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Tài liệu tham khảo chuyên ngành viễn thông Robust Cross-Layer Scheduling Design in Multi-user Multi-antenna Wireless Systems
Abstract of thesis entitled Robust Cross-Layer Scheduling Design in Multi-user Multi-antenna Wireless Systems submitted by Meilong Jiang for the degree of Doctor of Philosophy at The University of Hong Kong in October 2006 Cross-layer design for a multi-user multi-antenna system has been shown to offer high spectral efficiency which benefits from the inherent multi-user diversity and spatial multiplexing gain in wireless fading channels In this thesis, we consider the cross-layer scheduling design under various practical physical layer and network layer constraints for a wireless system with one base station (with N antennas) and K mobile users (each with a single antenna) In the first part of the thesis, we study the cross-layer scheduling design with imperfect channel state information (CSI) at the base station for delay-tolerant applications The CSI imperfectness may derive from the CSI estimation error or the CSI outdatedness due to feedback and duplexing delay With imperfect CSI at transmitter (CSIT), there exists a potential packet transmission error when the scheduled data rate exceeds the instantaneous channel capacity referring to packet outage Our objective is to maximize the average system goodput, which measures the average b/s/Hz delivered to the mobiles successfully In practical wireless systems, a discrete set of rates instead of an infinite continuous rate can only be supported due to the finite choice of error correction encoders and discrete level constellations To this end, the cross-layer design is formulated as a mixed convex and combinatorial optimization problem, with respect to the imperfect CSIT statistics and the discrete rate set constraint In the second part, we extend the scheduling design for the heterogeneous user applications such as voice and data services To take delay sensitive users into consideration, we employ both queueing theory and information theory to model the system dynamics A novel cross-layer heterogeneous scheduler is designed to exploit the spatial multiplexing gain as well as the multi-user selection diversity gain while also maintaining the delay constraints of the delay sensitive users Numerical results and comparison with various start-of-art scheduling schemes are provided to demonstrate the potential of our proposed schemes Specifically, by considering the CSIT error statistics, source statistics and queueing delay into the design, the proposed scheduling schemes provide significant performance enhancement in terms of system goodput, robustness with respect to imperfect CSIT, and quality of service (QoS) guarantees Robust Cross-Layer Scheduling Design in Multi-user Multi-antenna Wireless Systems by Meilong Jiang MSEE, Beijing University of Posts and Telecomms A thesis submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy Electrical and Electronic Engineering at The University of Hong Kong in October 2006 Copyright c 2006 Meilong Jiang i Declaration I declare that this thesis represents my own work, except where due acknowledgement is made, and that it has not been previously included in a thesis, dissertation or report submitted to this University or to any other institution for a degree diploma or other qualifications Signed Meilong Jiang ii Acknowledgments I AM DEEPLY INDEBTED to my supervisor, Prof Vincent K.N Lau for having invariably given me his patient guidance, stimulating encouragement, and deep insights into my research and my life as well His enthusiastic attitude and extremely high efficiency has not only had a great impact on my Ph.D study, but has also given me great impetus that I would be able to cherish in my entire life The completion of this thesis would not have been possible without his continual support I am sincerely grateful to the Graduate School of HKU for having provided the Postgraduate Studentship during the whole Ph.D program I would like to thank Dr N Wong, Prof J Wang, Dr W.H Lam, and Prof Roger Cheng for their insightful guidance, suggestions, and kind help during my study I would also like to thank Prof Ricky Kwok, Prof K.L Ho, and Prof Li chun Wang for serving on my thesis examination committee I truly appreciate the friendship of my friends for having created a pleasant working environment and for their helpful discussions Special thanks go to Mr Tyrone Kwok, Mr Gan Zheng, Mr Carson Hung, Mr David Hui, Doctors-to-be- Xiaoshan Liu , Guanghua Yang and Shaodan Ma, Dr Zhifeng Diao, Dr Xiaohui Lin, and Dr Yiqing Zhou for their kind help and insightful discussions Many thanks go to other friends in the lab and research group Finally, I would like to express my sincerest gratitude to my parents and my wife Ying Zheng for their deepest love and constant support iii Table of Contents Page Declaration i Acknowledgments ii List of Tables vi List of Figures vii Abstract x Abbreviations xii Notation and used symbols xv 1 1.1 1.2 1.3 1.4 Introduction Background 2.1 2.2 2.3 Evolution and Challenge of Wireless Technology Literature Survey Motivation and Problem Statement Thesis Research and Contributions Wireless Fading Channel - Characterizations and Mitigation 2.1.1 Large-Scale Fading 2.1.2 Small-Scale Fading 2.1.3 Mitigation Methods Cross-Layer Scheduling and Adaptive Design in Multi-user Wireless Network 2.2.1 Adaptive Design in Physical Layer 2.2.2 MAC Layer Scheduling Model Linear Transmit-receive Processing in Multi-antenna Base Station 2.3.1 Zero-forcing Processing 2.3.2 Transmit MMSE Processing 10 10 11 13 13 15 17 18 21 iv Page Uplink Scheduling Design with Outdated CSI 22 3.1 3.2 3.3 3.4 3.5 22 23 23 23 25 26 26 27 27 28 28 31 31 32 Cross-Layer Downlink Scheduling and Rate Quantization Design with Imperfect CSIT 37 4.1 4.2 4.3 4.4 4.5 Overview Multi-user SIMO System Model 3.2.1 Channel Model with Outdated CSIT 3.2.2 Multi-user Uplink Physical Layer Model 3.2.3 Packet Outage Model Uplink Space Time Scheduling Design 3.3.1 System Utility Function 3.3.2 Optimal Solution with Perfect CSI 3.3.3 Heuristic Solution with Perfect CSI - Genetic Algorithm Scheduling with Outdated CSI 3.4.1 Performance Degradation of Ideal Schedulers Due to Outdated CSI 3.4.2 Proposed Scheme A - Rate Quantization 3.4.3 Proposed Scheme B - Rate Discounting Numerical Results and Discussions Overview Multi-user MISO System Model 4.2.1 Downlink Channel Model 4.2.2 Imperfect CSIT Model 4.2.3 Multi-user Downlink Physical Layer Model Problem Formulation of Cross-Layer Scheduling 4.3.1 Instantaneous Channel Capacity and System Goodput 4.3.2 Cross-Layer Design Optimization Solutions of the Optimization Designs 4.4.1 Combined Scheduling and Rate Quantization Optimization 4.4.2 Optimal Inner Scheduling Based on Imperfect CSIT 4.4.3 Optimal Transmission Modes Design 4.4.4 Summary of the Scheduler Solution Numerical Results and Discussions 4.5.1 Performance of Regular Scheduler with Imperfect CSIT 4.5.2 Performance of Proposed Scheduler with Imperfect CSIT 37 38 38 40 40 41 43 44 45 45 47 50 52 53 54 54 Performance Analysis of Downlink Scheduling for Voice and Data Applications 65 5.1 Overview 65 v Page 5.2 5.3 5.4 66 67 67 69 69 69 72 77 78 78 80 Cross-layer Downlink Scheduling with Heterogeneous Delay Constraints 84 6.1 6.2 6.3 6.4 6.5 System Model 5.2.1 Channel model 5.2.2 Multi-user Physical Layer Model 5.2.3 Source Model - Voice and Data Space Time Scheduling for Heterogeneous Users 5.3.1 Asymptotic Spatial Multiplexing Gain 5.3.2 Scheduling Algorithm Numerical Results and Discussions 5.4.1 Delay Performance of VoIP users 5.4.2 Spatial Multiplexing Gains on System Capacity 5.4.3 Transient Performance Overview System Model 6.2.1 Multi-user Physical Layer Model 6.2.2 Source Model - Delay Sensitive and Delay Insensitive Formulation of the Cross-layer Design for Heterogeneous Users Solution of the Cross-Layer Optimization Problem 6.4.1 Convex Optimization on (p1 , , pK ) 6.4.2 Combinatorial Search on Admissible Set Numerical Results and Discussions 6.5.1 Delay Performance of the Proposed Scheduler 6.5.2 System Throughput Performance 6.5.3 Delay and Power Tradeoff 84 85 85 86 87 90 90 91 91 91 93 93 Conclusions and Future Work 102 7.1 7.2 Conclusions 102 Future Work 103 List of References 105 vi List of Tables Table Page 3.1 Optimal quantization levels with respect to CSI error variance 31 4.1 Design example of rate partition, rate centroid and modes for CSIT error σ = 0.1, Q = 8, SNR=10dB and target frame error rate (FER) = 0.01 52 vii List of Figures Figure Page 2.1 Wireless fading channel 2.2 System diagram for the cross-layer design framework 14 2.3 Block diagrams of the MAC layer scheduling 16 2.4 Linear downlink transmit strategy with isolated encoding and beamforming 19 3.1 Block diagram of the zero-forcing MUD (multi-user detection) at the base station with nR receive antennas 24 3.2 Performance degradation of naive cross-layer schedulers (designed for perfect CSI) with outdated CSI 29 3.3 Outage probability of naive scheduler versus various CSI error and SNR 30 3.4 System throughput (b/s/Hz successfully received) versus rate discounting factor where SNR = 6dB, nR =2, σ = 0.05 ∼ 0.1 33 3.5 Performance of maximal throughput schedulers of perfect CSIT, rate quantization and rate discounting with outdated CSIT, ideal (naive) scheduler with outdated CSIT 34 3.6 Illustration of crossover operation in Genetic algorithm 36 4.1 One cell system model of multi-user MISO system 39 4.2 Multi-antenna base station architecture with linear transmit processing 42 4.3 Block diagram of the cross-layer scheduling algorithm 46 4.4 Performance degradation of naive scheduler (scheduler designed for perfect CSIT), no quantization (Q = ∞): system goodput versus SNR in the presence of imperfect CSIT for nT = and K = 10 55 viii Figure Page 4.5 Average system goodput versus SNR for the naive scheduler (designed for perfect CSIT) with rate quantization (Q = 8) at imperfect CSIT cases, nT = 56 4.6 Packet outage probability versus CSIT errors of the naive scheduler (with no quantization), the naive scheduler (with rate quantization Q = 8) and the proposed scheduler (with rate quantization Q = 8) for nT = 57 4.7 Performance comparison of the proposed scheduler, the naive scheduler and the round robin scheduler with CSIT error σ = 0.05 at nT = 2, 4, 59 4.8 Sensitivity of average system goodput to the CSIT errors for the naive scheduler, the proposed scheduler and the round robin scheduler at nT = and Q = 60 4.9 Average system goodput versus number of users for proposed scheduler, RXZF scheduler, TDMA scheduler and opportunistic beamforming scheduler under outdated CSIT with speed= [20, 60] km/h and CSIT delay τ = 300µs (with equivalent error variance σ =[0.015, 0.13] for 5GHz carrier frequency); Transmit antenna nT = 4, receiver antenna for each user nR = (except for linear RXZF scheduler nT = nR = 4); SN R = 15dB 61 5.1 System model of a multi-user wireless system with a base station (nT transmit antennas), Kvoice voice client users and Kdata data client users 66 5.2 Scheduling and queueing model for voice and data users 70 5.3 Overall space time scheduling algorithm 73 5.4 Scheduling algorithm of the voice and data space time scheduler 75 5.5 Delay performance of VoIP users with background data traffic (BW = 20kHz, nT =4, Kdata =20, Kvoice =2, T0 = 20ms) 79 5.6 Spatial multiplexing gains of voice and data users (BW = 20kHz, Kdata =20, Kvoice =2, T0 = 20ms) 81 5.7 Transient performance of voice users in the presence of bursty data loading BW = 20kHz, nT =4, Kdata =20, Kvoice =2, T0 = 20ms 82 6.1 Queueing model and scheduling model 87 97 Mean packet transmission time E[X]: Considering i-th time slot for user k, si denotes the selection indicator which has si = when the user is selected and si = otherwise ri is the transmission rate for the user (unvarying within the time slot (ts = 2ms)) ni refers to the total number of transmitted packets (of user k) within time slot i which is given by ni = ri ts F The average packet transmission time of the investigated user can be approximated as the total transmission time divided by the total number of packets digested, i.e: N i=1 si ts N i=1 si ni E[X] = lim N −>∞ = N lim N −>∞ N N i=1 si ts N ri ts i=1 si F = E[sj ]F E[sj rj ] (6.18) Calculation of mean residual time E[R]: The mean residual time E[R] is calculated in a similar way to that of M/G/1 queues with vacations [67] which is given by: E[R] = lim t→+∞ t = t r(τ )dτ = lim t→+∞ M (t) t M (t) i=1 Xi M (t) λE[X ] λE[X] E[¯] s + ts 2 E[s] L(t) + t L(t) i=1 Zi L(t) (6.19) Expected waiting time modeling in terms of E[R] and E[s]: By expressing the selection probability as E[s] = E[R] + NQ E[X] E[s] NQ E[X] , NQ E[X]+E[ZT ] the mean waiting time in (6.17) is also given by E[W ] = By applying NQ = λE[W ] (Little’s theorem) and ρ = λE[X], we obtain the following modified Pollaczek-Khinchin formula: E[W ] = E[R] − ρ/E[s] (6.20) By substituting Eqn.(6.16) ∼ (6.20)to the delay constraint for user k on system time E[Tk ] = E[Wk ] + E[Xk ] ≤ τk , the delay constraint is equivalently given by: E[Xk ] + λE[Xk ] + ρ(E[sk ]/E[sk ])ts ¯ ≤ τk , 2(1 − ρ/E[sk ]) which is the expression in Eqn.(6.8) - Proof of Corollary (6.21) 98 With the fact that E[Xk ] = V ar[Xk ] + (E[Xk ])2 ≥ (E[Xk ])2 , a necessary condition for the delay requirement on system time in Eqn.(6.21) is achieved by: E[Xk ] + λ(E[Xk ])2 + ρ(E[sk ]/E[sk ])ts ¯ λE[Xk ] + ρ(E[sk ]/E[sk ])ts ¯ ≤ E[Xk ] + ≤ τj (6.22) 2(1 − ρ/E[sk ]) 2(1 − ρ/E[sk ]) From Eqn.(6.18), we have E[sk ]F E[sk λ( E[sk rk ] )2 + ρ( E[s¯ ]] )ts E[sk ]F k + ≤ τk ρ E[sk rk ] 2(1 − E[sk ] ) (6.23) Solving the standard quadratic inequality, a necessary condition for delay constraint C3 in Eqn.(6.7) is established in terms of rate rk : E[sk rk ] ≥ (2 − λk ts )E[sk ] + λj (2τk + ts ) + c− 4τk b2 4a F ρk , where a = (2−λk ts )2 +8λk τk , b = (4λk ts −8λk τk −2λk (λk ts )(2τk +ts )) and c = (λk (2τk +ts ))2 Appendix 6B: Iterative Lagrange Multiplier Search Algorithm The optimal rate allocation and power allocation strategies in (6.4) and (6.13) are obtained in terms of the Lagrange multipliers By substituting the power allocation (6.13) into (6.14), the Lagrange multipliers (γk and µ) is given by the solutions of the following equations: γk E log2 (1 − γk ) |hk wk |2 µ σz and E k∈A − γk σz − µ |hk wk |2 + − ρk =0 (6.24) + = P0 (6.25) There is no closed-form solution for the Lagrange multipliers µ and γk from the above system of equations ((6.24) and (6.25)) Thus we shall resort to the iterative numerical searching method to determine the µ and γk A Lagrangian multiplier finder algorithm is devised based on bisection searching method 99 According to the homogeneous characteristic among the same class of users, we only need to determine two distinct γk for all users (γ1 for class-1 user and γ2 for class-2 user) The flow chart of Lagrangian multiplier finder algorithm is illustrated in Fig 6.6 Without loss of generality, let index j = represent a particular user from class and index j = a particular user from class-2 The γ1 for class-1 user and γ2 for class-2 user can be respectively determined by the representative user (j = 1, 2) from each class To find the Lagrange multiplier µ and γ = {γ1 , γ2 }, we define the following bisection functions fj (µ, γ) = γj N N log2 n=1 and P (µ, γ) = P0 − N (1 + γj ) |hj (n)wj (n)|2 µ σz N n=1 k∈A + + γk σz − µ |hk (n)wk (n)|2 − ρj (6.26) + , (6.27) where N is the number of time slots that is to be averaged for approximating the ensemble average in (6.24) Eqn.(6.26) is the bisection function used to determine the optimal vector γ ∗ for given µ, which is based on the delay constraint equation (Eqn.(6.24)) Eqn.(6.27) is the bisection function to determine the optimal µ∗ based on the average total power constraint (Eqn.(6.25)) Notice that the ensemble average in the system of equations Eqn.(6.24) and Eqn.(6.25) is approximated by time average in the bisection functions The Lagrangian multiplier finder algorithm consists of the following three major steps: Step - Initialization: Choose an arbitrary µ, initialize a feasible search region for γ1 and γ2 f (µ, γ ) < j j,0 denoted as [γj,0 , γj,0 ], such that for j = [1, 2] f (µ, γ ) > j,0 j Step - bisection search on γ1 and γ2 : (a) For user j = [1, 2], we shall update γj,n and {γj,n , γj,n } based on Eqn (6.28) until |fj (µ, γ)|2 ≤ ε/2 γj,n = γj,n + γj,n , 100 γj,n+1 γ j,n = γ j,n if fj (µ, γj,n ) > if fj (µ, γj,n ) < and γj,n+1 γ j,n = γ j,n if fj (µ, γj,n ) > if fj (µ, γj,n ) < , where n is the iteration index; ε is the error tolerance for stopping criteria (b) Repeat bisection algorithm in (a) until we find a γ ∗ such that (|f1 (µ, γj,n )|2 +f2 (µ, γj,n )|2 ) ≤ ε where fj (µ, γ) is given by Eqn (6.26) for j = [1, 2] At this moment, the total consumed power reaches the minimum power level Pmin required to satisfy all users rate constraint (constraint C3 in Eqn 6.10) Step - Redistribution of the remaining power by adjusting µ using bisection method: Given γ ∗ (µ) obtained in step 2, determine the remaining power (P (µ, γ) = P0 − Pmin ) from Eqn (6.27) As illustrated in Fig 6.6, if P (µ, γ) < , the problem is infeasible because the provided power is insufficient to meet all the delay requirements If P (µ, γ) = , then {µ, γ ∗ } obtained in step is the solution If P (µ, γ) > 0, it means there exists remaining power to be allocated and the solution is obtained as follow Given {µ, γ ∗ } obtained in Step 2, we first initialize a feasible search region of µ , denoted as P (µ, γ(µ )) < 0 [µ0 , µ0 ] such that P (µ, γ(µ )) > 0 The search for the correct µ∗ is based on the following bisection iterative procedure: µn = µn+1 µ n = µ n µn + µn , ∗ if P (µn , γ (µn )) > if P (µn , γ ∗ (µn )) < and µn+1 µ n = µ n if P (µn , γ ∗ (µn )) > if P (µn , γ ∗ (µn )) < For each µn obtained from Eqn.6.28, repeat Step and Step to update γ ∗ (µn ) The iteration on µn in Eqn.6.28 terminates until |P (µn , γn )|2 < ε The final solution for Lagrange multiplier is given by {µ∗ , γ ∗ (µ∗ )} 101 Initialize µ with µ0 and feasible search region of {γ , γ } (Step 1) * Given µ , find {γ 1* , γ } with bisection method till f ( µ , γ * ) < ε (Step 2) Update µn based on bisection method using Eqn (17) (Step 3) Is P0 ≥ Pmin ? Yes No (Check once only) No Is P ( µ n , γ * ( µn )) < ε ? Yes E xit: Feasible Exit: Infeasible Figure 6.6 Flow chart of iterative lagrange multiplier algorithm 102 Chapter Conclusions and Future Work 7.1 Conclusions In this thesis, we first investigate the performance of cross-layer scheduling with imperfect CSIT (due to estimation error or outdatedness) in downlink multi-user MISO and uplink multiuser SIMO systems We found that while the system performance (average system throughput) improves quickly as the number of antennas in the base station (nT or nR ) increases (due to spatial multiplexing) with perfect CSI, the system performance suffers a significant loss on the system throughput gain in the presence of outdated CSI or imperfect CSI This is due to the mis-scheduling and packet outage problems To capture the effect of potential packet transmission outage due to the imperfect CSIT, we define the average system goodput, which measures the average b/s/Hz successfully delivered to the K mobiles, as the performance objective Followed by two heuristic schemes targeted for realizing the potential spatial multiplexing gains, a systematic and optimal framework for the cross-layer scheduling design with imperfect CSI constraint and discrete rate set constraint are proposed We introduce some special structures on the scheduling algorithm in order to simplify the optimization Specifically, the problem is decomposed into two parts: the optimal inner scheduling based on imperfect CSIT and the optimal transmission modes design based on inner scheduling output We formulate the cross-layer design as a mixed convex and combinatorial optimization problem with respect to the imperfect CSIT statistics By applying a Chi-square approximation on the outage probability, we obtain a closed-form solution for the rate and power adaptation for any given target 103 packet error probability The offline transmission mode design for rate adaptation is formulated as an optimization problem equivalent to the conventional scalar quantization problem, which can be effectively solved by Lloyd algorithm By considering the statistic of CSIT errors into the design, we have shown that the proposed schemes provide significant goodput enhancement Robust scheduling and rate adaptation with respect to imperfect CSIT assume delay-tolerant applications (homogeneous users), in which the objective is to maximize the system goodput regardless of how much delay a user’s packet may experience This is not realistic for wireless multimedia applications In Chapters and 6, cross-layer scheduling for heterogenous users application are investigated We first propose a heuristic heterogeneous scheduling in which scheduling priority is given to delay stringent users (voice users) in a naive way If there is any remaining resource (both power and spatial channel) after the voice user scheduling, it will be distributed to the data users in an optimal way The proposed heterogeneous scheduler ensures a stable quality of service for voice users Yet it cannot guarantee any explicit delay requirement due to the heuristic design In Chapter 6, we formulate the cross-layer heterogeneous scheduling as an optimization problem with a specific delay constraint for each user By considering the statistic of channel state and queue state into the design, the proposed scheme obtains significant throughput gain and desired delay performance 7.2 Future Work The work presented in this thesis can be extended in various ways Here we give some examples for potential future research • Cross-layer scheduling with heterogeneous delay constraints and imperfect CSIT In the thesis, the cross-layer scheduling design for heterogenous users application assumed perfect CSIT for simplicity It is straightforward to extend this work to study robust scheduling design with heterogeneous delay constraints for imperfect CSIT Instead of maximizing 104 the average system throughput subject to delay requirements, the objective could be maximizing the average system goodput (due to the transmission outage) or proportional fairness subject to heterogenous delay constraints • Robust beamforming with imperfect CSIT for the cross-layer scheduling We are assuming linear Zero-forcing processing and MMSE beamforming at the base station throughout the thesis The ZFBF and MMSE beamforming weights are obtained based on the estimated CSI as if they were perfect This actually simplifies the design and leads to suboptimal beamforming configuration An interesting extension is to regard the beamforming weight as the optimization parameters in the design framework It would be interesting to jointly optimize the beamforming weights, power allocation, rate allocation and user selection to maximize the average system goodput based on the error statistic of CSIT • Cross-layer scheduling design in multi-cell wireless systems In this thesis, we are considering cross-layer scheduling for a single cell wireless system with a centralized scheduler Basically the base station determines the scheduling output and coordinates the transmission among users to maximize the system capacity (with perfect CSIT) or goodput (with imperfect CSIT) In a practical multiple cells wireless system (with multiantenna base stations and single-antenna users), there exists multi-cell interference besides multi-user interference within the cell The multi-cell cooperation is expected to be able to effectively reduce the interference throughout the network An interesting approach is to put the MAC layer scheduler in the base station controller (BSC) side, which determines the rate and power allocation for the users in all cells The centralized scheduling in BSC needs cooperation among multiple cells and may require huge communication overheads One possible simplifying strategy is to apply a distributed/local cross-layer scheduling design within each cell, which has no cooperation with other cells When considering the imperfect CSIT, the above problems are even more 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Research Interest Cross layer design in multi-user OFDM/MIMO systems, Ad Hoc wireless systems, and relay-based wireless systems Physical layer algorithm design in OFDM/MIMO-based wireless systems such as channel estimation, timing and frequency synchronization, space time coding, multi-user detection, interference cancellation Digital baseband ASIC design, digital hardware design/prototyping for future wireless systems such as WLAN, UWB, and WiMAX Education PhD, Electrical and Electronic Engineering, Jan 2007, The University of Hong Kong, Hong Kong Master of Science, Electrical and Electronic Engineering, Apr 2002, Beijing University of Posts and Telecommunications, China Bachelor of Science, Physics, 1st Honor, Jul 1999, Nanchang University, China Research Experience Research Assistant Sep 2005 - Dec 2006 Cross-layer scheduling for downlink multi-user MIMO systems with heterogeneous delay constraints Research Assistant Sep 2004 - Aug 2005 Cross-layer scheduling and rate adaptation for downlink multi-user MIMO systems with imperfect channel state information (CSI) Research Assistant Sep 2003 - Aug 2004 Space-time uplink scheduling in multi-user multi-antenna systems; space-time downlink scheduling for voice and data application Technical Experience Research Assistant Feb 2005 - Dec 2006 Project on MBOA Ultra-wideband (UWB) Wireless Communications Chipset Design Research Assistant Project on High Capacity Wireless LAN Access Point Chipset Design Feb 2004 - Jan 2005 Hardware Design Engineer Mar 2002 - Aug 2003 Digital IF Design in CDMA2000-1X Base Station, Eastern Communications Ltd./Datang Mobile Ltd., Beijing, PRC Research Assistant Project on FPGA implementation of RFID Reader/Writer Sep 2000 - Feb 2002 List of Publications 1) K.N Lau, M L Jiang and Y.J Liu, ”Analysis of Space Time uplink Scheduling with Channel Estimation Error in Multiple Antennas System,” IEEE Transactions on Wireless Communications, Vol 5, No 6, Jun 2006, pp 1250-1253 2) K.N Lau and M L Jiang, ” Performance Analysis of Multi-user Downlink Space-time Scheduling for TDD Systems with Imperfect CSIT,” IEEE Transactions on Vehicular Technologies, Vol 25, No 1, Jan 2006, pp 296-305 3) K.N Lau, M L Jiang, ” On the Rate Adaptation and Scheduling Design of Downlink Multi-user, Multiple-antenna Base Station with Imperfect CSIT,” under second revision, IEEE Transactions on Wireless Communications, 2006 4) M.L Jiang, S.W Hui, K.N Lau and W.H Lam, ”Cross-layer Downlink Scheduling of Multiuser Multi-Antenna Systems for Wireless Multimedia Applications with Heterogeneous Delay Constraints,” to be submitted to IEEE Transactions on Wireless Communications, 2006 5) M.L Jiang, S.W Hui, K.N Lau and W.H Lam, ” Cross-layer Downlink Scheduling of Multiuser Multi-Antenna Systems for Wireless Multimedia Applications with Heterogeneous Delay Constraints,” IEEE ISIT2006, Jul 2006, Seattle, Washington, USA 6) K.N Lau and M.L Jiang, ” On the Rate Adaptation and Scheduling Design of Downlink Multiuser, Multiple-antenna Base Station with Imperfect CSIT,” IEEE GLOBECOM 2005, Nov 2005, St Louis, MO, USA 7) M.L Jiang and K.N Lau, ” On the design of Uplink Multi-Antenna Space Time Scheduling with CSI error,” IEEE PIMRC 2004, Sep 2004 Barcelona, Spain 8) K.N.Lau, M.L.Jiang, S Liew and O.C Yue, ” Performance Analysis of Downlink Multi-Antenna Scheduling for Voice and Data Applications,” Allerton Conference 2004, Sep 2004, the Allerton House, Monticello, IL, USA .. .Robust Cross-Layer Scheduling Design in Multi-user Multi-antenna Wireless Systems by Meilong Jiang MSEE, Beijing University of Posts and Telecomms A thesis submitted in partial fulfillment... Cross-Layer Scheduling and Adaptive Design in Multi-user Wireless Network 2.2.1 Adaptive Design in Physical Layer 2.2.2 MAC Layer Scheduling Model Linear... goodput and robustness in the presence of outdated CSI In Chapter 4, the downlink scheduling and rate adaptation are investigated in multi-user multipleinput single-output (MISO) systems with