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Ph. D. Defense

Downlink Adaptive Resource Allocation for a Multi-user MIMO OFDM System with and without Fixed Relays. Ph. D. Defense. M. Sc. Ying Zhang Reviewers: Prof. Dr.-Ing. Anja Klein Prof. Dr.-Ing. Dr. rer. nat. Holger Boche Examiners: Prof. Dr.-Ing. Peter Meißer Prof. Dr.-Ing. Han Eveking

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Ph. D. Defense

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  1. Downlink Adaptive Resource Allocation for a Multi-user MIMO OFDM System with and without Fixed Relays Ph. D. Defense M. Sc. Ying Zhang Reviewers: Prof. Dr.-Ing. Anja Klein Prof. Dr.-Ing. Dr. rer. nat. Holger Boche Examiners: Prof. Dr.-Ing. Peter Meißer Prof. Dr.-Ing. Han Eveking Institute for Telecommunications / Area of Communications Engineering Department for Electrical Engineering and Information Technology Darmstadt University of Technology 2014/11/19 | Institute of Telecommunications | Area of Communications Engineering

  2. time time frequency time space 1 MT frequency frequency signal interference time BS RN space Introduction (1): Downlink Multi-user OFDM MIMO System with and without Fixed Relays • Downlink multi-user:base station (BS) transmits, K user terminals (UTs) receive. • OFDM(Orthogonal frequency division multiplexing) • MIMO (Multiple-input multiple-output) BS: MT tx antennas; K UTs: K·MR rx antennas • Fixed relays(RN) • Challenges: • 3-dimensional resources • Two-hop communication • Interference among access points (APs), including BS and RNs N sub-carriers N 1 MT < K·MR time space 2014/11/19 | Institute of Telecommunications | Area of Communications Engineering

  3. channel gain UT 1 UT 2 UT 3 freq. 1 N w1 w2 UT1 AP UT2 AP Introduction (2): Adaptive Resource Allocation • Adaptive FDMA • Channel fading is time-varying, frequency-selective and independent among users. • Always allocate best resources to users. • Adaptive SDMA • Interference among co-located users is proportional to their spatial correlation. • Always allocate users with sufficiently low spatial correlation together. • Dynamic resource reuse among multiple APs • Inter-AP interference is time-varying, frequency-selective. • Reuse resource, i.e. multiple APs use the same resource, when the interference is sufficiently low. hk: channel vector wk: antenna vector Signal S1 = |h1T w1|2 Interference I1 = |h1T w2|2 max S1 w1=h1* I2 = h1T h2 high interference AP1 AP2 low inerference AP3 2014/11/19 | Institute of Telecommunications | Area of Communications Engineering

  4. Outline • Adaptive Resource Allocation in a Single Cell Joint adaptive FDMA/SDMA • Power minimization with user rate constraints • Rate maximization with user fairness constraints • Signaling Overhead for Adaptive Resource Allocation • Optimization of Chunk Dimension • Optimization of Chunk Update Interval • Resource Allocation with Reduced Channel Feedback • Optimization of Bandwidth Request Transmission • Adaptive Resource Allocation in a Relay-enhance Cell Two-level adaptive resource allocation • Construction of logic beams • Grouping of logic beams • Resource allocation among logic beams 2014/11/19 | Institute of Telecommunications | Area of Communications Engineering

  5. Adaptive Resource Allocation in a Single Cell- Joint Adaptive FDMA/SDMA • Assumption: using chunk as basic resource unit • State of the art • Adaptive FDMA in OFDMA system: no spatial dimension • Adaptive SDMA in narrow-band MIMO system: no frequency dimension Spatial correlation is frequency selective. Joint optimization of adaptive- FDMA and SDMA is required. Contribution 1: propose low-complexity algorithm performing joint optimization of adaptive FDMA/SDMA for power minimization problem and rate maximization problem. 2014/11/19 | Institute of Telecommunications | Area of Communications Engineering

  6. Joint Adaptive FDMA/SDMA- Power minimization problem (1) • Power minimization problem: • Solutions: • Optimal solution: exhaustive search by integer linear programming  huge complexity • Sub-optimal solution: Successive Bit Insertion (SBI) low complexity • Initialization: • Each iteration: where • End condition: k: user index n: chunk index Rk: min. data rate requirement rk,n: allocated data rate Pk,n: required transmit power cost for granting a given rate increase Δrto user k on chunk n 2014/11/19 | Institute of Telecommunications | Area of Communications Engineering

  7. Joint Adaptive FDMA/SDMA- Power minimization problem (2) • Three variants for the cost function • Original • With Priority Power increase required to increase the data rate of user k on chunk n by Δr Prioritize the user whose allocated rate is far away from the minimum requirement Rk. relative allocated rate • Weighted priority (WP) • First priority (FP) • Original variant is the worst. • FP variant is better than WP variant and approaches the optimal solution. 2014/11/19 | Institute of Telecommunications | Area of Communications Engineering

  8. Joint Adaptive FDMA/SDMA- Rate maximization problem (1) • Rate maximization problem: to maximize sum data rate while satisfying user fairness properties under power constraint. • Equal power distribution over chunks: • User fairness strategies Notes: average data rate update average data rate update after each iteration 2014/11/19 | Institute of Telecommunications | Area of Communications Engineering

  9. Joint Adaptive FDMA/SDMA- Rate maximization problem (2) • To further reduce the complexity  assume equal power sharing among users served on the same chunk • Successive User Insertion (SUI) • Initialization: • Each iteration: • End condition: sum rate cannot be increased without violating the power constraints set of users served on chunk n where 2014/11/19 | Institute of Telecommunications | Area of Communications Engineering

  10. Joint Adaptive FDMA/SDMA- Rate maximization problem (3) Suc. Bit Ins. vs. Suc. User Ins. Proportional Fair (WP) vs. Max-min Fair (FP) • Bit-Ins outperforms User-Ins by adaptive power loading among different spatial layers. • WP improve user satisfaction at the expense of total cell throughput compared to FP. 2014/11/19 | Institute of Telecommunications | Area of Communications Engineering

  11. Joint Adaptive FDMA/SDMA- Rate maximization problem (4) • SUI performs worse than SBI due to two factors: • No power adaptation among users • Discrete rate adaptation 2014/11/19 | Institute of Telecommunications | Area of Communications Engineering

  12. Joint Adaptive FDMA/SDMA- Rate maximization problem (5) Joint vs. Disjoint FDMA/SDMA • Disjoint(State of the art): • Allocate chunks in arbitrary order, e.g. one after another in order • For the given chunk, select the user that minimizes cost function • FP for max-min fairness: Joint approach achieves around 35% more throughput than disjoint approach. • WP for proportional fairness: The performance gain of joint approach over disjoint one is small. 2014/11/19 | Institute of Telecommunications | Area of Communications Engineering

  13. Signaling Overhead - Optimization of Chunk Dimension (1) • AP needs to inform users the results of adaptive resource allocation, resulting in additional signaling overhead Rate of signaling (bits/symbol) No. of trans. mode No. of users Chunk dimension: nsub subcarriers by nsymb symbols • Trade-off exists in choosing chunk dimension: • Increasing chunk dimension reduces signaling overhead; • Decreasing chunk dimension enhances performance of adaptive resource allocation. Contribution 2: Analytically deriving the relationship between the performance of adaptive resource allocation and the chunk dimension so as to derive the optimal chunk dimension. 2014/11/19 | Institute of Telecommunications | Area of Communications Engineering

  14. t 1 5 2 n 3 m 4 Signaling Overhead - Optimization of Chunk Dimension (2) • Roadmap of the analytical derivation Mean value within m-th chunk The user with highest performance is equivalent to highest PDF of , , can then be calculated through order statistics. • Assumptions in the analytical derivation: • An OFDMA system with one AP, K users and N subcarriers • Channel coefficients modeled as a stationary two dimensional zero-mean Gaussian process, whose variance is set to one without loss of generality • Perfect channel knowledge known at the AP • Performance evaluated in terms of Shannon capacity 2014/11/19 | Institute of Telecommunications | Area of Communications Engineering

  15. Signaling Overhead - Optimization of Chunk Dimension (3) Ceff,max = 3.06 bits/s/Hz at (8,18) Max delay spread: 3.2s; Velocity: 100km/h Optimal chunk dimension is 8 sub-carriers by 18 OFDM symbols. 2014/11/19 | Institute of Telecommunications | Area of Communications Engineering

  16. Channel is time-variant due to mobility  Channel knowledge shall be periodically updated. Signaling Overhead - Optimization of Channel Update Interval (1) • Trade-off exits in choosing channel update interval Tup: • Increasing Tup reduces signaling overhead • Decreasing Tup enhances adaptive resource allocation. r • Effective Throughput: Tup,opt=arg max ρ(Tup) too much overhead Fixed Allocation β: Signaling for channel knowledge update Tup: Channel update interval Tup 0 too less channel knowledge no adaptation gain Contribution 3: Derive the optimal channel update interval which maximizes the effective throughput. 2014/11/19 | Institute of Telecommunications | Area of Communications Engineering

  17. Approximate by a linear curve suitable for arbitrary velocity based on the numerical results: Signaling Overhead - Optimization of Channel Update Interval (2) frame duration Update interval relative to speed of the channel time variability: coherence time 2014/11/19 | Institute of Telecommunications | Area of Communications Engineering

  18. Signaling Overhead - Optimization of Channel Update Interval (3) Optimal update interval: 2014/11/19 | Institute of Telecommunications | Area of Communications Engineering

  19. Signaling Overhead - Allocation with Reduced Channel Feedback (1) • Different levels of channel knowledge: • short-term CSI (channel state information): full channel matrix • short-term CQI (channel quality matrix): channel gain • long-term CSI: channel covariance matrix • long-term CQI: average channel gain – average SNR Less signaling • State of the art Long-term Generalized Eigenbeamforming Opportunistic beamforming/SDMA Assumption: short-term CQI Assumption: long-term CSI own signal • Pre-determine Q beams wq • Each user selects the best beam. • Allocate each beam to the best user. interference to others No Adaptive switching between with and w/o SDMA. No adaptation in time- and freq.- domains. Contribution 4: perform joint adaptive FDMA/SDMA based on long-term CSI and short-term CQI. 2014/11/19 | Institute of Telecommunications | Area of Communications Engineering

  20. Signaling Overhead - Allocation with Reduced Channel Feedback (2) • Calculation of Actual SINR: require full channel feedback — short-term CSI • Prediction of SINR for long-term Generalized Eigenbeamforming : • Available channel knowledge: • long-term CSI • short-term CQI • Proposed methods: (a) Assuming single receive antenna (b) Approximate the channel matrix by the first r dominant eigenvector SINR overestimated SINR Underestimated Conservative resource allocation Aggressive resource allocation 2014/11/19 | Institute of Telecommunications | Area of Communications Engineering

  21. Signaling Overhead - Allocation with Reduced Channel Feedback (3) CCDF(error) CONS AGGR SNR =15 dB error = SINR-SINRest [dB] • Conservative resource allocation is beneficial than aggressive resource allocation. • GoB: less adaptive, more accurate SINR estimation  better in high SNR region. • GenEigBF: more adaptive, less accurate SINR estimation  better in low-middle SNR region. 2014/11/19 | Institute of Telecommunications | Area of Communications Engineering

  22. Signaling Overhead - Optimization of Bandwidth Request Transmission (1) • Users ask for allocation of resources by sending a bandwidth request (BW-REQ). • Each BW-REQ is transmitted in one transmission opportunity (TO). N TOs UL Data uplink frame • Performance: average delay - the time difference between the arrival and the successful transmission of the BW-REQ. No. of users • Two typical approaches: Average delay = frame 1. Polling User 1 User 2 User 3 Widely used scheme: slotted-Aloha with truncated binary exponential back-off (TBEB) algorithm 2. Random Access confliction State of the art: random access in WLAN (wireless local access network) under the assumption that users always have data to transmit has been well-studied by Bianch. Contribution 5: (a) derivative the performance of random access in a frame-based system under the assumption that the arrival of BW-REQ is modeled as Bernoulli process; (b) propose a novel user grouping approach which improves the performance of random access. 2014/11/19 | Institute of Telecommunications | Area of Communications Engineering

  23. Signaling Overhead - Optimization of Bandwidth Request Transmission (2) Slotted-Aloha with TBEB algorithm has two parameters: m: maximum back-off stage W0: initial window size. • In stage i, a back-off counter cibetween 0 to Wi-1is chosen, Wi=2iW0. The back-off counter ci indicates the number of TOs the user has to wait before a transmission attempt. • When a collision happens, the user goes to stage i+1 unless it reaches the maximum stage. • In case of successful transmission, the user goes to stage 0. Bianch models slotted-Aloha with TBEB algorithm with Markov Chain. 2014/11/19 | Institute of Telecommunications | Area of Communications Engineering

  24. Signaling Overhead - Optimization of Bandwidth Request Transmission (3) • Users know whether there is a collision: • immediately after the transmission in WLAN • at the beginning of the next frame in frame-based system Bianch’s analysis assumes that users always have some packets to transmit. The arrival of the BW-REQ is modeled as Bernoulli process with parameter λ, i.e. a new BW-REQ occurs with probability λ in every frame at the beginning of each frame. If a user transmits in the n-th TO, it won’t immediately know until the next frame and thus the back-off process will stop for (N-n)-th TOs. After successful transmission, when there is no new BW-REW coming, the back-off process will stop for N TOs. Introduce two kinds of idle states in the Markov chain. 2014/11/19 | Institute of Telecommunications | Area of Communications Engineering

  25. Signaling Overhead - Optimization of Bandwidth Request Transmission (4) Analytical results meet the simulation results pretty well. 2014/11/19 | Institute of Telecommunications | Area of Communications Engineering

  26. Signaling Overhead - Optimization of Bandwidth Request Transmission (5) • Problem: resources are not efficiently used • The resource required for BW-REQ transmission depends on the used data rate. • Users support different data rate according to their channel quality. • Each TO should be large enough for the transmission using the lowest data rate. Observations: the performance is almost the same when the ratio between the number of users and the number of TOs is constant. • Proposal: • Divide users into G groups, such that users in the same group have similar channel quality. • Divide resources into G groups such that Kg(No. of users in group g) = constant Ng(No. of TOs in group g) 2014/11/19 | Institute of Telecommunications | Area of Communications Engineering

  27. Comparing my proposal to the conventional method: • More TOs and so less delay, given the same amount of resources; • Alternatively, less resources are required to provide the same number of TOs. Signaling Overhead - Optimization of Bandwidth Request Transmission (6) Example: 144 symbols for BW-REQ Tx, 48-bits BW-REQ;12 users support 2 bits/symbol, 12 users only support 1 bits/symobl. Conventionally, 1 bits/symbol  48 symbols / BW-REQ  3 TOs K/N = 8 In my proposal, 96 symbols using 2 bits/symbol 2 TOs48 symbols using 2 bits/symbol 2 TOs N1/K1= N2/K2 = 6 2014/11/19 | Institute of Telecommunications | Area of Communications Engineering

  28. RN BS Adaptive Resource Allocation in a REC - Two-level adaptive resource allocation State of the Art: • Centralized approachadaptive resource allocation for the whole REC is carried out by the BS • high complexity • huge signaling overhead • additional delay due to two-hop communication • Distributed approachadaptive resource allocation is carried out by individual AP for its serving users independently • inter-AP interference is unpredictable • Resource Partitioning performed by BS on longer time scale, of few millisecs, to dynamically partition the resources among APs (i.e. BS and RNs) within a REC according to average traffic load and interference scenario • Resource Scheduling performed by each AP in each sub-cell on a shorter time-scale, of less than 1 ms, in order to obtain multi-user diversity through frequency-adaptive resource allocation Contribution 6: Propose a two-level adaptive resource allocation 2014/11/19 | Institute of Telecommunications | Area of Communications Engineering

  29. Proposed: dynamic logic beam Beam construction: identification of groups of spatially correlated AP-UT links at each AP (BS or RN). Each group of links is referred to as beam; Beam grouping: identification of spatially uncorrelated beams which are allowed to share the same time-frequency resources, i.e. chunks. Resource partitioning: assign resources to BS-RN links and logic beams (BS/RN-UT links): Two-level adaptive resource allocation- dynamic logic beam logic beam sector • Reference: sectorization — fixed logic beam • the sub-cell of each RN is divided into fixed sectors; • fixed sets of spatially uncorrelated sectors are allowed to share the same chunks in the spatial domain. 2014/11/19 | Institute of Telecommunications | Area of Communications Engineering

  30. Beam construction: Initialization: each user constitues a beam Each iteration: combining two beams with highest spatial correlation together End: spatial correlation of any two beams is sufficiently low 3 3 3 3 3 3 3 3 2 4 1 3 5 AP Two-level adaptive resource allocation- Beam construction beam A iterations 1 1 2 2 3 3 4 4 5 5 1 1 2 2 3 3 4 4 5 5 1 1 2 2 1 1 2 2 4 4 5 5 4 4 5 5 beam B 4 1 1 2 2 5 5 1 1 2 2 5 5 3 5 5 5 5 4 1 1 2 2 1 1 2 2 Definition of spatial correlation between beams A and B: Spatial correlation between users i and j 2014/11/19 | Institute of Telecommunications | Area of Communications Engineering

  31. Beam grouping:successively add best beam until group capacity decreases X Two-level adaptive resource allocation- Beam grouping iterations A B C D E A D Capacity increases Capacity increases A D B Capacity increases Capacity decreases A D B C A D C C E Group capacity / group data rate: Rate requirement of user i achievable rate of user i 2014/11/19 | Institute of Telecommunications | Area of Communications Engineering

  32. Two-level adaptive resource allocation- Resource partitioning End-to-end throughput is equal to the minimum between that of the first hop (BS-RN links) and that of the second-hop (BS/RN-UT links). Resources allocated to the first hops and the second hops should be balanced. • Chunk-by-chunk balancing (CBC) • Generate one beam group • Allocate one chunk for the group • Reserve resources for the first-hop links • Repeat 2-3 till at least one beam in the group is completely allocated • Repeat Steps 1-4 till • All beams are completely allocated, or • No resource is left • Iterative independent balancing (IIB) • Allocation for beams: calculate the amount of resources required to completely allocate all the beams • Allocation for first-hop links: calculate the amount of resources required for the first-hop links • If the sum of the required resources > the available resources, a) proportionally scaling down the required data rate of each user b) Go back to step 1 Completely allocated: allocated rate ≥ required rate 2014/11/19 | Institute of Telecommunications | Area of Communications Engineering

  33. Two-level adaptive resource allocation- Simulation Results Jain’s fairness index: totally fair unfair • Dynamic approach achieves higher cell throughput and better user fairness. • CCB achieves high cell throughput, but IIB guarantees user fairness. 2014/11/19 | Institute of Telecommunications | Area of Communications Engineering

  34. Summary This work • proposes low-complexity sub-optimal algorithms for joint optimization of adaptive FDMA and SDMA; • analytically derives the performance of adaptive FDMA as a function of the chunk dimension which facilitates the optimization of the chunk dimension; • investigates the optimal update interval of channel knowledge; • applies joint adaptive FDMA and SDMA to a system when only long-term CSI and short-term CQI are available at the transmitter; • analytically derives the performance of random access and proposes a grouping mechanism which enables more efficient usage of resources; • presents a hierarchical approach for the adaptive resource allocation in a relay-enhanced cell which achieves high adaptation gain at low signaling overhead. 2014/11/19 | Institute of Telecommunications | Area of Communications Engineering

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