In real world scenarios, low-resolution images often suffer from complex and unknown distortions during acquisition by edge Internet of Things (IoT) devices. To achieve a balance between super-resolution and distortion suppression, Real World Super Resolution (RWSR) models need to possess both global and local feature modeling capabilities. Moreover, a lightweight design is necessary for edge deployment without compromising performance. Therefore, this paper proposes a lightweight Channel Group Rearrange Transformer (CGRFormer), which exploits channel redundancy to reduce computational burden while enhancing local feature modeling capabilities. CGRFormer comprises two key components: the Channel Group and Rearrange Operation (CGRO) and the Multi-Scale Local Enhancement Feed Forward Network (MSLE-FFN). To address the issue of a heavy computational burden, CGRO divides the feature maps into active and inert groups. Heavy operations are applied only to the active group, significantly reducing overall complexity. A subsequent channel rearrange operation enhances information interaction between the two groups. To enhance local feature modeling, MSLE-FFN incorporates a Multi-Scale Convolutional Group (MSCG) and a Channel Excitation Module (CEM), enabling effective extraction and enhancement of local features at different receptive fields. Overall, CGRFormer effectively balances super-resolution and distortion suppression while maintaining a lightweight framework. Experimental results on the commonly used test datasets show that our model is still competitive with the SOTA model.

In this paper, a novel unmanned aerial vehicle (UAV) secure communication system enhanced by the passive coupled phase-shift simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR- RIS) is investigated. Considering the multiple-user and multiple-eavesdropper systems, we aim to maximize the minimum average secrecy rate by the joint design of the UAV base station's (UAV-BS's) beamforming, the UAV's trajectory, and the STAR-RIS's transmission and reflection (T&R) coefficients. Since the formulated optimization problem is highly non-convex with intricately coupled variables, we decompose it into three subproblems, and resolve them alternately by semi-definite relaxation, successive convex approximation, and penalty-based approach. Simulation results show that: 1) the practical coupled phase-shift model of passive STAR-RIS suffers a security performance degradation compared with the ideal independent phase-shift model, but it still outperforms the conventional RIS; 2) the UAV's trajectory tends to be closer to the STAR-RIS to fully exploit the benefits of STAR-RIS; 3) the amplitudes of T&R coefficients highly depend on the UAV's real-time trajectory.

In this paper, we investigate the energy-efficient multi-user mobile-edge computing offloading problem in massive MIMO enabled HetNets, where the CPU-cycle frequency of mobile devices, uplink power control, computational task offloading ratio and uplink transmission duration are jointly optimized. The problem is formulated as minimizing the energy consumption of all mobile devices while satisfying the maximum latency requirement. Specifically, to address this non-convex problem, a low-complexity algorithm is proposed relied on alternating optimization, where we address the joint computational task offloading ratio and uplink transmission duration optimization problem and the uplink power control problem iteratively. Besides, the effectiveness and convergence of the proposed iterative algorithm are analytically studied. Numerical results demonstrate that our proposed algorithm consumes less energy compared to local computing and full uploading schemes, and the application of massive MIMO in HetNets helps to reduce energy consumption of mobile devices.

In this paper, we concentrate on the robust multi-objective optimization (MOO) for the tradeoff between energy efficiency (EE) and spectral efficiency (SE) in device-to-device (D2D) communications underlaying heterogeneous networks (HetNets). Different from traditional resource optimization, we focus on finding robust Pareto optimal solutions for spectrum allocation and power coordination in D2D communications underlaying HetNets with the consideration of interference channel uncertainties. The problem is formulated as an uncertain MOO problem to maximize EE and SE of cellular users (CUs) simultaneously while guaranteeing the minimum rate requirements of both CUs and D2D pairs. With the aid of ε-constraint method and strict robustness, we propose a general framework to transform the uncertain MOO problem into a deterministic single-objective optimization problem. As exponential computational complexity is required to solve this highly non-convex problem, the power coordination and the spectrum allocation problems are solved separately, and an effective two-stage iterative algorithm is developed. Finally, simulation results validate that our proposed robust scheme converges fast and significantly outperforms the non-robust scheme in terms of the effective EE-SE tradeoff and the quality of service satisfying probability of D2D pairs.

In this paper, we propose a general framework to study the tradeoff between energy efficiency (EE) and spectral efficiency (SE) in massive multiple-input-multiple-output-enabled heterogenous networks while ensuring proportional rate fairness among users and taking into account the backhaul capacity constraint. We aim at jointly optimizing user association, spectrum allocation, power coordination, and the number of activated antennas, which is formulated as a multi-objective optimization problem maximizing EE and SE simultaneously. With the help of weighted Tchebycheff method, it is then transformed into a single-objective optimization problem, which is a mixed-integer non-convex problem and requires unaffordable computational complexity to find the optimum. Hence, a low-complexity effective algorithm is developed based on primal decomposition, where we solve the power coordination and number of antenna optimization problem and the user association and spectrum allocation problem separately. Both theoretical analysis and numerical results demonstrate that our proposed algorithm can fast converge within several iterations and significantly improve both the EE-SE tradeoff performance and rate fairness among users compared with other algorithms.

In this paper, the power allocation problem for downlink massive multiple-input multiple-output (MIMO) systems with inter-user interference is investigated considering the tradeoff between energy efficiency (EE) and spectral efficiency (SE). The minimum mean-square-error channel estimate and the rigorous closed-form expression of achievable downlink rate are first derived. Then, the multi-objective optimization problem (MOP) is formulated subject to maximum total transmission power constrain, where conflicting EE and SE are maximized simultaneously. To obtain the solution set characterized as a Pareto set, the MOP is transformed into a single-objective problem (SOP) using weighted sum method. We further convert the SOP into a convex optimization problem by adding an additional interference constraint, and the optimal power allocation algorithm is proposed by using dual method to balance EE and SE efficiently. Simulation results demonstrate the effectiveness of the proposed algorithm and illustrate the fundamental tradeoff between EE and SE for different parameter settings.