Search

Reconfigurable Intelligent Surfaces (RISs) have recently gained significant attention as they enable controlling the wireless propagation environment to improve information transmission with the help of reflective elements. This paper studies a RIS-assisted generalized spatial modulation (GSM) multiple input multiple output (MIMO) system to improve the signal quality and spectral efficiency. Due to inter-channel interference, signal detection in GSM becomes a critical issue. Therefore, we propose a low complexity block deep neural network (DNN) detector for the RIS-GSM MIMO system to detect the active antennas and the transmitted symbols at the receiver. In order to optimize the phase shifts at the RIS elements, we propose cosine similarity-based and semidefinite relaxation (SDR)-based algorithms. Further, conventional signal detectors such as maximum likelihood (ML), block zero-forcing (B-ZF), and block minimum mean square error (B-MMSE) are applied. In addition, we explore and analyze the performance of the proposed framework in terms of bit error rate (BER) and time complexity. Numerical results reveal that both optimize-phase-shifts algorithms assisted DNN signal detector has almost the same BER performance as ML, but it is four times faster than the linear detector (B-ZF and B-MMSE), and seven times faster than ML. Clearly, this scheme outperforms the alternative traditional signal detectors with the least time complexity.

This work investigates the performance of a downlink wireless network consisting of an unmanned aerial vehicle (UAV)-assisted base station (BS) that uses rate-splitting multiple access (RSMA) to serve multiple ground users (GUs) simultaneously. We analyze the network performance by deriving the closed-form expression of achievable ergodic capacity at each user. In addition to this, we maximize the ergodic capacity of the private message while guaranteeing the desired capacity of the common message. Moreover, we formulate an optimization problem to show that hovering the UAV at a certain optimal position can result in maximizing the sum capacity of the network. Monte-Carlo simulations are used to verify the accuracy of derived analytical expressions.

In this paper, an intelligent reflecting surface (IRS) aided uplink (UL) rate-splitting multiple access (RSMA) system is investigated for dead-zone users where the direct link between the users and the base station (BS) is unavailable and the UL transmission is carried out only through IRS. In the considered RSMA system, a message of each user is split into several sub-messages and each part contributes to the rate of that user and depending upon split proportions BS decodes them using appropriate decoding order. The problem of sum-rate maximization is formulated to jointly design the optimal power allocation at each UL user, passive beamforming at the IRS under optimal decoding order of sub-messages. Due to non-convexity and discrete non-linear programming of the formulated problem, the original problem is intractable and hence, we decouple the problem into different sub-problems in which the problems of power allocation and passive beamforming are alternatively solved under using successive convex approximation and Riemaniann conjugate gradient algorithms, respectively. Moreover, the decoding order strategy is analytically derived which confirm that the optimal decoding order strategy depend upon decreasing order of channel gain of users and increasing order of split proportions of sub-messages. Later, the unified solution based on block-coordinate descent (BCD) algorithm is proposed. Simulation results validate that the proposed decoding order scheme attains performance closer to the optimal solution with low computational complexity. Moreover, the proposed IRS aided RMSA system outperforms the system with non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA) schemes in terms of achievable sum-rate throughput.

Global agriculture production needs to be well-positioned to feed the fast-growing world population. Plant breeders increase overall plant performance to meet the global food demand, evaluate large genetic populations of breeding lines rapidly and accurately to identify variation underlying important traits; increasingly better adaption to changing environments. However, accurate, quick, and non-destructive characterization of all lines simultaneously, remains challenging. Therefore, there is a dire need to strengthen plant breeding not only for breeding techniques but also in other areas, such as improved phenotyping. Advanced remote and proximal sensing have increased the pace to rapidly overcome phenotyping shortcomings and address relevant biological questions related to plant breeding, field scouting, and crop management. However, it is still unclear how far and to what extent remote and proximal sensing has come to help plant breeding in the past two decades. Here we try to address this question by reviewing varied aspects of remote and proximal sensing applications in plant breeding and identify possible solutions to the existing shortcomings provided as the future direction for plant breeding programs.

In a reconfigurable intelligent surface (RIS) aided communication network, a RIS equipped with multiple passive reflecting elements assists in extending the coverage of wireless networks by overcoming the effects of non-line-of-sight propagation. Accordingly, to present the case for utilizing RIS panels in future consumer Internet of Things (IoT) networks, in this paper, we focus on a RIS-aided IoT network operating in in-band full-duplex (IBFD) mode. Since all the IoT node pairs communicate simultaneously through individual IBFD links at the same time-frequency resources, the nodes suffer from strong self-interference (SI) and inter-node interference. Consequently, in this paper, we investigate a joint active and passive beamforming design at each IoT node and the RIS, respectively, by minimizing the total transmission power of the network, subject to a minimum rate constraint for each IoT node and a constraint that guarantees a unit modulus phase shift at the RIS. As the original joint active and passive beamforming design problem is non-convex and intractable, we decompose the problem into two sub-problems. For a given passive beamformer design, we solve the original problem to obtain the optimal beamforming vectors at each IoT node. Next, for the obtained active beamformer, we propose two algorithms based on i) gradient descent (GD) search and ii) Riemannian conjugate gradient (RCG) to obtain the optimal passive beamformer at the RIS. Through extensive numerical analysis, we verify the effectiveness of the proposed algorithms and also show the effects of using RIS and transmitting in IBFD mode on the average rate and power of the considered consumer IoT network. We investigate the impact of several system parameters such as RIS elements, node pairs, level of hardware impairments at the transmitter and receiver, etc. We also highlight the trade-off between performance improvement and complexity due to elements used at RIS.

In this work, we investigate rate-splitting multiple access (RSMA) for a multiuser downlink wireless network consisting of an unmanned aerial vehicle (UAV)-assisted base station (BS) that serves multiple ground users (GUs) simultaneously. Considering two different transmission regimes, namely infinite blocklength (IBL), and finite blocklength (FBL), we analyze the performance of the considered network under the effect of imperfections in channel state information (CSI) estimation and successive interference cancellation (SIC) with the probabilistic line of sight fading channels. For IBL transmission, we derive the closed-form expressions of the outage probability, throughput, and achievable ergodic rate at each GU. Furthermore, for short packet communication, the closed-form expressions of block error rate (BLER), goodput, and achievable ergodic rate are determined with FBL transmission. Moreover, for the FBL regime we also formulate an optimization problem that jointly optimizes the 3D-position of the UAV, power allocated to each user, and common rate distribution at each user to maximize the ergodic sum rate subject to the practical constraints such as maximum tolerable BLER, minimum private and total rate at each user. We then propose an alternating optimization-based iterative algorithm to solve the problem. Monte-Carlo simulations are used to verify the accuracy of derived analytical results and demonstrate the trade-off between transmit power and achievable BLER. In addition to this, the effectiveness of RSMA in UAV-assisted communication under IBL and FBL transmission regimes is also observed compared to non-orthogonal multiple access.