Orthogonal Frequency Division Multiplexing is shortly abbreviated as OFDM which addresses the sub-carrier modulation and signal waveform. It is mainly introduced to enhance the digital transmission by splitting the whole into several closed-spaced sub-carriers. This modulated sub-carrier creates important benefits to the data link layer for better transmission.
This page provides info on the implementation procedure of OFDM transmitter and receiver using Matlab along with the latest research issues and ideas!!!
In recent days, OFDM is employed in many wireless cellular communication areas which have large bandwidth and data rates. For instance: Wi-Fi, Wi-Max, LTE, 5G, etc. As mentioned earlier, OFDM handles a huge volume of sub-carriers that have low bit rates. As a result, it is robust against interference, selective fading, and multipath/doppler effects. Further, it also provides the maximum degree of spectrum utilization and efficiency. Here, we have given you the basic functionalities of the OFDM transmitter and receiver for your awareness.
OFDM Transmitter
- Generate OFDM carrier signal which is the aggregation of total orthogonal sub-carriers
- Here, each sub-carrier along with based band data will autonomously modulate using traditional modulation types
- For instance: Phase-Shift Keying (PSK) and Quadrature Amplitude Modulation (QAM)
OFDM Receiver
- Collect signals which are transformed into quadrature-mixed down into baseband (by carrier frequency of sine and cosine waves)
- As a result, it generates N number of parallel streams
- Then, converts the sequences into bitstreams using a symbol detector
- Next, again re-join the streams to form sequences of streams
- Overall, it helps to estimate the original bits stream in the transmitter
However it is introduced for improving digital transmission, it has certain implications on real-time execution. Here, we have given you some significant research issues of OFDM. Our research team has gained several novel ideas to tackle these problems effectively. Also, we have achieved the best results in our unique problem-solving techniques. So, we are ready to provide the best solutions for any kind of challenging problem.
Research Issues in OFDM
- Decoupled Frequency Bands
- Timing Synchronization
- Out-of-band Noise Distribution
- Carrier Synchronization / Aggregation
- Neighbouring Channels Interference (Radiation)
In addition, we have also given you the latest research notions of OFDM transmitter and receiver. These ideas are found to be unique with a high degree of future scope. Beyond this list of ideas, we support you in other major research areas of OFDM. And, we assure you that our research notions are up-to-date to make your research work unique from others. Further, if you want to know more budding research ideas then approach our team. We provide our research support along with a code development service.
Research Ideas in OFDM Transmitter and Receiver
- Successive Interference Cancellation
- PAPR Reduction
- Secure Multiple Mode Index Modulation
- Adaptive Modulation for Performance improvement
- Subcarrier Index Power Modulation
For illustration purposes, we have selected “successive interference cancellation” and “PAPR reduction” techniques from the above-listed current ideas of the OFDM transmitter and receiver using Matlab. Here, we can see about few major techniques that are widely used for some key problems in OFDM. For instance: the following interference cancellation techniques are quite useful in facing challenges as doppler shift, carrier frequency offset, local oscillator mismatches. Let’s see the few effective techniques of OFDM transmitter and receiver.
Techniques for OFDM transmitter and Receiver
Interference Cancellation Techniques
- Intersymbol Interference (ISI)
- Self-Interference
- Intercarrier Interference (ICI)
- Fast Channel Time Difference
- Self and Multiuser Interference
- Co-Channel Interference (CCI)
- Fast Channel Time Difference
- Multicode Interference (MCI)
- Multiantenna Interference (MTI)
- Multipath Fading
- Self-Interference
PAPR Reduction Techniques
- Coding
- Golay Sequences
- Turbo Coding
- Linear Block Coding
- Signal Misrepresentation
- Companding
- Clipping and Filtering
- Peak Cancellation
- Pear Windowing
- Multi-Signaling and Probabilistic
- Constrained Constellation shaping
- Interleaved OFDM
- Tone injection
- Active Constellation Shaping
- Selective Mapping
- Tone Reservation
- Partial Transmit Sequences
Next, we can see the MATLAB toolboxes that are specifically introduced for the OFDM simulation. All these toolboxes support modeling of different wireless communication systems that enable OFDM, beamforming, MIMO, mmWave, etc. Then, it is used to generate a waveform, perform modulation, create references design, etc. Further, it enables the analysis of signals and computes their measurements. For instance: Throughput, EVM, BLER, ACLR, and more.
Best Matlab Toolboxes for OFDM Simulation
- Communication system Toolbox
- Channel (coding, characterization, interference, noise, fading, etc.)
- LTE System Toolbox
- Baseband transmitter and receiver
- Radio Frequency Toolbox (SImRF)
- RF non-linearities, dependency, mismatches, noise, etc.
- Antenna Toolbox
- Antenna array design, configuration, resistance, location, entities (types), etc.
- Other Toolboxes
- Instrument Control Toolbox
- DSP System Toolbox
- SDR Support Packages
- Phased Array System Toolbox
For add-on benefits of scholars, here we have selected the two most important toolboxes as the Antenna toolbox and LTE toolbox. In this, we have mentioned the specialization and key characteristics of these toolboxes for OFDM transmitter and receiver using Matlab. Let’s have a glance at the specifications of antenna and LTE toolboxes.
Antenna Toolbox
- Simple to design rectangular and linear antenna arrays
- Includes nearly 20+ parameterized antenna entities
- Offer moments field functions for performing surface analysis
- Provide functions to configure and simulate the model at ultra-speed
- Fast simulation and iteration of antenna scenarios (communication and radar design)
- Enable seamless network connectivity by modelling antenna with signal processing techniques
LTE System Toolbox
- Enriched with large volume of examples
- Enables nearly 200+ functions are designing physical and MAC layers
- Support Uplink / Downlink for data transmission and Frequency / Time Division Duplex for spectrum utilization
- Provide standards-compliant algorithms to design, simulate and analyse LTE systems
- Releases 8 to 11 (LTE 3GPP)
How a simple transmitter-channel-receiver simulation created using Matlab?
Now, we can see in what way the simple OFDM transmitter and receiver using Matlab will works. Here, this process deals with the functions of the LTE Toolbox for efficient transmitter-channel-receiver simulation. Similarly, our experts support you in all other toolboxes and functions based on your application requirements. In specific, this example produces the frame in which the data is adapted for one antenna port. Let’s see the implementation steps in the following,
- Step 1 Network configuration and settings
- Set the cell-wide settings in eNodeB
- Make sure that settings are made in eNodeB for upcoming functions being used
- Cell Identity
- Standard Cyclic Prefix
- Total Count of Resource Blocks
- Frequency Division Duplex Mode
- Single Transmit Antenna Port
- Step 2 – Configuration of SNR
- Compute SNRdB and pass over as input to configure functioning SNR in decibels
- Further, it will be transformed into linear SNR.
- Desired SNR (dB) and Linear SNR
- Configure the generators of random number
- Step 3 – Configuration of Channel Model
- Based on eNodeB structure configure the channel mode
- Utilize parameters like doppler frequency (120Hz), EVA latency and fading channel
- Further, use correlation of MIMO along with below specified channel model parameters for configuration
- EVA Latency Spread
- Initialized Time (0)
- Velocity of Channel
- Randomized Early Stages
- Number of Receiver Antenna
- Type of Rayleigh Fading Model
- Fading Model for Oscillators Phase
- Frequency of Doppler Shift
- Standardize Latency Profile Power and Transmit Antennas
- Least or Zero MIMO correlation
- Step 4 – Configuration of Channel Estimator
- Utilize customized window to compute average pilot symbol
- Intention – minimizing noise impact
- Use REs (Time and frequency) to configure averaging window size
- Averaging Window for Frequency and Time in REs
- Pilot based Averaging Window Technique
- Execute interpolation among pilot estimates using channel estimator
- Then, generate channel estimation for every RE
- For enhancement use interpolation windowing techniques and cubic interpolation (concurrently support up to 3 subframes)
- Utilize customized window to compute average pilot symbol
- Step 5 – Size of Subframe Resource Grid
- Create accessibility to dimensions of subframe resource grid
- Function – lteDLResourceGridSize
- Return Value – Array (number of (subcarriers, OFDM symbols and transmit antenna ports))
- Sum of Total Antenna Ports for Transmission
- Sim of Total OFDM symbols (1 subframe)
- Sum of Total Subcarriers
- Step 6 – Resource Grid for Transmission
- Create empty resource grid with subframes (Transceiver Grid)
- Step 7 – Generation of Payload Data
- No transport channel is required but use random QPSK modulated symbols
- Map symbols with RE
- Overwrite symbols which are needed for communication
- Perform Modulation on Input Bits
- Total Required bits = Size (Resource Grid (K*L*P) x Sum of Total bits/ symbol) (where 2 for QPSK)
- Produce Random Bit Series
- Step 8 – Generation of Frame
- Produce subframes to form the frame
- Here, append each subframe with preceding subframe (repeat for 10 times)
- TxGrid store the whole collection of appended subframes
- If time domain OFDM is transmitted via channel, the waveform will face latency
- Perform following operations for frame generation
- Total Subframes in one Frame
- Initialize Subframe Number
- Create Subframe (Empty)
- Map Input symbols with Resource Grid
- Create Signals in Synchronization
- Map Synchronized Signals with Resource Grid
- Create Indices and Symbols of Cell-specific Reference Signal
- Mapping of Cell-Specific Reference Signal with Resouce grid
- Add subframe to Grid at the end (transmitted)
- Step 9 – Modulation of OFDM
- Perform modulation for frequency domain OFDM
- Function – lteOFDMModulate
- Return Value – Sampling rate (collected from structure data) and matrix txWaveform
- Step 10 – Modelling of Fading Channel
- Transmit computed time domain waveform via channel model
- Function – lteFadingChannel (use cfg for structure-based configuration)
- Collect sampling rate for channel model
- Function – cfg.SamplingRate (forward data via fading channel model)
- Add noise prior to OFDM demodulation (use FFT for amplification)
- After demodulation, scale the noise and normalize SNR at the receiver
- Compute Noise Gain
- Generate White Gaussian Noise
- Insert Noise in Time Domain Waveform (Received)
- Step 11 – Harmonization / Synchronization
- Compute offset value during time domain OFDM
- Function – lteDLFrameOffset
- Return Value – Offset
- Specify the total number of samples where the waveforms experience latency
- Further, offset is assumed to be identical for all waveforms
- Step 12 – Demodulation of OFDM
- Perform OFDM demodulation on time domain waveform
- Intended to convert time-domain into frequency-domain for resource grid recreation
- Function – lteOFDMDemodulate
- Return Value – Resource Grid (3-D matrix)
- Number of rows = Number of subcarriers
- Number of columns = Number of OFDM symbols (1 subframe)
Summary of this page,
OFDM turns out to be the foundation for several telecommunication applications and services. It is an adaptive method to handle standard wireless communication channels which pose high data rates and wider bandwidth. Currently, it enables 4G networks to increase their transmission rate and capacity by improving MIMO techniques. Consequently, it increases the orders of magnitude which are more than the recent technologies.
OFDM is not only compatible with high data rates and wider bandwidth but also enhances the spectrum and power utilization. On the whole, it is the efficient method for several emerging wireless cellular technologies such as 5G New Radio communication. Further, other supporting technologies are given as follows,
- Device-to-Device (D2D)
- 5^{th} Generation Networks
- Massive MIMO (M-MIMO)
- Machine-to-Machine (M2M)
- Millimeter-wave (mm-wave)
- Heterogeneous Networks (HetNets)
- Femto / Macro / Micro cells
- Vehicle-to-Vehicle (V2V)
- Communication standards – IEEE 802.11p
Further, if you need more exciting information on OFDM systems from a research perspective then communicate with us. We let you know the current scientific developments and research demands of OFDM. Also, we provide you with code execution support in MATLAB for OFDM model simulation. Therefore, make use of our reliable services for your OFDM transmitter and receiver using MATLAB project.