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Prof. Aditya K. JagannathamMultimedia Wireless Networks MWN Lab
This presentation is also available for download on the MWN Lab website at http://www.iitk.ac.in/mwn/
• 3G Systems• UMTS – 384 Kbps (Mux)• Video Telephony, Streaming.
• 3.5G Systems• HSPA+ - 28 Mbps (Latency)• HSDPA – 14 Mbps• VOIP Video Conferencing• VOIP, Video Conferencing.
• 4G Wireless systems,• 3GPP LTE 100-150 Mbps.• WiMAX (802.16 d/e) –75 Mbps• True broadband• Interactive Gaming, MBMS.
• High Speed LAN• 802.11n – 600 Mbps• 802.11ac
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• Video over Wireless Applications• Video Streaming.g• Mobile/IP TV.• Video Conferencing.• Surveillance. • Interactive Gaming.
HDTV• HDTV.• 3.5G/4G support a rich plethora of
multimedia based services.• Unicast, Multicast/Broadcast.• Resource allocation is of key
interest in such networks.
• Uncompressed colour video (640 X 480) at 30 fps requires 220 Mbps.) p q p• Virtually impossible in existing
wireless/wireline systems.• Video compression standards include
MPEG-2/4, H.263, H.264.• Involve motion prediction and DPCMInvolve motion prediction and DPCM
based transmission.• Most schemes employ transform
domain VLE encoding (Huffman).• Codecs are substantially complex.
• Require low latency
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5
• Require low latency.• Hybrid ARQ.
0 5 10 15 20 25 30 35 40 45
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• Orthogonal Frequency Division Multiplexing – OFDM
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Orthogonal Subcarriers in OFDM for WiMAX
p g• Key standard included in all 4G
Cellular and 802.11n.• IFFT/FFT implementation.• Substantially reduces complexity
of wideband PHY processing.0
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p g• At the Heart of every modern
wireless broadband access.• LTE, WiMAX, 802.11n etc. are all
based on OFDM/OFDMA.• Resource allocation in Time
-60 -40 -20 0 20 40 60-0.2
Frequency (KHz)
• Resource allocation in Time, Frequency and Multiuser.
• Multiple-Input Multiple-Output (MIMO) RT(MIMO)• Multiple antennas at transmitter and
receiver.• Linear increase in throughput for
same power and bandwidth.• Employs spatial-multiplexing
Rx
Transmitter Receiver= Antenna
TX
102
MIMO Capacity vs. SNR (dB) for Different No. of Antennas
r = t = 1Employs spatial multiplexing.• Enables high data rates in 3.5/4G
systems HSPA+/LTE/WiMAX.• MIMO-OFDM blends MIMO and
OFDM technologies.Si lifi d b b d i f
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py
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r = t = 2
r = t = 4r = t = 8
• Simplified baseband processing for wireless broadband.
5 10 15 20 25 30 35 40 45 5010
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SNR (dB)
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• Multiple subcarriers per user in OFDMA based 4G/Cellular System.• Rayleigh subcarriers, significant
probability of deep fade.• Wireless transmitter (BS/Cluster
Head) has limited power.
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Orthogonal Subcarriers in OFDM for WiMAX
• Maximize reliability (ie minimize distortion) corresponding to Pt.
Full/partial Channel State Info (CSI)
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-60 -40 -20 0 20 40 60-0.2
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Frequency (KHz)
• Full/partial Channel State Info (CSI), QoS constraints etc.
• Mean-squared error, Rate Distortion (RD), Perception Quality
• Broadcast, Multicast, Unicast.
• Cognitive radio is a revolutionary new paradigm for wireless p gcommunication.
• Several Interesting Issues• Spectrum Sensing• Capacity Maximization• Interference Minimization• Interference Minimization
• Consider SU capacity maximization in OFDMA CR systems• The transmission rate for the ith
subcarrier of power Pi and channel fade hi
ss is,
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• Power allocation for rate maximization with interferencemaximization with interference constraints is given as,
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• Several other exciting paradigms such as Cancellation Carriers, Game Theory, Auctions, Dynamic Programming etc.
• Powerful framework for optimal resource allocation.• Convex objective and constraints,
feasibility, reduction to LP.
• Classes of CO, LP, QCQP, SOCP.• Fast solvers, polynomial root algos.• MATLAB environment (CVXOPT),
MOSEK PYTHONMOSEK, PYTHON.• Significant applications such as
robust beamforming, estimation etc.• Root of all modern signal processing
and resource allocation.
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• Wireless Sensor Networks• Low Power, Low Overhead ,
(Coding, Pilots).• Low processing complexity, High
correlation – Spatial, Temporal.• Reliability in WSNs
• Prediction History Trees (PHTs)Prediction History Trees (PHTs).• Incorporate Spatial Information.• Sensor Breakdown, Malicious
sensors – Sensor Management.• Time varying wireless channel,
Optimal FilteringOptimal Filtering.• AR Modeling and Yule-Walker.
• Compressive sensing is another hot research area.
• Intuitively, it means the following• Instead of oversampling an image
at a high rate and discarding most of the information, it is indeed possible to sense a sparse set of information.
• Compressed sensing has been revolutionized by the development of extremely fast L1 norm minimization algorithms.g
• It has significant applications in Wireless Sensor Networks for efficient sensing and transmission.
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• Video in 4G Mobile Wireless• Multiuser environment is highly
selective – Spatially, Temporally.• Unirate video coding results in
poor Quality/Latency.• Poor scalability for simulcast in 4G
Wireless networks.• H.264 SVC
• Temporal Scalability.• Spatial Scalability.• SNR or Fidelity Scalability.
• DPCM based base and• DPCM based base and enhancement layer encoding.• JSVM, MPEG-2/4 Scalability
Mode.
• Channel estimation is a key issue in MIMO-OFDM based wireless.
• Multimedia transmission involves substantial amount of overhead.• Headers, Synchronization, Padding
Streams.• JPEG is case in point.
• Several header components belong to structured data sets.
• Can be employed to extract information probabilistically – in the likelihood sense.
• An expectation-maximization like framework can be employed to compute the ML estimate in a MIMO wireless setting.
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• Cognitive Radio: Multimedia over OFDM CR networks.• Scheduling, Reliability,
latency.• Nash equilibrium and
Competitive efficiency of optimal resource allocation.• Pareto optimal resource
allocation . • Pricing mechanisms, Auctions.
• MATLAB, C/C+.• GNU Scientific Libraryy
• Multimedia repository on Xiph.org.• Standard JPEG images.• LTE/WiMAX standards.• MPEG coded video sequences.• Convex Optimization
• CVXopt (plans for MOSEK).• Wireless Networks.
• NS2• Plans for Qualnet (High fidelity• Plans for Qualnet (High fidelity
network environment 4G,WiMAX)• Plans for Crossbow sensor nodes.