to study the theoretical performance of the estimator in the large-system limit. Simulation results confirm our analytical results, which allow the efficient evaluation of the performance of quantized massive MIMO systems and provide insights into effective system design.

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arXiv2015

Wireless Powered Cooperative Jamming for Secrecy Multi-AF Relaying Networks

This paper studies secrecy transmission with the aid of a group of wireless energy harvesting (WEH)-enabled amplify-and-forward (AF) relays performing cooperative jamming (CJ) and relaying. The source node in the network does simultaneous wireless information and power transfer (SWIPT) with each relay employing a power splitting (PS) receiver in the first phase; each relay further divides its harvested power for forwarding the received signal and generating artificial noise (AN) for jamming the eavesdroppers in the second transmission phase. In the centralized case with global channel state information (CSI), we provide closed-form expressions for the optimal and/or suboptimal AF-relay beamforming vectors to maximize the achievable secrecy rate subject to individual power constraints of the relays, using the technique of semidefinite relaxation (SDR), which is proved to be tight. A fully distributed algorithm utilizing only local CSI at each relay is also proposed as a performance benchmark. Simulation results validate the effectiveness of the proposed multi-AF relaying with CJ over other suboptimal designs.

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arXiv2015

To Harvest and Jam: A Paradigm of Self-Sustaining Friendly Jammers for Secure AF Relaying

This paper studies the use of multi-antenna harvest-and-jam (HJ) helpers in a multi-antenna amplify-and-forward (AF) relay wiretap channel assuming that the direct link between the source and destination is broken. Our objective is to maximize the secrecy rate at the destination subject to the transmit power constraints of the AF relay and the HJ helpers. In the case of perfect channel state information (CSI), the joint optimization of the artificial noise (AN) covariance matrix for cooperative jamming and the AF beamforming matrix is studied using semi-definite relaxation (SDR) which is tight, while suboptimal solutions are also devised with lower complexity. For the imperfect CSI case, we provide the equivalent reformulation of the worst-case robust optimization to maximize the minimum achievable secrecy rate. Inspired by the optimal solution to the case of perfect CSI, a suboptimal robust scheme is proposed striking a good tradeoff between complexity and performance. Finally, numerical results for various settings are provided to evaluate the proposed schemes.

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arXiv2015

Safeguarding Massive MIMO Aided HetNets Using Physical Layer Security

This paper exploits the potential of physical layer security in massive multiple-input multiple-output (MIMO) aided two-tier heterogeneous networks (HetNets). We focus on the downlink secure transmission in the presence of multiple eavesdroppers. We first address the impact of massive MIMO on the maximum receive power based user association. We then derive the tractable upper bound expressions for the secrecy outage probability of a HetNets user.We show that the implementation of massive MIMO significantly improves the secrecy performance, which indicates that physical layer security could be a promising solution for safeguarding massive MIMO HetNets. Furthermore, we show that the secrecy outage probability of HetNets user first degrades and then improves with increasing the density of PBSs.

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arXiv2015

Grid-Based Belief Propagation for Cooperative Localization

We present a novel parametric message representation for belief propagation (BP) that provides a novel grid-based way to address the cooperative localization problem in wireless networks. The proposed Grid-BP approach allows faster calculations than non-parametric representations and works well with existing grid-based coordinate systems, e.g., NATO's military grid reference system (MGRS). This overcomes the hidden challenge inherent in all distributed localization algorithms that require a universally known global reference system (GCS), even though every node localizes using arbitrary local coordinate systems (LCSs) for a reference. Simulation results demonstrate that Grid-BP achieves similar accuracy at much reduced complexity when compared to common techniques that assume ideal reference

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arXiv2015

Location Identification of Power Line Outages Using PMU Measurements with Bad Data

The use of phasor angle measurements provided by phasor measurement units (PMUs) in fault detection is regarded as a promising method in identifying locations of power line outages. However, communication errors or system malfunctions may introduce errors to the measurements and thus yield bad data. Most of the existing methods on line outage identification fail to consider such error. This paper develops a framework for identifying multiple power line outages based on the PMUs' measurements in the presence of bad data. In particular, we design an algorithm to identify locations of line outage and recover the faulty measurements simultaneously. The proposed algorithm does not require any prior information on the number of line outages and the noise variance. Case studies carried out on test systems of different sizes validate the effectiveness and efficiency of the proposed approach.

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arXiv2015

Joint Channel-and-Data Estimation for Large-MIMO Systems with Low-Precision ADCs

The use of low precision (e.g., 1-3 bits) analog-to-digital convenors (ADCs) in very large multiple-input multiple-output (MIMO) systems is a technique to reduce cost and power consumption. In this context, nevertheless, it has been shown that the training duration is required to be {\em very large} just to obtain an acceptable channel state information (CSI) at the receiver. A possible solution to the quantized MIMO systems is joint channel-and-data (JCD) estimation. This paper first develops an analytical framework for studying the quantized MIMO system using JCD estimation. In particular, we use the Bayes-optimal inference for the JCD estimation and realize this estimator utilizing a recent technique based on approximate message passing. Large-system analysis based on the replica method is then adopted to derive the asymptotic performances of the JCD estimator. Results from simulations confirm our theoretical findings and reveal that the JCD estimator can provide a significant gain over conventional pilot-only schemes in the quantized MIMO system.

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arXiv2014

On Sparse Vector Recovery Performance in Structurally Orthogonal Matrices via LASSO

In this paper, we consider a compressed sensing problem of reconstructing a sparse signal from an undersampled set of noisy linear measurements. The regularized least squares or least absolute shrinkage and selection operator (LASSO) formulation is used for signal estimation. The measurement matrix is assumed to be constructed by concatenating several randomly orthogonal bases, referred to as structurally orthogonal matrices. Such measurement matrix is highly relevant to large-scale compressive sensing applications because it facilitates fast computation and also supports parallel processing. Using the replica method from statistical physics, we derive the mean-squared-error (MSE) formula of reconstruction over the structurally orthogonal matrix in the large-system regime. Extensive numerical experiments are provided to verify the analytical result. We then use the analytical result to study the MSE behaviors of LASSO over the structurally orthogonal matrix, with a particular focus on performance comparisons to matrices with independent and identically distributed (i.i.d.) Gaussian entries. We demonstrate that the structurally orthogonal matrices are at least as well performed as their i.i.d. Gaussian counterparts, and therefore the use of structurally orthogonal matrices is highly motivated in practical applications.

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arXiv2014

Performance Limits of Massive MIMO Systems Based on Bayes-Optimal Inference

This paper gives a replica analysis for the minimum mean square error (MSE) of a massive multiple-input multiple-output (MIMO) system by using Bayesian inference. The Bayes-optimal estimator is adopted to estimate the data symbols and the channels from a block of received signals in the spatial-temporal domain. We show that using the Bayes-optimal estimator, the interfering signals from adjacent cells can be separated from the received signals without pilot information. In addition, the MSEs with respect to the data symbols and the channels of the desired users decrease with the number of receive antennas and the number of data symbols, respectively. There are no residual interference terms that remain bounded away from zero as the numbers of receive antennas and data symbols approach infinity.

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arXiv2014

Power Scaling of Uplink Massive MIMO Systems with Arbitrary-Rank Channel Means

This paper investigates the uplink achievable rates of massive multiple-input multiple-output (MIMO) antenna systems in Ricean fading channels, using maximal-ratio combining (MRC) and zero-forcing (ZF) receivers, assuming perfect and imperfect channel state information (CSI). In contrast to previous relevant works, the fast fading MIMO channel matrix is assumed to have an arbitrary-rank deterministic component as well as a Rayleigh-distributed random component. We derive tractable expressions for the achievable uplink rate in the large-antenna limit, along with approximating results that hold for any finite number of antennas. Based on these analytical results, we obtain the scaling law that the users' transmit power should satisfy, while maintaining a desirable quality of service. In particular, it is found that regardless of the Ricean $K$-factor, in the case of perfect CSI, the approximations converge to the same constant value as the exact results, as the number of base station antennas, $M$, grows large, while the transmit power of each user can be scaled down proportionally to $1/M$. If CSI is estimated with uncertainty, the same result holds true but only when the Ricean $K$-factor is non-zero. Otherwise, if the channel experiences Rayleigh fading, we can only cut the transmit power of each user proportionally to $1/\sqrt M$. In addition, we show that with an increasing Ricean $K$-factor, the uplink rates will converge to fixed values for both MRC and ZF receivers.

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arXiv2014

Analysis of Compressed Sensing with Spatially-Coupled Orthogonal Matrices

Recent development in compressed sensing (CS) has revealed that the use of a special design of measurement matrix, namely the spatially-coupled matrix, can achieve the information-theoretic limit of CS. In this paper, we consider the measurement matrix which consists of the spatially-coupled \emph{orthogonal} matrices. One example of such matrices are the randomly selected discrete Fourier transform (DFT) matrices. Such selection enjoys a less memory complexity and a faster multiplication procedure. Our contributions are the replica calculations to find the mean-square-error (MSE) of the Bayes-optimal reconstruction for such setup. We illustrate that the reconstruction thresholds under the spatially-coupled orthogonal and Gaussian ensembles are quite different especially in the noisy cases. In particular, the spatially coupled orthogonal matrices achieve the faster convergence rate, the lower measurement rate, and the reduced MSE.

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arXiv2013

Large System Analysis of Cooperative Multi-cell Downlink Transmission via Regularized Channel Inversion with Imperfect CSIT

In this paper, we analyze the ergodic sum-rate of a multi-cell downlink system with base station (BS) cooperation using regularized zero-forcing (RZF) precoding. Our model assumes that the channels between BSs and users have independent spatial correlations and imperfect channel state information at the transmitter (CSIT) is available. Our derivations are based on large dimensional random matrix theory (RMT) under the assumption that the numbers of antennas at the BS and users approach to infinity with some fixed ratios. In particular, a deterministic equivalent expression of the ergodic sum-rate is obtained and is instrumental in getting insight about the joint operations of BSs, which leads to an efficient method to find the asymptotic-optimal regularization parameter for the RZF. In another application, we use the deterministic channel rate to study the optimal feedback bit allocation among the BSs for maximizing the ergodic sum-rate, subject to a total number of feedback bits constraint. By inspecting the properties of the allocation, we further propose a scheme to greatly reduce the search space for optimization. Simulation results demonstrate that the ergodic sum-rates achievable by a subspace search provides comparable results to those by an exhaustive search under various typical settings.

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arXiv2013

Performance of Joint Channel and Physical Network Coding Based on Alamouti STBC

This work considers the protograph-coded physical network coding (PNC) based on Alamouti space-time block coding (STBC) over Nakagami-fading two-way relay channels, in which both the two sources and relay possess two antennas. We first propose a novel precoding scheme at the two sources so as to implement the iterative decoder efficiently at the relay. We further address a simplified updating rule of the log-likelihood-ratio (LLR) in such a decoder. Based on the simplified LLR-updating rule and Gaussian approximation, we analyze the theoretical bit-error-rate (BER) of the system, which is shown to be consistent with the decoding thresholds and simulated results. Moreover, the theoretical analysis has lower computational complexity than the protograph extrinsic information transfer (PEXIT) algorithm. Consequently, the analysis not only provides a simple way to evaluate the error performance but also facilitates the design of the joint channel-and-PNC (JCNC) in wireless communication scenarios.

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