In some application scenarios, strong spatio-temporal correlations exist between remote sensing data streams. For instance, during earthquake relief efforts, users in nearby locations may simultaneously request remote sensing data from an area of interest within the same time period. This leads to large data volumes transmitted in a limited spatio-temporal range, causing network traffic imbalance and reduced transmission efficiency. To address this issue, this paper proposes the Aggregated Transmission Strategy for Remote Sensing Data based on Spatio-Temporal Association (AFRST). According to the spatio-temporal attributes of remote sensing data and the location of users, AFRST utilizes the mapping relationship between naming and demand in Named Data Networking(NDN) and generates a demand association set when the demand between users in the same area reaches the association threshold, and the data in the overlapping area in the set is transmitted only once. We also uniformly assign transmission paths to the association set to improve the data transmission efficiency. Furthermore, AFRST takes into account the network state and user demand, constructing a Transmission-Load Balancing Control Model (TLBCM) based on the network utility maximization framework. This model maximizes the data transmission rate and balances the network load under constraints such as link capacity and other factors in each time slot, optimizing network service performance. The performance of AFRST is evaluated using ndnSIM, and the experimental results demonstrate the effectiveness of AFRST in terms of transmission latency, throughput, and number of completions. Compared to DCT and DPCCP, the average completion time per demand is increased by 29.9% and 18.1%, the overall transmission rate is increased by 43.5% and 22.8%, the overall average increase in the number of completions is about 42.4% and 22.9%, and the throughput is about 16.1% and 15.7% higher.

Intelligent Omni-Surface (IOS) has attracted considerable attention for its advantages of high energy efficiency, which are similar to those of Reconfigurable Intelligent Surface (RIS), while also being able to overcome the limited scope of RIS services. In this paper, we provide a security energy efficiency (SEE) maximization design for IOS and artificial noise (AN) assisted cell-free massive MIMO (CF-mMIMO) networks, via jointly optimizing the transmission beamforming and AN covariance matrix of the AP, the reflection and transmission phase-shift matrices of the IOS, and the reflection-transmission power ratio of the IOS. To handle the formulated problem with non-convexity and high complexity, we first decouple it into two sub-problems. Then, we design low-complexity algorithms for each sub-problem i.e., an AP transmission beamforming and AN noise covariance matrix joint optimization algorithm based on the SSNCG-ALM, and an IOS reflection and transmission phase-shift matrix joint optimization algorithm based on the RPM-TR. Finally, a SEE maximization iterative algorithm based on block coordinate descent and successive convex approximation is established. The simulation results demonstrate that the proposed design significantly enhances the SEE of CF-mMIMO networks.

To meet the needs of high energy efficiency (EE) and various heterogeneous services for 6G, in this article, we probe into the EE of reconfigurable intelligent surfaces (RISs) subsurface (SSF) architecture-aided cell-free Internet of Things (CF-IoT) networks. Specifically, we jointly optimize the base station (BS)-RIS-IoT device (ID) joint associations, the RIS’s phase shift matrix (PSM), and the BS’s transmit power to enhance CF-IoT’s EE. The elevated complexity (NP-hard) and nonconvexity of the formulated problem pose significant challenges, making the solution highly difficult and intricate. To handle this challenging problem, we first develop an alternating optimization framework based on block coordinate descent, which can decouple the original problem into several subproblems. We then carefully design the corresponding low-complexity algorithm for each subproblem to solve it. Moreover, the proposed joint optimization framework serves as a versatile solution applicable to a wide range of scenarios aiming to maximize EE with the assistance of RISs. Simulations confirm that deploying RISs in CF-IoT scenarios is beneficial for improving the EE of the system, and the SSF architecture can further enhance the EE of the system.

Decoupled atomic norm minimization (D-ANM) is a computationally-efficient gridless two-dimensional parameter estimation method via dividing the two-level Toeplitz matrix into two one-level matrices, and can be applied to the one-snapshot direction-of-departure (DOD) and direction-of-arrival (DOA) estimation in bistatic multiple-input multiple-output (MIMO) radar with uniform linear arrays (ULAs). Nevertheless, in actual applications, the transmitting and receiving arrays are not always ULAs, which leads to the Toeplitz structure not satisfied and the regular D-ANM algorithm not available. In this work, the one-snapshot DOD and DOA estimation in bistatic MIMO radar with arbitrary linear arrays (ALAs) is investigated. The Prolate spheroidal wave functions (PSWFs) are utilized to express such a D-ANM problem followed by a semi-definite programming (SDP) solution. The PSWF-based method can extend the application of D-ANM from ULAs to ALAs, and avoid the grid partition of the conventional sparse representation methods. The numerical experiments are conducted to verify the proposed method.

The multipath fading and Doppler effect are well‐known phenomena affecting channel quality in mobile wireless communication systems. Within this context, the emergence of reconfigurable intelligence surfaces (RISs) brings a chance to achieve this goal. RISs as a potential solution are considered to be proposed in sixth generation (6G). The core idea of RISs is to change the channel characteristic from uncontrollable to controllable. This is reflected by some novel functionalities with wave absorption and abnormal reflection. In this paper, the multipath fading and Doppler effect are characterized by establishing a mathematical model from the perspective of reflectors and RISs in different mobile wireless communication processes. In addition, the solutions that improve the multipath fading and Doppler effect stemming from the movement of mobile transmitter are discussed by utilizing multiple RISs. A large number of experimental results demonstrate that the received signal strength abnormal fluctuations due to Doppler effect can be eliminated effectively by real‐time control of RISs. Meanwhile, the multipath fading is also mitigated when all reflectors deployed are coated with RISs.

Non-orthogonal multiple access (NOMA) is considered as a key candidate technology for the fifth generation (5G) mobile communication network. In this paper, we investigate the full-duplex (FD) relaying assisted underlay cognitive radio NOMA (CR-NOMA) network with coordinated direct and relay transmission (CDRT). Based on this framework, we derive the exact closed-form expressions of the outage probability (OP) of the secondary user with imperfect successive interference cancellation (SIC). Analytical and simulation results show that, with FD relaying, the far user's performance is significantly enhanced compared to the near user, and the FD relaying aided CR-NOMA network has a much better outage performance than that of the half-duplex (HD) relay network.

With the development of the non-orthogonal multiple access (NOMA) technology, cooperative non-orthogonal multiple access (C-NOMA) has received extensive attentions as an expanded version. This paper considers a full duplex (FD) cooperative NOMA (FD-C-NOMA) system, including one base station (BS), one near user and one far user, where the near user serves as the FD decode-and-forward relay to transmit the signals of the far user. As a common practical implementation problem, the impact of imperfect successive interference cancellation (SIC) is taken into account in system performance analysis. Under the given framework, we derive the closed-form outage probability and the approximate closed-form average rate for the near user and the far user. As analytically expected, simulation results verify that the system performance of the FD-C-NOMA is much better than those of non-cooperative NOMA and half duplex cooperative NOMA (HD-C-NOMA) systems. Finally, Monte Carlo simulation proves the accuracy of our calculation.

Ultra-dense HetNets (UDN)-based Millimeter-Wave (mmWave) massive MIMO is considered a promising technology for 5th generation (5G) wireless communications systems since it can offer massively available bandwidth and improve energy efficiency (EE) substantially. However, in UDN, the power consumption of the system increases sharply with the increase of network density. In this paper, we investigate the optimization of the EE in the mmWave massive MIMO systems with UDN. To develop the functions of massive MIMO, we first propose a system model where the massive MIMO harvests electromagnetic energy from the environment employing simultaneous wireless information and power transfer (SWIPT) technology, implemented at the base station (BS). Then, the EE optimization problem is formulated for 5G mmWave massive MIMO systems within the UDN. Considering the nonconcave feature of the objective function, an iterative EE algorithm is developed, based on Dinkelbach method. To utilize the role of coordinated multi-point transmission and reception (CoMP) for improving the EE, a coordinated user(UE)-BS association algorithm-based CoMP with maximum energy efficiency (MaxEE) is proposed. The simulation results demonstrate that the proposed algorithm has a substantially faster convergence rate and is very effective, compared with existing methods.

Fog-computing network combines the cloud computing and fog access points (FAPs) equipped with mobile edge computing (MEC) servers together to support computation-intensive tasks for mobile users. In this paper, we investigate the delay minimization problem for task offloading in a hierarchical fog-computing C-RAN network, which consists of three tiers of computational services: MEC server in radio units, MEC server in distributed units, and the cloud computing in central units. The receive beamforming vectors, task allocation, computing speed for offloaded tasks in each server and the transmission bandwidth split of fronthaul links are optimized by solving the formulated mixed integer programming problem. The simulation results validate the superiority of the proposed hierarchical fog-computing C-RAN network in terms of the delay performance.

Probability density function (PDF) of the modulating retro‐reflector (MRR) turbulence fading channels is crucial for the performance analysis of MRR communication systems. In this study, closed‐form expression for the PDF of normalised MRR free‐space optical (FSO) turbulence fading coefficient is obtained first. Moreover, it is applied to the evaluations of the closed‐form expressions for average capacity and outage probability of MRR FSO links. The effects of the parameters such as atmospheric turbulence conditions, communication distance, receiver aperture diameter and the average signal‐to‐noise ratio on the performance of MRR FSO links are discussed. Results show that in order to achieve successful MRR FSO communication, communication systems employing MRRs are suitable for short‐distance communication in the case of low transmission power, besides quite high transmission power and large receiver aperture diameter are required simultaneously by long‐distance MRR FSO communication.

In ultra dense heterogeneous networks (HetNets), the interference becomes more serious since multiple small cells coexist in the coverage area of the macrocells and share the same spectrum. An efficient interference coordination method is adjusting the transmit powers of all cells in a cooperative way. However, under practical serving cell selection rules in which serving cells of users alter with the variation of cell powers, finding optimal cell powers becomes a difficult problem. In this paper, an improved modified particle swarm optimization (MPSO) is proposed to tackle this difficult problem caused by the altering of serving cell. Local search and multi-restart process are introduced to guarantee the local and then global optimality, and the convergence conditions and global optimality are proved by mathematical deduction to guide the selection of the parameters. Simulations show that the proposed algorithm can significantly improve system throughput compared with existing algorithms which do not consider the alteration of serving cells. By improving MPSO, the proposed algorithm can spend less iteration time to achieve higher system throughput, and exhibit similar performance as exhaustive search in both system throughput and the signal to interference plus noise ratio (SINR) of users.

The intensive downlink (DL) inter-cell interference created by cell range expansion (CRE) of picocells is an urgent problem needing to be solved under macro-pico heterogeneous network (HetNet) scenario. A promising approach is taking advantage of additional degree of freedom brought by carrier aggregation (CA). However, poorly arranged carrier configurations or interference coordination schemes can lead to a degradation of system overall performance. In this paper, we propose a novel dynamic interference coordination scheme based on carrier aggregation to alleviate the DL interference from macrocells to users located in the cell range expansion area. Novel carrier configuration pattern and dynamic power control scheme based on price algorithm are applied to mitigate detrimental interference to picocell-edge users and improve the availability of macrocells. Simulation results show that the proposed scheme can boost the picocell-edge throughput significantly while ameliorating the overall system throughput.