In this paper, we propose a Satellite-Terrestrial Integrated Network (STIN) assisted vehicular multi-tier distributed computing (VMDC) system leveraging hybrid terahertz (THz) and radio frequency (RF) communication technologies. Task offloading for satellite edge computing is enabled by THz communication using the orthogonal frequency division multiple access (OFDMA) technique. For terrestrial edge computing, we employ non-orthogonal multiple access (NOMA) and vehicle clustering to realize task offloading. We formulate a non-convex optimization problem aimed at maximizing computation efficiency by jointly optimizing bandwidth allocation, task allocation, subchannel-vehicle matching and power allocation. To address this non-convex optimization problem, we decompose the original problem into four sub-problems and solve them using an alternating iterative optimization approach. For the subproblem of task allocation, we solve it by linear programming. To solve the subproblem of sub-channel allocation, we exploit many-to-one matching theory to obtain the result. The subproblem of bandwidth allocation of OFDMA and the subproblem of power allocation of NOMA are solved by quadratic transformation method. Finally, the simulation results show that our proposed scheme significantly enhances the computation efficiency of the STIN-based VMDC system compared with the benchmark schemes.

In this paper, we propose a Satellite-Terrestrial Integrated Network (STIN) assisted vehicular task offloading leveraging hybrid terahertz (THz) and radio frequency (RF) communication technologies. Task offloading for satellite edge computing is enabled by THz communication using the orthogonal frequency division multiple access (OFDMA) technique. For terrestrial edge computing, we employ non-orthogonal multiple access (NOMA) and vehicle clustering to realize task offloading. We formulate a non-convex optimization problem aimed at maximizing computation efficiency by jointly optimizing bandwidth allocation, task allocation and power allocation. To address this non-convex optimization problem, we decompose the original problem into three sub-problems and solve them using an alternating iterative optimization approach. For the subproblem of task allocation, we solve it by linear programming. The subproblem of bandwidth allocation of OFDMA and the sub-problem of power allocation of NOMA are solved by quadratic transformation method. Finally, the simulation results show that our proposed scheme significantly enhances the computation efficiency of the STIN-based vehicular task offloading compared with the benchmark schemes.

The frame-based precoding oriented from the frame structure under the DVB-S2 standard for satellite communications leads to the multicast transmission in each user frame. This paper investigates the multicast-aware user framing/grouping problem to facilitate the frame-based precoding that demands users of high channel similarity in each group. We propose two alternative approaches to increase the intra-group channel similarity by taking into account the channels of all users already in the group when selecting the parallel users for it. One approach extracts the first principle component vector from the channel matrix constituted by current group members and uses it to measure the similarity to the ungrouped users for the selection of the next group member. The other one adds up the projections of the ungrouped user's channel to the channels of the current group members to measure the similarity. Numerical results demonstrate that the proposed two algorithms outperform a benchmark algorithm in various scenarios, verifying the effectiveness of exploiting the channel information of all group members to constitute multicast groups with high intra-group similarity.

Accurate long-term predictions help governments make decisions and residents travel, which is essential for the development of smart cities. Fortunately, due to the deployment of low-cost sensors, a large amount of time-series data such as parking availability data and air quality data has been stored, which makes it possible for long-term predictions. Many state-of-the-art studies based on multiple graphs have shown excellent performance in long-term prediction tasks. However, few previous studies employ multiple attention mechanisms to their models based on multi-graphs and thus fail to comprehensively capture the dynamic spatio-temporal correlations as well as the inner relationships among graphs. To this end, we propose a spatio-temporal multi-attention multi-graph convolutional network (STM2CN) framework for long-term prediction. We applied four different graphs to mine the potential contextual relationships and employed three attention mechanisms to capture the multiple graph and spatio-temporal correlations. Experiments on two large-scale real-world datasets demonstrate that the proposed STM2CN framework outperformed the state-of-the-art baselines.

Existing researches on the theoretical analysis for Proportional Fair (PF) scheduling are mostly based on the full buffer assumption while the user always has a bursty traffic in reality. In this paper, an analytical framework for PF scheduling in an Orthogonal Frequency Division Multiple Access (OFDMA) wireless network is proposed, and the user traffic is set to follow a poisson arrival process. Detailed analysis for each user's Resource Block (RB) competition status is carried out, and an M/M/1 queuing model is adopted for the packet transmission process of each user, with which the analytical expressions for user throughput and packet latency are obtained. Simulation in a single-cell OFDMA network is carried out and the comparision between simulation and analysis results confirms the accuracy of our analysis.

In modern mobile communication networks, such as 3G and 4G networks, sectorized antennas have been widely used to divide each cell into multiple sectors in order to improve coverage, spectrum efficiency, and quality of service. Large-scale path loss from a transmitting antenna to a receiving antenna should include: 1) propagation attenuation that depends on transmission distance; 2) shadowing that depends on surrounding environment; and 3) antenna loss that depends on a sectorized antenna pattern and transmission angle. An in-depth analysis of statistical characteristics of large-scale path losses involving with these three factors is crucial for the design, operation, evaluation, and optimization of modern sectorized wireless networks. In this paper, a sectorized antenna pattern is, for the first time, considered in the derivation of a closed-form expression of a probability density function (pdf) of large-scale path losses. Specifically, we first discover that the normalized pdf of propagation attenuation plus shadowing, which can be approximated by the Gaussian mixture model (GMM) with all system parameters, is fully determined by our newly defined metric 10/ln 10β/σs, namely, the attenuation exponent β to standard deviation of shadowing σsratio (ASR). The convolution of GMM and antenna loss statistics is elaborately transformed to a series of differential equations. A closed-form pdf of large-scale path losses with sectorized antenna pattern can be obtained by solving these differential equations. To reduce the computational complexity, we further prove that the exciting sources of these differential equations can be tightly approximated by weighted Gaussian functions, and thus, the final solutions (i.e., pdf of path losses) can be derived in the form of Gaussian and Dawson functions. Our analytical results are verified by extensive numerical computation and Monte Carlo simulation results, e.g., the impact of ASR on the shape of pdf of propagation attenuation plus shadowing. Compared with traditional Gaussian-fitting approach, our newly derived pdf of large-scale path losses with sectorized antenna patterns is at least two orders of magnitude more accurate in terms of Kullback-Leibler divergence under typical propagation attenuation and shadowing conditions.

Facebook has recently announced its OpenCellular platform for promoting open-source wireless access technology developments and broader applications. In this demonstration, we would like to show a software defined mobile network that realizes both eNB and EPC key functions according to the 3GPP LTE standard on a general purpose processor (GPP) based computational platform. It is very adaptive and flexible for supporting a variety of internet of things (IoT) applications in vertical industries.

The statistical characteristics of the signal-to-interference plus noise ratio (SINR) are closely related to many performance metrics of cellular networks. In this paper, the downlink average rate and SINR distribution are studied for orthogonal frequency division multiple access (OFDMA)-based cellular networks subject to the distance-dependent path loss and shadow fading (SF). With the analytical approximate mean and moment generating function (MGF) of the SINR in the logarithmic domain, the closed-form approximation for the lower and upper bounds of the average rate is obtained. Then the distribution of the SINR in the logarithmic domain is proposed to be approximated as the normal inverse Gaussian (NIG) distribution whose parameters are computed explicitly through moment matching. Also, the closed-form expression for the cumulative distribution function (CDF) of the SINR based on the NIG approximation is derived. Simulation results not only verify the tightness of the bounds, but also show that the NIG approximation is up to one order of magnitude more accurate than the Pearson type IV approximation and at least one order of magnitude more accurate than the lognormal approximation when the SF correlation coefficient is small or the standard deviations of the SF are large or different.

In orthogonal frequency division multiple access (OFDMA)-based cellular networks subject to the distancedependent path loss and shadow fading (SF), the downlink inter-cell interference (ICI) for a given user equipment (UE) is essentially a sum of several lognormal random variables (RVs). So far, no method of approximating the lognormal sum distribution is explicitly accurate when the component lognormal RVs with different logarithmic means and logarithmic variances are correlated. In this paper, the Normal Inverse Gaussian (NIG) distribution is proposed to approximate the downlink ICI for a given UE with the correlated SF. First, the downlink ICI is modelled as a sum of several correlated lognormal RVs. Then original moments of the lognormal sum in the logarithmic domain are obtained analytically. Finally the estimated parameters of the NIG distribution are computed explicitly by the mean, variance, skewness and kurtosis of the lognormal sum in the logarithmic domain through moment matching. Numerical results verify the accuracy of the NIG approximation when the correlated component lognormal RVs have different logarithmic means and logarithmic variances, and show that the NIG approximation outperforms the MGF-based lognormal approximation in various scenarios.

In order to meet the explosive increasing demand of user application data in modern wireless networks, a variety of multiservice resource allocation algorithms have been proposed in the literature. Most of them can be modeled as optimization problems of minimizing a summation function indicated as Σi=1Nfi(xi) with additive nonlinear coupling inequality constraints. The existing subgradient methods can only achieve a convergence rate of O(1/√k), which is quite slow for handling big user data generated from modern heterogeneous wireless networks. To develop more efficient multiservice resource allocation algorithms, we consider the regularized Lagrangian function with smoothing accelerated techniques. Specifically, in this paper, we extend the previous research that mainly focuses on linear coupling equality constraints to a challenging scenario with nonlinear coupling inequality constraints. To solve the problem, we propose and analyze a Bregman-based inexact excessive gap (BIEG) algorithm, which, by rigorous mathematical proofs, can asymptotically achieve a faster convergence rate of O(1/k). Furthermore, the BIEG method is applied to develop a novel multiservice resource allocation algorithm, namely, BIEG-RA, which combines the accuracy control mechanism with the Bregman projection technique. Numerical results verify its fast convergence rate in heterogeneous wireless networks.

In frequency reuse one networks, the inter-cell interference (ICI) is severe especially for cell edge user equipments (UEs). Statistical information of ICI is helpful to design interference mitigation schemes. This paper proposes a statistical model for the uplink ICI in orthogonal frequency division multiple access (OFDMA) networks with power control. The distance-dependent attenuation and correlated shadowing are included in the channel model. Through analyzing the random part related to distribution of user's location and correlated shadowing, we derive the general parametric probability density function (PDF) of uplink ICI in logarithmic domain, and then use the derived model to investigate the distribution of ICI with typical power control schemes. Numerical results derived from theoretical calculations and Monte Carlo simulations verify the statistical model and the analysis.

Device-to-Device communication has been regarded as a promising technology to improve the cellular system efficiency, which allows mobile devices communicate directly with each other on the licensed frequency resources under the control of a base station (or eNB). However, the benefits of D2D using license exempt bands, such as ISM bands, have not been sufficiently taken into account yet. The main challenges arise from the coexistence of D2D and WLAN in the same frequency band and geographical area. In this paper, we propose a group-wise channel sensing and resource pre-allocation scheme for D2D user fairly contending and utilizing the ISM band resource with its WLAN rivals, where D2D pairs with different QoS or bandwidth requirements are grouped and pre-scheduled to approximately fill in an overall flexible resource block and then a representative D2D pair is appointed in each group for contending resource to avoid intra-group collisions. By using the proposed scheme, the resource on ISM band can be used more efficiently so that D2D applications can be applied to a much wider scope.

The energy consumption in radio access network is expected to increase significantly as the network expends to fulfill the explosively growing data traffics. However, when evaluating the energy saving mechanisms, the impact on other performance metrics such as spectral efficiency, should also be taken into account. In this paper, based on the multiple component carrier (CC) feature specified in Long Term Evolution-Advanced (LTE-A) systems, an energy-efficient coordinated scheduling mechanism is proposed to reduce the energy consumption in cellular networks by dynamically switching off CCs and base stations (BS) according to load variations, with special attention on the switching off order and BS transmit power adjustment to maintain service continuity of downlink users. Both the energy and spectral efficiency of the system under the proposed scheduling mechanism are analyzed. Simulation results show that when data traffics are below a quarter of the system capacity, with the proposed scheme more than half of the network power consumption can be saved, while the requirements for user data rate, service continuity, network coverage and spectral efficiency are guaranteed.

Tail-biting codes are efficient coding techniques to eliminate the rate loss in conventional known-tail convolutional codes at a cost of increased complexity in decoders. In addition, tail-biting trellis representation of block codes makes the trellis-based maximum likelihood (ML) decoder desirable for implementation. Circular Viterbi algorithm (CVA) is introduced to decode the tail-biting codes for its decoding efficiency. However, its decoding process suffers from circular traps, which degrade the decoding efficiency. In this paper, we propose an efficient checking rule for the detection of circular traps. Based on this rule, a novel maximum likelihood (ML) decoding algorithm for tail-biting codes is presented. On tail-biting trellis, computational complexity and memory consumption of this decoder are significantly reduced comparing to other available ML decoders, such as the two-phase ML decoder. To further reduce the decoding complexity, we propose a new near-optimal decoding algorithm based on a simplified trap detection strategy. The performance of the above algorithms is validated with simulation.

Soft Frequency Reuse (SFR) has been widely used to deal with the severe ICI (Inter-Cell Interference) especially for cell edge users. This paper presents an analytical method to investigate the statistics of inter-cell interference in uplink when SFR scheme is adopted in OFDM (Orthogonal Frequency Division Multiplexing) systems. PDF (Probability Density Function) of ICI from one adjacent interfering cell is derived and then used to deduce the expectation and variance of ICI from multiple interfering cells. The derived results are validated through comparison with Monte Carlo simulations. In addition, the relationships between system parameters and statistics of ICI in different frequency bands are investigated. These contributions will give inspect into important guidelines to system optimization.

An efficient receiving status feedback in multicast communications can improve the system performance considerably. In this paper, we propose a novel compressed hybrid automatic repeat request (HARQ) mechanism for the reliable multicast services in the cellular network controlled Device-to-Device (D2D) communications. Closely located D2D cluster devices transmit the acknowledgement and negative acknowledgement (ACK/NACK) message to a cluster head through D2D links directly, after that the cluster head feeds back the whole D2D cluster receiving status using 2-bit ACK/NACK to the cellular network. Performance analysis and numerical simulation results reveal that the proposed HARQ feedback mechanism is better than the conventional D2D multicast mechanisms in terms of the error probability and signaling overhead.

Tail-biting convolutional codes (TBCC) provide an efficient method to eliminate the rate loss caused by the known-tail encoding. To simplify the decoder design, circular Viterbi algorithm (CVA) has been proposed by recording and repeating the received block of (soft) symbols beyond the block boundary and continuing Viterbi decoding. However, CVA does not converge in the presence of circular trap. A checking rule is proposed for detecting the circular trap in existing CVA. Based on this rule, an efficient CVA-based decoding algorithm is obtained for tail-biting codes, which exhibits near-optimal performance for both short and long tail-biting codes. This new scheme provides faster convergence speed than the conventional CVA without increasing in complexity and storage space.

In dynamic HeNB networks, each user-installed HeNB autonomously selects component carriers (CCs) depending on offered traffic and interference relations with other cells. But it will cause that certain HeNB can't select feasible CC. Due to that the performance of BIM based ACCS scheme are sensitive to the predefined BIM threshold, we first introduce an adaptive binary criterion to determine whether two HeNBs can use the same CC. Furthermore, we propose a cooperative component carrier reselection algorithm based on backup CC list, to control inter-cell interference (ICI) and enhance the possibility of successful carrier selection. Simulation results show that CCCS can enhance the system performance both in terms of average user throughput and user outage rate.

Relay transmission technique can be utilized to increase coverage or enhance throughput for conventional cellular communication systems. There are two typical relaying schemes, Amplify-and-Forward (AF) and Decode-and-Forward (DF). This paper presents a new scheme named Soft symbol Estimation and Forward (SEF), which holds the advantages of both AF and DF, i.e. keeping soft information and obtaining decoding gain. This scheme has similar operations as DF except that the operations are performed in a soft-input-soft-output (SISO) fashion. The relay performs SISO decoding, SISO re-encoding and then the key point soft symbol estimation, which is derived based on Maximum Likelihood (ML) criteria. Simulation results prove that the proposed SEF scheme much outperforms both AF and DF.

The adaptive frequency domain interference cancellation and channel equalizer for MIMO-CP-CDMA systems is proposed to cancel the residual interference stage by stage. At each stage, the proposed scheme performs the adaptive forward filtering and interference cancellation in frequency domain so that the signal to interference plus noise ratio can be improved and the error propagation of the interference cancellation can be avoided. The achieved SINR of each code channel is utilized to compute the normalized correlation coefficient. Simulation results show that the proposed scheme can achieve the satisfactory performance and avoid the error propagation

Space-time transmission techniques, which make full use of the rich scattering channel with multiple transmit and receive antennas, can greatly increase the spectral efficiency. The conventional V-BLAST (Bell laboratories layered space-time) detection algorithm estimate symbols with the expected SNRs ordering. In this paper, a novel BLAST detection algorithm based instantaneous error ordering (IEO-BLAST) is proposed. The proposed BLAST detection algorithm obtains the additional diversity with instantaneous error ordering and reduces the effect of the error propagation. Therefore, the performance of the BLAST architecture is greatly improved. In addition, the simplified version of IEO-BLAST detection algorithm (SIEO-BLAST) is derived. And the performance of simplified algorithm has little degradation. The selection capacity (SC) is defined to represent the performance of the algorithm with successive interference cancellation.

Space-time transmission techniques, which make full use of the rich scattering channel with multiple transmit and receive antennas, can greatly increase the spectral efficiency. In this paper, a novel parallel multistage equalizer with partial decision feedback (PDF) is proposed for the layered space-time frequency-selective channel. It is comprised of a partial parallel interference canceller, a MIMO decision feedback equalizer and a linear combiner. Performance analysis shows that the proposed receiver is equivalent to the partial parallel interference cancellation (PPIC) using MMSE for initial estimation and tentative decisions. By using past- and post-symbol estimation to regenerate intersymbol and co-antenna interference, the proposed receiver can be further improved with partial decision feedback. Moreover, the proposed equalization schemes could be implemented by negligible complexity increase comparing with the other well-known approaches. Simulation results show that the proposed equalizers with PDF significantly outperform the conventional MIMO decision feedback equalizer without interference cancellation (IC) and MIMO equalizer with ordering successive interference cancellation (OSIC).

Space-time transmission techniques, which make full use of the rich scattering channel with multiple transmit and receive antennas, can greatly increase the spectral efficiency. In this paper, parallel multistage decision feedback equalizer (DFE), which is composed of parallel interference cancellation, multiple input single output decision feedback equalizer (MISO-DFE) and linear combination of the soft outputs of MISO-DFE and the previous soft decisions, is proposed for a single-carrier system over layered space-time frequency-selective channels. In addition, an algorithm is proposed to obtain the soft and hard decisions for initialization. And the proposed parallel multistage DFE can be implemented with lower complexity than MIMO-OFDM. Simulation results show that the parallel multistage DFE outperforms the MIMO-OFDM and various previous equalizers for a single-carrier system.