In today's digital era, data visualization (DV) technology has become indispensable for tasks involving data processing and graphical reasoning. In this demonstration, we introduce a novel automatic DV system named VoiceVisSystem. VoiceVisSystem is designed for transforming speech-form natural language questions (NLQs) into visual data representations, a task formally known as Speech-to-Vis. Unlike the existing cascaded method (e.g., Sevi), the core component of our system relies on an advanced end-to-end speech-to-vis model named SpeechVisNet, eliminating the need for text as an intermediate medium and directly facilitating the conversion from Speech-form to DV. Specifically, the speech encoder and the text encoder of the SpeechVisNet respectively take the user's NLQs and the corresponding database information as inputs and convert them into hidden representations. Then, a grammar-based decoder generates the corresponding DVs as the output. As a result, our system enjoys the benefits of avoiding error propagation, thereby enhancing accuracy. By offering a seamless solution for the speech-to-vis task, VoiceVisSystem presents a promising tool for practical applications in various domains. The demonstration video is available at https://1drv.ms/v/s!Ah2vhbolPBFMiSNPZLunJ6Qp6jqU?e=Shyq8R.

In this paper, we propose stacking codes and decoding algorithms to correct burst deletion/insertion and random substitution errors in a non-binary segment-Lmaxburst-Ddeletion and maxburst-Sinsertion (NB-SBDI(L,D,S)) channel without knowing the codeword boundaries. Specifically, in the NB-SBDI(L,D,S) channel, at most a single non-binary burst (a block of consecutive bits/symbols) of deletions or insertions of length up toDorS, respectively, exists in a block ofLconsecutive non-binary symbols. The proposed stacking-codes are constructed by stacking multiple Reed-Muller(RM)/modified BCH/modified binary representation of Reed-Solomon (BRRS) codes, respectively, and no marker codes are involved. We show that, in the NB-SBDI(L,D,S) channel, the proposed approach can not only correct both burst deletions/insertions and random substitution errors, but also achieve error-free decoding for stacking-RM/modified stacking-BCH/modified stacking-BRRS codes for burst deletions/insertions as long as no substitution errors exist. Also, the modified stacking-BRRS code is more rate efficient than the existing Song code [1], the stacking-RM/modified stacking-BCH codes are more rate efficient than the Song code if max(D,S) and code length are small.

In this paper, an effective approach is proposed to recognize the order among codewords from the randomly binary and non-binary interleaved linear block coded streams if the code minimum distance is known, where the interleaver lengthNn+dis not the multiple of the linear block code lengthn. Then, an efficient approach is developed without knowing linear block code parameters such as code rate and the code generator matrix (except the code length) to recognize the whole random interleaving pattern if the greatest common divisor (GCD) ofnanddis GCD(n, d) = 1 for both binary and non-binary interleaved linear block coded symbols. Further, for the case GCD(n, d) =q̸= 1, the whole random interleaving pattern can be recognized with the probability of no more than (q!)−(Nn+d)/qfor the following cases: (1) the non-binary interleaved coded symbols without knowing the non-binary code generator matrix; (2) the binary interleaved coded symbols.

Resource allocation is very important for achieving interference suppression and protecting the quality of service of users in heterogeneous networks (HetNets). However, the existing works with perfect channel state information (CSI) and ideal hardware ignored the impact of channel uncertainties and hardware impairments on system performance. In this paper, we design a robust secure resource allocation algorithm with imperfect CSI to achieve the energy efficiency (EE) maximization of femtocell users for a two-tier downlink HetNet with multiple passive eavesdroppers, where the residual hardware impairments are considered at the transceivers. The formulated EE problem is non-convex with the consideration of the maximum transmit power constraint of each base station, the cross-tier interference power constraint, as well as the secure rate constraint. By using the worst-case approach and successive convex approximation, the resource allocation problem with the infinite-dimensional constraints is converted into a convex one which is efficiently solved by using convex optimization theory. Simulation results verify that the proposed algorithm has a higher EE and causes less interference power to macrocell users by comparing it with the baseline algorithms.

Robustness and energy efficiency (EE) are of crucial importance in reconfigurable intelligent surface (RIS)-assisted wireless communication networks. However, a large portion of the current works assume that perfect channel state information (CSI) can be obtained, which is impractical because of the passive features of the RIS and the lack of radio frequency chains at the RIS. To handle this issue, we investigate an alternating optimization (AO) algorithm in an RIS-assisted non-orthogonal multiple-access (NOMA) network with imperfect CSI. The EE-based maximization resource allocation problem is formed with the maximum transmit power constraint at the base station, the continuous phase shifts constraint of the RIS, and the outage probability constraint of the signal-to-interference-noise ratio. To solve the tricky non-convex fractional problem, Dinkelbach’s method is used to convert the fractional objective function into parameter subtraction form, and the S-procedure is utilized to deal with the non-convex outage probability constraint with channel uncertainties. Moreover, by applying the AO algorithm the original optimization problem is converted into several semi-definite programming (SDP) subproblems. The overall simulation results illustrate that the proposed algorithm has good robustness and EE.

Edge computing has emerged as a prospective paradigm to meet ever-increasing computation demands in mobile target tracking wireless sensor networks (MTT-WSNs). This paradigm can offload time-sensitive tasks to sink nodes to improve computing efficiency. Nevertheless, it is intractable to execute dynamic and critical missions in the MTT-WSN network due to static property. Besides, the network cannot ensure consecutive tracking with limited energy. To address the problems, this article proposes a new hierarchical tracking structure based on the edge intelligence (EI) technology. The structure can integrate the computing resource of both mobile nodes and edge servers to provide high-efficient computing for real-time tracking. Based on the proposed structure, we propose a long-term dynamic resource allocation algorithm to obtain the optimal resource scheduling solution for accurate and consecutive tracking. Simulation results demonstrate that our algorithm outperforms the deep${Q}$-learning over 14.5% in terms of systematic energy consumption. It can also obtain a significant enhancement in tracking accuracy compared with the noncooperative scheme.

To ensure the security and stabilization of the communication networks, anomaly detection is the first line of defense. However, their learning process suffers two major issues: (1) inadequate labels : there are many different kinds of attacks but rare abnormal nodes in mt of these atstacks; and (2) inaccurate labels : considering the heavy network flows and new emerging attacks, providing accurate labels for all nodes is very expensive. The inadequate and inaccurate label problem challenges many existing methods because the majority normal nodes result in a biased classifier while the noisy labels will further degrade the performance of the classifier. To tackle these issues, we propose SemiADC, a Semi -supervised A nomaly D etection framework for dynamic C ommunication networks. SemiADC first approximately learns the feature distribution of normal nodes with regularization from abnormal ones. It then cleans the datasets and extracts the nodes sasainaccurate labels by the learned feature distribution and structure-based temporal correlations. These self-learning processes run iteratively with mutual promotion, and finally help increase the accuracy of anomaly detection. Experimental evaluations on real-world datasets demonstrate the effectiveness of our SemiADC, which performs substantially better than the state-of-art anomaly detection approaches without the demand of adequate and accurate supervision.

Mobile Edge Computing (MEC) can significantly promote the development of Internet of Vehicles (IoV) for providing a low-latency and high-reliability environment. Nevertheless, a huge amount of sensor data or computing requirements generated by massive vehicles in adjacent area may be duplicated. In order to realize the efficient diffusion of information, we propose a hierarchical end-edge framework with the aid of deep collaboration among data communication, computation offloading and content caching to minimize network overheads. Specially, duplicated perceived data and computation results are cached in advance to decrease repeated data uploading and duplicated computation in offloading process. In addition, the problem is formulated as a mixed integer non-linear programming (MINLP) problem, and the deep deterministic policy gradient (DDPG)-based resource allocation scheme is utilized to obtain a sub-optimal solution with low computation complexity. Performance evaluation demonstrates that the proposed scheme can significantly reduce network overheads compared with other benchmark methods.

In ad hoc networks, each node is required to forward packets for others. However, based on energy consumption considerations, a node may reject other nodes' forwarding requests to save the limited battery power for its own data transmission. Therefore, a lot of incentive schemes have been proposed to promote the cooperation of the nodes. Most of the existing research work assumes that all the nodes in the network should provide full cooperation in order to optimize the transmission efficiency. However, this assumption is too strict because the activities of the nodes in ad hoc networks have some inherent uncertainty. In this paper, we propose a power control mechanism in ad hoc networks under a dynamic repeated game-theoretic framework. A notion of nodes' evaluation levels for the future experiences is defined to take account of the non-cooperation due to the inherent uncertainty in the ad hoc network nodes' activities. Our scheme does not require all the nodes in the ad hoc network to absolutely cooperate with each other. The simulation results show that, compared with the existing schemes, our power control mechanism considering non-cooperative packet forwarding improves the average transmission efficiency by ∼25% and has good scalability.

Due to frequent handovers in broadband wireless communications in high-speed rail, communication interruption during handover could seriously degrade the experiences of passengers on the train. Aiming to reduce the interruption time, this paper proposes a seamless handover scheme based on a dual-layer and dual-link system architecture, where a Train Relay Station is employed to execute handover for all users in a train and two antennas are mounted at the front and rear of a train. In the proposed scheme, the front antenna executes handover while the rear antenna is still communicating with BS, so that the communication can keep non-interruptive throughout the handover. Moreover, bi-casting is adopted to eliminate the data forwarding delay between the serving BS and target BS. A complete handover protocol is designed and the performance of the proposed scheme is analyzed. It can be seen from analytical results that the handover failure probability decreases as cell overlap increases and the communication interruption probability decreases with the decrease of train handover location and the increase of cell overlap. The simulation results show that in the proposed scheme, the communication interruption probability is smaller than 1% when the handover location is properly selected and the system throughput is not affected by handover. In conclusion, both theoretical and simulation results show that the proposed scheme can efficiently perform seamless handover for high-speed rail with low implementation overhead.

It is a great challenge to design an efficient handover scheme of broadband wireless communication systems for high-speed rail to reduce the communication interruption time during handover. A seamless handover scheme based on the dual-layer and dual-link system architecture is proposed in this paper. In the scheme, two antennas are mounted at the front and rear of a train, and one of them executes handover while the other is still communicating with BS. Therefore, the communication will not be interrupted throughout the handover. Furthermore, bi-casting is adopted to eliminate the data forwarding delay between the serving BS and target BS. In order to reduce handover implementation overhead, a Train Relay Station is employed to represent all the users in a train to execute handover in the proposed scheme. The performance of the proposed scheme is analyzed in terms of handover probability, handover failure probability and communication interruption probability. Simulation and theoretical results show that the proposed scheme can efficiently support seamless handover for high-speed trains with low implementation overhead.

QoS provisioning in wireless mesh networks has been to known as a challenging issue. In a distributed scheduling based wireless mesh backhaul network, conventional connection-based QoS provision mechanism requires considerable amount of overhead for per-link QoS signaling, thus cannot support high speed real time traffic. In this paper,we propose a DiffServ-like QoS provision mechanism for IEEE 802.16 mesh networks with directional antennas to increase network capacity and enable real time traffic. We develop a distributed scheduling algorithm based on our proposed scheme. Simulation results demonstrate that directional antennas can achieve higher network capacity compared with omni-antennas, and our QoS scheme can effectively guarantee QoS requirements of real time traffic.

In ad hoc networks, reducing energy consumption and improving throughput are both important for high network performance. This paper presents a joint power and rate control adaptive algorithm to optimize the trade-off between power consumption and throughput in ad hoc networks. Each node chooses its own transmission power and rate based on limited environment information in order to achieve optimal transmission efficiency. In a fictitious game framework with strategy space transformation, our joint power and rate control adaptive algorithm can be viewed as a supermodular game. By interpreting the supermodular game using myopic best response updates, this algorithm can converge to the unique optimal transmission efficiency. Finally, the simulation results show that this supermodular game approach improves the average transmission efficiency by about 33%.

With its new features such as multi-homing and multi-streaming the Stream Control Transmission Protocol (SCTP) has become a promising candidate as a general-purpose transport layer protocol. Multi-homing in an SCTP association can make concurrent multi-path transfer an appealing candidate to satisfy the ever increasing user demands for bandwidth. Multiple streams provide an aggregation mechanism to accommodate heterogeneous objects, which belong to the same application but may require different QoS from the network. However, the current approach lacks an internal mechanism to support preferential treatment among its streams for concurrent multi-path transfer. In this paper, we introduce WM2-SCTP (Wireless Multi-path Multi-flow - Stream Control Transmission Protocol), a transport layer solution for concurrent multi-path transfer with parallel subflows. WM2-SCTP aims at exploiting SCTP's multi-homing and multi-streaming capability by grouping SCTP streams into subflows based on their required QoS and selecting best paths for each subflow to improve data transfer rates. The results show that under different scenarios WM2-SCTP, can effectively enhance transmission efficiency.

With the development of heterogeneous networks, multimode terminals are becoming more and more popular. However, when there are several different kinds of sessions requiring transmission simultaneously, how to distribute these sessions among the available access networks according to the different features of the flows and the current link conditions of the candidate networks is a new challenge. In this paper, we propose a new solution to the flow distribution problem for multimode terminals. Our proposal, automatic flow distribution (AFD), includes a network selection algorithm located at the terminals and an admission control algorithm located at the access points of the networks. Consequently, the terminals and the networks can cooperate with each other and realize automatic flow distribution among the different available access networks. We utilize the notion of priority, ensuring that the more important sessions have preferential use of the network resources. In addition, in order not to excessively deteriorate the transmission performance of the lower priority flows, a probabilistic suspension scheme is introduced. Finally, the AFD method utilizes the concept of "entropy" to automatically compute the weights of different attributes which influence the flow distribution decision making, thus avoiding the users' difficulty to specify the weights manually.

In order to improve the efficiency of paging management, various paging strategies have been proposed for tracking single mobile user in the wireless cellular network. However, most of the schemes have ignored the important problem of efficient search for multiple mobile users under delay and bandwidth constraints. Given the condition that the search is over only after all the users in the group are found, this problem is defined as the Conference Call Search (CCS) problem. As opposed to the single user tracking, for which one can always reduce the expected search cost, for a multicast search the dependency between the delay and the search cost is NP-hard. In this paper, we propose a non-optimal search method that yields a low search delay as well as a low search cost. The 2D Markov walk is used as the mobility model which describes a broad class of mobility patterns. The Weighted Bipartite Graph Matching is used to allocate paging bandwidth which keeps a total maximum location probability at each paging round. Experimental results show that our paging scheme also achieves a low paging cost and delay compared to other schemes proposed in the literature.

Location prediction is one of the key technologies of active mobility management in the next generation of mobile communication systems. Most of known location prediction models only take parts of predictive factors into account, which leads to a low prediction success ratio of these models. The motivation of this paper is to design a location prediction model considering multiple predictive factors to improve the prediction success ratio and improve the efficiency of the model. In this paper, a location prediction model based on Bayesian Network theory is proposed. The proposed model can effectively solve multi-factor location prediction. Firstly, the relative predictive factors are coded in the Bayesian Network node, and location prediction results can be calculated based on cell topology information integrated in the model structure. A factors distribution mechanism is designed to solve the problem when the nodes cannot obtain location prediction information directly. Subsequently, the method of calculating location prediction results for each cell is presented. The simulation results indicate that the proposed location prediction model is effective in improving accuracy of location prediction and the stability of the model is better than that in comparative schemes.

Network selection is an important issue in the next generation of heterogeneous wireless network and well studied for individual flows. However, researches of network selection for multi-flows at network side are seldom touched upon but also important from the global view of optimizing usage of network resources. In this paper, a weighted bipartite graph based network selection scheme for multi-flows is proposed, which adopt secondary exponential smoothing method to perform network resource prediction and assign flows to networks based on Matching Degree(MD). The network selection scheme is modeled as weighted bipartite graph with objectives of maximizing Matching Degrees and access ratio as well as the constraint of guaranteeing no network overloaded. Weighted Bipartite Graph Algorithm (WBGA) is designed to achieve the goals. Simulation results show that WBGA demonstrates best performance compared to other schemes, with regard to average MD, access ratio and utilization ratio of network resources.

Significant throughput gains and system fairness can be obtained by employing scheduling schemes based on precoding techniques. However, QoS guarantee requirements are seldom taken into account. In this paper, we propose a downlink scheduling algorithm for QoS-guaranteed services in multi-user multiple-input multiple-output (MIMO) systems with limited feedback. The proposed algorithm combines stream selection with multi-user packet scheduling. To maximize the overall capacity and reduce co-channel interference, streams are selected in accordance with preceding matrices. In multi-user packet scheduling, the base station first determines the SDMA region size for the primary streams. In addition, packet scheduling for the secondary streams is performed to completely exploit spatial multiplexing gains. Numerical results show that, at the cost of slightly lower system fairness, the proposed algorithm can achieve higher spectrum efficiency, and have a noticeable improvement in guaranteeing QoS requirements in terms of data rates and delay.

In multiuser MIMO systems, the achievable system data rate as well as quality of service (QoS) of individual users is limited by the inter-user interference (IUI). The interference balancing issue via power allocation can be usually formulated as a nonconvex optimization problem which will become more intractable with nonconvex QoS constraints. In this paper we focus on the challenging QoS-aware optimal power allocation problem, aiming to maximize the system sum rate and guarantee the users' minimum data rates. As a result, a "Polynomial Approximations based on Single Condensation" (PASC) strategy is proposed which transforms the underlying problem to a series of tractable Geometric Programming (GP) problems through single condensation scheme. Extensive simulations have been conducted and the results indicate that PASC is quite suitable to tackle the problem, which can achieve a good balance between the system sum rate and the individual QoS requirements.

Although significant throughput gains and system fairness can be obtained by slot-based scheduling algorithms, the corresponding complexities are considerable. In this paper, we propose a region-based downlink scheduling algorithm for QoS-guaranteed services in multi-user multiple-input multiple-output (MIMO) precoding systems. In contrast with conventional slot- based scheduling, region-based scheduling allocates bandwidth based on resource regions consisting of several slots. The proposed algorithm combines QoS-urgent-degree and the proportional fairness to select candidate connections, and determines the size of each SDMA region. In addition, multi-user scheduling is performed in all SDMA regions to completely exploit spatial multiplexing gains. Simulation results show that, at the cost of slightly lower spectrum efficiency and data rate guarantee, the proposed region-based scheduling algorithm has much lower complexity, while at the same time it can achieve higher performances in system fairness and delay guarantee.

This paper focuses on the issue of flow distribution for multimode mobile terminals in heterogeneous networks, and proposes a flow distribution algorithm based on priority and weight self-production mechanism. The algorithm utilizes the notion of "priority" , assigning higher priorities to more important sessions, therefore ensuring these sessions have the preferential utilization of the network resources. In addition, this paper proposes a new method to automatically determine the weights of the multiple attributes which influence the session flow distribution decision, thus avoiding the difficulty and irrationality when relying on the users to specify the weights directly. Simulation results show that our algorithm can efficiently distribute the session flows on a multimode mobile terminal to the appropriate networks for transmission based on the current conditions of different access networks, and on the QoS requirements of different types of sessions. The decisions made by our algorithm reduce the monetary cost and power consumption of the communication as well as guarantee the QoS of different types of sessions. Finally, our algorithm offers significant improvements in performance indicators such as adjacency to the optimal network and session completion rate.

In the IEEE 802.16e OFDMA systems, the data mapping algorithm maps the data to the appropriate rectangular regions in the two-dimensional matrix of time and frequency domain. Each region is described by an Information Element (IE) which is used for signaling and occupies a slot. The IEs as well as vacant slots in the allocated rectangular region result in a substantial amount of overhead. In order to minimize the overhead so as to increase system throughput, the paper proposes a "Mapping with Appropriate Truncation and Sort" (MATS) algorithm. Extensive simulations are conducted in terms of mapping efficiency, mapping cost and system throughput to evaluate the performance of MATS. The results show that compared with Raster, MATS can increase the mapping efficiency by up to 2.4% and reduce the mapping cost by up to 80% and 37% for constant bit rate traffic and variable bit rate traffic, respectively. Moreover, system throughput is increased by more than 3% in the 10 MHz bandwidth network. Consequently, MATS can substantially reduce the overhead and achieve high system throughput.

In this paper, a low-complexity SDMA-based greedy resource allocation (SGRA) algorithm is proposed for the uplink of IEEE 802.16 MIMO-OFDMA systems taking into account co-channel interference. The objective of SGRA is to allocate resources in the space-time-frequency domain in order to maximize system throughput while guaranteeing QoS requirements of real time services. By performing efficient interference management, SGRA can be carried out in two phases. In the first phase, greedy resource allocation, primarily involving uplink scheduling and subchannel allocation across the MAC and PHY layers, is performed in the time-frequency domain. In the second phase, the resource allocation is extended to the space-time-frequency domain. Simulation results show that SGRA can improve system throughput while at the same time guaranteeing the delay and minimum data rate requirements of users.

In this paper, a singular value decomposition based signature matrix inversion (SVD-SI) scheme is proposed for downlink in multiuser MIMO systems, which is more efficient and able to reduce the feedback overhead by limited feedback. The wireless channels are decomposed into several eigenmodes with the right singular vectors as the spatial signatures, which are quantized and fed back to the base station. Then, the operating users are selected according to their signatures, and the multiuser interference is eliminated by the signature matrix inversion scheme. Finally, we characterize the performance of SVD-SI under limited feedback, and give the recommended feedback rate under different scenarios. Numerical results show that the proposed scheme achieves significant throughput improvement while reducing the feedback overhead.

The random access scheme is used for initial and periodic ranging in the IEEE 802.16 protocol. The number of contention slots in the uplink subframe decides the system performance. However, how many contention slots should be allocated for ranging is not standardized in the protocol, so it is still necessary to determine the optimal number of contention slots. In this paper, the exact equations of the optimal numbers of initial ranging slots and periodic ranging slots are derived. An optimal dynamic allocator is also proposed to optimize the allocation of the initial and periodic ranging regions in the uplink subframe. The simulation results show that good system performance can be achieved with the optimal dynamic allocator.

In this paper, we propose a practically efficient Subcarrier Allocation scheme based on Lagrangian relaxation to solve the problem of subcarrier allocation in OFDMA wireless channels. The problem of subcarrier allocation is formulated into an Integer Programming (IP) problem, which is relaxed by replacing complicating constraints with Lagrange multipliers using Lagrangian Relaxation. A subgradient method is used to optimize the Lagrangian dual function and a heuristic is designed to obtain the feasible solution. Lagrangian Relaxation Subcarrier Allocation (LRSA) is proven to be of polynomial complexity and it provides bounds on the value of channel efficiency. Numerical results show that compared with other algorithms proposed in the literature, LRSA can result in a significant improvement in channel efficiency, while at the same time guaranteeing minimum data rates of users.

In this paper, we tackle the packet scheduling problem in IEEE 802.16e wireless metropolitan area network (WMAN), where the Sleep Mode is applied to save energy of mobile stations (MSs). Our objective is to design an energy efficient scheduling policy which works closely with the sleep mode mechanism so as to maximize battery lifetime in MSs. To the best of our knowledge no power saving scheduling algorithms based on sleep mode defined in IEEE 802.16e have been proposed so far in the literature. We propose a longest virtual burst first (LVBF) scheduling algorithm which schedules packets of MSs in a virtual burst mode where there is one primary MS and multiple secondary MSs sharing the wireless link resource. LVBF prolongs MSs' lifetime by reducing the average time when MSs stay in the idle state and the number of state transitions between the awake and sleep states. Simulation results show that, in comparison with the round robin scheduling scheme, LVBF can produce significant overall energy saving, while guaranteeing the QoS requirements of MSs in terms of their minimum data rates.

In this paper, the problem of scheduling multiple users sharing a time varying wireless channel is studied, in networks such as in 3G CDMA and IEEE 802.16. We propose a new generic wireless packet scheduling framework (WPSF), which takes into account not only the quality of service (QoS) requirements but also the wireless resource consumed. The framework is generic in the sense that it can be used with different resource constraints and QoS requirements depending on the traffic flow types. Subsequently, based on this framework a minimum rate and channel aware (MRCA) scheduling algorithm is presented. MRCA attempts to greedily enhance wireless channel efficiency by making use of multi-user channel quality diversity, while providing acceptable QoS in term of users' minimum rate constraints. Simulation results show the desirable properties identified in the algorithm.