**4. Review on test-bed implementation of interference alignment**

The idea of squeezing aligned interference signals into half of the signal space and accessing the other half of the signal space for desired transmission in an interference network is so tempting that a large body of works has been done since the introduction of interference alignment to implement this elegant approach, for instance look at [9], [18-26].

The first implementation of interference alignment is reported in [18]. A hybrid version of interference alignment combined with the successive interference cancellation (IAC) in a single carrier narrow-band MIMO wireless local area network (WLAN) is tested in this paper. Several interference alignment and interference alignment-like approaches are tested in MIMO-OFDM interference channels in [19]. This paper specifically studies the effects of poor channel conditions on the performance of interference alignment. Real-time implementation of different interference alignment scenarios are performed on different test-beds like the ones in [21-23]. [21] identifies practical issues that degrades the interference alignment performance such as channel estimation errors or collinearity between the desired signal and interference subspaces while in [23], in Vienna MIMO test-bed (VMTB), the typical delay is measured.

Implementation of interference alignment in frequency domain over measured channels is considered in [9] where the different variants of interference alignment are compared with frequency planning scenarios. The significant superiority of interference alignment perform‐ ance in terms of average sum rate is reported at high SNRs in this paper.

In all previously mentioned papers, the effects of hardware impairments are ignored. In [20], the effects of such impairments on the performance of interference alignment and coordinated multi-point1 (CoMP) is investigated on *KTH four-multi* test-bed and a measurement based model for signal-to-interference-noise-and-distortion ratio (SINDR) is suggested. Implemen‐ tation of interference alignment with power control and interference alignment with com‐ pressed feedback are also studied on the same test-bed in [24] and [25], respectively. These two approaches are further explained in the rest of this chapter. Table below summarizes some of the works that have been done in the test-bed implementation of interference alignment.

<sup>1</sup> CoMP is an approach similar to interference alignment with this main difference that in CoMP all the sources know the information to be transmitted to all destinations.


( )

= = *M M*

2 2

*d j j l l d d k kj j j kj kk k k kk*

*P P*

1 1

computing the transmitter and receiver filters.

**I**M/2 is M/2 × M/2 identity matrix and N

*K*

60 Contemporary Issues in Wireless Communications

*j l*

to be a MMSE filter as follows

where

considered in [

multi-point

1

destinations decode their desired signals by applying associated

k

*Max-SINR algorithm*, the receiver filtering vector *dk*

filters. An

extension of this iterative algorithm is proposed in [17] which instead of minimizing leakage at each destination tries to maximize signal-to-noise-plus-interference

ratio (SINR) corresponding to each steam. This algorithm is referred to as *Max-SINR algorithm* in the literature. According to

**U** instead of the one in (14) is selected

information to be transmitted to all destinations.

 11

*Q*

*k*

 *HV*

> *dk*

*U*

 *kk k*

 *d*

) M 2 \* \* \* \*

**4. Review on test-bed implementation of interference alignment**

alignment to implement this elegant approach, for instance look at [9], [18-26].

beamforming vectors are updated in the reverse transmissions. In the following sections, we will present test-bed implementation of algorithms which use Max-SINR algorithm for

The idea of squeezing aligned interference signals into half of the signal space and accessing the other half of the signal space for desired transmission in an interference network is so tempting that a large body of works has been done since the introduction of interference

The first implementation of interference alignment is reported in [18]. A hybrid version of interference alignment combined with the successive interference cancellation (IAC) in a single carrier narrow-band MIMO wireless local area network (WLAN) is tested in this paper. Several interference alignment and interference alignment-like approaches are tested in MIMO-OFDM interference channels in [19]. This paper specifically studies the effects of poor channel conditions on the performance of interference alignment. Real-time implementation of different interference alignment scenarios are performed on different test-beds like the ones in [21-23]. [21] identifies practical issues that degrades the interference alignment performance such as channel estimation errors or collinearity between the desired signal and interference subspaces while in [23], in Vienna MIMO test-bed (VMTB), the typical delay is measured.

Implementation of interference alignment in frequency domain over measured channels is

frequency planning scenarios. The significant superiority of interference alignment perform‐

*Dk*

*n*

In all previously mentioned papers, the effects of hardware impairments are ignored. In [20], the effects of such impairments on the performance of interference alignment and coordinated

 *kk*

0

 *I*

.

(17)

model for signal-to-interference-noise-and-distortion ratio (SINDR) is suggested. Implemen‐ tation of interference alignment with power control and interference alignment with com‐ pressed feedback are also studied on the same test-bed in [24] and [25], respectively. These two approaches are further explained in the rest of this chapter. Table below summarizes some of the works that have been done in the test-bed implementation of interference alignment.

 

 *V H*

*kk k k*

 *HV*

*d d*

 \*

 \*

*N*

1 CoMP is an approach similar to interference alignment with this main difference that in CoMP all the sources know the

 *M*

 *kj*

> 1 1

*j l*

 

 

*HV V H*

*kj j j*

*M*

*l l*

\*

2

*P*

*j*

*d* *k*

*Q*

*j*

*d K*

\*

 2

 *P*

 *j*

ance in terms of average sum rate is reported at high SNRs in this paper.

2

 *kk k*

( )*d*

*Q*

*k*

 *HV*

*d*

,

(16)

 *d*

9] where the different variants of interference alignment are compared with

in the reverse transmissions.

filters.

In the following sections,

(CoMP) is investigated on *KTH four-multi* test-bed and a measurement based

algorithms which use Max-SINR algorithm for computing the transmitter and receiver

**Review on test-bed implementation of interference alignment**

**4.** The idea of squeezing aligned

network is so tempting that a large

at [9],

[18-26].

The first implementation of interference alignment is reported in [18].

approaches are tested in MIMO-OFDM interference channels in [19].

interference alignment. Real-time implementation of different interference

identifies practical issues that degrades the interference

interference subspaces while in [23], in Vienna MIMO test-bed

Implementation of interference

in this paper.

In all previously mentioned papers, the effects of hardware impairments are ignored. In [20],

alignment and coordinated multi- point1

distortion ratio (SINDR) is suggested. Implementation of interference

studied on the same test-bed in [24] and [25],

(CoMP) is investigated

respectively. These two approaches

are further explained

on *KTH four-multi* test-bed and a measurement based model

alignment with power control and interference

the effects of such impairments on the performance of interference

in the rest of this chapter.

for signal-to-interference-noise-and-

alignment with compressed feedback are also

(IAC) in a single carrier narrow-band MIMO wireless local area network (WLAN) is tested in this paper. Several interference

alignment performance such as channel

(VMTB), the typical delay is measured.

alignment in frequency domain over measured channels is considered in [9] where the different variants of interference

compared with frequency planning scenarios. The significant superiority of interference alignment performance in terms of average sum rate is reported at high SNRs

alignment are

This paper

A hybrid version of interference alignment combined with the successive interference

alignment scenarios are performed on different test-beds like the ones in [21-23].

estimation errors or collinearity between the desired signal and

specifically studies the effects of poor channel

alignment and interference alignment-like

conditions on the performance of

 [21]

cancellation

interference

Similarly, the transmitter beamforming vectors are updated

(

**<sup>Q</sup>** = -+ åå **HV V H HV V H I** (17)

we will present test-bed implementation of

0 M2

*N*

.

look

0 is the noise power. Similarly, the transmitter

signals into half of the signal space and accessing the other half of the signal space for desired transmission in an interference

body of works has been done since the introduction of interference alignment to implement this elegant approach, for instance

Table 1. Figure 3.

**5.** *KTH*

*four-multi* is a

accompanies

hardware and

**5.1.**

**Hardware setup**

Ettus XCVR2450 RF

is illustrated in Fig. 3.

The frequency and

square generated by an

(CLK). All the

synchronization

In a

system

decisions.

with

interference

alignment,

transmitter

 will in any

would be

achieved

using

common

control and

source's local

oscillators are

locked to the

same

clock

while a synchronization

case

need 1CoMPisanapproachsimilartointerferencealignmentwiththismaindifferencethatinCoMPallthesourcesknowtheinformationtobetransmittedtoalldestinations.

some

kind of

back-haul to

provide a

common

time

reference and

disperse

scheduling

channels

(cellular

systems) or from

the

burst

preambles

(wireless local area

separate

clock is

provided for each of the

wave) and a

network is

designed to

transmit-receive

national

EM406A

GPS

module and

marine

electronics

association

(NMEA)

signal (an

ASCII

protocol that

provides

hour-minute-second

performed by

destinations.

 In a real

helps of 10

time),

respectively.

 Both

MHz implementation

 the networks).

same

reference

clocks

signals are

distributed

through the

network. The

frequency

synchronization

 is also

synchronizations.

 The time and

work at 2.49

GHz

center

frequency

with 12

MHz transmit-receive

bandwidth.

synchronizations

 are

Synchronization

 of the

done by

means of a

pulse-

per-second

(PPS)

signal (0-5 V, 1 Hz

network is

performed in

three

levels,

namely

time,

software

structure of the

test-bed is

described.

 the

hardware

setup of the test- bed

USRP-based

wireless

communication

which

facilitates the

rapid

testing of

The current version of the test-bed consists of six nodes where three of them are fixed and take the role of transmitting sources while the other three are movable

Twelve Ettus Research USRP N210 (see www.ettus.com) are used to govern the twelve antennas in the network. The source USRPs are equipped with the standard

dautherboards while the destination USRPs use custom boards to achieve sufficient noise figure and dynamic range. The output signal of each

source USRP is amplified by a ZRL-2400LN power amplifier. Two Linux computers control all the USRPs in the network. The network structure of four-multi test-bed

is 1.6 times of the carrier's wavelength.

receiving destinations. All the nodes are equipped with two vertically polarized dipole antennas spaced 20 cm apart which

multi-antenna

schemes (see

test-bed

consisting of

several

stationary

and

movable

multi-antenna

http://fourmulti.sourceforge.net/).

 In the

following,

 the

nodes. A

software

framework

**KTH four-multitest-bed setup**

Network structure of four-multi test-bed.

Makinji Potpis

"Book˙Chapter˙MFZNS" — 2014/10/14 — 15:09 — page 13 — #13

13

**Figure 3.** Network structure of four-multi test-bed.
