In recent years, physical layer security (PLS) techniques have been paid considerable attention due to its high-security level and strong compatibility. However, the request of the superior legitimate channel is the Achilles’ Heel of PLS. A significant challenge exists in ensuring information confidentiality when the eavesdropper locates at user’s direction in the multi-antenna system. To overcome this limitation, we propose a novel framework to achieve secure communication via distributed split single sideband (SSB) time modulated array (TMA). By strategically dividing the I/Q paths of the transmitted signals into geographically separated subarrays, we establish the non-aliasing zone to achieve error-free communication for legitimate user (LU), while the eavesdropper positioned in the aliasing zone receives irrecoverably disturbed waveform. To assess performance, we adopt the encoder-decoder-based deep neural network to optimize the time-switching sequence, ensuring the subarrays’ spatial radiation areas overlap exclusively at the LU. A specialized loss function is formulated to enhance the interference-to-signal ratio by increasing the −1stto the +1stharmonic power ratio in non-LU regions. Furthermore, the joint optimization improves security by focusing energy on the LU while generating interference in non-LU areas. The bit error rate (BER) is used as the metric, and the simulation results validate the effectiveness of the proposed method, ensuring reliable transmission and increasing Eve’s BER to approximately 0.5, thereby compromising her ability to intercept the communication.

Recently, blockchain has been widely used in important scenarios (e.g., finance and auditing). To fully meet the needs of various business scenarios and reduce deployment costs, cloud blockchain services (CBS) are now being offered by cloud computing providers. However, in high-frequency and large-scale transaction scenarios, blockchain performance faces serious challenges, limiting its further application. Therefore, blockchain performance optimization (BPO) has become a key field. Recent BPO methods that adjust blockchain configuration parameters like block size, offer benefits such as low cost and easy deployment. However, these methods face challenges including unsuitability for dynamic environments, high optimization overhead, and failure to consider marginal utility (MU) in BPO. MU describes the decreasing effectiveness of BPO as transaction arrival rates increases, eventually leading to limited BPO benefits. This paper proposes a data-driven BPO framework (BPO-CBS) for CBS. First, a blockchain performance prediction model is trained using ensemble learning. Second, a performance scoring and adjustment mechanism is designed to identify optimal configuration parameters and adjust them to enhance BPO. Finally, extensive quantitative and qualitative comparisons with related works show that BPO-CBS achieves more effective BPO with low optimization overhead.

Integrated sensing and communications (ISAC), supported by massive multiple-input multiple-output (MIMO), can provide simultaneously improvement of sensing capability and communication capacity. However, employing the conventional fully digital beamforming architecture with a large-scale antenna array will incur the prohibitively high hardware cost and power consumption. In this paper, we propose a hybrid beamforming design with the overlapped subarrays (OSA)-based hybrid architecture for massive MIMO-ISAC systems. We design the analog and digital beamformers by jointly optimizing the spectral efficiency of communication and beampattern mean squared error of sensing under the specific constraints of OSA structures, power budget, and constant modulus. To tackle the resulting non-convex problem, we relax it as a weighted summation minimization problem, where the Euclidean distance between the designed hybrid beamformers and the optimal communication/desired sensing beamformers is minimized. We further decompose the formulated problem into three subproblems and develop an effective alternating minimization algorithm. Numerical simulations demonstrate the effectiveness and flexibility of the proposed OSA-based hybrid beamforming design in terms of spectral efficiency and sensing beampattern performance.

The parking lot is one of the important components of the intelligent transportation system (ITS). The current parking lots mainly use instant parking, which has low parking efficiency, during peak hours, which leads to traffic congestion. To guarantee the stable operation of parking lots, we propose a blockchain-enabled parking reservation framework, called BPR. Traditional parking reservation systems may exist the condition of malicious reservations, and resulting in wasted parking spaces. Therefore, we design a reputation mechanism to manage the parking reservation behavior of vehicles and reduce the number of malicious nodes. In addition, to balance the performance of the blockchain at different times (especially during peak hours), we use deep learning (DL) to dynamically adjust the block size to make the blockchain run more efficiently and stably. We deploy the system in Hyperledger Fabric and conduct effectiveness experiments. The comprehensive evaluation results and analysis show that the proposed reputation mechanism can effectively curb malicious nodes from reserving parking spaces and reduce the waste of parking resources. And the block size will be dynamically adjusted to balance the performance of the blockchain at different periods, this method is also applicable to other blockchain performance-sensitive scenes. Finally, this paper is compared with related work to demonstrate the innovation and feasibility of this work from various aspects.

Abstract Time‐modulated arrays (TMAs) have been widely studied owing to their convenient control mode and simple structure. Although direction‐of‐arrival (DOA) estimation based on TMA has garnered considerable attention, underdetermined DOA estimation in TMA is yet to be studied. In this study, a novel method is proposed for underdetermined DOA estimation based on the higher‐order cumulants of harmonic signal in a TMA. The periodic modulation of radio‐frequency switches leads to a TMA generating harmonics in space. Cumulants of harmonic statistics can be used for DOA estimation, and with higher‐order harmonics cumulants, underdetermined DOA estimation can be realised with a TMA using the multiple signal classification method, and the degree of freedom can be improved further. Another advantage of the proposed method is its simple structure, which only requires one receiver with several filters. Numerical simulations show that the proposed method can achieve good resolution and precision performance.

Summary form only given: We study compressed representations of web graphs. Among previous work, the solution by Hernandez and Navarro [1] supports more queries than alternative approaches, including in-neighbour queries, out-neighbour queries and a set of mining queries. Their main strategy is to extract dense subgraphs from the given graph, and encode them using succinct data structures such as wavelet trees. Previous experimental studies on wavelet trees, however, test performance using textual data, and more engineering work is needed for the data generated from web graphs.Our strategy is to use different implementations to encode bit vectors at different levels of the wavelet trees constructed for dense subgraphs, based on the observation that bit vectors at top levels are more compressible than the rest. These implementations are considered: RRR, practical implementations [2] of the structure by Raman et al. [3]; RLEG, a bit vector structure based on run-length and Elias gamma codes [4]; and Plain, an uncompressed representation with low overheads [5]. Two specific approaches are used to combine them: The first approach encodes bit vectors using RRR starting from the root of a wavelet tree, until a level for which Plain uses less space is reached. Then, starting from this level downwards, Plain is used to encode bit vectors. The second approach uses RLEG, RRR and Plain in a similar top-down fashion, and different tradeoffs can be achieved by using different block sizes for RLEG.We implemented these approaches with code from [1, 4] and the compact structures library libcds (http://recoded.cl/), to encode data sets from the WebGraph Framework project (http://webgraph.di.unimi.it/). We obtained a rich set of time/space tradeoffs that can not be achieved using a single bit vector structure for all levels. The following three tradeoffs are particularly interesting: A new encoding scheme that decreases the space cost of Hernandez and Navarro's structure by 9% to 19% (more than 13% for all but one graph), while only doubling query time; a new scheme that decreases the space cost by 4% to 12% (10% or more for most graphs), with roughly the same query time; and a new scheme that decreases the space cost and the query time by about 2% and 1%-9% (5% or more for most graphs), respectively.