In this paper, we investigate antenna selection strategies for multiple-input-multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) wireless systems from an energy efficiency (EE) perspective. We first derive closed-form expressions of the EE and the energy efficiency-spectral efficiency (EE-SE) tradeoff in conventional antenna selection MIMO-OFDM systems. The obtained results show that these systems suffer from a significant loss in EE. To achieve a better EE performance, we propose an adaptive antenna selection method where both the number of active radio-frequency chains and the antenna indexes are selected depending on the channel condition. This selection scheme could be implemented by an exhaustive search technique for a small number of antennas. Moreover, we develop a greedy algorithm that achieves a near-optimal performance with much lower complexity compared with the (optimal) exhaustive search method when the number of antennas is large. In addition, the efficacy of power loading across subcarriers for improved EE in the conventional and proposed antenna selection MIMO-OFDM systems is considered. Monte Carlo simulation results are provided to validate our analyses.

Self-interference (SI) cancellation for full-duplex (FD) multiple input multiple output (MIMO) systems is challenging due to both hardware and signal processing complexity. In this paper, a beam-based adaptive filter structure with analog least mean square (ALMS) loops is proposed to significantly reduce the complexity of SI cancellation for FD MIMO systems. With this structure, the number of adaptive filters required for SI cancellation scales linearly with the number of transmit beams rather than quadratically with the number of antennas. Furthermore, to avoid additional transmit chains used to up-convert the beam signals to generate reference signals for the ALMS loops, a novel method is proposed to select the optimized reference signals from all transmitted signals. In addition, our stationary analysis shows that the proposed structure for FD MIMO systems outperforms the ALMS loop employed for an FD single input single output system. Simulations are conducted to confirm the theoretical analyses.

With the advances in sonar imaging technology, sonar imagery has increasingly been used for oceanographic studies in civilian and military applications. High-resolution imaging sonars can be mounted on various survey platforms, typically autonomous underwater vehicles, which provide enhanced speed and improved data quality with long-range support. This paper addresses the automatic detection of mine-like objects using sonar images. The proposed Gabor-based detector is designed as a feature pyramid network with a small number of trainable weights. Our approach combines both semantically weak and strong features to handle mine-like objects at multiple scales effectively. For feature extraction, we introduce a parameterized Gabor layer which improves the generalization capability and computational efficiency. The steerable Gabor filtering modules are embedded within the cascaded layers to enhance the scale and orientation decomposition of images. The entire deep Gabor neural network is trained in an end-to-end manner from input sonar images with annotated mine-like objects. An extensive experimental evaluation on a real sonar dataset shows that the proposed method achieves competitive performance compared to the existing approaches.

This paper proposes a general framework of space-time-frequency codes (STFCs) for multi-band orthogonal frequency division multiplexing (MB-OFDM) ultra-wide band (UWB) communications systems. A great similarity between the STFC MB-OFDM UWB systems and conventional wireless complex orthogonal space-time block code (CO STBC) multiple-input multiple-output (MIMO) systems is discovered. This allows us to quantify the pairwise error probability (PEP) of the proposed system and derive the general decoding method for the implemented STFCs. Based on the theoretical analysis results of PEP, we can further quantify the diversity order and coding gain of MB-OFDM UWB systems, and derive the design criteria for STFCs, namely diversity gain criterion and coding gain criterion. The maximum achievable diversity order is found to be the product of the number of transmit antennas, the number of receive antennas, and the FFT size. We also show that all STFCs constructed based on the conventional CO STBCs can satisfy the diversity gain criterion. Various baseband simulation results are shown for the Alamouti code and a code of order 8. Simulation results indicate the significant improvement achieved in the proposed STFC MB-OFDM UWB systems, compared to the conventional MB-OFDM UWB ones.

Source positioning using hybrid angle-of-arrival (AOA) estimation and received signal strength indicator (RSSI) is attractive because no synchronization is required among unknown nodes and anchors. Conventionally, hybrid AOA/RSSI localization combines the same number of these measurements to estimate the agents’ locations. However, since AOA estimation requires anchors to be equipped with large antenna arrays and complicated signal processing, this conventional combination makes the wireless sensor network (WSN) complicated. This paper proposes an unbalanced integration of the two measurements, called 1AOA/nRSSI, to simplify the WSN. Instead of using many anchors with large antenna arrays, the proposed method only requires one master anchor to provide one AOA estimation, while other anchors are simple single-antenna transceivers. By simply transforming the 1AOA/1RSSI information into two corresponding virtual anchors, the problem of integrating one AOA and N RSSI measurements is solved using the least square and subspace methods. The solutions are then evaluated to characterize the impact of angular and distance measurement errors. Simulation results show that the proposed network achieves the same level of precision as in a fully hybrid nAOA/nRSSI network with a slightly higher number of simple anchors.