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Sarah Zhang

on 20 September 2014

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Transcript of 2013_03_13


Meet & Present 1. The first WP in the project we talked about, I would love to be able to work on that. Looking forward to have more description about it. Network Coding
since 2006 Summary ask for more task description!
when to start... Massive-MIMO CSC scholarship...

Work on remaining tasks

Identify future tasks...

Next meeting? 2. Talk with Denys and Will about the project that are more MAC layer related? Is this the mini-morphs project,which is about meshing? 
3. I will follow up the Broadcom project as well. Using Broadcom boxes for the train project, work with Di to get the boxes working through C8; also with Denys, we did several tests with SISO links with different channels (Static/ mobile/leaky feeder/multi-user), basics about MAC related parameters and their effect on TP: Guard interval, packet size, TXOP, advanced MPDU, APSD

Can I ask to be added to the mail list, so I can receive reading materials! Now have all 4 versions of LTEsimulator matlab codes, have basic understanding of codes, will focus more on the channels models...(Multipath Channel Model and Spatial CM) 4. LTE/LTE-Advanced channel models: talk with Di and Will, Evangelos. 5. Short presentation on following topics based on previous and later reading GFDM Applications in scenarios exhibiting high degrees of spectrum fragmentation: TVWS (2) and CR (3) 1. With large number of antennas, energy can be focused extremely sharply into small regions in space.

2. No ultra-linear high-power AMP is required. M-MIMO reduces the constraints on accuracy and linearity of each individual AMP and RF chain.

3. MRC @the each terminal is trivial (distribute computing at each antenna unit); plus we would likely operate in the nearly noise-limited regime.

4. Robust and cheap antenna arrays M-MIMO uses antenna arrays with a few hundred antennas, that simultaneously serve much smaller number of terminals in the same time-frequency resource. PHY Layer Network Layer The general concept of PNC is to make use of the mixing of superimposed EM waves that occurs in nature to realize a desired network coding operation.
The key requirement is that nodes 1 and 2 must be able to extract the information from the other end node from the mapped signal transmitted by relay R.

1. noise superposition and propagation
2. channel coding
3. synchronization(packet& symbol level&freq&phase)
4. Non-symmetric Fading Channels and Channel Estimation(OFDM-PNC)
5. General Network Topologies and Higher-layer Issues

PNC can be used to detect malicious attacks on wireless networks, eg.: Sybil attack. PNC detection does not require any special hardware or time synchronization in the wireless network. HongKong University

--''Physical-Layer Network Coding: Tutorial, Survey, and Beyond''

--"PNC in wireless ad-hoc networking" PNC can yield higher capacity than network layer network coding when applied to wireless networks. UNC Charlotte:

"Exploring the Security Capabilities of Physical Layer Network Coding" Recapture EPFL and MIT:
"Wireless Network Coding: Opportunities &Challenges"

EPFL and Bell Lab
Network Coding Applications Linear NC Applications:

1. Digital file distribution and P2P file sharing. e.g.: Avalanche from Microsoft

2. Throughput increase in wireless mesh networks. e.g. : COPE,Coding-aware routing

3. Bidirectional low energy transmission in wireless sensor networks.

4. Alternative to forward error correction and ARQ in traditional and wireless networks. e.g.: Multi-user ARQ

5. Many-to-many broadcast network capacity augmentations.

6. Robust and resilient to network attacks like snooping, eavesdropping or replay attacks.
... ... Random Network Coding Nodes transmit random linear combinations of the packets they receive, with coefficients chosen from a Galois field.

In broadcast transmission schemes, it allows close to optimal throughput using a decentralized algorithm,with and without packet erasures. Simulation and test of a 20-node mesh network Katti et al., "XORs in The Air: Practical Wireless Network Coding", MIT and Cambridge COPE largely increases network throughput.The gains vary from a few percent to several folds depending on the traffic pattern, congestion level, and transport protocol COPE Vodafone Chair Mobile Communications Systems

(1)"GFDM Interference Cancellation for Flexible Cognitive Radio PHY Design"
(2)"Integration of a GFDM Secondary System in an OFDM Primary System"
(3)"GFDM in Cognitive Radio Sensing" "GFDM - Generalized Frequency Division Multiplexing", Invited Paper by Gerhard Fettweis, et al. Flexibility & Simplicity Beyond Tail biting:
Further CP reduction of RX side filter can be done if precoding is used. The drawback is potentially higher tx power, which could be mitigated by using non-linear precoding and modulo constellations. zero-forcing loss of subcarrier orthogonality (1) superior PAPR 1. when the aperture of the array grows, the resolution of the array increases. Interestingly, the performance of the system in M-MIMO regime depends less on the actual statistics of the propagation channel but on the aggregated properties of the propagation.

2. the thermal noise can be scale down, so system performance is predominantly limited by interference from other transceivers.

3. the asymptotics of random matrix theory kicks in. Previous random values become deterministic, for instance, the singular values of the channel matrix. Matrix dimension imbalance makes inversion easy by using series expansion techniques. Linear Processing is optimal with unlimited number of antennas at BS. 1. pilot contamination in multi-cell scenario. When non-orthogonal training sequences are used in uplink training, CSI is imperfect at base station.

2. interaction between antenna elements. The reduced antenna spacing and fixed overall aperture induce coupling problem, whose primary impact is power loss.

3. intra-cell interference and signal processing algorithm. When the number of transmit antenna is very large compared to receivers, then simple linear decoder is good enough; otherwise, interference from other users will significantly impair performance.

4. inter-cell interference. Intercell interference prediction accuracy depends on coherence time, which ultimately dictates the number of users can be served. Time-division duplex has a strong advantage in the case where there is asymmetry of the UL and DL data rates.

5. hardware imperfections. Because Massive MIMO depends on the phase-coherent, therefore hardware induced phase noise is particular bad. There is also the I/Q imbalance problem, Analogue Digital Converter and Power Amplifiers design.
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