arXiv subjects

Ying Jun Zhang

Publications and source records attributed to Ying Jun Zhang.

At least 19 recordsLinked to original sources

arXiv2018

Device-to-Device Load Balancing for Cellular Networks

Small-cell architecture is widely adopted by cellular network operators to increase network capacity. By reducing the size of cells, operators can pack more (low-power) base stations in an area to better serve the growing demands, without causing extra interference. However, this approach suffers from low spectrum temporal efficiency. When a cell becomes smaller and covers fewer users, its total traffic fluctuates significantly due to insufficient traffic aggregation and exhibiting a large "peak-to-mean" ratio. As operators customarily provision spectrum for peak traffic, large traffic temporal fluctuation inevitably leads to low spectrum temporal efficiency. In this paper, we advocate device-to-device (D2D) load-balancing as a useful mechanism to address the fundamental drawback of small-cell architecture. The idea is to shift traffic from a congested cell to its adjacent under-utilized cells by leveraging inter-cell D2D communication, so that the traffic can be served without using extra spectrum, effectively improving the spectrum temporal efficiency. We provide theoretical modeling and analysis to characterize the benefit of D2D load balancing, in terms of total spectrum requirements of all individual cells. We also derive the corresponding cost, in terms of incurred D2D traffic overhead. We carry out empirical evaluations based on real-world 4G data traces to gauge the benefit and cost of D2D load balancing under practical settings. The results show that D2D load balancing can reduce the spectrum requirement by 25% as compared to the standard scenario without D2D load balancing, at the expense of negligible 0.7% D2D traffic overhead.

cs.NI↗

arXiv2017

Optimal Relaying in Energy Harvesting Wireless Networks with Wireless-Powered Relays

In this paper, we consider a wireless cooperative network with an energy harvesting relay which is powered by the energy harvested from ambient RF waves, such as that of a data packet. At any given time, the relay operates either in the energy harvesting (EH) mode or the data decoding (DD) mode, but not both. Separate energy and data buffers are kept at the relay to store the harvested energy and decoded data packets, respectively. In this paper, we optimize a time switching policy that switches between the EH mode and DD mode to maximize the system throughput or minimize the average transmission delay. Both static and dynamic time switching policies are derived. In particular, static policies are the ones where EH or DD mode is selected with a pre-determined probability. In contrast, in a dynamic policy, the mode is selected dynamically according to the states of data and energy buffers. We prove that the throughput-optimal static and dynamic policies keep the relay data buffer at the boundary of stability. More specifically, we show that the throughput-optimal dynamic policy has a threshold-based structure. Moreover, we prove that the delay-optimal dynamic policy also has a threshold-based structure and keeps at most one packet at the relay. We notice that the delay-optimal and throughput-optimal dynamic policies coincide in most cases. However, it is not true for optimal static policies. Finally, through extensive numerical results, we show the efficiency of optimal dynamic policies compared with the static ones in different conditions.

cs.NI↗

arXiv2017

Scalable Uplink Signal Detection in C-RANs via Randomized Gaussian Message Passing

Cloud Radio Access Network (C-RAN) is a promising architecture for unprecedented capacity enhancement in next-generation wireless networks thanks to the centralization and virtualization of base station processing. However, centralized signal processing in C-RANs involves high computational complexity that quickly becomes unaffordable when the network grows to a huge size. Among the first, this paper endeavours to design a scalable uplink signal detection algorithm, in the sense that both the complexity per unit network area and the total computation time remain constant when the network size grows. To this end, we formulate the signal detection in C-RAN as an inference problem over a bipartite random geometric graph. By passing messages among neighboring nodes, message passing (a.k.a. belief propagation) provides an efficient way to solve the inference problem over a sparse graph. However, the traditional message-passing algorithm is not guaranteed to converge, because the corresponding bipartite random geometric graph is locally dense and contains many short loops. As a major contribution of this paper, we propose a randomized Gaussian message passing (RGMP) algorithm to improve the convergence. Instead of exchanging messages simultaneously or in a fixed order, we propose to exchange messages asynchronously in a random order. The proposed RGMP algorithm demonstrates significantly better convergence performance than conventional message passing. The randomness of the message update schedule also simplifies the analysis, and allows the derivation of the convergence conditions for the RGMP algorithm. In addition, we propose a blockwise RGMP (B-RGMP) algorithm for practical implementation. The average computation time of B-RGMP remains constant when the network size increases.

cs.IT↗

arXiv2016

Graph-based Cyber Security Analysis of State Estimation in Smart Power Grid

Smart power grid enables intelligent automation at all levels of power system operation, from electricity generation at power plants to power usage at households. The key enabling factor of an efficient smart grid is its built-in information and communication technology (ICT) that monitors the real-time system operating state and makes control decisions accordingly. As an important building block of the ICT system, power system state estimation is of critical importance to maintain normal operation of the smart grid, which, however, is under mounting threat from potential cyber attacks. In this article, we introduce a graph-based framework for performing cyber-security analysis in power system state estimation. Compared to conventional arithmetic-based security analysis, the graphical characterization of state estimation security provides intuitive visualization of some complex problem structures and enables efficient graphical solution algorithms, which are useful for both defending and attacking the ICT system of smart grid. We also highlight several promising future research directions on graph-based security analysis and its applications in smart power grid.

eess.SY↗

arXiv2016

A Model Predictive Control Approach for Low-Complexity Electric Vehicle Charging Scheduling: Optimality and Scalability

With the increasing adoption of plug-in electric vehicles (PEVs), it is critical to develop efficient charging coordination mechanisms that minimize the cost and impact of PEV integration to the power grid. In this paper, we consider the optimal PEV charging scheduling, where the non-causal information about future PEV arrivals is not known in advance, but its statistical information can be estimated. This leads to an "online" charging scheduling problem that is naturally formulated as a finite-horizon dynamic programming with continuous state space and action space. To avoid the prohibitively high complexity of solving such a dynamic programming problem, we provide a Model Predictive Control (MPC) based algorithm with computational complexity $O(T^3)$, where $T$ is the total number of time stages. We rigorously analyze the performance gap between the near-optimal solution of the MPC-based approach and the optimal solution for any distributions of exogenous random variables. Furthermore, our rigorous analysis shows that when the random process describing the arrival of charging demands is first-order periodic, the complexity of proposed algorithm can be reduced to $O(1)$, which is independent of $T$. Extensive simulations show that the proposed online algorithm performs very closely to the optimal online algorithm. The performance gap is smaller than $0.4\%$ in most cases.

math.OC↗

arXiv2016

Fundamental Limits of Training-Based Multiuser MIMO Systems

In this paper, we endeavour to seek a fundamental understanding of the potentials and limitations of training-based multiuser multiple-input multiple-output (MIMO) systems. In a multiuser MIMO system, users are geographically separated. So, the near-far effect plays an indispensable role in channel fading. The existing optimal training design for conventional MIMO does not take the near-far effect into account, and thus is not applicable to a multiuser MIMO system. In this work, we use the majorization theory as a basic tool to study the tradeoff between the channel estimation quality and the information throughput. We establish tight upper and lower bounds of the throughput, and prove that the derived lower bound is asymptotically optimal for throughput maximization at high signal-to-noise ratio. Our analysis shows that the optimal training sequences for throughput maximization in a multiuser MIMO system are in general not orthogonal to each other. Furthermore, due to the near-far effect, the optimal training design for throughput maximization is to deactivate a portion of users with the weakest channels in transmission. These observations shed light on the practical design of training-based multiuser MIMO systems.

cs.IT↗

arXiv2015

Competitive Charging Station Pricing for Plug-in Electric Vehicles

This paper considers the problem of charging station pricing and plug-in electric vehicles (PEVs) station selection. When a PEV needs to be charged, it selects a charging station by considering the charging prices, waiting times, and travel distances. Each charging station optimizes its charging price based on the prediction of the PEVs' charging station selection decisions and the other station's pricing decision, in order to maximize its profit. To obtain insights of such a highly coupled system, we consider a one-dimensional system with two competing charging stations and Poisson arriving PEVs. We propose a multi-leader-multi-follower Stackelberg game model, in which the charging stations (leaders) announce their charging prices in Stage I, and the PEVs (followers) make their charging station selections in Stage II. We show that there always exists a unique charging station selection equilibrium in Stage II, and such equilibrium depends on the charging stations' service capacities and the price difference between them. We then characterize the sufficient conditions for the existence and uniqueness of the pricing equilibrium in Stage I. We also develop a low complexity algorithm that efficiently computes the pricing equilibrium and the subgame perfect equilibrium of the two-stage Stackelberg game.

cs.GT↗

arXiv2014

Dynamic Nested Clustering for Parallel PHY-Layer Processing in Cloud-RANs

Featured by centralized processing and cloud based infrastructure, Cloud Radio Access Network (C-RAN) is a promising solution to achieve an unprecedented system capacity in future wireless cellular networks. The huge capacity gain mainly comes from the centralized and coordinated signal processing at the cloud server. However, full-scale coordination in a large-scale C-RAN requires the processing of very large channel matrices, leading to high computational complexity and channel estimation overhead. To resolve this challenge, we exploit the near-sparsity of large C-RAN channel matrices, and derive a unified theoretical framework for clustering and parallel processing. Based on the framework, we propose a dynamic nested clustering (DNC) algorithm that not only greatly improves the system scalability in terms of baseband-processing and channel-estimation complexity, but also is amenable to various parallel processing strategies for different data center architectures. With the proposed algorithm, we show that the computation time for the optimal linear detector is greatly reduced from $O(N^3)$ to no higher than $O(N^{\frac{42}{23}})$, where $N$ is the number of RRHs in C-RAN.

cs.IT↗

arXiv2014

A Guard Zone Based Scalable Mode Selection Scheme in D2D Underlaid Cellular Networks

Underlaying the cellular networks, Device to Device (D2D) communication brings the possibility to significantly improve the spectral efficiency in cellular networks and offload the traffic relayed by the base station. However, it creates new challenge for interference management as well. In this paper, we consider the multi-cell D2D underlaid cellular network in which many D2D links reuse the uplink resource simultaneously. The infeasibility of interference cancellation/alignment motivates us to force the D2D users near the base stations to work in cellular mode. Based on that, we present a distributed and scalable mode selection scheme based on guard zone to make a good tradeoff between decreasing interference penalty to base stations and improving spectrum utilization. With the help of stochastic geometry, we develop an analytically tractable framework to analyze the interference and then throughput for the two kinds of users in the considered scenario. The results obtained by the framework reveal that enabling D2D communication does improve the per user throughput and that a proper guard zone can further significantly increase the average throughput of both kinds of users. Thanks to the tractability of our model, the optimal guard zone, which is the key parameter in our scheme, can be efficiently obtained by finding the root of the first order derivative of the throughput expression. Through extensive numerical analysis, we show insights of the system and give some guidelines in system design aspect.

cs.NI↗

arXiv2014

Using Covert Topological Information for Defense Against Malicious Attacks on DC State Estimation

Accurate state estimation is of paramount importance to maintain the power system operating in a secure and efficient state. The recently identified coordinated data injection attacks to meter measurements can bypass the current security system and introduce errors to the state estimates. The conventional wisdom to mitigate such attacks is by securing meter measurements to evade malicious injections. In this paper, we provide a novel alternative to defend against false-data injection attacks using covert power network topological information. By keeping the exact reactance of a set of transmission lines from attackers, no false data injection attack can be launched to compromise any set of state variables. We first investigate from the attackers' perspective the necessary condition to perform injection attack. Based on the arguments, we characterize the optimal protection problem, which protects the state variables with minimum cost, as a well-studied Steiner tree problem in a graph. Besides, we also propose a mixed defending strategy that jointly considers the use of covert topological information and secure meter measurements when either method alone is costly or unable to achieve the protection objective. A mixed integer linear programming (MILP) formulation is introduced to obtain the optimal mixed defending strategy. To tackle the NP-hardness of the problem, a tree pruning-based heuristic is further presented to produce an approximate solution in polynomial time. The advantageous performance of the proposed defending mechanisms is verified in IEEE standard power system testcases.

cs.CR↗

arXiv2013

The Cost of Mitigating Power Law Delay in Random Access Networks

Exponential backoff (EB) is a widely adopted collision resolution mechanism in many popular random-access networks including Ethernet and wireless LAN (WLAN). The prominence of EB is primarily attributed to its asymptotic throughput stability, which ensures a non-zero throughput even when the number of users in the network goes to infinity. Recent studies, however, show that EB is fundamentally unsuitable for applications that are sensitive to large delay and delay jitters, as it induces divergent second- and higher-order moments of medium access delay. Essentially, the medium access delay follows a power law distribution, a subclass of heavy-tailed distribution. To understand and alleviate the issue, this paper systematically analyzes the tail delay distribution of general backoff functions, with EB being a special case. In particular, we establish a tradeoff between the tail decaying rate of medium access delay distribution and the stability of throughput. To be more specific, convergent delay moments are attainable only when the backoff functions $g(k)$ grows slower than exponential functions, i.e., when $g(k)\in o(r^k)$ for all $r>1$. On the other hand, non-zero asymptotic throughput is attainable only when backoff functions grow at least as fast as an exponential function, i.e., $g(k)\inΩ(r^k)$ for some $r>1$. This implies that bounded delay moments and stable throughput cannot be achieved at the same time. For practical implementation, we show that polynomial backoff (PB), where $g(k)$ is a polynomial that grows slower than exponential functions, obtains finite delay moments and good throughput performance at the same time within a practical range of user population. This makes PB a better alternative than EB for multimedia applications with stringent delay requirements.

cs.NI↗

arXiv2012

Pragmatic Physical Layer Encryption for Achieving Perfect Secrecy

Conventionally, secrecy is achieved using cryptographic techniques beyond the physical layer. Recent studies raise the interest of performing encryption within the physical layer by exploiting some unique features of the physical wireless channel. Following this spirit, we present a novel physical layer encryption (PLE) scheme that randomizes the radio signal using a secret key extracted from the wireless channel under the assumption of channel reciprocity. Specifically, we propose to jointly design the encryption function and the secret-key generation method. On one hand, we establish a sufficient and necessary condition for the encryption function to achieve perfect secrecy. Based on that, several candidate encryption functions are proposed and compared. We show that, given the secret key available to the legitimate users, perfect secrecy can be achieved without compromising the capability of the communication channel. On the other hand, we study the practical design of the secret-key generation method based on the channel reciprocity. We show that, by introducing marginal system overhead, the key agreement between the legitimate users can be done with a high success probability. The performance advantages of the proposed PLE method is verified through comparisons against other existing PLE methods.

cs.CR↗

arXiv2010

Optimal Spectrum Sharing in MIMO Cognitive Radio Networks via Semidefinite Programming

In this paper, we study the optimal secondary-link beamforming pattern that balances between the SU's throughput and the interference it causes to PUs in MIMO cognitive radio networks. In particular, we aim to maximize the throughput of the SU, while keeping the interference temperature at the primary receivers below a certain threshold. Unlike traditional MIMO systems, SUs may not have the luxury of knowing the channel state information (CSI) on the links to PUs. This presents a key challenge for a secondary transmitter to steer interference away from primary receivers. In this paper, we consider three scenarios, namely when the secondary transmitter has complete, partial, or no knowledge about the channels to the primary receivers. In particular, when complete CSI is not available, the interference-temperature constraints are to be satisfied with high probability, thus resulting in chance constraints that are typically hard to deal with. Our contribution is fourfold. First, by analyzing the distributional characteristics of MIMO channels, we propose a unified homogeneous QCQP formulation that can be applied to all three scenarios. The homogeneous QCQP formulation, though non-convex, is amenable to semidefinite programming (SDP) relaxation methods. Secondly, we show that the SDP relaxation admits no gap when the number of primary links is no larger than two. Thirdly, we propose a randomized polynomial-time algorithm for constructing a near-optimal solution to the QCQP problem when there are more than two primary links. Finally, we show that when the secondary transmitter has no CSI on the links to primary receivers, the optimal solution to the QCQP problem can be found by a simple matrix eigenvalue-eigenvector computation, which can be done much more efficiently than solving the QCQP directly.

cs.NI↗

arXiv2008

Bounded Mean-Delay Throughput and Non-Starvation Conditions in Aloha Network

This paper considers the requirements to ensure bounded mean queuing delay and non-starvation in a slotted Aloha network operating the exponential backoff protocol. It is well-known that the maximum possible throughput of a slotted Aloha system with a large number of nodes is 1/e = 0.3679. Indeed, a saturation throughput of 1/e can be achieved with an exponential backoff factor of r = e/(e-1)=1.5820. The binary backoff factor of r = 2 is assumed in the majority of prior work, and in many practical multiple-access networks such as the Ethernet and WiFi. For slotted Aloha, the saturation throughput 0.3466 for r = 2 is reasonably close to the maximum of 1/e, and one could hardly raise objection to adopting r = 2 in the system. However, this paper shows that if mean queuing delay is to be bounded, then the sustainable throughput when r = 2 is only 0.2158, a drastic 41% drop from 1/e . Fortunately, the optimal setting of r = 1.3757 under the bounded mean-delay requirement allows us to achieve sustainable throughput of 0.3545, a penalty of only less than 4% relative to 1/e. A general conclusion is that the value of r may significantly affect the queuing delay performance. Besides analyzing mean queuing delay, this paper also delves into the phenomenon of starvation, wherein some nodes are deprived of service for an extended period of time while other nodes hog the system. Specifically, we propose a quantitative definition for starvation and show that the conditions to guarantee bounded mean delay and non-starved operation are one of the same, thus uniting these two notions. Finally, we show that when mean delay is large and starvation occurs, the performance results obtained from simulation experiments may not converge. A quantitative discussion of this issue is provided in this paper.

cs.NI↗

arXiv2008

MAPEL: Achieving Global Optimality for a Non-convex Wireless Power Control Problem

Achieving weighted throughput maximization (WTM) through power control has been a long standing open problem in interference-limited wireless networks. The complicated coupling between the mutual interferences of links gives rise to a non-convex optimization problem. Previous work has considered the WTM problem in the high signal to interference-and-noise ratio (SINR) regime, where the problem can be approximated and transformed into a convex optimization problem through proper change of variables. In the general SINR regime, however, the approximation and transformation approach does not work. This paper proposes an algorithm, MAPEL, which globally converges to a global optimal solution of the WTM problem in the general SINR regime. The MAPEL algorithm is designed based on three key observations of the WTM problem: (1) the objective function is monotonically increasing in SINR, (2) the objective function can be transformed into a product of exponentiated linear fraction functions, and (3) the feasible set of the equivalent transformed problem is always normal although not necessarily convex. The MAPLE algorithm finds the desired optimal power control solution by constructing a series of polyblocks that approximate the feasible SINR region in increasing precision. Furthermore, by tuning the approximation factor in MAPEL, we could engineer a desirable tradeoff between optimality and convergence time. MAPEL provides an important benchmark for performance evaluation of other heuristic algorithms targeting the same problem. With the help of MAPEL, we evaluate the performance of several respective algorithms through extensive simulations.

cs.NI↗

arXiv2008

Delay Analysis for Wireless Local Area Networks with Multipacket Reception under Finite Load

To date, most analysis of WLANs has been focused on their operation under saturation condition. This work is an attempt to understand the fundamental performance of WLANs under unsaturated condition. In particular, we are interested in the delay performance when collisions of packets are resolved by an exponential backoff mechanism. Using a multiple-vacation queueing model, we derive an explicit expression for packet delay distribution, from which necessary conditions for finite mean delay and delay jitter are established. It is found that under some circumstances, mean delay and delay jitter may approach infinity even when the traffic load is way below the saturation throughput. Saturation throughput is therefore not a sound measure of WLAN capacity when the underlying applications are delay sensitive. To bridge the gap, we define safe-bounded-mean-delay (SBMD) throughput and safe-bounded-delay-jitter (SBDJ) throughput that reflect the actual network capacity users can enjoy when they require bounded mean delay and delay jitter, respectively. The analytical model in this paper is general enough to cover both single-packet reception (SPR) and multi-packet reception (MPR) WLANs, as well as carrier-sensing and non-carrier-sensing networks. We show that the SBMD and SBDJ throughputs scale super-linearly with the MPR capability of a network. Together with our earlier work that proves super-linear throughput scaling under saturation condition, our results here complete the demonstration of MPR as a powerful capacity-enhancement technique for both delay-sensitive and delay-tolerant applications.

cs.NI↗

arXiv2008

On capacity of wireless ad hoc networks with MIMO MMSE receivers

Widely adopted at home, business places, and hot spots, wireless ad-hoc networks are expected to provide broadband services parallel to their wired counterparts in near future. To address this need, MIMO techniques, which are capable of offering several-fold increase in capacity, hold significant promise. Most previous work on capacity analysis of ad-hoc networks is based on an implicit assumption that each node has only one antenna. Core to the analysis therein is the characterization of a geometric area, referred to as the exclusion region, which quantizes the amount of spatial resource occupied by a link. When multiple antennas are deployed at each node, however, multiple links can transmit in the vicinity of each other simultaneously, as interference can now be suppressed by spatial signal processing. As such, a link no longer exclusively occupies a geometric area, making the concept of "exclusion region" not applicable any more. In this paper, we investigate link-layer throughput capacity of MIMO ad-hoc networks. In contrast to previous work, the amount of spatial resource occupied by each link is characterized by the actual interference it imposes on other links. To calculate the link-layer capacity, we first derive the probability distribution of post-detection SINR at a receiver. The result is then used to calculate the number of active links and the corresponding data rates that can be sustained within an area. Our analysis will serve as a guideline for the design of medium access protocols for MIMO ad-hoc networks. To the best of knowledge, this paper is the first attempt to characterize the capacity of MIMO ad-hoc networks by considering the actual PHY-layer signal and interference model.

cs.NI↗

arXiv2008

Proportional Fairness in Multi-channel Multi-rate Wireless Networks-Part II: The Case of Time-Varying Channels

This is Part II of a two-part paper series that studies the use of the proportional fairness (PF) utility function as the basis for capacity allocation and scheduling in multi-channel multi-rate wireless networks. The contributions of Part II are twofold. (i) First, we extend the problem formulation, theoretical results, and algorithms to the case of time-varying channels, where opportunistic capacity allocation and scheduling can be exploited to improve system performance. We lay down the theoretical foundation for optimization that "couples" the time-varying characteristic of channels with the requirements of the underlying applications into one consideration. In particular, the extent to which opportunistic optimization is possible is not just a function of how fast the channel characteristics vary, but also a function of the elasticity of the underlying applications for delayed capacity allocation. (ii) Second, building upon our theoretical framework and results, we study subcarrier allocation and scheduling in orthogonal frequency division multiplexing (OFDM) cellular wireless networks. We introduce the concept of a W-normalized Doppler frequency to capture the extent to which opportunistic scheduling can be exploited to achieve throughput-fairness performance gain. We show that a "look-back PF" scheduling can strike a good balance between system throughput and fairness while taking the underlying application requirements into account.

cs.PF↗