In this article, we consider a new unmanned ariel vehicles (UAV)-aided intelligent wireless sensing scheme, where the UAVs are deployed for smart sensing and collecting data from Internet-of-Things (IoT) devices. In particular, we propose optimal UAVs’ path planing approaches for minimizing the completion time and total energy consumption of the UAVs’ deployment for data collection. Two optimal schemes, namely, optimal energy consumption by peer-to-peer UAV-IoT sensing networks and optimal energy consumption by clustering UAV-IoT sensing networks, are considered. The low-complexity procedures of our advanced optimization techniques are suitably applied to disaster relief networks when the solving time must be strictly adhered to. Our real-time optimization algorithms result in low computational complexity with fast deployment and low processing time for solving the problem of tracking and gathering sensor data, i.e., in very short time (milliseconds). Through simulations results we demonstrate that our proposed approaches in UAV-aided intelligent IoT wireless sensing are suitable for time-critical mission applications such as emergency communications, public safety, and disaster relief networks.

This paper proposes a deep learning (DL)-aided multicarrier (MC) system operating on fading channels, where both modulation and demodulation blocks are modeled by deep neural networks (DNNs), regarded as the encoder and decoder of an autoencoder (AE) architecture, respectively. Unlike existing AE-based systems, which incorporate domain knowledge of a channel equalizer to suppress the effects of wireless channels, the proposed scheme, termed as MC-AE, directly feeds the decoder with the channel state information and received signal, which are then processed in a fully data-driven manner. This new approach enables MC-AE to jointly learn the encoder and decoder to optimize the diversity and coding gains over fading channels. In particular, the block error rate of MC-AE is analyzed to show its higher performance gains than existing hand-crafted baselines, such as various recent index modulation-based MC schemes. We then extend MC-AE to multiuser scenarios, wherein the resultant system is termed as MU-MC-AE. Accordingly, two novel DNN structures for uplink and downlink MU-MC-AE transmissions are proposed, along with a novel cost function that ensures a fast training convergence and fairness among users. Finally, simulation results are provided to show the superiority of the proposed DL-based schemes over current baselines, in terms of both the error performance and receiver complexity.

In this paper, a low-complexity linear precoding algorithm based on the principal component analysis technique in combination with the conventional linear precoders, called Principal Component Analysis Linear Precoder (PCA-LP), is proposed for massive MIMO systems. The proposed precoder consists of two components: the first one minimizes the interferences among neighboring users and the second one improves the system performance by utilizing the Principal Component Analysis (PCA) technique. Numerical and simulation results show that the proposed precoder has remarkably lower computational complexity than its low-complexity lattice reduction-aided regularized block diagonalization using zero forcing precoding (LC-RBD-LR-ZF) and lower computational complexity than the PCA-aided Minimum Mean Square Error combination with Block Diagonalization (PCA-MMSE-BD) counterparts while its bit error rate (BER) performance is comparable to those of the LC-RBD-LR-ZF and PCA-MMSE-BD ones.

In this paper, a parallel group detection (PGD) algorithm is proposed in order to address the degradation in the bit error rate (BER) performance of linear detectors when they are used in high-load massive MIMO systems. The algorithm is constructed by converting the equivalent extended massive MIMO system into two subsystems, which can be simultaneously detected by the classical detection procedures. Then, using the PGD and the classical ZF as well as the QR-decomposition- (QRD-) based detectors, we proposed two new detectors, called ZF-based PGD (ZF-PGD) and QRD-based PGD (QRD-PGD). The PGD is further combined with the sorted longest basis (SLB) algorithm to make the signal recovery more accurate, thereby resulting in two new detectors, namely, the ZF-PGD-SLB and the QRD-PGD-SLB. Various complexity evaluations and simulations prove that the proposed detectors can significantly improve the BER performance compared to their classical linear and QRD counterparts with the practical complexity levels. Hence, our proposed detectors can be used as efficient means of estimating the transmitted signals in high-load massive MIMO systems.

In this paper, we propose a spatially modulated space-time block coding scheme, called DT-SM, by combining the conventional double space time transmit diversity (DSTTD) with spatial modulation. First, we design a set of four basic spatial constellation (SC) codewords and an extended set of 16 SC codewords for four transmit antennas by using computer search based on the rank and determinant criterion. We then present a generalized algorithm to design SC codewords for an arbitrary number of transmit antennas greater than four. Compared with DSTTD, the proposed scheme attains the same transmit diversity order, while offering higher spectral efficiency. Besides, we derive a union bound for the bit error rate (BER) of the proposed DT-SM scheme in a quasi-static Rayleigh fading channel with spatial correlation. In order to achieve the performance of maximum-likelihood (ML) estimation at reduced detection complexity, we develop a sphere decoder based on the conventional Schnorr-Euchner sphere decoder. BER performance and detection complexity of the DT-SM scheme are evaluated by computer simulations. It is shown that the proposed DT-SM outperforms many existing MIMO and SM-based MIMO schemes at reasonable detection complexity, even in correlated MIMO channels.

This paper proposes an enhanced sub-optimal detector for the High Rate-Spatial Modulation (HR- SM) systems presented in [1] by incorporating the ISQRD detector previously proposed in [2] with a new search space. The proposed decoder, called Enhanced ISQRD, achieves not only significant improvement in the bit error rate (BER) performance but also complexity reduction as compared to the ISQRD detector. In addition, we propose a new bit mapping approach that allows the HR-SM system to improve its BER performance. Simulation results and complexity analysis are presented to verify the effectiveness of the proposed approaches in terms of performance improvement and detection complexity reduction when they are applied to the HR-SM systems.

In this paper, we present a new upper bound for the bit error probability (BEP) of the so-called Space Time Block Coded Spatial Modulation (STBC-SM) system introduced by E. Basar et al. in [1] over a quasi-static Rayleigh fading channel. Based on the Verdu's theorem [2], the concept of the spatial constellation (SC) codewords and maximum likelihood (ML) decoder in [3], the upper bound is obtained by eliminating a number of redundant pairwise error probabilities (PEPs). Our approach leads to a new upper bound, which is tighter than the union bound. Consequently, it allows us to evaluate the bit-error performance of STBC-SM systems more exactly, particularly when the signal-to-noise power ratio (SNR) is sufficiently high.

This paper proposes a high rate spatial modulation transmission scheme based on the concept of Spatial Constellation (SC) and SC codewords introduced in [1]. In the proposed scheme, transmitted signal vectors are generated by multiplying spatial SC codewords with constellation symbols drawn from a conventional QAM or PSK constellation. The entries of SC codewords will determine how the constellation symbols are weighted and which transmit antennas among a total of nT transmit antennas are activated to transmit the signals. Different SC codewords allow us to have different ways to transmit the constellation symbols. Thus, information bits are born not only by constellation symbols but also by SC codewords. The additional spectral efficiency offered by SC codewords is proportional to the number of SC codewords which can be designed for each antenna configuration. A new low-complexity maximum-likelihood (ML) decoder is also devised for signal recovery. In addition, a theoretical union bound on the bit error probability is derived. Simulation results, supported by the theoretical upper bound, are given to compare bit error rate (BER) performance of the proposed scheme with those of existing MIMO transmission counterparts in the literature for various antenna configurations.

This paper proposes a multiple-input multiple-output (MIMO) transmission scheme for M-ary modulations, called Spatially Modulated Orthogonal Space-Time Block Coding (SM—OSTBC), based on the concept of Spatial Constellation (SC) codewords introduced by Le at el. . In the proposed scheme, transmit codeword matrices are generated by multiplying SC matrices with codewords constructed from Orthogonal Space-time Block Codes (O—STBC). The maximum spectral efficiency of the proposed scheme is equal to (n_T-2+ log_2 M) bpcu, where n_T is the number of transmit antennas and M is the modulation order. The SC matrices provide a means of carrying information bits together with the O—STBC codewords and allow the SM—OSTBC scheme to achieve second-order transmit diversity by satisfying the non-vanishing determinant property. A systematic method to design the SC codewords for an even number of transmit antennas greater than 3 is presented. A single-stream maximum-likelihood (ML) decoder, which requires a low computational complexity thanks to the structure of the SM—OSTBC codewords and to the orthogonality of the O—STBCs, and a sphere decoder with further reduced signal processing complexity are developed. The bit error rate (BER) performance of the proposed scheme is studied by using both theoretical union bound analysis and computer simulations. Finally, simulation results are presented in order to compare BER performance, energy efficiency and decoding complexity of the proposed scheme with those of several existing MIMO transmission schemes.

This letter proposes an efficient decoder for signal detection in orthogonal space-time block coded systems employing phase-shift keying modulation without channel state information at the receiver. The proposed decoder consists of a sphere decoder and a QRD-based decoder. Simulation results show that the proposed decoder enables the system to trade off performance improvement against complexity

In this work, we propose a low-complexity maximum-likelihood decoding approach based on QR decomposition (QRD) for signal detection in VBLAST-STBC systems, which employ less receive antennas than transmit antennas. With the example illustration of (6,3) system using rate 3/4 STBC for three transmit antennas, computer simulations are implemented to verify the performance and complexity of the proposed decoder

This letter proposes a very low-complexity maximum-likelihood (ML) detection algorithm based on QR decomposition for the quasi-orthogonal space-time block code (QSTBC) with four transmit antennas, called the LC-ML decoder. The proposed algorithm enables the QSTBC to achieve ML performance with significant reduction in computational load for any high-level modulation scheme.