This paper investigates the downlink of an internet-of-things (IoT) network with a base station serving multiple IoT devices (IoTDs) with the assistance of two far-apart reconfigurable intelligent surfaces (RISs). We propose joint design of the BS’s beamformer and RISs’ quantized programmable reflecting elements (PREs). Considering the IoTDs’ minimum rate (MR) as the primary optimization objective, we further aim to optimize the multi-objective function of both the MR and sum rate (SR) in the Pareto-optimal sense. We develop convex-solver and closed-form algorithms. Simulations demonstrate that the latter, with scalable complexity, performs as well as the former, which exhibits polynomially increasing complexity. Furthermore, the simulations reveal the advantages of the double-RIS assisted solution over its single-RIS assisted counterpart of the same size.

This is the first treatise on multi-user (MU) beamforming designed for achieving long-term rate-fairness in full-dimensional MU massive multi-input multi-output (m-MIMO) systems. Explicitly, based on the channel covariances, which can be assumed to be known beforehand, we address this problem by optimizing the following objective functions: the users’ signal-to-leakage-noise ratios (SLNRs) using SLNR max-min optimization, geometric mean of SLNRs (GM-SLNR) based optimization, and SLNR soft max-min optimization. We develop a convex-solver based algorithm, which invokes a convex subproblem of cubic time-complexity at each iteration for solving the SLNR max-min problem. We then develop closed-form expression based algorithms of scalable complexity for the solution of the GM-SLNR and of the SLNR soft max-min problem. The simulations provided confirm the users’ improved-fairness ergodic rate distributions.

This paper considers a scenario involving a network where two reconfigurable intelligent surfaces (RISs) contribute to enhancing the security of multi-user secure downlink communication, even in the presence of multiple potential eavesdroppers. The objective is to optimize the base station (BS)’s beamforming and the quantized programmable reflecting elements (PREs) of both RISs to maximize the geometric mean of secrecy rates (GM-SECR). To tackle this intricate non-convex penalized optimization problem, the paper introduces an alternating descent iteration algorithm based on closed-form solutions. Through simulations, the study highlights the advantages presented by the proposed double-RIS system and validates the efficacy of the algorithm. Notably, the results demonstrate a marked enhancement in achieving fair distributions of secrecy rates.

This paper is concerned with distributed consensus control of unknown multiagent systems. As the system’s dynamics is unknown, an adaptive Fourier decomposition (AFD) based iterative learning control (ILC) dynamics adaptive matching method in frequency domain is put forward to deal with it. First, large amounts of input and output measurement data are used to estimate the frequency domain characteristics of the system by Takenaka-Malmquist functions. Second, convert the traditional time domain ILC to the frequency domain to establish a matching relationship with the estimated frequency domain features. Then, an adaptive iterative learning rate is constructed to achieve the optimal convergence at each sampling point. The feasibility of the proposed algorithm is guaranteed by the convergence of AFD in Hardy space$H^{2}(\mathbb {D})$under the maximum selection principle. Compared with the reinforcement learning data-driven control scheme, the method proposed in this paper has obvious advantages in the control accuracy and convergence efficiency. In addition, this paper takes two kinds of denoising algorithms based on unwinding AFD to deal with the multi-agent systems with channel noise. Finally, the feasibility and effectiveness of the developed method are verified by a series of simulations.

In this paper, a Takenaka-Malmquist (TM) basis function based equivalent data-model is established by an adaptive rational decomposition for the finite-time interval trajectory tracking control and real-time control of a class of nonlinear systems in the frequency domain. This data model can adaptively learn and match the control process of nonlinear systems. As a result, the proposed trajectory tracking as well as real-time control method can reflect the feature of adaptive learning in order-by-order decomposition, and the feasibility of the proposed method is guaranteed by the convergence of adaptive decomposition by TM basis function under the maximum selection principle (MSP) in Hardy space$H^{2}(\mathbb {D})$. Compared with the traditional model-free control method, this data learning model which matches the control process has obvious advantages in the system model expression and control accuracy. Simulation results at the end of this paper show the effectiveness of the proposed method.

In this paper, an unmanned aerial vehicle (UAV) enabled secure downlink communication is considered, where a single-antenna UAV serves multiple ground users facilitated by orthogonal frequency-division multiple access (OFDMA), in the presence of an eavesdropper (EV) with imperfect channel state information. To enhance the secrecy rate (SR), we employ a power splitting approach, where part of the transmit power is used for communication while the rest is used for jamming. We maximize the average secrecy rate (ASR) by jointly optimizing the bandwidth, trajectory, power allocation, and power splitting ratio. To tackle this non-convex and computationally intractable optimization problem, we propose a novel algorithm by employing successive convex approximation, block coordinate descend and$S$-procedure. Numerical results show that our proposed joint optimization algorithm outperforms the benchmark schemes.

A multi-user fog radio access network (F-RAN) is designed for supporting content-centric services. The requested contents are partitioned into sub-contents, which are then ‘beamformed’ by the remote radio heads (RRHs) for transmission to the users. Since a large number of beamformers must be designed, this poses a computational challenge. We tackle this challenge by proposing a new class of regularized zero forcing beamforming (RZFB) for directly mitigating the inter-content interferences, while the ‘intra-content interference’ is mitigated by successive interference cancellation at the user end. Thus each beamformer is decided by a single real variable (for proper Gaussian signaling) or by a pair of complex variables (for improper Gaussian signaling). Hence the total number of decision variables is substantially reduced to facilitate tractable computation. To address the problem of energy efficiency optimization subject to multiple constraints, such as individual user-rate requirement and the fronthauling constraint of the links between the RRHs and the centralized baseband signal processing unit, as well as the total transmit power budget, we develop low-complexity path-following algorithms. Finally, we confirm their performance by simulations.

The paper considers the problem of beamforming design for a multi-cell network of downlink users, who either harvest energy or decode information or do both by receiving signals from the multi-antenna base station (BS) within a time slot and over the same frequency band. Our previous contributions have showed that the time-fraction based energy and information transmission, under which first the energy is transferred within the initial fraction of time and then the information is transferred within the remaining fraction, is the most efficient design alternative both in terms of its practical implementation and network performance. However, at the time of writing, both energy and information beamforming has only been implemented for proper Gaussian signaling (PGS), which has limited the network's throughput. Although the network throughput could be improved in some specific scenarios by using non-orthogonal multi-access (NOMA), this may compromise the user secrecy. In order to circumvent the above implementations, we conceive improper Gaussian signaling (IGS) for information beamforming, which enables the network to substantially improve its throughput in any scenario without jeopardizing the user secrecy despite its low-complexity signal processing at the user end. A simpler subclass of IGS is also considered, which also outperforms NOMA PGS and works under any arbitrary scenario.

Internet-of-things (IoT) applications continue to drive advancements in serving as many heterogeneous low-latency downlinks and uplinks as possible within a constrained communication bandwidth. Full-duplexing (FD) transceivers have been introduced to implement simultaneous signal transmission and reception (STR) over the entire available frequency band. However, both inter-link interference and FD loop-interference are hardly suppressed to a necessary level for the effectiveness of FD-based STR even for microcells. This paper proposes an alternative STR technique per one time-slot for macrocells, where a fraction of a time-slot is used for downlinks and the remaining complementary fraction of the time-slot is used for uplinks. Thus, STR over the entire available bandwidth can be implemented in a way with no loop interference. Furthermore, another approach of using a fraction of the available bandwidth for downlinks and the remaining complementary fraction of the bandwidth for uplinks over the whole time-slot is also proposed. The problem of both downlink and uplink beamforming to maximize the energy efficiency of such heterogeneous networks subject to the quality-of-service in terms of downlink and uplink throughput is examined for all three possible STRs. Numerical results demonstrate the advantages of the time-fraction-wise STR and bandwidth-fraction-wise STR over the FD-based STR, where the time-fraction-wise STR is not only the best in serving the same numbers of downlinks and uplinks but also is capable of serving many more downlinks and uplinks with a higher energy efficiency.

Although almost blank subframes (ABS) proposed in heterogeneous networks (HetNet) can enhance the performance of user equipments (UEs) in Pico-cell range expansion (CRE) area, it also significantly degrades the Macro-cell throughput. To address this issue, further-enhanced inter-cell interference coordination (FeICIC) scheme is considered in 3GPP Release 11, where low power ABS (LP-ABS) are adopted for the Macro-cell center region users to improve the Macro-cell throughput. However, LP-ABS power, Pico CRE bias and Pico base station (PBS) density will jointly affect on the system performance, which eventually deteriorates the network energy efficiency (EE) without careful configuration. In this paper, we first deduce the closed-form expression of network EE as a function of PBS density, Pico CRE bias and LP-ABS power based on stochastic geometry model. Then we provide Monte Carlo simulations to verify the accuracy of theoretical derivation of the network EE and analyze the impacts of these parameters on the network EE. The simulation results show that the reasonable PBS density, Pico CRE bias and LP-ABS power can improve the network EE obviously.

Spherical arrays have been widely used in direction-of-arrival (DOA) estimation in recent years, and the high-resolution estimation of signal parameter via rotational invariance technique (ESPRIT) was developed in the spherical harmonics domain. However, the spherical harmonics ESPRIT (SHESPRIT) cannot estimate the DOA when the elevation approaches 90°. To solve this problem, we present a two-step SHESPRIT (TS-SHESPRIT) based on two new recurrence relations of complex spherical harmonics. Furthermore, we develop a real-valued two-step SHESPRIT (RTS-SHESPRIT) that exploits a unitary matrix to obtain a real-valued relation between the signal subspace and the steering matrix to further reduce the computational complexity. However, the number of sources that are estimated by RTS-SHESPRIT is limited. Therefore, we propose the semi-RTS-SHESPRIT method, which reduces the computational complexity associated with eigenvalue decomposition (EVD) and avoids the limitations of RTS-SHESPRIT. Relative to SHESPRIT and TS-SHESPRIT, RTS-SHESPRIT and semi-RTS-SHESPRIT reduce the computational burden by 75% during EVD. Furthermore, we derive the mean square errors (MSEs) of the above algorithms and significantly simplify the MSE expressions. Different expressions for the MSEs are due to different recurrence relations used by different SHESPRIT-type algorithms. All proposed two-step SHESPRIT-type algorithms have higher accuracy than traditional SHESPRIT. The simulation results demonstrate the satisfactory performance of our methods.

Spherical arrays have been widely used in direction-of-arrival (DOA) estimation in recent years. In this paper, we develop a unitary matrix to transform the complex spherical harmonics into real ones and obtain a real-valued covariance matrix after forward-backward (FB) averaging. Based on this transformation, a two-stage decoupled approach (TSDA) is proposed to decouple the estimation of the elevation and the azimuth. First, we propose a unitary spherical harmonics estimation of signal parameter via rotational invariance technique using one recurrence relation of real spherical harmonics to obtain the elevation estimation. Because of the limitation of using another recurrence relation to estimate the azimuth; second, we propose a unitary spherical harmonics root multiple signal classification (U-SHRMUSIC) to obtain the azimuth. Moreover, using the phase shift characteristic of real spherical harmonics, we also propose a low-complexity U-SHRMUSIC that can further decrease the computational load in the second stage. The proposed TSDA can not only achieve more accurate DOA estimation via FB averaging and two-stage estimation after decoupling but also reduce the computational complexity by exploiting real operations. Computer simulations, especially simulations of the real environment, validate the effectiveness of the proposed method.

This paper considers power allocation between data and pilot symbols for an orthogonal frequency division multiplexing (OFDM) system in a high-speed train (HST) environment. The channel gains are very quickly time-varying within an OFDM symbol so both their estimation and symbol detection must be simultaneously implemented with unavoidable inter-carrier interferences. The average channel complex gains are estimated and are used to calculate the basis expansion model (BEM) coefficients of the HST channel in data detection. We choose the effective signal-to-interference and noise ratio (SINR) in symbol detection as the cost function and propose the effective algorithm to maximize it. The simulation results confirm the viability of our proposed algorithm.

Time domain enhanced intercell interference coordination with Almost Blank Subframe (ABS) configuration for Macrocell is proposed in 3GPP to mitigate the inter-tier interference for heterogeneous networks (HetNets) consisting of Macrocells and overlaying Picos. The existing schemes for ABS power optimization are mainly base on the same level muted power of ABS, which will either cause time-frequency resource waste for Macrocell with zero power ABS or reduce the Pico cell range expansion capability with low power ABS. In this paper, we propose a multilevel power ABS scheme with resource management to conquer these problems. The simulation results show that our proposed scheme can improve the network throughput and protect the cell range expansion (CRE) area user effectively.

A novel subcarrier-pair based opportunistic DF protocol is proposed for cooperative downlink OFDMA transmission aided by a decode-and-forward (DF) relay. Specifically, user message bits are transmitted in two consecutive equal-duration time slots. A subcarrier in the first slot can be paired with a subcarrier in the second slot for the DF relay-aided transmission to a user. In particular, the source and the relay can transmit simultaneously to implement beamforming at the subcarrier in the second slot for the relay-aided transmission. Each unpaired subcarrier in either the first or second slot is used by the source for direct transmission to a user without the relay's assistance. The sum rate maximized resource allocation (RA) problem is addressed for this protocol under a total power constraint. It is shown that the novel protocol leads to a maximum sum rate greater than or equal to that for a benchmark one, which does not allow the source to implement beamforming at the subcarrier in the second slot for the relay-aided transmission. Then, a polynomial-complexity RA algorithm is developed to find an (at least approximately) optimum resource allocation (i.e., source/relay power, subcarrier pairing and assignment to users) for either the proposed or benchmark protocol. Numerical experiments illustrate that the novel protocol can lead to a greater sum rate than the benchmark one.