Counterfeit and substandard product crimes pose significant threats to consumer safety and market stability, yet the incompleteness of knowledge graphs in this domain hampers effective case analysis and decision-making. Link prediction (LP) captures both structural and semantic representations of entities to infer latent relationships, thereby mitigating graph incompleteness and enabling more accurate investigation and law enforcement. To achieve this, most existing works employ graph neural networks to aggregate neighbor information of entities, aiming to capture both structural and semantic features in the embedding process. However, due to the heterogeneity of the neighbor information, directly aggregating it can negatively impact the prediction accuracy. In this paper, we propose a neighbor-aware embedding interaction (NEI) model that computes interactions between semantic and structural neighbors to sufficiently aggregate heterogeneous neighbor information, which improves LP performance. Specifically, we first propose dependency direction enhancement module that extracts high-quality semantic neighbors to improve understanding for entities with sparse relationships. Subsequently, a neighbor-aware interaction module captures valuable hidden information from both semantic and structural neighbors, enriching the neighbor embedding representation. Then, we propose a neighbor-aware convolution method to improve the prediction performance by incorporating rich neighbor embedding vectors for head and relation embedding vectors of each triple through interaction. We conducted a series of experiments on three public LP datasets to evaluate the effectiveness of the NEI model. The NEI model outperforms the suboptimal baseline by 7. 3% and 5. 3% in the WN18RR dataset in terms of MRR and Hits@1, respectively.

This paper proposes a Cluster-based On-demand DElay tolerant routing (CODE) algorithm for VANETs. The CODE algorithm considers a higyway scenario and performs route discovery for data dissemination based on a cluster-based routing structure. To build the routing structure, CODE first configures vehicles moving on the highway into clusters and uses the relative speeds of vehicles in cluster-head election. A vehicle moving at a speed closer to the average speed of all vehicles has a higher probability to be elected as cluster head. The purpose is to maintain the stability of a cluster in order to achieve a good data delivery performance. Based on this routing structure, an on-demand delay tolerant route discovery mechanism is employed to find an available route for disseminating data packets from a source node to a destination node. Simulation results show that the proposed CODE algorithm has a better cluster stability, and can achieve a better performance in terms of end-to-end delay, packet loss ratio, and routing overhead as compared with existing LID and HD algorithms.

This paper proposes a destination and moving direction information based probabilistic routing protocol, called Advanced DIP (ADIP), for vehicular ad hoc networks (VANETs). The ADIP protocol uses different strategies for routing and data forwarding in different network scenarios. For a high-density scenario, it only considers the position of a neighbor in selecting the next hop for forwarding a data packet. For a low-density scenario, it considers not only the position of a neighbor but also the moving direction of a neighbor in selecting the next hop. The concept of an average two-hop neighbor number is introduced to identify different network scenarios. Simulation results show that in an urban street scenario ADIP does not exhibit an obvious advantage while in a highway scenario ADIP can significantly reduce the packet loss as compared with an existing routing protocol called DIP.

3D-MIMO, using two dimensional antenna array at base station, demonstrates promising throughput gain over conventional antenna system during recent academic and industry studies. To realize the performance gain, accurate channel state information (CSI) feedback is one essential aspect. One effective way to obtain this CSI is taking advantage of channel reciprocity between uplink and downlink in TDD system. This paper analyzes the uplink pilot contamination problem in practical TDD LTE system using a novel pilot contamination model, besides, pilot contamination elimination methods in terms of increasing pilot power and reducing pilot collision probability are evaluated. The simulation results show that system performance degradation is less than 21% considering the practical pilot contamination, which still outperform the traditional 2D-MIMO system. Moreover, about 15% performance gain could be benefit from pilot contamination reduction approaches.

Broadcasting is an effective routing paradigm for data dissemination in vehicular ad hoc networks (VANETs). One concern that arises with broadcasting is the broadcast storm problem, which would cause node contentions and data collisions, and thus degrade the transmission efficiency of a network. This paper proposes a Dynamic trAnsmission delaY based broadcast (DAYcast) protocol for a VANET. To alleviate the effect of the broadcast storm and improve the transmission efficiency of the network, DAYcast only allows the effective neighbors of a source vehicle to broadcast a received data packet and the selection of the effective neighbors are based on the position information on the one-hop neighbors of the source vehicle. Meanwhile, it allows each effective neighbor to wait a certain transmission delay before it broadcasts a received packet. The transmission delay of an effective neighbor depends on the distance between the neighbor and the source vehicle, and the number of effective neighbors of the source vehicle. Simulation results show that DAYcast can effectively improve the network performance in terms of network reachability and the successful delivery ratio as compared with existing weighted p-persistence broadcasting (WPB) and slotted 1-persistence broadcasting (SPB).

This paper proposes a Destination Information based Probabilistic (DIP) routing protocol for vehicular sensor networks (VSNs). In DIP, the destination information of a packet is used to determine candidate neighbors and the moving pattern of a vehicle is used to help select the next hop for forwarding data. The design goal of this protocol is to minimize the packet loss in the network and thus improve network throughput. Simulation results show that DIP achieves a better performance than the well-known existing AODV protocol in terms of packet loss by about 20%. Moreover, DIP also has a better scalability to different packet loads than AODV.

Routing is a critical issue in vehicular ad hoc networks (VANETs). This paper considers the routing issue in both vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) communications in VANETs, and proposes a Moving dirEction and DestinAtion Location based routing (MEDAL) algorithm for supporting V2V and V2I communications. MEDAL takes advantage of both the moving directions of vehicles and the destination location to select a neighbor vehicle as the next hop for forwarding data. Unlike most existing routing algorithms, it only uses a HELLO message to obtain or update routing information without using other control messages, which largely reduces the number of control messages used in routing. Simulation results show that MEDAL can significantly improve the packet delivery ratio of the network as compared with the well-known Ad hoc On-demand Distance Vector Routing (AODV) algorithm.

For downlink multi-user multiple-input multiple-output (MU-MIMO) system, the precoding scheme of maximizing signal-to-leakage-and-noise (SLNR) at base station (BS) relies on channel covariance matrix. However, the CSI feedback defined in LTE/LTE-A does not directly correspond to channel covariance. Instead, precoding matrix indicator (PMI) and channel quality indicator (CQI) are fed back from user equipment (UE) to the base station. It is possible to reconstruct channel covariance matrix based on PMI/CQI. However such reconstruction is quite rough and may cause MU-MIMO performance degradation.

This paper investigates leakage-based precoding schemes that support coordinated multi-point (CoMP) operation for Long-Term Evolution (LTE) systems studied in the 3rd Generation Partnership Project. CoMP operation, which improves cooperative transmission among cells and reduces or eliminates intercell interference, enables performance improvement in cell edge and average throughputs. Although leakage-based precoding has been investigated for multiuser multiple-input multiple-output (MIMO) systems, its extension to CoMP requires further analysis of the near-far effect under multicell deployment scenarios. To this end, we propose to incorporate the channel between a victim user equipment (UE) and the cell that serves the victim UE into the leakage-based precoding calculation. We investigated optimal linear scaling of the covariance matrix involving this channel. To determine the benefits of the investigated algorithm, multicell system level evaluations were carried out. This confirmed the improved performance of the system's average and cell edge throughputs.

The original diversity-multiplexing tradeoff work by Zheng and Tse assumes multi-input-multi-output (MIMO) systems with frequency flat, spatially independent, and temporally block fading. Several following works have extended the tradeoff to either frequency selective or spatially correlated channels. However, all of them assume that fadings are temporally block. Compared with block fading assumption, it is more realistic to model Rayleigh temporal fadings as a zero-mean stationary complex Gaussian random process . Correspondingly, its power spectral density (or Doppler spectrum) is a sufficient description of the random process, because the correlation between two arbitrary fadings can be calculated through the Fourier transform of the Doppler spectrum. The main contribution of this paper is to derive the diversity- multiplexing tradeoff in frequency flat single-input-single-output (SISO) channels with arbitrary Doppler spectrum. Moreover, the derivation process itself, which is based on the spike sharpening effect, may be extended to facilitate other more complicated diversity-multiplexing tradeoff works.

Current multi-input-multi-output (MIMO) capacity analysis focuses on the optimization of ergodic capacity, i.e., mean of capacity, if the capacity in random channels is considered as a random variable. In addition to ergodic capacity, outage probability is another important criteria of MIMO systems, as already stated by many researchers. However, it is not easy to optimize outage probability, because it is in fact a function of outage capacity. Different from previous outage analysis, in this work we are particularly interested in the relationship between outage probability and outage capacity, when outage capacity is asymptotically low, which is called asymptotic outage. Analogous to deep fading, the proposed asymptotic outage is the major exponent of error event. Moreover, it is convenient to optimize the proposed asymptotic outage, in contrast to conventional outage analysis. Detailed study is conducted in large size (number of antenna elements goes to infinity) MIMO systems, accompanied with a recently proposed correlated MIMO channel model, virtual channel representation. It is interesting to find that logarithm of asymptotic outage probability scales linearly with respect to logarithm of asymptotic outage capacity. Even more importantly, it is found that asymptotic outage performance degrades (higher outage probability) when channel correlation decreases.

Recent Multi-Input-Multi-Output research demonstrates that real wireless environments exhibit spatially correlated fadings: The independent fading assumption does not hold in all cases. Accordingly, spatially correlated MIMO channel models have attracted much attention. The Kronecker product form (KPF) is the currently most widely used MIMO channel model describing spatial correlations. However, its validity in various environments have not been well examined. In this work, first the correlation structure of the KPF is discussed. It is shown that the KPF exhibits a separable correlation structure. Second, it is claimed that the separable correlation structure corresponds to separable scatterers in physical environment. Using this utility, the KPF is examined with two popular scatterer distributions: two ring models and elliptic models. It is shown that the KPF is feasible for describing two ring models, but not capable of describing elliptic models. Numerical ray-tracing simulations verify the theoretical results.

Previous multi-input-multi-output (MIMO) designs optimize either reliability (average error probability) or capacity (average data rate, or ergodic capacity). However, a proper MIMO design ought to consider both of them; Just one of them is not sufficient. In this work, we analyze both capacity and error performance of correlated MIMO system, via a recently proposed channel model, virtual channel representation. We derive the error probability in correlated channels, and establish its relationship with experimental capacity results. The results leads to a good hint that there should be a tradeoff between capacity and error performance in MIMO systems.

In this paper, we introduce a simple merger of beamforming (BF) and maximal ratio combining (MRC) in single-input-multi-output (SIMO) wireless communication systems. It is well known that either BF or MRC in SIMO systems does not perform properly in spatially correlated channels, because the former assumes completely coherent fades over the entire antenna elements while the latter assumes independent fades. Here, we study a merger of BF and SIMO via virtual channel representation (VCR). VCR is used as a consistent description of coherent, correlated and independent channels to derive the proposed algorithm. Specifically, this study is performed for a simple SIMO system which leads to high performance gains in spatially correlated channels. The study can be easily generalized to Multi-Input-Multi-Output (MIMO) systems. This approach replaces the traditional channel codes by complex codes generated via signal-to-noise (SNR) optimization at the receiver.

Wavelet and Fourier analyses are widely used in various fields of image processing. They can be utilized in land use remote sensing dynamic monitoring to detect change information automatically. The procedures are as followed: firstly do wavelet transform or Fourier transform to each band or principal component of the multi-spectrum images of two different periods, the coefficients after transform are subtracted, then do inverse transform. The result images display land using changing areas with high brightness. In this paper, we do experiments using the two transforms, and find the result of wavelet transform is better.

In this paper, we analyze the perturbation effects on the performance of a novel wireless local position system (WLPS) with applications in homeland security, defense, vehicle collision avoidance, and multi-robot collaboration and coordination. This WLPS has two main components: (1) a dynamic base station (DBS), and (2) a transponder (TRX), mounted at the monitoring and target mobiles, respectively. The DBS detects TRX via unique ID codes assigned to them. This structure leads to a unique wireless structure. In this work, we assume direct-sequence code division multiple access (DS-CDMA) transmission in the uplink (i.e., from TRX to DBS), and antenna array with conventional beamforming (BF) mounted at the DBS. This structure leads to a high probability-of-detection (POD) performance and capacity (in terms of number of TRX) at the DBS receiver. We analyze the effect of perturbations, which lead to antenna weight vector error, reduce the POD performance and the capacity of the WLPS system. Simulation are performed specifically for applications of WLPS systems in vehicle collision avoidance.

We solve the adaptive call admission control (CAC) problem in multimedia networks via reinforcement learning (RL). The problem requires that network revenue be maximized while simultaneously meeting quality of service (QoS) constraints that forbid entry into certain states and use of certain actions. We show that RL provides a solution to this constrained semi-Markov decision problem and is able to earn significantly higher revenues than alternative heuristics. Unlike other model-based algorithms, RL does not require the explicit state transition models to solve the decision problems. This feature is very important if one considers large integrated service networks supporting a number of different service types, where the number of states is so large that model-based optimization algorithms are infeasible. Both packet-level and call-level QoS constraints are addressed, and both conservative and aggressive approaches to the QoS constraints are considered. Results are demonstrated on a single link and extended to routing on a multilink network.