A framework for spectrum reuse based on primary-secondary

cooperation

Omar BakrBen Wild

Mark JohnsonProfessor Kannan Ramchandran

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

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

Dual Citizenship Nodes

Primary Network

Secondary Network

Application: Cellular Uplink Reuse

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)

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

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

Review: interference cancellation Two radios: a single antenna receiver PR, and an N-

antenna transmitter CR Let hi[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](w1h1[0] + w2h2[0]) wi is the complex weight at antenna i y[0] = 0 w1 = -h2[0], w2 = h1[0]

interference cancellation: two taps Let x[n] = {x[0], 0, 0, …, 0}, hi[n] = {hi[0], hi[1]} => y[0] = x[0](w1h1[0] + w2h2[0]) => y[1] = x[0](w1h1[1] + w2h2[1]) Setting y[0] = y[1] = 0? Not enough degrees of freedom if equations are linearly

independent! Need more antennas.

3-antennas, 2-taps y[0] = x[0](w1h1[0] + w2h2[0] + w3h3[0]) = 0 y[1] = x[0](w1h1[1] + w2h2[1] + w3h3[1]) = 0 Set w1 = 1:

w2h2[0] + w3h3[0] = -h1[0] w2h2[1] + w3h3[1] = -h1[1]

Two equations, and two unknowns! Number of antennas must be at least the number of

required nulls + 1

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

Primary user 1

Primary user 2

Primary user 3

Primary user 4

Cognitive Radio

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, 100µS slot duration.

=> 312Hz max feedback rate per subcarrier.

Time

Primary user 1

Primary user 2

Primary user 3

Primary user 4

Cognitive Radio

OFDM Summary

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

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