Difference between Broadband and Baseband Transmission

Broadband systems use modulation techniques to reduce the effect of noise in the environment. Broadband transmission employs multiple channel unidirectional transmission using a combination of phase and amplitude modulation. 

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Baseband is a digital signal transmitted on the medium using one of the signal codes like NRZ, RZ Manchester biphase-M code, etc. called baseband transmission. 

These are the following differences between Broadband and Baseband transmission. 

Baseband transmission:

1. Digital signaling.
2. Frequency division multiplexing is not possible.
3. Baseband is the bi-directional transmission.
4. A short-distance signal traveling.
5. The entire bandwidth is for single signal transmission.
6. Example: Ethernet is using Basebands for LAN.

Broadband transmission:

1. Analog signaling.
2. The transmission of data is unidirectional.
3. Signal traveling distance is long.
4. Frequency division multiplexing is possible.
5. Simultaneous transmission of multiple signals over different frequencies.
6. Example: Used to transmit cable TV to premises.

S. No Basis of Comparison Baseband Transmission Broadband Transmission 1. Type of Signal In baseband transmission, the type of signaling used is digital.  In broadband transmission, the type of signaling used is analog. 2. Direction Type Baseband Transmission is bidirectional in nature. Broadband Transmission is unidirectional in nature. 3. Signal Transmission The Signal can be sent in both directions. Sending of Signal in one direction only. 4. Distance covered by the signal Signals can only travel over short distances. For long distances, attenuation is required. Signals can be traveled over long distances without being attenuated. 5. Topology It works well with bus topology. It is used with a bus as well as tree topology. 6. Device used to increase signal strength Repeaters are used to enhance signal strength. Amplifiers are used to enhance signal strength. 7. Type of Multiplexing used It utilizes Time Division Multiplexing. It utilizes Frequency Division Multiplexing. 8. Encoding Techniques In baseband transmission, Manchester and Differential Manchester encoding are used. Only PSK encoding is used. 9. Transfer medium Twisted-pair cables, coaxial cables, and wires are used as a transfer medium for digital signals in baseband transmission.  Broadband signals were sent through optical fiber cables, coaxial cables, and radio waves. 10. Impedance Baseband transmission has a 50-ohm impedance.  Broadband transmission has a 70-ohm impedance.  11. Data Streams  It can only transfer one data stream at a time in bi-directional mode. It can send multiple signal waves at once but in one direction only. 12. Installation and Maintenance Baseband transmission is easy to install and maintain. Broadband transmission is difficult to install and maintain. 13. Cost This transmission is cheaper to design. This transmission is expensive to design. 14. Application Typically seen in Ethernet LAN networks.  Typically found in cable and networks. 15. Frequency In this, capacity of frequency is less than 100 kHz. In this, capacity of frequency is higher than 100 kHz. 16. Suitable for It is best for wired networks. It is best for non-wired networks. 17. Structure The structure is very simple, and no special hardware is required. The structure is complex as it needs unique hardware.

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For most file formats, one can simply import baseband and use baseband.open to access the file. This gives one a filehandle from which one can read decoded samples:

For other file formats, a bit more information is needed. Below, we cover the basics of inspecting files, reading from and writing to files, converting from one format to another, and diagnosing problems. We assume that Baseband as well as numpy and the astropy.units module have been imported:

The one format for which file_info works a bit differently is GSB , as this format requires separate time-stamp and raw data files. Only the timestamp file can be inspected usefully.

The information is gleaned from info properties on the various file and stream readers (see below).

The same function will also tell you when more information is needed. For instance, for Mark 5B files one needs the number of channels used, as well as (roughly) when the data were taken:

Baseband allows you to quickly determine basic properties of a file, including what format it is, using the baseband.file_info function. For instance, it shows that the sample VDIF file that comes with Baseband is very short (sample files can all be found in the baseband.data module):

Reading Files¶

Opening Files¶

As shown at the very start, files can be opened with the general baseband.open function. This will try to determine the file type using file_info, load the corresponding baseband module, and then open the file using that modules master input/output function.

Generally, if one knows the file type, one might as well work with the corresponding module directly. For instance, to explicitly use the DADA reader to open the sample DADA file included in Baseband, one can use the DADA modules open function:

>>> 
from
 
baseband
 
import
 
dada
>>> 
from
 
baseband.data
 
import
 
SAMPLE_DADA
>>> 
fh
 
=
 
dada
.
open
(
SAMPLE_DADA
,
 
'rs'
)
>>> 
fh
.
read
(
3
)
array([[ -38.-38.j,  -38.-38.j],
       [ -38.-38.j,  -40. +0.j],
       [-105.+60.j,   85.-15.j]], dtype=complex64)
>>> 
fh
.
close
()

In general, file I/O and data manipulation use the same syntax across all file formats. When opening Mark 4 and Mark 5B files, however, some additional arguments may need to be passed (as was the case above for inspecting a Mark 5B file, and indeed this is a good way to find out what is needed). Notes on such features and quirks of individual formats can be found in the API entries of their open functions, and within the Specific file format documentation.

For the rest of this section, we will stick to VDIF files.

Decoding Data and the Sample File Pointer¶

By giving the openers a 'rs' flag, which is the default, we open files in stream reader mode, where a file is accessed as if it were a stream of samples. For VDIF, open will then return an instance of VDIFStreamReader, which wraps a raw data file with methods to decode the binary data frames and seek to and read data samples. To decode the first 12 samples into a ndarray, we would use the read method:

>>> 
from
 
baseband
 
import
 
vdif
>>> 
from
 
baseband.data
 
import
 
SAMPLE_VDIF
>>> 
fh
 
=
 
vdif
.
open
(
SAMPLE_VDIF
,
 
'rs'
)
>>> 
d
 
=
 
fh
.
read
(
12
)
>>> 
type
(
d
)
<... 'numpy.ndarray'>
>>> 
d
.
shape
(12, 8)
>>> 
d
[:,
 
0
]
.
astype
(
int
)
    
# First thread.
array([-1, -1,  3, -1,  1, -1,  3, -1,  1,  3, -1,  1])

As discussed in detail in the VDIF section, VDIF files are sequences of data frames, each of which is comprised of a header (which holds information like the time at which the data was taken) and a payload, or block of data. Multiple concurrent time streams can be stored within a single frame; each of these is called a channel. Moreover, groups of channels can be stored over multiple frames, each of which is called a thread. Our sample file is an 8-thread, single-channel file (8 concurrent time streams with 1 stream per frame), and in the example above, fh.read decoded the first 12 samples from all 8 threads, mapping thread number to the second axis of the decoded data array. Reading files with multiple threads and channels will produce 3-dimensional arrays.

fh includes shape, size and ndim, which give the shape, total number of elements and dimensionality of the files entire dataset if it was decoded into an array. The number of complete samples - the set of samples from all available threads and channels for one point in time - in the file is given by the first element in shape:

>>> 
fh
.
shape
    
# Shape of all data from the file in decoded array form.
(, 8)
>>> 
fh
.
shape
[
0
]
 
# Number of complete samples.

>>> 
fh
.
size

>>> 
fh
.
ndim
2

The shape of a single complete sample, including names indicating the meaning of shape dimensions, is retrievable using:

>>> 
fh
.
sample_shape
SampleShape(nthread=8)

By default, dimensions of length unity are squeezed, or removed from the sample shape. To retain them, we can pass squeeze=False to open:

>>> 
fhns
 
=
 
vdif
.
open
(
SAMPLE_VDIF
,
 
'rs'
,
 
squeeze
=
False
)
>>> 
fhns
.
sample_shape
    
# Sample shape now keeps channel dimension.
SampleShape(nthread=8, nchan=1)
>>> 
fhns
.
ndim
            
# fh.shape and fh.ndim also change with squeezing.
3
>>> 
d2
 
=
 
fhns
.
read
(
12
)
>>> 
d2
.
shape
             
# Decoded data has channel dimension.
(12, 8, 1)
>>> 
fhns
.
close
()

Basic information about the file is obtained by either by fh.info or simply fh itself:

>>> 
fh
.
info
VDIFStream information:
start_time = -06-16T05:56:07.
stop_time = -06-16T05:56:07.
sample_rate = 32.0 MHz
shape = (, 8)
format = vdif
bps = 2
complex_data = False
verify = fix
readable = True

checks:  decodable: True
         continuous: no obvious gaps

VDIFFile information:
edv = 3
number_of_frames = 16
thread_ids = [0, 1, 2, 3, 4, 5, 6, 7]
number_of_framesets = 2
frame_rate = .0 Hz
samples_per_frame = 
sample_shape = (8, 1)

>>> 
fh
<VDIFStreamReader name=... offset=12
    sample_rate=32.0 MHz, samples_per_frame=,
    sample_shape=SampleShape(nthread=8),
    bps=2, complex_data=False, edv=3, station=,
    start_time=-06-16T05:56:07.>

Not coincidentally, the first is identical to what we found above using file_info.

The filehandle itself also shows the offset, the current location of the sample file pointer. Above, it is at 12 since we have read in 12 (complete) samples. If we called fh.read (12) again we would get the next 12 samples. If we instead called fh.read(), it would read from the pointers current position to the end of the file. If we wanted all the data in one array, we would move the file pointer back to the start of file, using fh.seek, before reading:

>>> 
fh
.
seek
(
0
)
      
# Seek to sample 0.  Seek returns its offset in counts.
0
>>> 
d_complete
 
=
 
fh
.
read
()
>>> 
d_complete
.
shape
(, 8)

We can also move the pointer with respect to the end of file by passing 2 as a second argument:

>>> 
fh
.
seek
(
-
100
,
 
2
)
    
# Second arg is 0 (start of file) by default.

>>> 
d_end
 
=
 
fh
.
read
(
100
)
>>> 
assert
 
np
.
array_equal
(
d_complete
[
-
100
:],
 
d_end
)

-100 means 100 samples before the end of file, so d_end is equal to the last 100 entries of d_complete. Baseband only keeps the most recently accessed data frame in memory, making it possible to analyze (normally large) files through selective decoding using seek and read.

Note

As with file pointers in general, fh.seek will not return an error if one seeks beyond the end of file. Attempting to read beyond the end of file, however, will result in an EOFError.

To determine where the pointer is located, we use fh.tell():

>>> 
fh
.
tell
()

>>> 
fh
.
close
()

Caution should be used when decoding large blocks of data using fh.read. For typical files, the resulting arrays are far too large to hold in memory.

Seeking and Telling in Time With the Sample Pointer¶

We can use seek and tell with units of time rather than samples. To do this with tell, we can pass an appropriate astropy.units.Unit object to its optional unit parameter:

>>> 
fh
 
=
 
vdif
.
open
(
HUAXUN Product Page
SAMPLE_VDIF
,
 
'rs'
)
>>> 
fh
.
seek
(
)

>>> 
fh
.
tell
(
unit
=
u
.
ms
)
<Quantity 1.25 ms>

Passing the string 'time' reports the pointers location in absolute time:

>>> 
fh
.
tell
(
unit
=
'time'
)
<Time object: scale='utc' format='isot' value=-06-16T05:56:07.>

We can also pass an absolute astropy.time.Time, or a positive or negative time difference TimeDelta or astropy.units.Quantity to seek. If the offset is a Time object, the second argument to seek is ignored.:

>>> 
from
 
astropy.time.core
 
import
 
TimeDelta
>>> 
from
 
astropy.time
 
import
 
Time
>>> 
fh
.
seek
(
TimeDelta
(
-
5e-4
,
 
format
=
'sec'
),
 
2
)
  
# Seek -0.5 ms from end.

>>> 
fh
.
seek
(
0.25
*
u
.
ms
,
 
1
)
  
# Seek 0.25 ms from current position.

>>> 
# Seek to specific time.
>>> 
fh
.
seek
(
Time
(
'-06-16T05:56:07.'
))

We can retrieve the time of the first sample in the file using start_time, the time immediately after the last sample using stop_time, and the time of the pointers current location (equivalent to fh.tell(unit='time')) using time:

>>> 
fh
.
start_time
<Time object: scale='utc' format='isot' value=-06-16T05:56:07.>
>>> 
fh
.
stop_time
<Time object: scale='utc' format='isot' value=-06-16T05:56:07.>
>>> 
fh
.
time
<Time object: scale='utc' format='isot' value=-06-16T05:56:07.>
>>> 
fh
.
close
()

Extracting Header Information¶

The first header of the file is stored as the header0 attribute of the stream reader object; it gives direct access to header properties via keyword lookup:

>>> 
with
 
vdif
.
open
(
SAMPLE_VDIF
,
 
'rs'
)
 
as
 
fh
:
... 
    
header0
 
=
 
fh
.
header0
>>> 
header0
[
'frame_length'
]
629

The full list of keywords is available by printing out header0:

>>> 
header0
<VDIFHeader3 invalid_data: False,
             legacy_mode: False,
             seconds: ,
             _1_30_2: 0,
             ref_epoch: 28,
             frame_nr: 0,
             vdif_version: 1,
             lg2_nchan: 0,
             frame_length: 629,
             complex_data: False,
             bits_per_sample: 1,
             thread_id: 1,
             station_id: ,
             edv: 3,
             sampling_unit: True,
             sampling_rate: 16,
             sync_pattern: 0xacabfeed,
             loif_tuning: ,
             _7_28_4: 15,
             dbe_unit: 2,
             if_nr: 0,
             subband: 1,
             sideband: True,
             major_rev: 1,
             minor_rev: 5,
             personality: 131>

A number of derived properties, such as the time (as a Time object), are also available through the header object:

>>> 
header0
.
time
<Time object: scale='utc' format='isot' value=-06-16T05:56:07.>

These are listed in the API for each header class. For example, the sample VDIF files headers are of class:

>>> 
type
(
header0
)
<class 'baseband.vdif.header.VDIFHeader3'>

and so its attributes can be found here.

Reading Specific Components of the Data¶

By default, fh.read() returns complete samples, i.e. with all available threads, polarizations or channels. If we were only interested in decoding a subset of the complete sample, we can select specific components by passing indexing objects to the subset keyword in open. For example, if we only wanted thread 3 of the sample VDIF file:

>>> 
fh
 
=
 
vdif
.
open
(
SAMPLE_VDIF
,
 
'rs'
,
 
subset
=
3
)
>>> 
fh
.
sample_shape
()
>>> 
d
 
=
 
fh
.
read
(
)
>>> 
d
.
shape
(,)
>>> 
fh
.
subset
(3,)
>>> 
fh
.
close
()

Since by default data are squeezed, one obtains a data stream with just a single dimension. If one would like to keep all information, one has to pass squeeze=False and also make subset a list (or slice):

>>> 
fh
 
=
 
vdif
.
open
(
SAMPLE_VDIF
,
 
'rs'
,
 
subset
=
[
3
],
 
squeeze
=
False
)
>>> 
fh
.
sample_shape
SampleShape(nthread=1, nchan=1)
>>> 
d
 
=
 
fh
.
read
(
)
>>> 
d
.
shape
(, 1, 1)
>>> 
fh
.
close
()

Data with multi-dimensional samples can be subset by passing a tuple of indexing objects with the same dimensional ordering as the (possibly squeezed) sample shape; in the case of the sample VDIF with squeeze=False, this is threads, then channels. For example, if we wished to select threads 1 and 3, and channel 0:

>>> 
fh
 
=
 
vdif
.
open
(
SAMPLE_VDIF
,
 
'rs'
,
 
subset
=
([
1
,
 
3
],
 
0
),
 
squeeze
=
False
)
>>> 
fh
.
sample_shape
SampleShape(nthread=2)
>>> 
fh
.
close
()

Generally, subset accepts any object that can be used to index a numpy.ndarray, including advanced indexing (as done above, with subset=([1, 3], 0)). If possible, slices should be used instead of list of integers, since indexing with them returns a view rather than a copy and thus avoid unnecessary processing and memory allocation. (An exception to this is VDIF threads, where the subset is used to selectively read specific threads, and thus is not used for actual slicing of the data.)

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