Ultra-dense network (UDN) is a promising technology for future wireless networks to meet the explosively increasing wireless traffic demands. Sleeping strategy that switches base station (BS) on/off to reduce the energy consumption of UDN is widely investigated. However, the energy saving of sleeping strategy may come at the cost of increased delay. Hence, energy-delay tradeoff (EDT) which means how much energy can be saved by trading off tolerable delay is an important topic.<;/p> <;p>In this paper, we develop a theoretical framework for EDT considering BS sleeping and cell association in UDNs. Firstly, an M/G/1/N processor sharing vacation queueing is introduced to model small cell considering sleeping strategy and cell association. Mean delay and energy consumption are quantitatively studied based on the queueing model. The general expressions for mean delay and energy consumption are derived. It shows that sacrificing delay will not always bring about energy saving. Then, EDT problem is formulated as a cost minimization problem, and the optimal sleeping time and association radius are investigated to achieve the optimal tradeoff between energy consumption and delay. Since there is no closed-form expression for the optimal sleeping time for arbitrary number of users associating with one small cell, the optimal sleeping time for one and infinite number of users are derived. Simulation results demonstrate that the optimal sleeping time for infinite number of users is a reasonable approximation for arbitrary number when it is more than one.

In this article, we investigate the energy efficiency (EE) of reconfigurable intelligent surfaces (RISs) aided cell-free ultra-dense HetNets (CFUDN). To maximize the EE of CFUDN, users (UEs) association and clustering, RISs subsurface (SSF) associations are carefully designed. Then, the phase shift matrix of RISs and transmission power of base stations (BSs) are jointly optimized. Due to the non-convexity and high complexity of the formulated problem, this jointly optimized problem is usually difficult to solve. At present, the most commonly adopted method to deal with joint optimization problems is the block coordinate descent (BCD) algorithm based on an alternative optimization framework. However, as we all know, the BCD algorithm has some degree of performance loss due to the alternate optimization of variables. To overcome this challenging issue, a novel joint optimization framework based on Riemannian product manifolds (RPM) is proposed, in which the phase shift matrix of RISs and transmission power of BSs can be simultaneously optimized rather than alternating ways. The simulation results and computational complexity analysis demonstrate that the joint optimization framework based on the RPM exhibits superior performance and lower complexity compared to the joint optimization algorithm based on the BCD.

Estimating the polarization properties of objects from polarization images is still an important but extremely undefined problem. Currently, there are two types of methods to probe the polarization properties of complex materials: one is about the equipment acquisition, which makes the collection of polarization information unsatisfactory due to the cumbersome equipment and intensive sampling, and the other is to use polarized imaging model for probing. Therefore, the accuracy of the polarized imaging model will be crucial. From an imaging perspective, we propose an end-to-end learning method that can predict accurate, physically based model parameters of polarimetric BRDF from a limited number of captured photographs of the object. In this work, we first design a novel pBRDF model as a powerful prior knowledge. This hybrid pBRDF model completely defines specular reflection, body scattering and directional diffuse reflection in imaging. Then, an end-to-end inverse rendering is performed to connect the multi-view measurements of the object with the geometry and pBRDF parameter estimation, and a reflectance gradient consistency loss is introduced to iteratively estimate the per-pixel normal, roughness, and polarimetric reflectance. Real-world measurement and rendering experiments show that the results obtained by applying our method are in strong agreement with ground truth, validating that we can reproduce the polarization properties of real-world objects using the estimated polarimetric reflectance.

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.