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1. Wideband Array Signal Processing with Real-Time Adaptive Interference Mitigation

   https://doi.org/10.3390/s23146584  

   Whipple, Adam; Ruzindana, Mark W; Burnett, Mitchell C; Kunzler, Jakob W; Lyman, Kayla; Jeffs, Brian D; Warnick, Karl F (July 2023, Sensors)

   Wideband beamforming and interference cancellation for phased array antennas requires advances in signal processing algorithms, software, and specialized hardware platforms. A high-throughput array receiver has been developed that enables communication in radio frequency interference-rich environments with field programmable gate array (FPGA)-based frequency channelization and packetization. In this study, a real-time interference mitigation algorithm was implemented on graphics processing units (GPUs) contained in the data pipeline. The key contribution is a hardware and software pipeline for subchannelized wideband array signal processing with 150 MHz instantaneous bandwidth and interference cancellation with a heterogeneous, distributed, and scaleable digital signal processing (DSP) architecture that achieves 30 dB interferer cancellation null depth in real time with a moving interference source. 

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2. Bayesian Iterative Channel Estimation and Turbo Equalization for Multiple-Input–Multiple-Output Underwater Acoustic Communications

   https://doi.org/10.1109/JOE.2019.2956299  

   Qin, Xiangzhao; Qu, Fengzhong; Zheng, Yahong Rosa (July 2020, IEEE Journal of Oceanic Engineering)

   This article investigates a robust receiver scheme for a single carrier, multiple-input–multiple-output (MIMO) underwater acoustic (UWA) communications, which uses the sparse Bayesian learning algorithm for iterative channel estimation embedded in Turbo equalization (TEQ). We derive a block-wise sparse Bayesian learning framework modeling the spatial correlation of the MIMO UWA channels, where a more robust expectation–maximization algorithm is proposed for updating the joint estimates of channel impulse response, residual noise, and channel covariance matrix. By exploiting the spatially correlated sparsity of MIMO UWA channels and the second-order a priori channel statistics from the training sequence, the proposed Bayesian channel estimator enjoys not only relatively low complexity but also more stable control of the hyperparameters that determine the channel sparsity and recovery accuracy. Moreover, this article proposes a low complexity space-time soft decision feedback equalizer (ST-SDFE) with successive soft interference cancellation. Evaluated by the undersea 2008 Surface Processes and Acoustic Communications Experiment, the improved sparse Bayesian learning channel estimation algorithm outperforms the conventional Bayesian algorithms in terms of the robustness and complexity, while enjoying better estimation accuracy than the orthogonal matching pursuit and the improved proportionate normalized least mean squares algorithms. We have also verified that the proposed ST-SDFE TEQ significantly outperforms the low-complexity minimum mean square error TEQ in terms of the bit error rate and error propagation. 

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3. Residual self-interference cancellation and data detection in full-duplex communication systems

   https://doi.org/10.1109/ICC.2017.7997326  

   Koohian, Abbas; Mehrpouyan, Hani; Nasir, Ali Arshad; Durrani, Salman; Blostein, Steven D. (January 2017, IEEE International Conference on Communicaitons)

   Residual self-interference cancellation is an important practical requirement for realizing the full potential of full-duplex (FD) communication. Traditionally, the residual self-interference is cancelled via digital processing at the baseband, which requires accurate knowledge of channel estimates of the desired and self-interference channels. In this work, we consider point-to-point FD communication and propose a superimposed signaling technique to cancel the residual self-interference and detect the data without estimating the unknown channels. We show that when the channel estimates are not available, data detection in FD communication results in ambiguity if the modulation constellation is symmetric around the origin. We demonstrate that this ambiguity can be resolved by superimposed signalling, i.e., by shifting the modulation constellation away from the origin, to create an asymmetric modulation constellation. We compare the performance of the proposed detection method to that of the conventional channel estimation-based detection method, where the unknown channels are first estimated and then the data signal is detected. Simulations show that for the same average energy over a transmission block, the bit error rate performance of the proposed detection method is better than that of the conventional method. The proposed method does not require any channel estimates and is bandwidth efficient. 

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4. A Joint Detection and Decoding Receiver Design for Polar Coded MIMO Wireless Transmissions

   https://doi.org/10.1109/ISIT.2018.8437879  

   Jalali, Amin; Ding, Zhi (June 2018, 2018 IEEE International Symposium on Information Theory (ISIT))

   This work develops a novel design of joint detection and decoding receiver for multiple-input multiple output (MIMO) wireless transmissions that utilizes polar codes in forward error correction (FEC). To optimize the overall receiver performance, we integrate the polar code constraints during signal detection by relaxing and transforming FEC code constraints from the original Galois field to the real field. We propose a novel joint linear programming (LP) optimization formulation that takes into consideration the transformed polar code constraints when designing a novel receiver robust against practical obstacles including channel state information (CSI) errors, additive noises, co-channel interferences, and pilot contamination. Our newly proposed joint LP formulation can also be integrated with reduced complexity polar decoders such as successive cancellation (SC) and successive cancellation list (SCL) decoders to deliver superior receiver performance at low cost. 

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5. OFDM Pilot-Based Radar for Joint Vehicular Communication and Radar Systems

   https://doi.org/10.1109/VNC.2018.8628347  

   Ozkaptan, Ceyhun D.; Ekici, Eylem; Altintas, Onur; Wang, Chang-Heng (December 2018, 2018 IEEE Vehicular Networking Conference (VNC))

   With the large-scale deployment of connected and autonomous vehicles, the demand on wireless communication spectrum increases rapidly in vehicular networks. Due to increased demand, the allocated spectrum at the 5.9 GHz band for vehicular communication cannot be used efficiently for larger payloads to improve cooperative sensing, safety, and mobility. To achieve higher data rates, the millimeter-wave (mmWave) automotive radar spectrum at 76-81 GHz band can be exploited for communication. However, instead of employing spectral isolation or interference mitigation schemes between communication and radar, we design a joint system for vehicles to perform both functions using the same waveform. In this paper, we propose radar processing methods that use pilots in the orthogonal frequency-division multiplexing (OFDM) waveform. While the radar receiver exploits pilots for sensing, the communication receiver can leverage pilots to estimate the time-varying channel. The simulation results show that proposed radar processing can be efficiently implemented and meet the automotive radar requirements. We also present joint system design problems to find optimal resource allocation between data and pilot subcarriers based on radar estimation accuracy and effective channel capacity. 

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