Software Tools: LabVIEW NXG, GNU Radio, and MATLAB
- M. Alaee-Kerharoodi, S. M. R. Bhavani, K. V. Mishra and B. Ottersten, “Information Theoretic Approach for Waveform Design in Coexisting MIMO Radar and MIMO Communications,” ICASSP 2020 – 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain, 2020, pp. 1-5.
- M. Alaee-Kerahroodi, K. V. Mishra, M. R. Bhavani Shankar and B. Ottersten, “Discrete-Phase Sequence Design for Coexistence of MIMO Radar and MIMO Communications,” 2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Cannes, France, 2019, pp. 1-5.
A prototype for co-existence of multiple-input multiple-output (MIMO) radar and vehicle-to-vehicle MIMO communications (MRMC) is developed for the first time by the radar group at SnT. The radar system emulates transmission of code-division-multiplexed (CDM) signal in which the probing waveforms from different antennas are discrete phase sequences; several well-known as well as optimized sequences can be selected in real-time using an User Interface. Simultaneously, the set up supports a MIMO communication link emulating a Base-station-user-terminal pair. This is a commercial grade 802.11p system and can support OFDM based waveforms; it is currently, is being upgraded to support the latest communication waveforms. Models for target and communication channel are emulated. The radar processing of MRMC receiver estimates the range and Doppler of the target present in an environment contaminated with noise and clutter. It is currently being upgraded to perform DoA estimation based on sparse arrays. The communication receiver performs the traditional operations as specified in the appropriate standard. The performance metrics including SINR, Bit error rate, estimation accuracy are displayed on a User Interface.
Extreme crowding of electromagnetic spectrum in recent years has led to emergence of complex challenges in designing radar and communications systems . Both systems need wide bandwidth to provide a designated quality-of-service (QoS) thus resulting in competing interests in exploiting the spectrum. Current approaches for mitigating this problem are broadly grouped in two classes. In spectral co-design, a common waveform is employed by the radar and communications system while sharing the hardware resources at the Tx and/or Rx. In spectral coexistence, radar and communications individually address the suppression of interference from the other system. The overall architecture usually promotes the performance of only one system leading to a radar-centric or communications-centric performance.
With the introduction of 5G; which requires very high bandwidths; the operation of communication links are being pushed into the radar bands, creating spectrum congestion. For example, 5.6 GHz and 24 GHz band which is of interest to both 5G as well as weather radars.
This prototype aims to demonstrate the spectral co-existence of Radar and Communication systems operating on overlapped frequency bands. None of the systems applies any specific signal processing to counter interference caused by the other; however, power control is exercised by both the systems in order to lessen the interference caused to the other.
MIMO radar systems has become a growing topic of research in recent years due to the performance advantages it offers over the conventional phased-array counterparts, mostly due to the diversity in the transmit waveform. Based on different antenna configurations,
MIMO radar systems can be classified into colocated and widely separated (distributed) architectures.
In the colocated MIMO radar systems, spatial diversity is not provided due to the antennas viewing the same aspect of a target. Yet, spatial resolution is increased in that the number of uniquely identifiable targets is N times larger than the number of targets identified in a phased-array system with N as the number of transmit antennas. Moreover, colocated MIMO radars can provide a variety of beampattern by allowing the user to design the signal covariance matrix such that the system transmits power only in specific regions.
Let us assume a MIMO radar system with N number of transmit antennas and M number of receive antenna elements. The baseband equivalent of the received signals from K point-like targets located at at n-th time-sample is,
where is the noise vector with zero mean with covariance matrix and is the noise vector with zero mean with covariance matrix and are respectively transmit and receive arrays steering vectors. is the transmit waveform that should be adopted from a set of orthogonal waveforms to create the possibility of separating them in the receive side.
Receiver Processing and Performance Metrics
- Matched filtering, Range, Angle and Doppler processing
- SINR, MSE,
- Better angle estimation by performing a possibly filled virtual antenna array with elements instead of elements.
A MIMO communications system consists of Nc transmit and receive Mc receive antennas. It transmits correlated waveforms from all the transmit antennas.
Receiver Processing and Performance Metrics
- Frame Synchronization, Frequency Offset Correction, FFT, MAC Decode
- Spatial Multiplexing
Coexisting MIMO-radar-MIMO-comm (MRMC) desirable due to spectrum congestion
- Radar and comm share the same antennas
- MIMO comm crucial in 5G communications
- MIMO radar offers better angular resolution, parameter identifiability, broadens application areas
- High-dimensional system design
- Waveform multiplexing
- Interference management
- Accurately decoding of data and estimation target parameters including DoA
System Architecture for wireless experiments
In our prototype, the Radar system is capable of transmitting Polyphase, Binary and Frequency Modulated waveforms. On the communication side, we used IEEE 802.11p waveforms, which is based on OFDM and 10 MHz wide.
List of waveforms are reported in the following Table.
|Radar waveform||Communications waveform|
|Polyphase : (Frank, Golomb, Legendre, |
|OFDM (IEEE 802.11p with BPSK, QPSK, 16-QAM, and 64-QAM modulations)|
|Binary (Barker, m-Sequence, Gold, Kasami, Random)|
|Frequency Modulated (Up-LFM, Down-LFM)|
Transmitter and receiver were modified to perform MIMO operation. For simplicity, we used two-antenna transmitter and two-antenna receive for both Radar and Communication systems. System architecture is shown in Fig. 1.
An IEEE 802.11p waveform is generated and time multiplexed with two radar waveforms. We use single USRP RIO for both transmission and reception of radar waveforms. On the other hand, we use a separate USRP RIO, connected to separate CPU for the reception of communication waveform.
System Architecture for wired experiments
In order to avoid interference during demonstrations, we chose to emulate the wireless operation Radar and communication systems over wire, i.e., RF cables. Some major changes include:
- Random phase addition to the two communication channels in order to emulate a multipath wired channel.
- Target addition at either Radar transmitter or Radar receiver side.
An actual prototype is shown in Fig . 2.
The front panel of the Radar transceiver system is shown in Fig. 4, which is developed in LabVIEW NXG. This front panel enables the user to control Radar transmit waveforms, Communication transmit waveforms, transmit power etc., as well as monitor the received Radar spectrum, target information, target distance, target size among others.
The front panel of the communication receiver is shown in Fig. 4. It is developed in GNU Radio. It enables the user to decode the IEEE 802.11p waveforms, monitor the constellation among other.
We are conducting measurements in order to record the distortion caused by a Radar to a collocated and spectrally co-existing communication link. Preparation of test set-up using LabVIEW LTE Application framework is ongoing. In the next step, we are working on optimization of radar waveforms in order to cause minimal distortion to collocated and spectrally co-existing communication link without compromising the Radar performance.