logging in or signing up CHANNEL capacity pps_k Download Post to : URL : Related Presentations : Share Add to Flag Embed Email Send to Blogs and Networks Add to Channel Uploaded from authorPOINT lite Insert YouTube videos in PowerPont slides with aS Desktop Copy embed code: Embed: Flash iPad Dynamic Copy Does not support media & animations Automatically changes to Flash or non-Flash embed WordPress Embed Customize Embed URL: Copy Thumbnail: Copy The presentation is successfully added In Your Favorites. Views: 941 Category: Entertainment License: All Rights Reserved Like it (0) Dislike it (0) Added: June 21, 2010 This Presentation is Public Favorites: 0 Presentation Description No description available. Comments Posting comment... By: icdragon (36 month(s) ago) It's a very good introduction, such as Channel capacity derivation. Saving..... Post Reply Close Saving..... Edit Comment Close By: pinudiku (39 month(s) ago) pls sir sending this documentation Saving..... 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Edit Comment Close Premium member Presentation Transcript Wireless CommunicationElec 534Set ISeptember 9, 2007 : Wireless CommunicationElec 534Set ISeptember 9, 2007 Behnaam Aazhang The Course : The Course Light homework Team project Individual paper presentations Mid October Team project presentations Early December Multiuser Network : Multiuser Network Multiple nodes with information Outline : Outline Transmission over simple channels Information theoretic approach Fundamental limits Approaching capacity Fading channel models Multipath Rayleigh Rician Outline : Outline Transmission over fading channels Information theoretic approach Fundamental limits Approaching achievable rates Communication with “additional” dimensions Multiple input multiple (MIMO) Achievable rates Transmission techniques User cooperation Achievable rates Transmission techniques Outline : Outline Wireless network Cellular radios Multiple access Achievable rate region Multiuser detection Random access Why Information Theory? : Why Information Theory? Information is modeled as random Information is quantified Transmission of information Model driven Reliability measured Rate is established Information : Information Entropy Higher entropy (more random) higher information content Random variable Discrete Continuous Communication : Communication Information transmission Mutual information Channel Useful Information Noise; useless information Maximum useful information Wireless : Wireless Information transmission Channel Useful Information Noise; useless information Maximum useful information Interference Randomness due to channel Multiuser Network : Multiuser Network Multiple nodes with information References : References C.E. Shannon, W. Weaver, A Mathematical Theory Communication, 1949. T.M. Cover and J. Thomas, Elements of Information Theory, 1991. R. Gallager, Information Theory and Reliable Communication, 1968. J. Proakis, Digital Communication, 4th edition D. Tse and P. Viswanath, Fundamentals of Wireless Communication, 2005. A. Goldsmith “Wireless Communication” Cambridge University Press 2005 References : References E. Biglieri, J. Proakis, S. Shamai, Fading Channels: Information Theoretic and Communications, IEEE IT Trans.,1999. A. Goldsmith, P. Varaiya, Capacity of Fading Channels with Channel Side Information, IEEE IT Trans. 1997. I. Telatar, Capacity of Multi-antenna Gaussian Channels, European Trans. Telecomm, 1999. A. Sendonaris, E. Erkip, and B. Aazhang, “User cooperation diversity, Part I. Systemdescription,” IEEE Trans. Commun., Nov. 2003. ——, “User cooperation diversity. Part II. Implementation aspects and performance analysis,” IEEE Trans. Commun., Nov. 2003. J. N. Laneman, D. N. C. Tse, and G. W. Wornell, “Cooperative diversity in wireless networks: Efficient protocols and outage behavior,” IEEE Trans. Inform. Theory, Dec. 2004. M.A. Khojastepour, A. Sabharwal, and B. Aazhang, “On capacity of Gaussian ‘cheap’ relay channel,” GLOBECOM, Dec. 2003. Reading for Set 1 : Reading for Set 1 Tse and Viswanath Chapters 5.1-5.3, 3.1 Appendices A, B.1-B.5 Goldsmith Chapters 1, 4.1,5 Appendices A, B, C Single Link AWGN Channel : Single Link AWGN Channel Model where r(t) is the baseband received signal, b(t) is the information bearing signal, and n(t) is noise. The signal b(t) is assumed to be band-limited to W. The time period is assumed to be T. The dimension of signal is N=2WT Signal Dimensions : Signal Dimensions A signal with bandwidth W sampled at the Nyquist rate. W complex (independent) samples per second. Each complex sample is one dimension or degree of freedom. Signal of duration T and bandwidth W has 2WT real degrees of freedom and can be represented 2WT real dimensions Signals in Time Domain : Signals in Time Domain Sampled at Nyquist rate Example: three independent samples per second means three degrees of freedom time Voltage 1/W 1 second Signal in Frequency Domain : Signal in Frequency Domain Bandwidth W at carrier frequency fc frequency Power W Carrier frequency fc Baseband Signal in Frequency Domain : Baseband Signal in Frequency Domain Passband signal down converted Bandwidth W frequency Power W Sampling : Sampling The baseband signal sampled at rate W Where Sinc function is an example of expansion basis Model : Model There are N orthonormal basis functions to represent the information signal space. For example, The discrete time version Noise : Noise Assumed to be a Gaussian process Zero mean Wide sense stationary Flat power spectral density with height Passed through a filter with BW of W Samples at the rate W are Gaussian Samples are independent Noise : Noise Projection of noise Projections, ni onto orthonormal bases fi(t) are zero mean Gaussian Variance Noise : Noise The samples of noise are Gaussian and independent The received signal given the information samples are also Gaussian Model : Model The discrete time formulation can come from sampling the received signal at the Nyquist rate of W The final model The discrete time model could have come from projection or simple sampling Statistical Model : Statistical Model Key part of the model The discrete time received signals are independent since noise is assumed white Entropy : Entropy Differential entropy Differential conditional entropy with Example : Example A Gaussian random variable with mean and variance The differential entropy is If complex then it is Among all random variables with fixed variance Gaussian has the largest differential entropy Proof : Proof Consider two zero mean random variables X and Y with the same variance Assume X is Gaussian Variance of X Proof : Proof Kullback-Leibler distance Due to Gibbs inequality! Gibbs’ Inequality : Gibbs’ Inequality The KL distance is nonnegative Capacity : Capacity Formally defined by Shannon as where the mutual information with Capacity : Capacity Maximum reliable rate of information through the channel with this model. In our model Mutual Information : Mutual Information Information flow Channel Useful Information Noise; useless information Maximum useful information Capacity : Capacity In this model the maximum is achieved when information vector has mutually independent and Gaussian distributed elements. AWGN Channel Capacity : AWGN Channel Capacity The average power of information signal The noise variance AWGN Capacity : AWGN Capacity The original Shannon formula per unit time An alternate with energy per bit Achievable Rate and Converse : Achievable Rate and Converse Construct codebook with N-dimensional space Law of large numbers Sphere packing Sphere Packing : Sphere Packing Number of spheres (ratio of volumes) Non overlapping As N grows the probability of codeword error vanishes Higher rates not possible without overlap Achievable Rate and Converse : Achievable Rate and Converse Construct codebook with bits in N channel use Achieving Capacity : Achieving Capacity The information vector should be mutually independent with Gaussian distribution The dimension N should be large Complexity Source has information to transmit Full buffer Channel is available No contention for access Point to point Achieving Capacity : Achieving Capacity Accurate model Statistical Noise Deterministic Linear channel Signal model at the receiver Timing Synchronization Approaching Capacity : Approaching Capacity High SNR: Coded modulation with large constellation size Large constellation with binary codes Low SNR: Binary modulation Turbo coding LDPC coding Constellations and Coding : Constellations and Coding You do not have the permission to view this presentation. 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