Power allocation for coordinated multi-cell systems with imperfect channel and battery-capacity-limited receivers

This letter studies the transmit power allocation in downlink coordinated multi-cell systems with the batterycapacity-limited receivers, where the battery level of receivers is considered. The power allocation is formulated as an optimization problem to maximize the minimum signal-to-interference noise ratio of users under the per-base station power constraints and the feasible maximum received data rate constraints determined by the receiver battery level. The optimal solutions are derived by the proposed monotonic optimization technique based algorithm. The proposed algorithm can extend the battery lifetime of the receivers with lower battery level. Simulation results illustrate the performance of the proposed algorithm

Power allocation for coordinated multi-cell systems with imperfect channel and battery-capacity-limited receivers

This letter studies the transmit power allocation in downlink coordinated multi-cell systems with the batterycapacity-limited receivers, where the battery level of receivers is considered. The power allocation is formulated as an optimization problem to maximize the minimum signal-to-interference noise ratio of users under the per-base station power constraints and the feasible maximum received data rate constraints determined by the receiver battery level. The optimal solutions are derived by the proposed monotonic optimization technique based algorithm. The proposed algorithm can extend the battery lifetime of the receivers with lower battery level. Simulation results illustrate the performance of the proposed algorithm

User preference aware caching deployment for device-to-device caching networks

Content caching in the device-to-device (D2D) cellular networks can be utilized to improve the content delivery efficiency and reduce traffic load of cellular networks. In such cache-enabled D2D cellular networks, how to cache the diversity contents in the multiple cache-enabled mobile terminals, namely, the caching deployment, has a substantial impact on the network performance. In this paper, a user preference aware caching deployment algorithm is proposed for D2D caching networks. First, the definition of the user interest similarity is given based on the user preference. Then, a content cache utility of a mobile terminal is defined by taking the transmission coverage region of this mobile terminal and the user interest similarity of its adjacent mobile terminals into consideration. A general cache utility maximization problem with joint caching deployment and cache space allocation is formulated, where the special logarithmic utility function is integrated. In doing so, the caching deployment and the cache space allocation can be decoupled by equal cache space allocation. Subsequently, we relax the logarithmic utility maximization problem, and obtain a low complexity near-optimal solution via a dual decomposition method. Compared with the existing caching placement methods, the proposed algorithm can achieve significant improvement on cache hit ratio, content access delay, and traffic offloading gain

Caching deployment algorithm based on user preference in device-to-device networks

In cache enabled D2D communication networks, the cache space in a mobile terminal is relatively small compared with the huge amounts of multimedia contents. As such, a strategy for caching the diverse contents in a multiple cache-enabled mobile terminals, namely caching deployment, will have a substantial impact to network performance. In this paper, a user preference aware caching deployment algorithm is proposed for D2D caching networks. Firstly, based on the concept of the user preference, the definition of user interest similarity is given, in which it can be used to evaluate the similarity of user preferences. Then a content cache utility of a mobile terminal is defined by taking the communication coverage of this mobile terminal and the user interest similarity of its adjacent mobile terminals into consideration. The logarithmic utility maximization problem for caching deployment is formulated. Subsequently, we relax the logarithmic utility maximization problem, and obtain a low complexity near-optimal solution via dual decomposition method. The convergence of the proposed caching deployment algorithm is validated by simulation results. Compared with the existing caching placement methods, the proposed algorithm can achieve significant improvement on cache hit ratio, content access delay and traffic offloading gain

Correlation between Chlamydia Pneumoniae IgG Positive in Lung Cancer Patients and Cytokines Related to Radiation-induced Pulmonary Lesion

Background and objective There exsits intimate relationship between infection with chlamydia pneumoniae (Cpn) and lung cancer incidence. But few studies have been reported about radiation-induced pulmonary lesion in lung cancer patients infected with Cpn. The aim of this study is to explore the correlation between cytokines related to radiation-induced pulmonary lesion and Cpn IgG positive in lung cancer patients. Methods A total of 69 patients with lung cancer received chest radiotherapy. Blood samples were collected and frozen before radiotherapy (pre-RT), middle radiotherapy (mid-RT) and after radiotherapy (post-RT). Cpn IgG and levels of IL-1β, SP-A, TGF-β, and TNF-α were measured by enzymelinked immunosorbent assay (ELISA). Results In the total of 69 patients, 21 patients were Cpn IgG positive, 48 patients negative. The positive rate was 30.43%. In mid-RT concentration of IL-1β in Cpn IgG positive and negative group were (35.82±10.09) ng/L and (30.01±6.46) ng/L, with statistically significant difference (P < 0.05). Pre-RT and post-RT concentrations of IL-1β in Cpn IgG positive and negative group had no statistically significant difference. Mid-RT concentrations of SP-A in Cpn IgG positive group and negative group were (641.78±106.81) ng/L and (100.86±61.4) ng/L respectively, with statistically significant difference (P < 0.05). Post-RT concentration of SP-A in Cpn IgG positive and negative group were (657.47±115.19) ng/L and (93.23±47.15) ng/L respectively, with statistically significant difference (P < 0.05). Concentrations of TNF-α in Cpn IgG positive and negative group had no statistically significant difference. Concentrations of TGF-β in Cpn IgG positive group were (710.67±358.16) pg/mL in pre-RT, (1,002.06±542.16) pg/mL in mid-RT, (2,125.16±1,522.29) pg/mL in post-RT; those in negative group were (867.77±412.48) pg/mL, (914.05±425.70) pg/mL, (1,073.36±896.01) pg/mL. Concentration of TGF-β in post-RT between Cpn IgG positive and negative group had statistically significant difference (P < 0.05). Conclusion Cpn IgG positive in lung cancer patients influenced levels of IL-1β, SP-A, TGF-β during chest radiotherapy. This might aggravate radiation-induced pulmonary lesion

Joint Computing Offloading and Resource Allocation for Classification Intelligent Tasks in MEC Systems

Mobile edge computing (MEC) enables low-latency and high-bandwidth applications by bringing computation and data storage closer to end-users. Intelligent computing is an important application of MEC, where computing resources are used to solve intelligent task-related problems based on task requirements. However, efficiently offloading computing and allocating resources for intelligent tasks in MEC systems is a challenging problem due to complex interactions between task requirements and MEC resources. To address this challenge, we investigate joint computing offloading and resource allocation for intelligent tasks in MEC systems. Our goal is to optimize system utility by jointly considering computing accuracy and task delay to achieve maximum system performance. We focus on classification intelligence tasks and formulate an optimization problem that considers both the accuracy requirements of tasks and the parallel computing capabilities of MEC systems. To solve the optimization problem, we decompose it into three subproblems: subcarrier allocation, computing capacity allocation, and compression offloading. We use convex optimization and successive convex approximation to derive closed-form expressions for the subcarrier allocation, offloading decisions, computing capacity, and compressed ratio. Based on our solutions, we design an efficient computing offloading and resource allocation algorithm for intelligent tasks in MEC systems. Our simulation results demonstrate that our proposed algorithm significantly improves the performance of intelligent tasks in MEC systems and achieves a flexible trade-off between system revenue and cost considering intelligent tasks compared with the benchmarks.Comment: arXiv admin note: substantial text overlap with arXiv:2307.0274

A limited feedback scheme for massive MIMO systems based on principal component analysis

Massive multiple-input multiple-output (MIMO) is becoming a key technology for future 5G cellular networks. Channel feedback for massive MIMO is challenging due to the substantially increased dimension of the channel matrix. This motivates us to explore a novel feedback reduction scheme based on the theory of principal component analysis (PCA). The proposed PCA-based feedback scheme exploits the spatial correlation characteristics of the massive MIMO channel models, since the transmit antennas are deployed compactly at the base station (BS). In the proposed scheme, the mobile station (MS) generates a compression matrix by operating PCA on the channel state information (CSI) over a long-term period, and utilizes the compression matrix to compress the spatially correlated high-dimensional CSI into a low-dimensional representation. Then, the compressed low-dimensional CSI is fed back to the BS in a short-term period. In order to recover the high-dimensional CSI at the BS, the compression matrix is refreshed and fed back from MS to BS at every long-term period. The information distortion of the proposed scheme is also investigated and a closed-form expression for an upper bound to the normalized information distortion is derived. The overhead analysis and numerical results show that the proposed scheme can offer a worthwhile tradeoff between the system capacity performance and implementation complexity including the feedback overhead and codebook search complexit