Providing rich and useful information regarding spectrum activities and propagation channels, radiomaps characterize the detailed distribution of power spectral density (PSD) and are important tools for network planning in modern wireless systems. Generally, radiomaps are constructed from radio strength measurements by deployed sensors and user devices. However, not all areas are accessible for radio measurements due to physical constraints and security considerations, leading to non-uniformly spaced measurements and blanks on a radiomap. In this work, we explore distribution of radio spectrum strengths in view of surrounding environments, and propose two radiomap inpainting approaches for the reconstruction of radiomaps that cover missing areas. Specifically, we first define a propagation-based priority before integrating exemplar-based inpainting with radio propagation model for fine-resolution small-size missing area reconstruction on a radiomap. We next introduce a novel radio depth map and propose a two-step template-perturbation approach for large-size restricted region inpainting. Our experimental results demonstrate the power of the proposed propagation priority and radio depth map in capturing PSD distribution, as well as their efficacy in radiomap reconstruction.

Cloud gaming (CG) has gradually gained popularity. By leveling shared computing resources on the cloud, CG technology allows those without expensive hardware to enjoy AAA games using a low-end device. However, the bandwidth requirement for streaming game video is high, which can cause backbone network congestion for large-scale deployment and expensive bandwidth bills. To address this challenge, the authors proposed an innovative edge-assisted computing architecture that collaboratively uses AI-powered foveated rendering (FR) and super-resolution (SR). Using FR, the cloud server can stream gaming video in lower resolution, significantly reducing the transmitted data volume. The edge server will then upscale the video using a game-specific SR model, recovering the quality of the video, especially for the areas players pay the most attention. The authors built a prototype system called FRSR and did thorough, objective comparative experiments to demonstrate that this architecture can reduce bandwidth usage by 39.47% compared with classic CG implementation for similar perceived quality.

Radio map describes network coverage and is a practically important tool for network planning in modern wireless systems. Generally, radio strength measurements are collected to construct fine-resolution radio maps for analysis. However, certain protected areas are not accessible for measurement due to physical constraints and security considerations, leading to blanked spaces on a radio map. Non-uniformly spaced measurement and uneven observation resolution make it more difficult for radio map estimation and spectrum planning in protected areas. This work explores the distribution of radio spectrum strengths and proposes an exemplar-based approach to reconstruct missing areas on a radio map. Instead of taking generic image processing approaches, we leverage radio propagation models to determine directions of region filling and develop two different schemes to estimate the missing radio signal power. Our test results based on high-fidelity simulation demonstrate efficacy of the proposed methods for radio map reconstruction.

In this paper, we investigate a cloud radio access network (Cloud-RAN) in which both fronthaul and radio access links use massive MIMO millimeter-wave (mmWave) transmissions. Such an all-mmWave Cloud-RAN architecture provides a flexible and cost-effective means for deployment of next-generation (5G and beyond) cellular networks to meet the demands of fast-growing mobile data traffic. Nevertheless, the design of transmit power allocation schemes for multiple massive MIMO beams on both the fronthaul and access links in a mmWave Cloud-RAN is challenging. In particular, the traffic and wireless channel states of multiple mobile terminals (MTs) change over time, while their statistics may not be known a priori. We formulate the joint fronthaul-access link massive MIMO beam power allocation problem as a Markov decision process (MDP) with an objective to optimize the long-term quality of service to all the MTs in a Cloud-RAN. A reinforcement learning algorithm is designed, which learns the optimal beam power allocation policy on the fly and adapts to the network dynamics. Further, by leveraging the structure of the underlying problem, a post-decision state is introduced and a function decomposition technique is developed to reduce the search space during the learning process. The evaluation results validate the convergence of our proposed scheme and demonstrate its superior performance over the state-of-the-art baselines.

By analyzing the characteristics of energy consumption in wireless sensor networks, this study finds out that the pivotal aspect in controlling energy consumption is the media access control protocol, and introduces the media access control protocol of typical wireless sensor network in various categories, meanwhile, abstractly divides the context of use into three conditions. Based on the OPNET, this paper simulates different scenarios in terms of the energy consumption and delay performance under the varieties of protocol, generating the result that none of them could provide the optimal performance in the different scenarios. Therefore, for the sake of adapting into complex application scenarios, it is necessary to develop the media access control protocol with further improvements, in terms of the general applicability or business mode with adaptive ability.

Based on the findings of an empirical survey of EC application in SMEs in the East of Guangdong Province, the author analyses the actuality and questions of the EC application, then puts forward the countermeasures on how SMEs apply EC successfully at present.

Dynamic bit-loading for ADSL-DMT is driven by the signal-to-noise ratio (SNR) achieved per tone. 14.3 dB of minimum SNR is required to load a tone with the smallest square 4QAM constellation, for a fixed 10/sup -7/ bit error rate (BER). In the presence of physical layer impairments such as loop attenuation, bridge taps or crosstalks, many tones cannot be used because their individual SNR values are too small. On the other hand the associated total unloaded bandwidth exhibits a significant capacity. To benefit from parts of this unloaded bandwidth capacity, trellis coding with one bit or fractional bit loading has been proposed, unfortunately requiring some trellis decoding modifications. This paper introduces a new method called clustering modulation (CM) that does not require any trellis change and which clusters unloaded tones together to increase the SNR after maximum likelihood cluster payload demodulation in the frequency domain. Clustering modulation achieves up to 424 kbits/s of rate improvement on a 2.8 km (9 kfeet) 26AWG loop and up to 200 m of reach increase against SDSL noise B crosstalks.