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Trends in Wireless Communications

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Trends in Wireless Communications

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    1. November, 2007 1 Trends in Wireless Communications Geert Leus Delft University of Technology g.leus@tudelft.nl Acknowledgements: STW via VIDI-TVCOM and VICI-SPCOM TNO via UCAC University of Perugia Michigan Technological University Katholieke Universiteit Leuven

    2. November, 2007 2 Outline Communications over time-varying channels Feedback in single-user and multi-user MIMO systems Ultra wideband communications Cognitive radio

    3. November, 2007 3 Communications over time-varying channels

    4. November, 2007 4 Problem Statement Many wireless communications standards assume the channel is time-invariant over a block (mainly OFDM): IEEE802.11, IEEE802.16d, DVB-T, … When used in high mobility situations, problems occur and the orthogonality among subcarriers gets lost: IEEE802.16e, DVB-H, underwater communications, ... Special transceiver signal processing techniques are required to solve this self-interference problem

    5. November, 2007 5 OFDM Input-output relation

    6. November, 2007 6 OFDM How does this circular convolution look like?

    7. November, 2007 7 OFDM We take IDFT and DFT at transmitter and receiver:

    8. November, 2007 8 OFDM We assume guard intervals are removed:

    9. November, 2007 9 OFDM Equalization

    10. November, 2007 10 Improving Band Assumption Transmitter and receiver windows or pulse shapes have been developed to improve the subcarrier orthogonality and reduce the cyclic prefix length We relax these windows to improve the band approximation instead of the subcarrier orthogonality These methods are low-complexity in the sense that they have a complexity that is linear in the number of subcarriers Such schemes have also been labeled as generalized multicarrier systems

    11. November, 2007 11 Channel Estimation There are too many unknowns to estimate We need a reduced model that exploits the correlation

    12. November, 2007 12 Channel Estimation Polynomial BEM: Complex Exponential BEM:

    13. November, 2007 13 Channel Estimation Pilots are inserted in the frequency domain

    14. November, 2007 14 Extensions Non-linear equalization Decision-feedback equalization Iterative equalization Improved channel estimation Semi-blind channel estimation Iterative channel estimation Extensions to MIMO Spatial multiplexing (with or without precoding) Space-time block coding

    15. November, 2007 15 Simulation Results

    16. November, 2007 16 Application UCAC project “UUV - Covert Acoustic Communications”

    17. November, 2007 17 Application Different partners are testing different technologies to transfer a certain number of bits at the lowest SNR TNO and Delft University of Technology study OFDM Loss of orthogonality among subcarriers is a major problem when using OFDM in this set-up The proposed methods can be used to solve problem Multi-band OFDM is used to reduce complexity

    18. November, 2007 18

    19. November, 2007 19 Feedback for Single- and Multi-User MIMO Systems

    20. November, 2007 20 Why feedback? Feedback of the channel state information (CSI) in a single-user multiple-input multiple-output (MIMO) system allows for improved capacity, SNR, BER, … Example: Feedback in a multi-user MIMO system allows for the exploitation of the so-called multi-user diversity by selecting the right set of users

    21. November, 2007 21 Feedback for Single-User MIMO Both spatial multiplexing and space-time coding are incorporated in the above model The precoder adapts the transmitted signal to the current channel conditions

    22. November, 2007 22 Feedback for Single-User MIMO Many different feedback schemes have been proposed Statistical feedback of the CSI: useful if the channel varies too rapidly to track accurately Quantized feedback of the CSI: can exploit strong spatial modes if channel varies slowly We focus on quantized feedback

    23. November, 2007 23 Quantized Feedback Quantization steps are related to vector quantization

    24. November, 2007 24 Quantized Feedback Codebook design procedures Grassmannian sphere packing: the precoders are optimally packed w.r.t. some subspace distance Generalized Lloyd algorithm: the precoders are designed by iteratively minimizing the average distortion (done in 2 steps) Monte-Carlo algorithm: randomly generate a large set of codebooks and select the one that minimizes the average distortion Last two approaches make use of a large training set of channels, generated according to some statistics

    25. November, 2007 25 Quantized Feedback Extensions MIMO-OFDM systems Correlation between carriers can be exploited to reduce feedback and/or improve performance Entropy coding Clustering Finite-state vector quantization Time-varying MIMO systems Similar methods can be used to exploit the time correlation of the channel to reduce feedback and/or improve performance

    26. November, 2007 26 Feedback for Multi-User MIMO In this case feedback is also used for user scheduling Let us consider the single-antenna users case

    27. November, 2007 27 Feedback for Multi-User MIMO Basic scheme: opportunistic beamforming (OBF) The base station broadcasts a random beam Every user estimates its received SNR This received SNR is fed back to the base station Base station selects the user with the highest SNR Extensions: OBF with beam selection (OBF-S) Opportunistic SDMA (OSDMA) OSDMA with beam selection (OSDMA-S) Fairness and delay play an important role here Difficult to exploit frequency- and time-correlation

    28. November, 2007 28 Feedback for Multi-User MIMO

    29. November, 2007 29 Feedback of Multi-User MIMO Frequency- and time-correlation can for instance be exploited by predictive vector quantization

    30. November, 2007 30 Ultra Wideband Communications

    31. November, 2007 31 UWB Drivers Demand for short-range high-rate wireless capability Smaller semiconductor costs and power consumption Fragmented spectrum and discontinuous use of bands

    32. November, 2007 32 Key Features of UWB High rate for short range Low-complexity and low-cost equipment Low transmit power and noise-like spectrum Multipath and interference immunity High penetration capability Accurate positioning Use of radio as a sensor (radar features)

    33. November, 2007 33 IEEE Standardization IEEE 802.15.3a High-rate Not restricted to UWB but lends itself to it 100 Mbps within 10 m and 480 Mbps within 2 m Activities stopped in February 2006 IEEE 802.15.4a Low-rate / low-complexity Operate in unlicensed bands Focus on WPAN, sensor networks, smart badges, … Standard is being finalized

    34. November, 2007 34 Generic Pulsed UWB Receiver

    35. November, 2007 35 Subsampling UWB

    36. November, 2007 36 Subsampling UWB PAM

    37. November, 2007 37 Subsampling UWB PPM

    38. November, 2007 38 Transmitted Reference UWB

    39. November, 2007 39 Transmitted Reference UWB

    40. November, 2007 40 Transmitted Reference UWB

    41. November, 2007 41 UWB Testbed

    42. November, 2007 42 Cognitive Radio

    43. November, 2007 43 Introduction Current wireless systems are characterized by wasteful static spectrum allocation Dynamic spectrum allocation (DSA) shows promises to alleviate the inefficient usage of the spectrum Frequency-agile cognitive radios (CRs) are key to this

    44. November, 2007 44 Introduction The term “cognitive radio” was first coined by Mitola in 1999 and can be defined as in 2006 by IEEE: “A type of radio that can sense and autonomously reason about its environment and adapt accordingly. This radio could employ machine learning mechanisms in establishing, conducting or concluding communication and networking functions with other radios” Two CR-related standards are under development: IEEE 802.22: high rate access (1.5 Mb/s) in rural areas up to 100 km in coverage IEEE 802.11h: WLANs with dynamic frequency selection transmit power control capabilities

    45. November, 2007 45 Considered Set-Up A peer-to-peer CR network where each user corresponds to a single transmitter-receiver pair On top of that there is interference from primary users

    46. November, 2007 46 How does it work? CRs dynamically decide the allocation of the available resources to improve the network-wide spectrum efficiency, a.k.a. dynamic resource allocation (DRA) The DRA task can be efficiently performed in a distributed fashion where every CR iteratively senses the available resources, and adjusts its own usage accordingly The resources can be represented by transmitter and receiver basis functions (carriers, pulses, codes, wavelets, etc.) which can be chosen to enable various agile platforms, such as frequency-, time-, or code-division multiplexing (FDM, TDM, CDM)

    47. November, 2007 47 How does it work? Sensing part Sensing its own link is done by training techniques Sensing the interference is difficult due to the large number of possible resources, but since the actual number of used resources is small, compressive sampling mechanisms can be used Adapting part Given its own link and the interference, the CR optimizes its spectral efficiency under certain power and spectral mask constraints

    48. November, 2007 48 Some Results

    49. November, 2007 49 Discussion and Extensions Generally, DRA is done independently from waveform optimization, but this has a number of cons: DRA has to run on a central level If distributed DRA is used, every CR requires the knowledge of the links to the other CRs and the decisions taken at the other CRs Sparsity constraints can be included in the optimization to limit the actual number of used resources Band-limited feedback is required from the receiver to the transmitter, which can be taken into account in the optimization procedure

    50. November, 2007 50 Comments? Questions?

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