ofdm_simo.m File Reference

Functions
Scale for Tx	waveform ([0:1]) else nt
Theoretical BER	fprintf ("Channel type: %s \n", chan_type)
Modulation	order (2/4/16/64=BSPK/QPSK/16-QAM/64-QAM) % OFDM params SC_IND_PILOTS
Number of data	symbols (one per data-bearing subcarrier per OFDM symbol) N_LTS_SYM
id	FFT_OFFSET ()
Number of CP samples to use in	FFT (on average) LTS_CORR_THRESH=0.6
other subcarriers will remain at	ifft_in_mat (SC_IND_DATA, :)
	ifft_in_mat (SC_IND_PILOTS, :)
Insert the cyclic prefix	if (CP_LEN > 0) tx_cp
1, N_ZPAD_POST	zeros ()
abs(tx_vec_iris	max ()
	if (N_BS_NODE > 1) b_scheds
	sdr_params (2)

Variables
	Author (s)[version, executable, isloaded] = pyversion
if ~isloaded pyversion usr bin python py print() %weird bug where py isn 't loaded in an external script end % Params Enable writing plots to PNG	CHANNEL = 11
Channel to tune Tx and Rx radios	SIM_MOD = 1
if SIM_MOD	chan_type = "awgn"
	nt = 100
	sim_SNR_db = 1:20
	nsnr = length(sim_SNR_db)
	snr_plot = 20
	TX_SCALE = 1
end	ber_SIM = zeros(nt,nsnr)
BER	berr_th = zeros(nsnr,1)
Iris	params
	N_UE = 1
	TX_FRQ = 2.5e9
	RX_FRQ = TX_FRQ
	TX_GN = 40
	RX_GN = 20
	SMPL_RT = 5e6
	N_FRM = 10
	b_ids = string.empty()
	b_scheds = string.empty()
	ue_ids = string.empty()
	ue_scheds = string.empty()
Waveform params	N_OFDM_SYM = 46
Number of OFDM symbols for	burst
Number of OFDM symbols for it needs to be less than	MOD_ORDER = 16
Pilot subcarrier indices	SC_IND_DATA = [2:7 9:21 23:27 39:43 45:57 59:64]
Data subcarrier indices	SC_IND_DATA_PILOT = [2:27 39:64]'
	N_SC = 64
Number of subcarriers	CP_LEN = 16
Cyclic prefix length	N_DATA_SYMS = N_OFDM_SYM * length(SC_IND_DATA)
Number of	N_SYM_SAMP = N_SC + CP_LEN
Number of samples that will go over the air	N_ZPAD_PRE = 90
Normalized threshold for LTS correlation	DO_APPLY_PHASE_ERR_CORRECTION = 1
Enable Residual CFO estimation correction Define the preamble LTS for fine CFO and channel estimation	lts_f
	lts_t = ifft(lts_f, 64)
time domain	preamble = [lts_t(33:64) lts_t lts_t]
Generate a payload of random integers	tx_data = randi(MOD_ORDER, 1, N_DATA_SYMS) - 1
	tx_syms = mod_sym(tx_data, MOD_ORDER)
Reshape the symbol vector to a matrix with one column per OFDM symbol	tx_syms_mat = reshape(tx_syms, length(SC_IND_DATA), N_OFDM_SYM)
Define the pilot tone values as BPSK symbols	pilots = [1 1 -1 1].'
Repeat the pilots across all OFDM symbols	pilots_mat = repmat(pilots, 1, N_OFDM_SYM)
IFFT Construct the IFFT input matrix	ifft_in_mat = zeros(N_SC, N_OFDM_SYM)
Insert the data and pilot	values
Perform the IFFT	tx_payload_mat = ifft(ifft_in_mat, N_SC, 1)
end Reshape to a vector	tx_payload_vec = reshape(tx_payload_mat, 1, numel(tx_payload_mat))
	tx_vec = [preamble tx_payload_vec]
Leftover from zero	padding
for	isnr
	rx_vec_iris = rx_vec_iris.'
just to agree with what the hardware spits out else Init Iris nodes Set up the Iris experiment Create a two Iris node	objects
just to agree with what the hardware spits out else Init Iris nodes Set up the Iris experiment Create a two Iris node	RF3E000024
	b_prim_sched = "PGGGGGRG"
BS primary noede s	schedule
	ue_sched = "GGGGGGPG"
for	iu
end	n_samp = length(tx_vec_iris)
Iris nodes parameters	sdr_params
	l_rx_dec =length(rx_vec_iris)
Correlate for LTS	a = 1

Function Documentation

FFT()

Number of CP samples to use in FFT ( on  average )

pure virtual

FFT_OFFSET()

id FFT_OFFSET ( )

virtual

fprintf()

Theoretical BER fprintf	(	"Channel type: %s \n"	,
		chan_type
	)

if() [1/2]

Insert the cyclic prefix if	(	CP_LEN	,
		0
	)

if() [2/2]

if	(	N_BS_NODE	,
		1
	)

ifft_in_mat() [1/2]

other subcarriers will remain at ifft_in_mat	(	SC_IND_DATA	,
		:
	)

ifft_in_mat() [2/2]

ifft_in_mat	(	SC_IND_PILOTS	,
		:
	)

max()

abs(tx_vec_iris max ( )

staticvirtual

order()

Modulation order

(

2/4/16/

64 = BSPK/QPSK/16-QAM/64-QAM

)

sdr_params()

sdr_params

(

2

)

symbols()

Number of data symbols

(

one per data-bearing subcarrier per OFDM

symbol

)

waveform()

Scale for Tx waveform

(

)

zeros()

1,N_ZPAD_POST zeros ( )

virtual

Variable Documentation

a

Correlate for LTS a = 1

Author

Author(s)[version, executable, isloaded] = pyversion

b_ids

b_ids = string.empty()

b_prim_sched

b_prim_sched = "PGGGGGRG"

b_scheds

UE schedule b_scheds = string.empty()

ber_SIM

end ber_SIM = zeros(nt,nsnr)

berr_th

BER berr_th = zeros(nsnr,1)

burst

Number of OFDM symbols for burst

chan_type

if SIM_MOD chan_type = "awgn"

CHANNEL

if ~isloaded pyversion usr bin python py print () %weird bug where py isn't loaded in an external script end % Params Enable writing plots to PNG CHANNEL = 11

CP_LEN

Number of subcarriers CP_LEN = 16

DO_APPLY_PHASE_ERR_CORRECTION

Normalized threshold for LTS correlation DO_APPLY_PHASE_ERR_CORRECTION = 1

ifft_in_mat

IFFT Construct the IFFT input matrix ifft_in_mat = zeros(N_SC, N_OFDM_SYM)

isnr

for isnr

Initial value:

= 1:nsnr
    for it = 1:nt
if (SIM_MOD)        
    rx_vec_iris = getRxVec(tx_vec_iris, N_BS_NODE, N_UE, chan_type, sim_SNR_db(isnr))

iu

for iu

Initial value:

= 1:N_UE
        ue_scheds(iu,:) = ue_sched

l_rx_dec

l_rx_dec =length(rx_vec_iris)

lts_f

Enable Residual CFO estimation correction Define the preamble LTS for fine CFO and channel estimation lts_f

Initial value:

= [0 1 -1 -1 1 1 -1 1 -1 1 -1 -1 -1 -1 -1 1 1 -1 -1 1 -1 1 -1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 ...
    1 1 -1 -1 1 1 -1 1 -1 1 1 1 1 1 1 -1 -1 1 1 -1 1 -1 1 1 1 1]

lts_t

lts_t = ifft(lts_f, 64)

MOD_ORDER

Number of OFDM symbols for it needs to be less than MOD_ORDER = 16

N_DATA_SYMS

Cyclic prefix length N_DATA_SYMS = N_OFDM_SYM * length(SC_IND_DATA)

N_FRM

N_FRM = 10

N_OFDM_SYM

Waveform params N_OFDM_SYM = 46

n_samp

end n_samp = length(tx_vec_iris)

N_SC

N_SC = 64

N_SYM_SAMP

Number of N_SYM_SAMP = N_SC + CP_LEN

N_UE

N_UE = 1

N_ZPAD_PRE

Number of samples that will go over the air N_ZPAD_PRE = 90

nsnr

nsnr = length(sim_SNR_db)

nt

nt = 100

objects

just to agree with what the hardware spits out else Init Iris nodes Set up the Iris experiment Create a two Iris node objects

padding

Leftover from zero padding

params

Iris params

pilots

Define the pilot tone values as BPSK symbols pilots = [1 1 -1 1].'

pilots_mat

Repeat the pilots across all OFDM symbols pilots_mat = repmat(pilots, 1, N_OFDM_SYM)

preamble

time domain preamble = [lts_t(33:64) lts_t lts_t]

RF3E000024

just to agree with what the hardware spits out else Init Iris nodes Set up the Iris experiment Create a two Iris node RF3E000024

RX_FRQ

RX_FRQ = TX_FRQ

RX_GN

RX_GN = 20

rx_vec_iris

end rx_vec_iris = rx_vec_iris.'

SC_IND_DATA

Pilot subcarrier indices SC_IND_DATA = [2:7 9:21 23:27 39:43 45:57 59:64]

SC_IND_DATA_PILOT

Data subcarrier indices SC_IND_DATA_PILOT = [2:27 39:64]'

schedule

BS primary noede s schedule

sdr_params

Iris nodes parameters sdr_params

Initial value:

= struct(...
        'id', b_ids, ...
        'n_chain',N_BS_NODE, ...
        'txfreq', TX_FRQ, ...
        'rxfreq', RX_FRQ, ...
        'txgain', TX_GN, ...
        'rxgain', RX_GN, ...
        'sample_rate', SMPL_RT, ...
        'n_samp', n_samp, ...          % number of samples per frame time.
        'n_frame', N_FRM, ...
        'tdd_sched', b_scheds, ...     % number of zero-paddes samples
        'n_zpad_samp', (N_ZPAD_PRE + N_ZPAD_POST) ...
        )

SIM_MOD

Channel to tune Tx and Rx radios SIM_MOD = 1

sim_SNR_db

sim_SNR_db = 1:20

SMPL_RT

SMPL_RT = 5e6

snr_plot

snr_plot = 20

tx_data

Generate a payload of random integers tx_data = randi(MOD_ORDER, 1, N_DATA_SYMS) - 1

TX_FRQ

TX_FRQ = 2.5e9

TX_GN

TX_GN = 40

tx_payload_mat

tx_payload_mat = ifft(ifft_in_mat, N_SC, 1)

tx_payload_vec

id tx_payload_vec = reshape(tx_payload_mat, 1, numel(tx_payload_mat))

TX_SCALE

TX_SCALE = 1

tx_syms

tx_syms = mod_sym(tx_data, MOD_ORDER)

tx_syms_mat

Reshape the symbol vector to a matrix with one column per OFDM symbol tx_syms_mat = reshape(tx_syms, length(SC_IND_DATA), N_OFDM_SYM)

tx_vec

tx_vec = [preamble tx_payload_vec]

ue_ids

ue_ids = string.empty()

ue_sched

ue_sched = "GGGGGGPG"

ue_scheds

end ue_scheds = string.empty()

values

Insert the data and pilot values