Optimal Caching in 5G Networks With Opportunistic Spectrum Access

The Voxel Imaging PET (VIP) Pathfinder project intends to show the advantages of using pixelated solid-state technology for nuclear medicine applications. It proposes designs for Positron Emission Tomography (PET), Positron Emission Mammography (PEM) and Compton gamma camera detectors with a large number of signal channels (of the order of 106). For Compton camera, especially with a large number of readout channels, image reconstruction presents a big challenge. In this work, results are presented for the List-Mode Ordered Subset Expectation Maximization (LM-OSEM) image reconstruction algorithm on simulated data with the VIP Compton camera design. For the simulation, all realistic contributions to the spatial resolution are taken into account, including the Doppler broadening effect. The results show that even with a straightforward implementation of LM-OSEM, good images can be obtained for the proposed Compton camera design. Results are shown for various phantoms, including extended sources and with a distance between the field of view and the first detector plane equal to 100 mm which corresponds to a realistic nuclear medicine environment.

Reliability analysis of a channel restoration mechanism for opportunistic spectrum access

Wireless content caching in small cell networks has recently been considered as a promising way to alleviate the congestion of the backhaul in emerging heterogenous cellular network. However, how to select files which are cached in SBSs and how to make SBSs work together is an important issue for cooperative cache research for the propose of reducing file download time. In this paper, a Cooperative-Greedy strategy (CGS) among cache-enabled small base stations (SBSs) in small cell network is proposed, in order to minimize the download time of files. This problem is formulated as a Chinese restaurant game.Using this game model, we can configure file caching schemes based on file popularity and the spectrum resources allocated to several adjacent SBSs. Both the existence and uniquencess of a Nash equilibrium are proved. In the theoretical analysis section, SBSs cooperate with each other in order to cache popular files as many as possible near UEs. Simulation results show that the CGS scheme outperforms other schemes in terms of the file-download time.

Performance analysis of coverage-centric heterogeneous cellular networks using dual-slope path loss model

As Digital Television Broadcasting spreads over the world, existing (and more) TV channels can be distributed in less spectrum in the spectrum traditionally allocated to TV broadcasting. This freed spectrum is also referred to as the "Digital Dividend" and its use has been debated around the world. In addition, there is also a debate about the potential use of the "white space" within the TV-bands. This is due to the sparse frequency planning with large interference margins, which is typical in wide area broadcasting. Various technical approaches using Opportunistic Spectrum Access (OSA) have been proposed for unlicensed "white space" access to the TV bands. Most of previous studies have focused on spectrum sensing, i.e. detecting "free channels", where secondary users, utilizing White Space Devices (WSD) could avoid causing harmful interference to the TV receivers. However, interference caused by WSD is not only limited to co-channel interference. In particular, in short-range scenarios, the adjacent channel interference is an equally severe problem. Assessing the feasibility of WSDs in short-range indoor scenarios, taking more interference mechanisms into account is the objective of this paper. An Indoor home scenario with Cable, Rooftop antenna and Set-top antenna reception of DVB-T, has been analyzed. The spectrum reuse opportunities for WSDs have been determined, using the number of channels where it is possible to transmit without causing harmful interference to DVB-T receivers as performance measure. Simulation results show that the number of available channels for indoor unlicensed white space transmission appears to be significant in most of the studied scenarios.

In this work we investigate optimal geographical caching in heterogeneous cellular networks where different types of base stations (BSs) have different cache capacities. Users request files from a content library according to a known probability distribution. The performance metric is the total hit probability, which is the probability that a user at an arbitrary location in the plane will find the content that it requires in one of the BSs that it is covered by. We consider the problem of optimally placing content in all BSs jointly. As this problem is not convex, we provide a heuristic scheme by finding the optimal placement policy for one type of base station conditioned on the placement in all other types. We demonstrate that these individual optimization problems are convex and we provide an analytical solution. As an illustration, we find the optimal placement policy of the small base stations (SBSs) depending on the placement policy of the macro base stations (MBSs). We show how the hit probability evolves as the deployment density of the SBSs varies. We show that the heuristic of placing the most popular content in the MBSs is almost optimal after deploying the SBSs with optimal placement policies. Also, for the SBSs no such heuristic can be used; the optimal placement is significantly better than storing the most popular content. Finally, we show that solving the individual problems to find the optimal placement policies for different types of BSs iteratively, namely repeatedly updating the placement policies, does not improve the performance.

This paper presents the stochastic geometry analysis and design of a primary exclusive region (PER) for cognitive radio vehicular ad hoc networks (CR-VANETs). Recently, to satisfy the increasing demands for vehicular communications, cognitive radio (CR) technology has been applied to broaden the vehicular communication spectrum. However, while utilizing the licensed spectrum opportunistically as secondary transmitters (STs), vehicles must avoid harmful interference with primary receivers (PRs), e.g., TV broadcasters. In contrast to cooperative spectrum sensing and stand-alone spectrum sensing in most existing works, in this paper, we proposed a geo-location-database-driven opportunistic spectrum access (OSA) approach to maximize the spectrum opportunity of SUs. For the proposed OSA approach, this paper presents a line segment model for CR-VANETs and introduces the allowable transmission probability of STs in each line segment. We theoretically analyze the outage probability of the primary user using stochastic geometry. From these analyses, we introduce the optimization problem to maximize the spectrum opportunity of STs while ensuring a minimal outage threshold for the PR. Subsequently, we numerically evaluate the solution of the optimization problem. Our results show that a PER for CR-VANETs has been successfully designed. With the decrease in the length of the line segment and further detailed information, a smaller and more complex-shaped PER can be achieved.

In the scenario with many small base stations (SBSs) deployed, edge caching technology could bring contents closer to users by caching files at SBSs. Considering these SBSs with limited storage capacity, how to effectively cache files is a difficult and interesting problem. Many factors should be considered, such as the popularity of files, user download delay and average hit rate. In this paper, we investigate this problem and propose a minimizing user download delay caching (MUDDC) algorithm. It decides which files should be cached and where to cache them for reducing download delay. There is a conflict between hit rate and download delay with the limited SBS storage capacity. We target the average user download delay and model an optimization problem with the constraint of average hit rate and find the optimal solution. The simulation results show that the system performance is improved.

Caching content of small base stations (SBSs) is a promising approach in the fifth generation (5G) wireless networks for decreasing the transmission delay and energy consumption. The key issue in caching is effective cooperation among SBSs for full usage of the caching to reduce the systems cost. In this paper, we first introduce the architecture of wireless cellular networks with caching in 5G. We divide the caching space into two parts: One caches the fixed content purchased by telecom operators from the remote service providers, and the other one caches contents for wireless transmission. Then, we propose the cooperative caching scheme based on content-exchanged, where each user can get its demanded data from either local SBS or purchasing the contents stored in the neighboring cooperative SBSs. We further prove the concavity of system costs regarding to the proportion between the two parts. We also propose a mechanism to search for the minimal system costs. Finally, by simulations, we prove that the performance of our proposed cooperative caching scheme is better than that of the traditional cache schemes without purchasing the contents between SBSs.

In existing works on performance analysis, cell selection is distance-based, and the relation of backhaul capacity variance and cell selection has not been explicitly modelled. Using stochastic geometry, this letter analyzes the performance of backhaul-aware cell selection by deriving the analytical expression for the outage probability, where high-backhaul-capacity small base stations (SBSs) have larger association biases to offload users from low-backhaul-capacity SBSs. Specifically, the general case of cell group (CG) selection is considered instead of single cell selection, and a CG consists of multiple SBSs with the largest biased reference signal received power, where the bias value is the SBS's backhaul capacity. In addition, the serving SBS changes adaptively which is the one with the largest transmission rate (minimum of access and backhaul rate) inside CG, thus exploiting the diversity of SBSs within CG. The results show that the outage probability is decreased by 30.6% when the CG size equals 5, compared with the distance-based CG formation strategy.

Coded caching is able to exploit accumulated cache size in a wireless network by storing fractions of files in each node. In this work, we model, analyze, and optimize coded caching in cache-enabled small-cell networks (SCNs). We first propose a content delivery framework for coded caching, where multiple small base stations (SBSs) that cache different coded packets of a desired file transmit simultaneously upon a user request and the packets are decoded using successive interference cancellation (SIC)-based receiver. A closed-form expression for the successful transmission probability with SIC receiver in the high signal-to-noise ratio (SNR) region is derived by using stochastic geometry. We next optimize the cache placement to maximize the average fraction of offloaded traffic by the cache-enabled SBSs. The coded cache placement problem is formulated as a multiple-choice knapsack problem (MCKP). By exploiting the analytic properties of the average traffic offload fraction, we propose a greedy but optimal algorithm. Numerical results show our proposed coded caching scheme facilitated by simultaneous transmission and SIC-based receiving significantly outperforms uncoded caching. In addition, the coded caching only needs to split each file into a small number of fragments to approach an ideal performance.

We consider a network model where small base stations (SBSs) have caching capabilities as a means to alleviate the backhaul load and satisfy users’ demand. The SBSs are stochastically distributed over the plane according to a Poisson point process (PPP) and serve their users either (i) by bringing the content from the Internet through a finite rate backhaul or (ii) by serving them from the local caches. We derive closed-form expressions for the outage probability and the average delivery rate as a function of the signal-to-interference-plus-noise ratio (SINR), SBS density, target file bitrate, storage size, file length, and file popularity. We then analyze the impact of key operating parameters on the system performance. It is shown that a certain outage probability can be achieved either by increasing the number of base stations or the total storage size. Our results and analysis provide key insights into the deployment of cache-enabled small cell networks (SCNs), which are seen as a promising solution for future heterogeneous cellular networks.

To solve low-resource utilization, complex storage, and busy traffic in space–air–ground integrated networks (SAGINs), a new multilayered decentralized coded caching (ML-DCC) scheme is proposed in hierarchical networks with nonuniform file popularity and multilevel cache capacity. First, a file popularity prediction is performed by a random forest model with grid search. Second, a multipopularity coded caching (MPCC) strategy by grouping files is executed in a two-hop network with fixed cache size. Finally, a new ML-DCC scheme is proposed to adopt nonuniform file popularity and multilevel cache capacity with both block-coded caching and zero-bit padding to obtain high-resource utilization and efficient file exchange in file transmissions. The innovations are random forest classifier with bagging integration and grid search to improve network traffic and link load, decentralized coded caching to reduce average transferred files, and coded caching and grouping files by popularity to improve network load. Simulation results show that the data payload of the proposed scheme is significantly improved by about 1.88, 1.18, 1.38, 3.53, and 4.39 times, when compared with those of the shared cache, multilevel popularity, hierarchical coded caching, MPCC, and uncoded caching schemes, respectively. Under the expected load of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R=211.57F$ </tex-math></inline-formula> bits of the shared link, the cache size used by the proposed ML-DCC is only 1/42.85 and 1/24.39 of those of the highest popularity first (HPF) and nonuniform cache schemes, respectively.

Accurate sleep staging for patients with sleep disorders requires the integration of multiple channels and various signal modalities. While graph convolutional networks (GCNs) have significantly improved sleep staging accuracy by effectively processing complex data, current GCN-based methods often require a large number of signal channels and struggle to capture temporal correlations effectively. They are leading to a large limitation in the scope of graph network models in sleep staging. To address these challenges, we propose an attention-based spatial-temporal graph network for sleep staging, named ASTGSleep. ASTGSleep effectively addresses the temporal continuity of sleep data and overcomes limitations related to the number of channels by integrating the temporal graph construction and feature extraction module (TGFEM). TGFEM captures dynamic temporal features through temporal graph construction, complementing spatial features and enhancing sensitivity to sleep stage transitions. Moreover, the multidimensional fusion module (MDFM) optimizes and fuses these spatiotemporal features using a graph attention network (GAT), further improving accuracy and robustness in sleep staging. Experimental results demonstrate that ASTGSleep achieves competitive accuracy on datasets with different numbers of channels, such as MASS-SS3 with 26 channels (87.0%) and SleepEDF-20 with three channels (86.5%). Furthermore, its performance on sleep disorder datasets, including CAP (81.4%) and clinical REM Sleep Behavior Disorder from Changzheng Hospital (CZ-RBD) (82.7%), shows robust generalization and enhanced accuracy, making it a promising tool for automatic sleep staging in clinical diagnostics.

Coverage analysis of cache-enabled small cell networks with stochastic geometry methods