This paper studies a full-duplex filter-and-forward (FD-FF) relay system in frequency-selective channels. Conventionally, the loop-back signal at the FD relay is treated as harmful self- interference and needs to be significantly suppressed via both analog- and digital-domain cancellation. However, the performance of the conventional self-interference cancellation approach is fundamentally limited due to the quantization error induced by the analog-to-digital converter (ADC) with limited dynamic range. In this paper, we consider an analog filter-and-forward design to help avoid the quantization error, and surprisingly show that the maximum achievable rate of such an FD-FF relay system is in fact regardless of the loop- back channel at the FD relay. We characterize the maximum achievable rate of this channel by jointly optimizing the transmit power allocation over frequency at the source and the frequency response of the filter at the relay, subject to their individual power constraints. Although this problem is non- convex, we obtain its optimal solution by applying the Lagrange duality method. By simulations it is shown that the proposed joint source and relay optimization achieves rate gains over other heuristic designs, and is also advantageous over the conventional approach by cancelling the relay loop- back signal as self-interference, especially when the residual self-interference after cancellation is still significant.
Chip-embedded mm-wave antenna-integrated modules are demonstrated, for the first time, on panel-scale ultra-thin glass substrates, for high-speed 5G communication standards in the n257 band (26.5 – 29.5 GHz) defined by 3GPP. Co-packaging of amplifiers, filters, and antennas with minimal package parasitics is the key to realize mm-wave package systems. Parasitics arise from on-package and chip-to-package interconnects. This paper focuses on reduced chip-to-package losses and implementation of filters and antennas with chip-embedding structures in glass substrates. To demonstrate the benefits of glass-panel embedding (GPE) for 5G communications, the interconnect losses are benchmarked with the C4-bump based flip-chip technique. The electrical performance shows that the chip-embedding structure with a glass substrate lead to 3X lower insertion loss from chip to antenna than the flip-chip assembly method with C4 bumps. This reduced chip-to-antenna insertion loss brings about the enhanced efficiency and gain of the patch antennas integrated on top of the glass substrates. The process development and electrical performance are benchmarked with emerging 5G substrate technologies such as fan-out wafer level packaging.
Optimal transmit power allocation strategies are proposed for an energy harvesting estimation system, where energy can be harvested from the environment and buffered in a battery for future use. With the aim of minimizing the mean squared error at the receiver, two types of side information (SI) available to the transmitter are considered: causal SI (energy harvested in the past) and non-causal SI (energy harvested in the past, present and future). For the case where non-causal SI is available and battery storage is unlimited, it is shown that the optimal power allocation can be attained by a simple water-filling-like procedure, where the water level follows a non-decreasing staircase function. Dynamic programming is used to optimize the allocation policy when causal SI is available. The issue of unknown transmit power at the receiver is also addressed.
To achieve the more significant passive beamforming gain in the double-intelligent reflecting surface (IRS) aided system over the conventional single-IRS counterpart, channel state information (CSI) is indispensable in practice but also more challenging to acquire, due to the presence of not only the single- but also double-reflection links that are intricately coupled and also entail more channel coefficients for estimation. In this paper, we propose a new and efficient channel estimation scheme for the double-IRS aided multi-user multiple-input multiple-output (MIMO) communication system to resolve the cascaded CSI of both its single- and double-reflection links. First, for the single-user case, the single- and double-reflection channels are efficiently estimated at the multi-antenna base station (BS) with both the IRSs turned ON (for maximal signal reflection), by exploiting the fact that their cascaded channel coefficients are scaled versions of their superimposed lower-dimensional CSI. Then, the proposed channel estimation scheme is extended to the multi-user case, where given an arbitrary user's cascaded channel (estimated as in the single-user case), the other users' cascaded channels can also be expressed as lower-dimensional scaled versions of it and thus efficiently estimated at the BS. Simulation results verify the effectiveness of the proposed channel estimation scheme and joint training reflection design for double IRSs, as compared to various benchmark schemes.
The effectiveness of many existing high-dimensional indexing structures is limited to specific types of queries and workloads. For example, while the Pyramid technique and the iMinMax are efficient for window queries, the iDistance is superior for kNN queries. We present a new structure, called the P/sup +/-tree, that supports both window queries and kNN queries under different workloads efficiently. In the P/sup +/-tree, a B/sup +/-tree is employed to index the data points as follows. The data space is partitioned into subspaces based on clustering, and points in each subspace are mapped onto a single dimensional space using the Pyramid technique, and stored in the B/sup +/ -tree. The crux of the scheme lies in the transformation of the data which has two crucial properties. First, it maps each subspace into a hypercube so that the Pyramid technique can be applied. Second, it shifts the cluster center to the top of the pyramid, which is the case that the Pyramid technique works very efficiently. We present window and kNN query processing algorithms for the P/sup +/-tree. Through an extensive performance study, we show that the P/sup +/-tree has considerable speedup over the Pyramid technique and the iMinMax for window queries and outperforms the iDistance for kNN queries.
The discourse construction of ideological and political education in colleges and universities is an important task to strengthen the leadership and discourse power of ideological work in colleges and universities. The relevant theories of western multidisciplinary discourse research provide rich theoretical reference for the discourse construction of ideological and political education in colleges and universities. Based on linguistics, sociology, political science, philosophy and other disciplines, ideological and political education in colleges and universities should reasonably absorb its theoretical achievements on the sociality, power, democracy and authenticity of discourse, providing theoretical and practical references for the construction of ideological and political education discourse in colleges and universities in terms of content, power, expression and value.
An intelligent reflecting surface (IRS) can adaptively adjust the phase shifts of its reflecting units to strengthen the desired signal and/or suppress the undesired signal. In this letter, we investigate an IRS-aided secure wireless communication system where a multi-antenna access point (AP) sends confidential messages to a single-antenna user in the presence of a single-antenna eavesdropper. In particular, we consider the challenging scenario where the eavesdropping channel is stronger than the legitimate communication channel and they are also highly correlated in space. We maximize the secrecy rate of the legitimate communication link by jointly designing the AP's transmit beamforming and the IRS's reflect beamforming. While the resultant optimization problem is difficult to solve, we propose an efficient algorithm to obtain high-quality suboptimal solution for it by applying the alternating optimization and semidefinite relaxation methods. Simulation results show that the proposed design significantly improves the secrecy communication rate for the considered setup over the case without using the IRS, and outperforms a heuristic scheme.
Combination of edge computing technologies and machine learning help to put edge intelligence into practice. Industrial Internet of Things (IIoT) is one of its most typical applications. But this system can be easily attacked in the process of using edge computing center to process localized perception data. Intrusion detection technologies based on machine learning provide strong security for edge computing center, in which the most widely used is gradient boosting decision tree (i.e., GBDT). But still this model faces with problems such as imbalanced data, high dimensional data characteristics, and low efficiency of parameter optimization. To solve these problems, this paper proposes an optimization scheme for GBDT to improve its detection precision and training efficiency. First, to solve the problem of imbalanced data in data set, we propose a margin synthetic minority oversampling technique (i.e., MSMOTE), which can expand the non-noise data with less sample size, namely, small sample, to ensure equilibrium distribution of data. Second, to lower the data feature dimensionality, we propose a recursive feature elimination-hierarchy cross validation algorithm (i.e., RFE-HCV). The new algorithm eliminates redundant data features recursively according to feature weight, to strengthen the relationship between features and goals. It also designs hierarchy system to ensure equal proportionment of data category (attack category) in training set and testing set at cross validation stage. Next, in order to improve the efficiency of parameter optimization in model training process, we develop a flexible grid search algorithm (i.e., FGS) to improve retrieval efficiency of optimum parameters. Finally, the detailed experimental results show that our new scheme ensures data balance in dataset and eliminates redundant data features, and helps the efficiency of parameter optimization increase by three times. Moreover, the new scheme defends against intrusion more effectively.
With the fast development of Radio Frequency Identification(RFID) and tag usage,security has become the key issue for RFID related applications.In this paper,a new RFID secure protocol is proposed,which uses a synchronized random number created in the backend system,for tag authentication.After tag successful authentication each time,both tag and random number update synchronously.Meanwhile,the performance and security of the protocol are analyzed theoretically.Also the BAN logic is used to prove its security in further.The result show that this protocol can provide authentication between tag and reader,moreover,it can resist attacks such as replay,location tracking,traffic analysis,counterfeiting,denial of service,and illegal access,etc.From the implementation point,it just uses Hash and exclusive XOR operations,which reduces the computing complexity and makes it fitting for low price tags applications.
This paper presents a multi-antenna cognitive radio (CR) system that is capable of operating concurrently with the primary radio (PR) link. The operation of the CR system consists of three stages: environmental learning, CR channel training and CR data transmission. In environmental learning stage, partial channel information between PR and CR are obtained blindly, based on which the transmit beamforming and the receive beamforming strategies are designed at CR to remove/reduce the interference to and from PR, respectively. We characterize all the interference values analytically and study the problem of learning/training tradeoff associated with the proposed scheme. The optimal balancing between learning and training is examined via the minimum mean square error (MSE) of the channel estimation. It is shown that for a given total learning/training time, there indeed exists a optimal learning time that minimizes the MSE of the channel estimation, yet the interference power to the PR is regulated.
The cloud radio access network (C-RAN) concept, in which densely deployed access points (APs) are empowered by cloud computing to cooperatively support mobile users (MUs), to improve mobile data rates, has been recently proposed. However, the high density of active APs results in severe interference and also inefficient energy consumption. Moreover, the growing popularity of highly interactive applications with stringent uplink (UL) requirements, e.g., network gaming and real-time broadcasting by wireless users, means that the UL transmission is becoming more crucial and requires special attention. Therefore in this paper, we propose a joint downlink (DL) and UL MU-AP association and beamforming design to coordinate interference in the C-RAN for energy minimization, a problem which is shown to be NP hard. Due to the new consideration of UL transmission, it is shown that the two state-of-the-art approaches for finding computationally efficient solutions of joint MU-AP association and beamforming considering only the DL, i.e., group-sparse optimization and relaxed-integer programming, cannot be modified in a straightforward way to solve our problem. Leveraging on the celebrated UL-DL duality result, we show that by establishing a virtual DL transmission for the original UL transmission, the joint DL and UL optimization problem can be converted to an equivalent DL problem in C-RAN with two inter-related subproblems for the original and virtual DL transmissions, respectively. Based on this transformation, two efficient algorithms for joint DL and UL MU-AP association and beamforming design are proposed, whose performances are evaluated and compared with other benchmarking schemes through extensive simulations.
The active measurement technology widely used today is mostly based on ICMP, TCP and UDP, which consequently results in the limitation of the relevant measurement tools. IPMP protocol is designed to provide underlying protocol support for various measurement mechanism. With router's assistance, it can accomplish the accurate measurement of network parameters such as one way delay and the bottleneck of link. The fundamentals of IPMP and the existent shortcomings are discussed in the article, with the improved scheme being presented.
In this paper, we propose a new six-dimensional (6D) movable antenna (6DMA) system for future wireless networks to improve the communication performance. Unlike the traditional fixed-position antenna (FPA) and existing fluid antenna/two-dimensional (2D) movable antenna (FA/2DMA) systems that adjust the positions of antennas only, the proposed 6DMA system consists of distributed antenna surfaces with independently adjustable three-dimensional (3D) positions as well as 3D rotations within a given space. In particular, this paper applies the 6DMA to the base station (BS) in wireless networks to provide full degrees of freedom (DoFs) for the BS to adapt to the dynamic user spatial distribution in the network. However, a challenging new problem arises on how to optimally control the 6D positions and rotations of all 6DMA surfaces at the BS to maximize the network capacity based on the user spatial distribution, subject to the practical constraints on 6D antennas' movement. To tackle this problem, we first model the 6DMA-enabled BS and the user channels with the BS in terms of 6D positions and rotations of all 6DMA surfaces. Next, we propose an efficient alternating optimization algorithm to search for the best 6D positions and rotations of all 6DMA surfaces by leveraging the Monte Carlo simulation technique. Specifically, we sequentially optimize the 3D position/3D rotation of each 6DMA surface with those of the other surfaces fixed in an iterative manner. Numerical results show that our proposed 6DMA-BS can significantly improve the network capacity as compared to the benchmark BS architectures with FPAs or MAs with limited/partial movability, especially when the user distribution is more spatially non-uniform.
This letter studies a wireless powered communication system with a bidirectional relay, where the relay helps forward wireless power from the access point (AP) to the user, and also amplify-and-forward information from the user to the AP in the reverse direction. We propose two protocols based on power splitting and time switching at the relay, respectively. The optimal power splitting and time switching ratios are derived in each case to maximize the information throughput from the user to the AP subject to the energy harvesting and consumption balance constraint at both the relay and the user. Numerical results show that a significant throughput gain is achieved by the optimized power splitting scheme over the optimized time switching scheme.
