Announcements Homework 2 due Friday (flexible). Graded Paper 2 summaries ready. HW 1 and solutions ready this week. Third paper summary on ad-hoc networks due today (flexible). Final project reports due by noon next Tuesday (inflexible). Post to website. Extra credit assignment: Read the final report for the project you originally gave comments on. Send an critique to the authors with a copy to me (by midnight Tuesday).

Ad-Hoc Wireless Networks Main Characteristics Each node generates independent data Any node can communicate with any other. No centralized controller (self-configuring) Data transmitted in (short) packets Links typically symmetric. Nodes may be mobile and/or power constrained. Typically a large number of nodes

What has changed since 1985? Better, cheaper, low power DSPs Advanced communication techniques Powerful channel codes and decoders. Equalization/SS/Multicarrier High level modulation Diversity/MUD/smart antennas Advances in routing Signal strength measuring techniques available in radios. Adaptive radios. How would we leverage these developments to make better ad-hoc networks?

Sensor Networks Energy is the driving constraint. Data highly correlated in time and space. Node location information critical Low homogeneous rates. Links typically asymmetric. Data flows to centralized location ,000 Nodes Have a common mission. Very different from typical ad-hoc networks

Link Layer Design Fundamental limits “Shannon capacity” versus energy Processing to reduce transmit power Diversity (multinode combining) Coding Adaptive modulation (probing) Adaptive framing Beamforming Processing vs. transmitting bits Data Processing Compression via local decisions Data prioritization Data distribution (“need-to-know”) Variable node alertness Sleep modes Hierarchical power conservation modes

Network Layer Design Network Capacity Routing Delay/throughput/energy tradeoffs Distributed control Topology Dense deployment How many hops per connection? Effect of adaptive link techniques Supernodes vs. homogeneous nodes Adaptive Techniques Multiple access. Link adaptation to maximize throughput Network optimization versus link optimization.

Application Design Design Optimization What is the “mission” of the network. Tradeoff between longevity and capability. Longevity driven by application Data Prioritization Collective Data Processing Can compensate for node limitations Compression and clustering Requires additional communication between nodes Multiuser game theoretic approaches Energy optimization: minimize total energy (between processing and communication) required for mission success

Network Capacity Capacity limits of ad-hoc 2D networks. Measured as throughput of each node to another randomly selected node Assumptions n users uniformly distributed over the interior of a unit cube. Each user communicates with another user randomly chosen among all users. Nodes communicate at fixed rate W or when a minimum threshold SIR is met. Interference from nodes outside a disk around receiving node negligible Alternate SIR model (iuterference as AWGN) No channel division or diversity

Capacity Bounds Capacity Definition: Average rate (bps) transmitted by any user to another randomly selected user. Lower Bound Based on deterministic routing scheme and partition of network area. Upper Bound Uses convexity and aggregate rate In 3D, bounds proportional to 1/n 1/3 Capacity goes to zero as n increases

Summary and Open Problems Multiple Access Techniques Capacity Random Access Cellular System Design Cellular Capacity and ASE Power Control Dynamic Resource Allocation Ad-Hoc Networks Sensor/Energy Efficient Networks Wireless impact on higher level protocols

Multiple access techniques TD, FD, and orthogonal CD support same number of users DS spread spectrum typically supports fewer users capacity flexible (soft capacity) Improved by MUD, activity, etc. FH not typically used alone as a MAC technique Averages out-of-cell interference Open Problems Tradeoffs in wideband channels. Tradeoffs without perfect CSI

Capacity (1 cell) User capacity Computed for DSSS systems Inherent assumptions needed l BER, channel, voice activity, etc. Shannon capacity Obtained for fading broadcast and MAC channels Optimizes resource allocation TD and FD equal, CD best or same as TD depending on MUD Outage capacity Keeps rate constant over all fading Optimizes resource allocation Useful for delay-constrained data Open Problems Wideband channels/Imperfect CSI Combined Shannon/outage capacity Capacity with multiple antennas

Random Access ALOHA inefficient Channel sensing ineffective Busy tones work well in some topologies, but not ad-hoc nets Reservation protocols inefficient for short messaging Different media types require different access techniques Open Problems: Multimedia techniques Satisfying QOS/delay constraints

Minimize reuse distance and cell size Optimal access technique is in the eyes of the beholder (stockholder). User capacity calculations skewed Tradeoffs in complex systems hard to assess - implementation considerations Interference reduction is good!!! Sectorized/Smart antennas Power control Dynamic resource allocation Multiuser detection Open Problems Optimizing/implementing interference reduction techniques Impact of multiple antennas Impact of packet data and multimedia Cellular System Design

Cellular Capacity and ASE Preliminary Shannon capacity results Simple channel model TD scheme Base station coordination (uplink) ASE general formula (bps/Hz/Km 2 ) Based on MAC or broadcast channel capacity region Interference treated as noise No base station coordination Open Problems (Lots!!!) Expand exisiting capacity/ASE results l Channels, multiple antennas, coordination,... Propose new capacity/ASE definitions Develop outage capacity results

Power Control Extremely powerful tool Increases battery life Maintains link SIR Reduces interference Component of resource allocation Aids in smooth handoff Reduces delays Increases capacity/throughput Distributed vs. Centralized Active link protection Combine with channel access Open Problems Impact of estimation errors Throughput/delay/power optimization Impact of noncooperative users Group vs. individual optimization

Dynamic Resource Allocation Optimally assigns available resources based on traffic, user conditions, etc. Channels (time, codes, BW) Power (for transmission or processing) Rate Antennas Optimal dynamic channel allocation (MP) is NP hard Heuristics often used (work well) Little exact analysis - some bounds FCA optimal at high loads Optimal resource allocation NP harder. Open Problems (Lots!!!) Combinations (e.g. power/channels) Antenna allocation Processing power allocation

Ad-Hoc Networks Wide open design issues at all layers of the protocol stack Access Channel allocation/freq. reuse Power adaptation Connectivity and Routing QOS Synergies across layers should be exploited Performance measures and capacity Open Problems (Everything!!!)

Sensor and Energy Efficient Networks Network optimization for energy- constrained nodes Power tradeoffs for processing vs. transmitting bits Longevity vs. network function Energy-conserving modes Collective data processing Diffuse routing Open Problems (Everything!!)

Wireless impact on higher layers Routing must take user mobility into account Mobile IP not designed for rapid movement Base stations may not be available for handoffs Network protocols react to errors using congestion control Does not correct for link failures due to fading Significantly slows down network when links fade Open Problems Design of protocols that take wireless channel into account without breaking the great features of current protocols