Existing optimization and deep learning approaches for resource management optimization cannot achieve satisfactory performance and computation time trade-offs for interference networks with arbitrary device locations. This paper aims to address this issue. Specifically, we investigate an orthogonal frequency-division multiplexing (OFDM)-based wide-band device-to-device (D2D) network with multiple multi-antenna transceiver pairs accessing various subcarriers. We optimize resource management (RM), i.e., beamforming and power control, to maximize the worst-case achievable rate among all transceiver pairs under the beamforming and power constraints. The RM problem is a challenging large-scale non-convex problem. We propose joint and separate designs offering different worst-case achievable rate and computation time trade-offs by solving the RM problem and a resultant power control (PC) problem for separately optimized beamforming using optimization and deep learning techniques. First, we propose two parallel successive convex approximation (PSCA)-based parallel iterative algorithms to obtain stationary points of the RM and PC problems. Both can achieve completely parallel and closed-form per-iteration updates, significantly reducing the computation times. Next, we propose two data and model-driven deep learning methods that optimally select good channel state information (CSI)-adaptive initial points for RM-PSCA and PC-PSCA and effectively unroll the two PSCA-based algorithms into neural networks with the algorithm parameters as tunable parameters. Both further reduce the underlying algorithms’ computation times while achieving appealing worst-case achievable rates based on training over vast samples. Numerical results demonstrate the proposed approaches’ superior advantages over the state of the art.

Existing optimization and deep neural network (DNN) approaches for beamforming and power control cannot achieve satisfactory performance and computation time trade-offs for interference networks with arbitrary device locations, due to optimization algorithms' expensive computation costs and DNNs' universal approximation and lack of generality. To address this issue, we propose new beamforming and power control approaches by elegantly leveraging optimization and deep learning techniques. We investigate the maximization of the worst-case achievable rate of multiple transceiver pairs under power constraints in orthogonal frequency-division multiplexing (OFDM)-based wideband device-to-device (D2D) networks with multiple subcarriers and arbitrary device locations. First, we obtain each transceiver's transmit and receive beamforming using the standard singular value decomposition (SVD) method. Then, we propose an iterative algorithm named PSCA to obtain joint power control for all transceivers using the parallel successive convex approximation (PSCA) method. PSCA allows parallel and closed-form per-iteration updates and has a short per-iteration computation time. Next, we propose two PSCA-driven deep unrolling neural networks, namely PSCANet and S-PSCANet to reduce the overall computation time. PSCANet and S-PSCANet marry PSCA's parallel computation mechanism with the parallelizable neural network architecture and effectively optimize PSCA's algorithm parameters based on vast samples of random channel fading coefficients and device locations. Moreover, S-PSCANet successfully resolves the training problem due to vanishing or exploding gradients when unrolling many PSCA iterations. Numerical results demonstrate the superior advantages of PSCANet and S-PSCANet over the existing approaches.

Existing works on grant-free access, proposed to support massive machine-type communication (mMTC) for the Internet of Things (IoT), mainly concentrate on narrow band systems under flat fading. In contrast, this paper investigates massive grant-free access in a wideband system under frequency-selective fading. First, we present an orthogonal frequency division multiplexing (OFDM)-based massive grant-free access scheme. Then, we propose two different but equivalent models for the received pilot signal. Specifically, one directly models the received signal for actual devices, whereas the other can be interpreted as a signal model for virtual devices. The two signal models are insightful and essential for designing various device activity detection and channel estimation methods for OFDM-based massive grant-free access. Next, we systematically investigate statistical device activity detection under frequency-selective Rayleigh fading based on the two signal models. In particular, in the case without prior knowledge of device activities, we model device activities as deterministic but unknown binary constants and propose three maximum likelihood (ML) estimation-based device activity detection methods with different detection accuracies and computation times. In the case with prior knowledge of device activities, we model device activities as realizations of Bernoulli random variables with a known joint distribution, which appropriately incorporates the prior knowledge, and propose three maximum a posterior probability (MAP) estimation-based device activity methods, which further enhance the accuracies of the corresponding ML estimation-based methods at the cost of increased computational complexities. The proposed methods can meet diverse practical needs for OFDM-based massive grant-free access.

The impacts of channel estimation errors, inter-cell interference, phase adjustment cost, and computation cost on an intelligent reflecting surface (IRS)-assisted system are severe in practice but have been ignored for simplicity in most existing works. In this paper, we investigate a multi-antenna base station (BS) serving a single-antenna user with the help of a multi-element IRS in a multi-cell network with inter-cell interference. We consider imperfect channel state information (CSI) at the BS, i.e., imperfect CSIT, and focus on the robust optimization of the BS’s instantaneous CSI-adaptive beamforming and the IRS’s quasi-static phase shifts in two scenarios. In the scenario of coding over many slots, we formulate a robust optimization problem to maximize the user’s ergodic rate. In the scenario of coding within each slot, we formulate a robust optimization problem to maximize the user’s average goodput under the successful transmission probability constraints. The robust optimization problems are challenging two-timescale stochastic non-convex problems. In both scenarios, we obtain closed-form robust beamforming designs for any given phase shifts and more tractable stochastic non-convex approximate problems only for the phase shifts. Besides, we propose an iterative algorithm to obtain a Karush-Kuhn-Tucker (KKT) point of each of the stochastic problems for the phase shifts. It is worth noting that the proposed methods offer closed-form robust instantaneous CSI-adaptive beamforming designs which can promptly adapt to rapid CSI changes over slots and robust quasi-static phase shift designs of low computation and phase adjustment costs in the presence of imperfect CSIT and inter-cell interference. Numerical results further demonstrate the notable gains of the proposed robust joint designs over existing ones and reveal the practical values of the proposed solutions.

Device activity detection and channel estimation for grant-free massive access under frequency-selective fading have unfortunately been an outstanding problem. This paper aims to address the challenge. Specifically, we present an orthogo-nal frequency division multiplexing (OFDM)-based grant-free massive access scheme for a wideband system with one M- antenna base station (BS),$N$single-antenna Internet of Things (IoT) devices, and$P$channel taps. We obtain two different but equivalent models for the received pilot signals under frequency-selective Rayleigh fading. Based on each model, we formulate device activity detection as a non-convex maximum likelihood estimation (MLE) problem and propose an iterative algorithm to obtain a stationary point using optimal techniques. The two proposed MLE-based methods have the identical computational complexity order O(NPL2), irrespective of M, and degrade to the existing MLE-based device activity detection method when P = 1. Conventional channel estimation methods can be readily applied for channel estimation of detected active devices under frequency-selective Rayleigh fading, based on one of the derived models for the received pilot signals. Numerical results show that the two proposed methods have different preferable system parameters and complement each other to offer promising device activity detection design for grant-free massive access under frequency-selective Rayleigh fading.

The impacts of channel estimation errors, inter-cell interference, phase adjustment cost, and computation cost on an intelligent reflecting surface (IRS)-assisted system are severe in practice but have been ignored for simplicity in most existing works. In this paper, we investigate a multi-antenna base station (BS) serving a single-antenna user with the help of a multi-element IRS in the presence of channel estimation errors and inter-cell interference. We consider imperfect channel state information (CSI) at the BS, i.e., imperfect CSIT, and focus on the robust optimization of the BS's instantaneous CSI-adaptive beamforming and the IRS's quasi-static phase shifts. First, we formulate the robust optimization of the BS's instantaneous channel state information (CSI)-adaptive beamforming and IRS's quasi-static phase shifts for the ergodic rate maximization as a very challenging two-timescale stochastic non-convex problem. Then, we obtain a closed-form beamformer for any given phase shifts and a more tractable single-timescale stochastic non-convex problem only for phase shifts. Next, we propose a low-complexity stochastic algorithm to obtain quasi-static phase shifts which correspond to a KKT point of the single-timescale stochastic problem. It is worth noting that the proposed method offers a closed-form robust instantaneous CSI-adaptive beamforming design that can promptly adapt to rapid CSI changes over slots and a robust quasi-static phase shift design of low computation and phase adjustment costs in the presence of channel estimation errors and inter-cell interference. Finally, numerical results demonstrate the notable gains of the proposed robust joint design over existing ones and reveal the practical values of the proposed solutions.

It is important to deploy the capability to locate a User Equipment (UE) using the Time-Difference-of-Arrival (TDOA) measurements. One of the sources of error is the presence of Non-Line-of-Sight (NLOS) errors in the localization problem, which leads to severe degradation in the localization performance using conventional localization algorithms such as the two-step weighted least squares (2WLS) method. In this work, a TDOA localization scheme with NLOS mitigation is proposed. The scheme uses the multidimensional scaling (MDS) framework and it contains three steps. Firstly, the set of all the TDOA measurements is used to form a scalar product matrix in the MDS framework. Secondly, the deletion diagnostics approach is applied to the scalar product matrix to find a subset of the TDOA measurements which forms a submatrix whose eigenvalues satisfy a specific distribution. Finally, the above subset of TDOA measurements is used to compute the estimate of the UE position using 2WLS method. Simulation results show that the proposed scheme successfully eliminates the adverse effects of the NLOS errors.

This work deals with source localization with time-difference-of-arrival (TDOA) measurements in two-dimensional (2-D) scenarios. Although the celebrated two-step weighted least squares (2WLS) method is quite successful, its drawback lies in an ill-conditioning problem when the sensor array is quasi-linear. This work presents a multidimensional scaling (MDS)-based localization scheme. Based on the subspace analysis of the scalar product matrix, an auxiliary line is defined in the plane, close to the global minimizer of the cost function. Then, the minimizer on the auxiliary line is found as the estimation of the source position. Simulations show that the proposed scheme achieves high localization accuracy for all kinds of sensor arrays including quasi-linear arrays.