VSMC MIMO: A Spectral Efficient Scheme for Cooperative Relay in Cognitive Radio Networks 1

VSMC MIMO: A Spectral Efficient Scheme for Cooperative Relay in Cognitive Radio Networks

Outline

Introduction

Fundamental Components of VSMC MIMO

scheme

VSMC MIMO Scheme in Cooperative MIMO

Resource Allocation

MAC Layer Design

Testbed Description and Experimental

Results

Conclusion 2VSMC MIMO: A Spectral Efficient Scheme for Cooperative Relay in Cognitive Radio Networks

Introduction

SISO (single input single output)

MIMO (Multiple-Input Multiple-Output)

spatial multiplexing gain

diversity gain

VSMC MIMO: A Spectral Efficient Scheme for Cooperative Relay in Cognitive Radio Networks 3

Introduction

Traditional MIMO schemes are designed mainly for the scenario

of contiguous spectrum ranges.

In cognitive radio networks, the available spectrum is discontiguous, making traditional MIMO schemes inefficient for

spectrum usage.

We proposed a new scheme: VSMC MIMO (Variable Numbers of Streams on Multiple Channels of MIMO system)

Introduction

Simple example of VSMC MIMO

The arrow lines indicate the numbers of data streams between transmitters and receivers, and do not mean the actual signals at the antennas. All the figures

in this paper adopt this approach for simpler illustration.

Contributions:

Designed a feasible and efficient scheme for MIMO nodes to transmit variable numbers of streams on multiple channels.

Solved resource allocation problem to implement cooperative MIMO relay in cognitive radio networks.

Built a system of cooperative MIMO relay that involved VSMC MIMO scheme based on USRP platform.

Introduction

Outline

Introduction

scheme

Resource Allocation

MAC Layer Design

Results

To implement VSMC MIMO scheme, we have two considerations.

One radio should transmit or receive signals on multiple channels

----- D-OFDM scheme (Discontiguous Orthogonal Frequency Division Multiplexing)

Interferences of several concurrent transmissions on the same channels should be eliminated.

----- view transmitters and receivers as a “virtual MIMO” system, thus adopting existing pre-coding methods

Fundamental Components

Discontiguous OFDM

One channel

Sub-channel 1

Sub-channel 2

Sub-channel 3

Sub-channel 4

Traditional OFDM Discontiguous OFDM

Combined D-OFDM scheme with MIMO techniques to implement VSMC MIMO scheme

Four basic components in VSMC MIMO scheme

Baseband Signal Processing

Preambles on Part of Subcarriers

Coding Methods in VSMC MIMO scheme

Channel States Information

Baseband Signal Processing

A low-pass filter is used to get the desired signal on certain channels in baseband while eliminate signal on other channels.

Frequency range of one desired channel is

Shift frequency by

Pass a low-pass filter

Shift back by

Preambles on Part of Subcarriers

the preambles for time synchronization and training sequences for channel estimation are added individually on the corresponding subcarriers.

The preambles are used to detect the beginning of each packet and calculate the carrier frequency offset.

1 2[ , ]f f1 2

Coding Methods in VSMC MIMO scheme

adopt V-BLAST pre-coding method

adopt Zero-Forcing(ZF) decoding algorithm

Channel States Information (CSI)

adopt Linear Least Square Estimate (LLSE)

Outline

Introduction

scheme

Resource Allocation

MAC Layer Design

Results

An example of implementing cooperative MIMO relay in cognitive radio networks as shown in Fig. 2.

Data rate of each stream on one channel is 10 Kbps.

MIMO nodes try to obtain their spatial multiplexing gain to achieve larger throughput.

Cooperative MIMO Relay

Select R1 as a relay, adopt the VSMC MIMO scheme in implementing cooperative MIMO relay.

Relay transmission process is divided into two time slots.

Cooperative MIMO Relay

Outline

Introduction

scheme

Resource Allocation

MAC Layer Design

Results

We move on to the network perspective

There are N Secondary User(SU)s, served by one AP in a certain area.

We only consider downlink transmission

We have to allocate spectrum and select relay nodes

Resource Allocation

Resource allocation problem is solved by three steps.

A. Network Model

B. Problem Formulation

C. Heuristic Solution

A. Network Model

Resource Allocation

A graph denotes the network

represents the nodes

： AP node : SU nodes

represents direct links between nodes

: exits a link between node and node

: otherwise

: direct link is symmetrical

denotes data demands that SU nodes need to receive from AP.

A. Network Model

Resource Allocation

Antenna matrix

: number of antennas equipped with

Frequency range is divided into channels with same

bandwidth (channel ) .

Channel matrix

: the channel is available at node

: unavailable

1,2,..., k1 2[ , ]f f

Resource Allocation

Channel assignment matrix :

: channel is assigned to link

: otherwise

The constraints of channel assignment:

Resource Allocation

Data streaming matrix :

: number of data streams transmitted over on channel

The constraints of data stream:

Resource Allocation

Relay matrix :

: node works as a relay for node

: otherwise

The constraints for relays:

1)Channel availability:

2)Half duplex:

Resource Allocation

Data transmission rate matrix :

: data transmission rate of link

on channel

: constant transmission rate for each data stream

Data transmission rate for link :

Resource Allocation

Throughput for SU nodes:

If node receives data from on channel directly,

the average throughput of link on channel is

If node acts as a relay , then

Resource Allocation

1) direct-receiving node:

We divide nodes into three categories: direct-receiving nodes,

relay nodes and relay-assisted nodes, and calculate their throughputs.

2) Relay node:

Resource Allocation

3) relay-assisted node:

We combine three categories together:

Our final problem:

C. Heuristic Solution for Resource Allocation

Resource Allocation

The optimization problem (15) is complex and difficult to obtain the close-form solution, so we propose a heuristic

solution.

1) Find potential relay nodes and relay-assisted nodes

in direct transmission

2) Select relays and allocate spectrum resource

Resource Allocation

Direct transmission:

The optimization problem (15) can be simplified to

We can transform problem (16) to a max flow problem, thus we can solve it by various kinds of methods, such as

Hungarian Algorithm.

Resource Allocation

Max flow problem:

k channel nodes N SU nodes

Sink node

Resource Allocation

After solving the max-flow problem, we can get the direct transmission throughput of each node

If , add to potential relay set

If , add to potential relay-assisted set

In , we first calculate relay potential

Where , if , is removed from

In , we calculate data gap

Resource Allocation

Another Max flow problem:

Source node

Nodes in set

Channel nodes

Satisfying:

Sink node

Nodes in set

Outline

Introduction

scheme

Resource Allocation

MAC Layer Design

Results

Using VSMC MIMO scheme to implement cooperative MIMO relay in cognitive radio networks needs good organization and

coordination of all the nodes in the network. To make cooperative MIMO relay scheme workable and reliable, we propose a MAC

layer design.

MAC Layer Design

The whole network is in global

synchronization

The transmission process is based on

frames

Outline

Introduction

scheme

Resource Allocation

MAC Layer Design

Results

We implement cooperative MIMO relays system based on the existing codes of MIMO technology introduced in [17] on USRP—

LABVIEW platform.

Testbed Description

NI USRP 2920 series

Transmitting process

Testbed Description

Receiving process

Testbed Description

Experiment parameters

Experiments

I/Q rate: 400 K/second

Carrier frequency: 915 MHz

Pulse shaping samples per symbol: 8

Channel: composed of 20 subcarriers (2 pilot subcarriers)

Guard subcarriers: 12

Each data stream supports about 10 Kbps

Packet: 2880 bits

Each run transfers 20 packets in downlink

Experiment times: 100

Experiments of two end users

Experiments

Setup 1:

Traffic demand for D : 25 Kbps

Traffic demand for R : 10 Kbps

Setup 2:

Experimental results of two end users

Experiments

Set up 1:

Theoretical throughput gain: 50%

Average throughput gain: 34.74%

Set up 2:

Experiments of three end users

Experiments

Traffic demand for D1: 15 Kbps

Traffic demand for D2: 15 Kbps

Traffic demand for R: 0 Kbps

Experimental results:

Evaluation of Experimental results

Experiments

The gap between the theoretical analysis and experimental results are caused mainly by these reasons:

Relay nodes need time to switch from receiving mode to transmitting mode

Relay nodes have to spend time on decoding and recoding message

Signal processing rate of USRP—LabVIEW platform is slow and variable

Outline

Introduction

scheme

Resource Allocation

MAC Layer Design

Results

44VSMC MIMO: A Spectral Efficient Scheme for Cooperative Relay in Cognitive Radio Networks

Conclusions

We proposed VSMC MIMO scheme.

We solved the resource allocation problem in implementing cooperative MIMO relay in cognitive radio networks and

designed a MAC protocol.

We built a cooperative MIMO relay system based on USRP.

In the future, we may enlarge the scale of the testbed to evaluate the performance of VSMC MIMO in large-scale

MIMO relays system.

Thank you!

VSMC MIMO: A Spectral Efficient Scheme for Cooperative Relay in Cognitive Radio Networks 1

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