1. A framework for spectrum reuse based on primary-secondary cooperation Omar Bakr Ben Wild Mark Johnson Professor Kannan Ramchandran

2. Opportunistic Spectrum Sharing Primary detection through spectrum sensing Cognitive radios operate without primary knowledge Good news: Backwards compatible with legacy systems Secondary systems do not have to pay for spectrum usage Bad news: Primary not always easy to detect (especially for passive radios and shadowy environments) Little guarantees for both primary (interference) and secondary (usage) systems Places too much burden on secondary systems

3. Collaborative spectrum reuse Better primary control over interference levels Secondary systems are much easier to design and operate More consistent secondary access to the spectrum Primary systems can monetize spectrum usage More incentives for both systems to adopt this model Exact nature of cooperation depends on many factor

4. Dual Citizenship Nodes Primary Network Secondary Network

5. Application: Cellular Uplink Reuse

6. Cellular uplink Reuse Secondary user (cognitive radio) joins a cellular network as a subscriber Network allocates resources on both uplink and downlink Secondary user transmits on its allocated channel Base station continues to provide feedback (through the downlink) When transmission falls below a threshold, Secondary can reuse the entire uplink Primary can terminate the contract any time In FDD systems, only explicit feedback is possible In TDD systems, both explicit and implicit feedback available (channel reciprocity)

7. Why Uplink? Very few primary receivers (base stations) Location are relatively static Much easier for network operators to make changes to base stations than to cell phones

8. Interference from the Primary Cell phones transmit on the uplink Transmit powers are much lower than the base station Secondary user can use coding or interference averaging Interference can be further reduced by streering nulls towards jammers

9. Review: interference cancellation Two radios: a single antenna receiver PR, and an N- antenna transmitter CR Let h i [n] be the complex discrete-time baseband channel response from PR to antenna i in CR Assume a single tap channel (flat fading), N=2 If x[n] is the transmitter sequence: => received sequence y[n] = x[n](w 1 h 1 [0] + w 2 h 2 [0]) w i is the complex weight at antenna i y[0] = 0  w1 = -h2[0], w2 = h1[0]

10. interference cancellation: two taps Let x[n] = {x[0], 0, 0, …, 0}, h i [n] = {h i [0], h i [1]} => y[0] = x[0](w 1 h 1 [0] + w 2 h 2 [0]) => y[1] = x[0](w 1 h 1 [1] + w 2 h 2 [1]) Setting y[0] = y[1] = 0? Not enough degrees of freedom if equations are linearly independent! Need more antennas.

11. 3-antennas, 2-taps y[0] = x[0](w 1 h 1 [0] + w 2 h 2 [0] + w 3 h 3 [0]) = 0 y[1] = x[0](w 1 h 1 [1] + w 2 h 2 [1] + w 3 h 3 [1]) = 0 Set w 1 = 1: w 2 h 2 [0] + w 3 h 3 [0] = -h 1 [0] w 2 h 2 [1] + w 3 h 3 [1] = -h 1 [1] Two equations, and two unknowns! Number of antennas must be at least the number of required nulls + 1

12. OFDMA Cellular Network  OFDMA is a convenient multiple access scheme to implement adaptive nulling.  Cellular network assigns CR 1 time/frequency hopping pattern.  Network reports channel information of CR subcarriers through cellular downlink.  Nature of the feedback depends on the hardware and algorithms used. Time Frequency Primary user 1 Primary user 2 Primary user 3 Primary user 4 Cognitive Radio

13. OFDMA Cellular Network  Once CR learns nulling weights for all subcarriers, it can use all subcarriers at the same time.  Assumption is that CR to BS channel varies slowly enough to where nulling algorithm can adapt quickly enough.  Example: 32 time/frequency slots, 100  S slot duration.  => 312Hz max feedback rate per subcarrier. Time Frequency Primary user 1 Primary user 2 Primary user 3 Primary user 4 Cognitive Radio

14. OFDM Summary

15. Multiple base stations Multiple base stations (sectors) in practice Same process can be repeated for each All must be satisfied More antennas required Details are algorithm dependent

16. Work in Progress How much interference cancellation can achieved in practice? Noise, doppler, quantization noise can limit signal rejection in practice, but by how much? More in depth analysis on the effectiveness of different nulling algorithms. Extend this model to more applications