Next-Generation IoT Networks: Integrated Sensing Communication and Computation

peer reviewedTo enable the exponential expansion of Internet of Things (IoT) applications, IoT devices must gather and transmit massive amounts of data to the server for further processing. By employing the same signals for both radar sensing and data transmission, the integrated sensing and communication (ISAC) approach provides simultaneous data gathering and delivery in the physical layer. Over-the-air computation (AirComp), which leverages the analog-wave addition property in multi-access channels, is a communication method that also supports function computation. In order to leverage the individual benefits of ISAC and AirComp, this work focuses on Integrated Sensing Communication and Computation (ISCCO) framework for the IoT network. Since the IoT sensors are small size low cost devices and each is equipped with single antenna, and hence to make the processing of received echo simple this work assume that the waveform transmitted by each sensor is orthogonal to each other. Furthermore, joint optimal power allocation for each sensor in the IoT network and the combining vector at the EC is designed such that the signal-to-noise (SNR) ratio at the EC is maximized. However, the design challenge lies in the non-convex joint optimal power allocation for each IoT device and the combining vector at the server. To address this, an iterative algorithm is proposed which provides closed-form solution for each quantity in each iteration. Results show that the proposed optimal power allocation and orthogonal waveform design scheme outperforms the equal power allocation-based design.9. Industry, innovation and infrastructur

Cramer-Rao Bound on DOA Estimation of Finite Bandwidth Signals Using a Moving Sensor

In this paper, we provide a framework for the direction of arrival (DOA) estimation using a single moving sensor and evaluate performance bounds on estimation. We introduce a signal model which captures spatio-temporal incoherency in the received signal due to sensor motion in space and finite bandwidth of the signal, hitherto not considered. We show that in such a scenario, the source signal covariance matrix becomes a function of the source DOA, which is usually not the case. Due to this unknown dependency, traditional subspace techniques cannot be applied and conditions on source covariance needs to imposed to ensure identifiability. This motivates us to investigate the performance bounds through the Cramer-Rao Lower Bounds (CRLBs) to set benchmark performance for future estimators. This paper exploits the signal model to derive an appropriate CRLB, which is shown to be better than those in relevant literature

IRS-Aided Wideband Dual-Function Radar-Communications with Quantized Phase-Shifts

peer reviewedIntelligent reflecting surfaces (IRS) are increasingly considered as an emerging technology to assist wireless communications and target sensing. In this paper, we consider the quantized IRS-aided wideband dual-function radar-communications system with multi-carrier signaling. Specifically, the radar receive filter, frequency-dependent transmit beamforming and discrete phase-shifts are jointly designed to maximize the average signal-to-interference-plus-noise ratio (SINR) for radar while guaranteeing the communication SINR among all users. The resulting optimization problem has a fractional quartic objective function with difference of convex and discrete phase constraints and is, therefore, highly non-convex. Thus, we solve this problem via the alternating maximization framework, in which the alternating direction method of multipliers and Dinkelbach's algorithm are integrated to tackle the related subproblems. Numerical results demonstrate that the proposed method, even with the low-resolution IRS, achieves better sensing performance compared with non-IRS system

Multiple IRS-Assisted Wideband Dual-Function Radar-Communication

peer reviewedWe propose a novel dual-function radar-communications (DFRC) system that relies on multiple intelligent reflecting surfaces (IRSs) to enhance detection of non-line-of-sight (NLoS) targets. In particular, we consider a wideband OFDM transmit signal for which we jointly design the frequency-dependent beamforming and phase shifts to maximize the average SINR of radar and the minimal communication SINR among all users. We solve the resulting highly nonconvex problem comprising maximin objective function with a difference of convex (DC) constraint through an alternating maximization (AM) framework of alternating direction method of multipliers (ADMM) and Dinkelbach's method. Numerical experiments demonstrate that the proposed method with multiple IRS can achieve 3.3 dB radar SINR enhancement and 0.9 dB minimal communication SINR improvement compared with single IRS scenario

Simultaneous Active-Passive Beamformer Design in IRS-Enabled Multi-Carrier DFRC System

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Joint waveform and precoding design for coexistence of MIMO radar and MU-MISO communication

peer reviewedThe joint design problem for the coexistence of multiple-input multiple-output (MIMO) radar and multi-user multiple-input-single-output (MU-MISO) communication is investigated. Different from the conventional design schemes, which require defining the primary function, we consider designing the transmit waveform, precoding matrix and receive filter to maximize the radar SINR and the minimal SINR of communication users, simultaneously. By doing so, the promising overall performance for both sensing and communication is achieved without requiring parameter tuning for the threshold of communication or radar. However, the resulting optimization problem which contains the maximin objective function and the unit sphere constraint, is highly nonconvex and hence difficult to attain the optimal solution directly. Towards this end, the epigraph-form reformulation is first adopted, and then an alternating maximisation (AM) method is devised, in which the Dinkelbach’s algorithm is used to tackle the nonconvex fractional-programing subproblem. Simulation results indicate that the proposed method can achieve improved performance compared with the benchmarks

Distributed Sensing and Centric Computing via FMCW Waveform in Wireless Sensor Network

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Widely Distributed Radar Imaging: Unmediated ADMM Based Approach

This paper presents a novel approach to reconstruct a unique image of an observed scene via synthetic apertures (SA) generated by employing widely distributed radar sensors. The problem is posed as a constrained optimization problem in which the global image which represents the aggregate view of the sensors is a decision variable. While the problem is designed to promote a sparse solution for the global image, it is constrained such that a relationship with local images that can be reconstructed using the measurements at each sensor is respected. Two problem formulations are introduced by stipulating two different establishments of that relationship. The proposed formulations are designed according to consensus ADMM (CADMM) and sharing ADMM (SADMM), and their solutions are provided accordingly as iterative algorithms. We drive the explicit variable updates for each algorithm in addition to the recommended scheme for hybrid parallel implementation on the distributed sensors and a central processing unit. Our algorithms are validated and their performance is evaluated by exploiting the Civilian Vehicles Dome dataset to realize different scenarios of practical relevance. Experimental results show the effectiveness of the proposed algorithms, especially in cases with limited measurements