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**Backhauling in TV White Space**

Narayan B. Mandayam (joint work with Cyrus Gerami, Larry Greenstein, Ivan Seskar) WINLAB, Rutgers University IEEE Distinguished Lecture

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**What is White Space? X TV Band Devices: Fixed or Portable**

Max. Fixed antenna height = 30m, Portable < 3m Permissible channels (6MHz each) Transmit Restrictions Protected region around primary TV transmitters Sense and avoid protected devices TX power: Fixed:30 dBm (6dBi antenna gain) = 4W EIRP, Co- and Adjacent-channel not allowed Portable: 20 dBm (no antenna gain) = 100mW, Co-channel not allowed, Adjacent = 16 dBm Additional Ruling on Sep X

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**What is “Really” White Space?**

Economist Markets, Property Regulator/Politician Social Good Engineer New Technology, Cognitive Radios Folks who are “out there” Free speech, Bill of Rights Communication/Information Theorist W ≈ $ ≈ $, votes ≈ ¢ priceless

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**How much TV White Space is there in NJ?**

TV Towers around NY City and Philadelphia Lots of white space spectrum available in NJ! # of channels (fixed) vs. # of 5X5 sq. mi. grids 7 – 31 channels available per cell (42 – 186 MHz)

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**Radio Coverage Prime spectrum with a wide range of applications**

~ MHz available depending on TV transmitter density Power constraints result in achievable bit-rate profile for fixed-fixed, fixed-mobile, and mobile-mobile ~5 2Km range for LOS fixed-mobile ~3-5x WiFi range for non-LOS services, e.g. ~50 250m

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White Space Networks Range of possible usage scenarios, with sweet spot in outdoor networks with medium range and speed Bit-Rate 100 m

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**Sample Applications: Cellular Data Boost**

“Cellular data boost” network can be used to offload fast-growing cellular traffic using dual-mode radio Mesh network of outdoor white space hot spots; backhaul data to existing BTS Intended for transport of non-real time data such as mail, content, facebook … Potential for ~2-5x capacity boost depending on % coverage & service mix

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**Sample Applications: Distribution/Backhaul**

Distribution and Backhaul using White Space

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**Sample Applications: Long range V2V/Emergency Network**

Long-range V2V useful for traffic control/warnings, geographic apps, p2p content, etc. Supplements short-range p/DSRC V2V links (from mandated car radios?) can be used to form a high capacity emergency backup network using ad hoc mesh between cars and fixed AP’s Application requirements well matched with WS range/bit-rate properties

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**Sample Applications: Cognitive Digital Home**

Service Provision Device Provides end-user service GENIE NODE Central spectrum manager Relay and Wireless Access Devices Provides relay/connectivity support

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**Design Implications for White Space Networks**

White space radio systems require the following building blocks: Flexible BW PHY, preferably operating in non-contiguous spectrum Spectrum sensing for TV primary and other incumbents, coordination with data base Opportunistic link layer access with distributed congestion control procedures for fair sharing among secondary users Discovery and bootstrap protocols for ad hoc network formation Common coordination channels and/or spectrum servers for improved coordination among multiple types of secondary users, e.g. Databases … and of course, cognitive SDR platforms (wideband, flexible, low-cost)

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**WS Building Blocks: NC OFDMA PHY**

NC OFDMA approach used to opportunistically fill spectrum Allows for flexible spectrum sharing for secondary coexistence Center freq White Space Primary freq Min. tones needed for freq. synchronization

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**Case for Noncontiguous OFDMA - I**

Three available channels Node A transmits to node C via node B. Node B relays node A’s data and transmits its own data to node C. Node X, an external and uncontrollable interferer, transmits in channel 2. 1 2 3 2 B X A If we use max-min rate objective and allocate channels, node B requires two channels; node A requires one channel Scheduling options for Node A and Node B?

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**Case for Noncontiguous OFDMA - II**

(NC-OFDMA) #1: Contiguous OFDM #2: Multiple RF front ends Nulled Subcarrier C C C 1 3 1 2 1 3 B B B 2 2 2 X X X 2 3 2 A A A Spectrum fragmentation limited by number of radio front ends NC-OFDM accesses multiple fragmented spectrum chunks with single radio front end Transmission in link BC suffers interference in channel 2 14

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**NC-OFDM Operation AP 1 3 B 2 X 2 A Non-Contiguous OFDM Nulled**

Subcarrier X[2] = AP 1 3 X[1] x[1] X[1] X[3] Serial to Parallel IFFT Parallel to Serial D/A Modulation x[2] x[3] B X[3] 2 X 2 Node B places zero in channel 2 and avoids interference Node A, far from the interferer node X, uses channel 2. Both nodes use better channels. Node B spans three channels, instead of two. Sampling rate increases. A NC-OFDM accesses multiple fragmented spectrum chunks with single radio front end

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**Resource Allocation in NC-OFDMA**

Benefits: Avoids interference, incumbent users Uses better channels Each front end can use multiple fragmented spectrum chunks Challenges: Increases sampling rate Increases ADC & DAC power Increases amplifier power Increases peak-to-average-power-ratio (PAPR) Develop centralized, distributed and hybrid algorithms for carrier and forwarder selection, power control

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**WS Building Blocks: NC OFDMA MAC**

NC OFDMA offers the possibility of a simple FDMA MAC instead of CSMA or TDMA (..CSMA may still be used for end-user access) Simplifies ad hoc network operation and avoid classical mesh self interference and exposed node problems Requires a cooperative access policy (i.e. not greedy, and with some form of congestion backpressure) f2 f3 f1 rate r1 rate r2 rate r3 freq LINK 1 LINK 2 LINK 3 Rates r1, r2, r3 periodically adjusted via cooperative procedures

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**Architectures for Secondary Coexistence**

Secondary co-existence an important requirement for WS Various schemes possible depending on system model Completely autonomous, using performance feedback only Common coordination channel Common Internet based spectrum server Spectrum Server (optional) freq Secondary A Spectrum Secondary B Spectrum Internet WS AP w/ backhaul Control information WS Mobile Access Protocol Secondary System A Common Coordination Channel (optional) Secondary System B

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**Distribution and Backhaul using White Space**

WHITE SPACES WIFI FIBER BACKHAUL NETWORK WINLAB

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**Outline The Proposed System First order Methodology**

Achievable Capacity Traffic Demand How many cells would need fiber? Aggregating Flows Conclusion and Future Directions White Space: Where are we? Where do we go? This is our outline. First, we present the idea and lay the groundwork of our proposed system. Then, we show a first order simplified version of the methodology used to determine the feasibility of the system. In that process the ingredients will be achievable capacity which will be calculated using the available channels, and also traffic demand which will be estimated using various sources of information and calculations. Then we answer the question “how many cells would be able to carry information through white spaces and how many will need fiber?” After that, we propose a solution for the problem of multiple flows. And finally, we conclude our study and present some possible future work. 1.40 WINLAB

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**How will it look? WINLAB NJ as case study Proximity to NY & Philly**

Highest population density WINLAB in NJ! Cells of 5 mi X 5 mi total 307 Antenna (base-station) in each FCC’s max allowed height=30 m FCC’s max TX power=4 W How will our system look? First of all, we have used NJ as our case study because: it’s close to two major metropolitan areas: New York and Philadelphia. It has the highest population density in the US and has large pockets of urban, suburban and rural areas. And of course because WINLAB is located in NJ! Looking at the map of NJ, we divide it into square cells of 5 mi X 5 mi. this will result in about 300 cells. we consider an antenna (base-station) in or near the middle of each of these cells. Now we will have a network of base stations and we will connect them through white spaces. Keep in mind that these base stations will have a max height of 30 meters (which is the max allowed by FCC) and their transmit power will be limited to 4 W (again FCC limitations). And also, we will use FCC rules for Fixed devices. Note that our system will not end up having base stations in all cells, but only in the ones that qualify based on our criteria. And since our focus for this system is rural areas, you will see that we will end up having base stations in those type of areas. 2.54 Based on fixed devices rules of FCC WINLAB

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**for more concentrated transmission**

What will it do? Use of Sector antennas for more concentrated transmission Internet user Wifi Fiber 4 sector antennas Antenna coverage Coming back to our grid, these base stations in each cell will be used to wirelessly connect local traffic to a wired connection using our network, so that from there (e.g. internet) data can be routed to their final destinations. In other words, if some local client connects to the grid by any means (e.g. wifi), the traffic will be routed through the network to the nearest fiber point which is best located to route the traffic to it’s final destination. Each of our base-stations will consist of 4 sector antennas pointing to all neighboring base-stations. By using directional antennas we can have more concentrated transmission and better reception with greater distance (considering max allowed transmission power) 3.40 Wireless Distribution and Backhaul WINLAB

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**Can White Spaces be used?**

Available Bandwidth Spectral Efficiency Received SNR TX Power 4W Noise Thermal and Noise Figure Path Loss ITU Terrain Achievable Capacity Demand (per cell) (per cell) > < FIRST ORDER CRITERION Use Radio Use Fiber Resources used: Now to answer the question of whether white spaces can be used or not, we use this first order criterion. For each link, if the achievable capacity is more that the traffic demand, that link can use white spaces. If not we need to use fiber. In our calculations we use these sources among others: FCC rules on TV white spaces, ITU propagation models, statistics on NJ population and internet usage and a survey conducted by Cisco. Now first the achievable capacity: to calculate this, we need the available bandwidth and spectral efficiency. 4.17 FCC rules Propagation models NJ pop statistics Census 2000 Internet usage statistics Internet traffic survey WINLAB

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**NJ towers at a glance WINLAB Available Bandwidth**

Towers in NJ, NY, DE & PA Coverage can be 100km (r) To calculate the available bandwidth, let’s first take a look at TV towers in NJ proximity. Since TV tower ranges can span up to 100 km in radius, we are required to also consider neighboring states: NY, DE and PA. This is an approximate location map of major TV towers considered. And this is a superposition of their coverage (just for illustrative purposes) 4.46 WINLAB

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**FCC’s Protection rule WINLAB Available Bandwidth Primary TV tower**

Protected radius Additional separation ring Secondary White Space radio Next we come to FCC’s rules to protect these primary TV towers. For this matter, the FCC has set each tower’s coverage radius as a protected radius, in which no secondary transmission can be present. On top of that, there is an additional separation ring in which no secondary antenna can be located. These rules will limit the area in which each channel is available for white spaces use. 5.13 WINLAB

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**Available space per channel**

Channel availability including adjacent channel effect Available space per channel Available Bandwidth 25 TV tower coverage Additional separation ring Available for possible use Available as White Spaces 24 25 26 As an example, we look at channels 24, 25 and 26. the dark and light blues areas are TV tower coverages with the extra ring which are prohibited. So we have the white colored areas which are potentially available for white spaces for this channel. But now let’s calculate the actual available space for channel 25. Now we need to take into consideration not only space prohibited in channel 25 but also spaces prohibited in adjacent channels., i.e. if we want to use channel 25, we need to make sure no TV transmission is going on in 24, 25 and 26. so we need to superimpose 24, 25 and 26 coverage areas and whatever area left, will be available for our use. As you can see this limits channel 25’s availability to two very small pockets. 6.11 WINLAB

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Bandwidth Database Available Bandwidth 25 Repeat this for each cell and you get bandwidth database Each channel is 6 MHz 7 – 31 channels available per cell (42 – 186 MHz) No islands Similar channels available in neighboring cells INTERFERENCE! X Now in terms of our grid, we can see in which cells this channel is available. And we can use the same method for all channels (UHF) allowed by the FCC and come up with a database of available channels (i.e. we will have a database of available channels in each cell) We note that each channel is 6 MHz. we have found out that in all cells of our grid we will have at least 7 channels (42 MHz) of bandwidth available. So now a couple of observations: There are no channels available in only one cell. (i.e. we don’t have any available channels as islands) Mostly the same channels are available for neighboring cells (i.e. channels are shared) So using these channels we cause interference problems. 7.00 WINLAB

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**Frequency reuse planning**

Available Bandwidth reuse factor (r) of 2 : SNR at cell-2 = 19 dB SNR at cell-4 = 5 dB Interference 14 dB isolation for r=2 Median path loss: ITU terrain model for LOS Obstruction height:15m for sparse population and 30m for dense population 1% outage with 8 dB shadowing variance For that reason we need to use frequency planning. To model signal propagation we use the ITU model and plug in these parameters: we divide the population into two classes: sparse and dense, and assign different obstruction heights to each. We consider obstructions as buildings or trees and in the middle of the links. Our link height is the height of the antennas (30 m) and the distance is the distance between two neighboring antennas (5 mi). We take a sample UHF frequency as an average case. And use the max TX power allowed by the FCC. This will give of a median path loss and we model variations along the median as a gaussian variable and add a 1% outage probability with an 8 dB variance to compensate for statistical variations along the median. We can use either 2 or 3 for our reuse factor. Considering r=2 and transmitting to cell-2, the SNR at the desired antenna (cell-2) will be 19 dB and the SNR an undesired antenna (interference at cell-4) is 5 dB. So we will have an isolation of 14 dB, which is enough to be dealt with and canceled. So a reuse factor of 2 will suffice. 8.25 WINLAB

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**Thermal (-136dBW) + Noise Figure (10dB)**

Achievable Capacity Demand > < Available Bandwidth Bandwidth Database Spectral Efficiency Received SNR TX Power 4W (6dBW) Noise Thermal (-136dBW) + Noise Figure (10dB) Path Loss ITU Terrain Coming back to our comparison, we have the available bandwidth for each cell. Now for spectral efficiency, we would need the received SNR and in order to get that, we need the TX power (which is 4W or 6 dB), noise ( Thermal + a 10 dB noise figure) and path loss (for which we again use the ITU model + shadowing effects). Having these factors we calculate the spectral efficiency. So now we can calculate the achievable capacity. 8.56 WINLAB

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**Let’s consider one cell**

54 MHz (9 channels) available 27 MHz usable (reuse) Spectral Efficiency: 6.23 bps/Hz (path loss and population and building heights) Max Achievable Capacity: ~168 Mbps ~75.7 GB/hour For example let’s consider one cell. This particular cell, has 9 channels available (that’s 54 MHz), after reuse we have 27 MHz. Spectral efficiency in this cell is about 6 bps/Hz. So the max achievable capacity will be 168 Mbps or 75.7 GB/hr 9.18 WINLAB

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**Simultaneous Active Users (α)**

Achievable Capacity Demand > < Usage per Household Simultaneous Active Users (α) α = 10% α = 30% α = 50% US Census Our Approximation Pop/sq mi pop/cell 3 people per household 74.2% have internet internet clients/cell 18MB/hr (Cisco Survey) MB/hr (5 times more) MB/hr (7 times more) MB/hr (10 times more) So now Demand. For calculating the demand per cell, we have two parameters: Usage per household and simultaneous active users. For usage per household, we look at the population of NJ, we estimate the US census2000 to fit our grid and divide the population into 10 grades. So for each cell we have population. We know each household is on average 3 people and 74% of people have an internet connection. From this information we can have an estimate for the total internet clients per cell. Now Cisco had a survey in which they had concluded that in peak internet hours internet connections use on average 18 MB/hr and that by 2013 we will have a 5 to 7 fold increase. So we have 4 cases: 18 MB/hr from Cisco, 5 times that, 7 times that and 10 times that for future considerations. Using this, we can see how much internet usage we have per cell. But not all connections will have traffic at the same time and not all people will be connected at the same time. To take this into account, we have a parameter alpha for simultaneous active users—Which is either 10, 30 or on worst case 50%. 10.38 10 grades WINLAB

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**Let’s consider one cell**

Cell pop: 8750 Cell households: 2917 Cell internet connections: 2164 Cell traffic using α = 30% : MB/hr/link: 11.7 GB/hr 90 MB/hr/link: 58.4 GB/hr MB/hr/link: 81.8 GB/hr MB/hr/link: GB/hr Coming back to our example cell. This particular cell has a population of about 87 hundred people, so it has about 29 hundred households and 21 hundred connections. If we consider a 30% active users, we will have these different traffic numbers: 11.7, 58.4, 81.8 and GB per hr. 11.08 WINLAB

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**Let’s consider one cell**

Achievable Capacity Demand > < α = 30% & 18 MB/hr/link : > 11.7 α = 30% & 90 MB/hr/link : > 58.4 α = 30% & 126 MB/hr/link : < 81.8 FIBER So now we have the achievable capacity for this cell and we have the demand. Now we can make a comparison. In the first case in which our alpha is 30% and our usage per hr is 18 MB, demand is much less than capacity, so radio can be used. For 90 MB/hr, we are still good. Now for 126 MB/hr traffic we see that capacity is less than demand, so in this case we cannot use radio and fiber should be used. Same goes for 180 MB/hr. So performing this kind of analysis for each cell in our grid will give us a database of how many cells will need fiber for different scenarios. 11.46 α = 30% & 180 MB/hr/link : < FIBER WINLAB

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**How many cells need fiber? (out of 307)**

18 MB/hr 90MB/hr 126 MB/hr 180 MB/hr Cells requiring fiber connection (Change NJ pictures) This table shows that. These are the number of cells we cannot support with radio (out of 307 cells). To illustrate how this looks on the map, we can show the alpha=30% cases. As you can see, most places which need fiber are cells near New York city and Philadelphia. Well these places already have a fiber infrastructure in place! And we are not targeting these places, our target is places in which currently there is no fiber and as the results show, we have sufficient capacity for such a system in these areas. 12.22 WINLAB

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**Aggregating Multiple Flows**

CLUSTER CLUSTER HEAD Proposed Solution: Use Excess Capacity for aggregation Excess Capacity = Achievable Capacity - Demand Clustering Plant more fiber at cluster heads Plant cluster heads in high capacity cells to route traffic through Detailed routing study FIBER So now we come to the problem of multiple flows. Till now, we have established that by our method we can determine that when connected to a node, a link from one cell to it’s neighbor will hold. But what if the destination is another cell? and what if other cells are being routed through the same node? And have the same destination? Then we cannot guaranty sufficient capacity only by this method. We believe that this will not be much of a problem since we are not talking about a stand alone radio grid with sort of an avalanche effect, rather a radio network to aid fiber networks. So our system’s function would be to get local data and reach it to the closest fiber. And with some provision we can overcome this aggregation issue. Our proposed solution for this problem is to use the excess capacity for this aggregation. In places where achievable capacity is higher than demand, in most cases it’s not marginally higher but is greater by some significant amount. This excess capacity can be used for routing traffic. Also, we can use clustering techniques and plant a couple more fiber connections at cluster heads and route the traffic trough them. A better clustering technique would be to have cluster heads at highest capacity cells among a cluster so that it would be able to handle routed traffic more easily. A detailed routing study would also help mitigate this issue. 13.54 WINLAB

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**Example of aggregation**

Group cells into clusters (illustrated in figure) Have 1 fiber connected cell in each cluster If in each cluster: Excess Capacity > Total Demand X (2 or 3) Then: 1 fiber per cluster is sufficient! Else: Add 1 fiber to cluster After calculations for α = 30% & 126 MB/h: Worst case requires 10 more fiber cells One example of the aggregation solution is illustrated here. We can group cells into clusters. In this case 8 groups. And have fiber connections in each cluster head. So one example solution would be to compare excess capacity with twice (or three times) the total demand in each cell. If we have enough excess capacity, we are fine with adding only that one cluster head fiber node. If not, we can add another fiber connection in another high capacity node in that group. For example, for alpha = 30% and 126 MB/hr, we would need ten more fiber connections for routing traffic. 14.33 WINLAB

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**Conclusions and future work**

Feasibility study of a distribution plan in NJ First order study promising in spite of conservative assumptions on traffic and propagation system more cost effective than a fiber layout Most effective in rural areas (where it’s needed) No prior high speed internet connectivity No fiber infrastructure More bandwidth available and better propagation Same methodology for other states/regions Further issues that need to be studied: Detailed routing strategies Cost/benefit analysis So in essence, we’ve done a feasibility study on this issue and presented a distribution plan. Although our assumptions on traffic and propagation were quite conservative, the results came out very promising and our system is a cost-effective substitution for laying out fiber. And as mentioned throughout the presentation, our system functions best in rural areas which is actually what we have meant the system for. That’s because of having no prior high speed connection, no fiber infrastructure and more available bandwidth to work with. Finally, this study can be used as a method for any other similar studies in the future. This concludes our preliminary study and for future studies, we can look at routing algorithms with more detail and we can have an extensive cost analysis to have a clear comparison with other alternatives. 15.15 WINLAB

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**White Space: Where are we today?**

Database Testing and Trials Google, Microsoft, Spectrum Bridge, Telcordia, etc. No TVBDs and services rolled out yet! Wireless Service Providers and TV Broadcasters still continue to resist Service providers want more licensed spectrum Broadcasters worry about interference FCC working on next round of spectrum auctions Reverse Auctions, Repackaging and Incentive Auctions So in essence, we’ve done a feasibility study on this issue and presented a distribution plan. Although our assumptions on traffic and propagation were quite conservative, the results came out very promising and our system is a cost-effective substitution for laying out fiber. And as mentioned throughout the presentation, our system functions best in rural areas which is actually what we have meant the system for. That’s because of having no prior high speed connection, no fiber infrastructure and more available bandwidth to work with. Finally, this study can be used as a method for any other similar studies in the future. This concludes our preliminary study and for future studies, we can look at routing algorithms with more detail and we can have an extensive cost analysis to have a clear comparison with other alternatives. 15.15 WINLAB

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**White Space: Where will we go?**

“Green” trumps “White”? So in essence, we’ve done a feasibility study on this issue and presented a distribution plan. Although our assumptions on traffic and propagation were quite conservative, the results came out very promising and our system is a cost-effective substitution for laying out fiber. And as mentioned throughout the presentation, our system functions best in rural areas which is actually what we have meant the system for. That’s because of having no prior high speed connection, no fiber infrastructure and more available bandwidth to work with. Finally, this study can be used as a method for any other similar studies in the future. This concludes our preliminary study and for future studies, we can look at routing algorithms with more detail and we can have an extensive cost analysis to have a clear comparison with other alternatives. 15.15 WINLAB

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**PILOT PROJECT: “Broadband to Bivalve”**

So in essence, we’ve done a feasibility study on this issue and presented a distribution plan. Although our assumptions on traffic and propagation were quite conservative, the results came out very promising and our system is a cost-effective substitution for laying out fiber. And as mentioned throughout the presentation, our system functions best in rural areas which is actually what we have meant the system for. That’s because of having no prior high speed connection, no fiber infrastructure and more available bandwidth to work with. Finally, this study can be used as a method for any other similar studies in the future. This concludes our preliminary study and for future studies, we can look at routing algorithms with more detail and we can have an extensive cost analysis to have a clear comparison with other alternatives. 15.15 WINLAB

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**PILOT PROJECT: “broadband to bivalve”**

Set up WiFi Hotspots in Bivalve, NJ Backhaul to Bridgeton, NJ where Internet (T1) connectivity exists Use “Fixed Towers” and available TV White Space to provide backhaul as shown in exemplary figure Could reuse water towers or weather towers as feasible for installing radios Towers requires power supply The set-up will also serve as a “research testbed” for protocol and application development to benefit rural areas If an ISP partner is available, mobile hotspot service could be provided along the way to farms, etc. So in essence, we’ve done a feasibility study on this issue and presented a distribution plan. Although our assumptions on traffic and propagation were quite conservative, the results came out very promising and our system is a cost-effective substitution for laying out fiber. And as mentioned throughout the presentation, our system functions best in rural areas which is actually what we have meant the system for. That’s because of having no prior high speed connection, no fiber infrastructure and more available bandwidth to work with. Finally, this study can be used as a method for any other similar studies in the future. This concludes our preliminary study and for future studies, we can look at routing algorithms with more detail and we can have an extensive cost analysis to have a clear comparison with other alternatives. 15.15 WINLAB

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**PILOT PROJECT: “broadband to bivalve”**

Hardware: Radio Router Node based on currently available second generation ORBIT platform Multiple radio interfaces: (wifi), (wimax), LTE, ZigBee, Bluetooth, CRKit (whitespace capable) Software: Whitespace Routing Protocol optimized for throughput Local (hotspot) support Caching capabilities So in essence, we’ve done a feasibility study on this issue and presented a distribution plan. Although our assumptions on traffic and propagation were quite conservative, the results came out very promising and our system is a cost-effective substitution for laying out fiber. And as mentioned throughout the presentation, our system functions best in rural areas which is actually what we have meant the system for. That’s because of having no prior high speed connection, no fiber infrastructure and more available bandwidth to work with. Finally, this study can be used as a method for any other similar studies in the future. This concludes our preliminary study and for future studies, we can look at routing algorithms with more detail and we can have an extensive cost analysis to have a clear comparison with other alternatives. 15.15 WINLAB

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References C. Gerami, N. B. Mandayam, and L. J. Greenstein, “Backhauling in TV white spaces,” Proceedings of IEEE GLOBECOM 2010, December 2010 O. Ileri and N. B. Mandayam. Dynamic spectrum access models: Toward an engineering perspective in the spectrum debate. IEEE Communications Magazine, 46(1): , January 2008. D. Zhang, R. Shinkuma, N. B. Mandayam, “Bandwidth Exchange: An Energy Conserving Incentive Mechanism for Cooperation” in IEEE Transactions on Wireless Communications, vol. 9, No. 6, pp , June 2010 D. Zhang and N. B. Mandayam, “Bandwidth Exchange for Fair Secondary Coexistence in TV White Space,” in Proceedings of International ICST Conference on Game Theory for Networks (GameNets), Shanghai, April 2011 M. N. Islam, N. B. Mandayam, and S. Kompella. Optimal resource allocation in a bandwidth exchange enabled relay network. In Proc. IEEE MILCOM’2011, pages 242–247, November 2011 C. Raman, R. Yates, N. B. Mandayam, ”Scheduling Variable Rate Links via a Spectrum Server” in Proceedings of IEEE DySpan 2005, November 2005, Baltimore, MD D. Raychaudhuri, N. B. Mandayam, J. B. Evans, B. J. Ewy, S. Seshan, and P. Steenkiste. Cognet: an architectural foundation for experimental cognitive radio networks within the future internet. In Proc. ACM MobiArch’ 2006 N. Krishnan, R. D. Yates, N. B. Mandayam, J. S. Panchal, “Bandwidth Sharing for Relaying in Cellular Systems” in IEEE Transactions on Wireless Communications, vol. 11, No. 1, pp , January 2012

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**Acknowledgments U.S. National Science Foundation**

Office of Naval Research IEEE COMSOC Debi Siering WINLAB Collaborators: Cyrus Gerami, Larry Greenstein, Nazmul Islam, Ivan Seskar, Dipankar Raychaudhuri

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