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In recent years, semantic communication has received significant attention from both academia and industry, driven by the growing demands for ultra-low latency and high-throughput capabilities in emerging intelligent services. Nonetheless, a comprehensive and effective theoretical framework for semantic communication has yet to be established. In particular, finding the fundamental limits of semantic communication, exploring the capabilities of semantic-aware networks, or utilizing theoretical guidance for deep learning in semantic communication are very important yet still unresolved issues. In general, the mathematical theory of semantic communication and the mathematical representation of semantics are referred to as semantic information theory. In this paper, we introduce the pertinent advancements in semantic information theory. Grounded in the foundational work of Claude Shannon, we present the latest developments in semantic entropy, semantic rate-distortion, and semantic channel capacity. Additionally, we analyze some open problems in semantic information measurement and semantic coding, providing a theoretical basis for the design of a semantic communication system. Furthermore, we carefully review several mathematical theories and tools and evaluate their applicability in the context of semantic communication. Finally, we shed light on the challenges encountered in both semantic communication and semantic information theory. [ABSTRACT FROM AUTHOR]

This paper focuses on the ultimate limit theory of image compression. It proves that for an image source, there exists a coding method with shapes that can achieve the entropy rate under a certain condition where the shape-pixel ratio in the encoder/decoder is O (1 / log t) . Based on the new finding, an image coding framework with shapes is proposed and proved to be asymptotically optimal for stationary and ergodic processes. Moreover, the condition O (1 / log t) of shape-pixel ratio in the encoder/decoder has been confirmed in the image database MNIST, which illustrates the soft compression with shape coding is a near-optimal scheme for lossless compression of images. [ABSTRACT FROM AUTHOR]

Semantic communication is not focused on improving the accuracy of transmitted symbols, but is concerned with expressing the expected meaning that the symbol sequence exactly carries. However, the measurement of semantic messages and their corresponding codebook generation are still open issues. Expansion, which integrates simple things into a complex system and even generates intelligence, is truly consistent with the evolution of the human language system. We apply this idea to the semantic communication system, quantifying semantic transmission by symbol sequences and investigating the semantic information system in a similar way as Shannon's method for digital communication systems. This work is the first to discuss semantic expansion and knowledge collision in the semantic information framework. Some important theoretical results are presented, including the relationship between semantic expansion and the transmission information rate. We believe such a semantic information framework may provide a new paradigm for semantic communications, and semantic expansion and knowledge collision will be the cornerstone of semantic information theory. [ABSTRACT FROM AUTHOR]

As for Generative Adversarial Networks (GANs), its interpretability may be closely related to optimization objective functions, that is, information metrics play important roles in networks training and data generation. In terms of original GAN, the objective function based on Kullback-Leibler (KL) divergence has limitations on the performance of data training and generation. Therefore, it is significant to investigate objective functions for the optimization in GANs to bring gains on the efficiency of network learning from the perspective of metrics. In this paper, the objective function with exponential form, referred from the Message Importance Measure (MIM), is adapted to replace that with logarithm form in the optimization for adversarial networks. This approach named MIM-based GAN, may provide more hidden information in terms of interpretability on training process and probability events generation. Specifically, we first analyze the intrinsic relationship between the proposed approach and other classical GANs. Moreover, compared with the original GAN, LSGAN and WGAN, we discuss its advantages on training performance in theory including sensitivity, convergence rate and so on. In addition, we do simulations on the datasets to confirm why the MIM-based GAN achieves state-of-the-art performance on training process and data generation. [ABSTRACT FROM AUTHOR]

In disease diagnosis, medical image plays an important part. Its lossless compression is pretty critical, which directly determines the requirement of local storage space and communication bandwidth of remote medical systems, so as to help the diagnosis and treatment of patients. There are two extraordinary properties related to medical images: lossless and similarity. How to take advantage of these two properties to reduce the information needed to represent an image is the key point of compression. In this paper, we employ the big data mining to set up the image codebook. That is, to find the basic components of images. We propose a soft compression algorithm for multi-component medical images, which can exactly reflect the fundamental structure of images. A general representation framework for image compression is also put forward and the results indicate that our developed soft compression algorithm can outperform the popular benchmarks PNG and JPEG2000 in terms of compression ratio. [ABSTRACT FROM AUTHOR]

Decode and forward (DF) and compress and forward (CF) are two efficient cooperation strategies for relay networks. The combination of the DF and CF strategies is promising to improve the achievable rate of cooperative communication systems. To thoroughly aggregate the advantage of the DF and CF strategies, we put forward a joint power allocation and strategy selection (PASS) scheme for the half-duplex relay channel. The PASS scheme is shown to achieve a higher rate compared with a regular hybrid DF/CF scheme. The rate gain obtained in the static relay channel results from actively adjusting and optimizing the relay power, followed by selecting the better strategy between DF and CF. In particular, when the relay receives and transmits in sequential time slots, we characterize the positive rate improvement area of the PASS scheme analytically and give a near-optimal setting for approaching the best rate performance. The PASS scheme is extended to fading relay channels. We show that the rate increment obtained from the PASS scheme is further magnified by employing an advanced relay power allocation technique over channel states. The corresponding optimal power allocation is established based on the concavity of the rate achieved by the PASS scheme in static relay channel. Numerical results are presented to validate the analysis. [ABSTRACT FROM PUBLISHER]

Energy harvesting technique is a potential way for relay node energy supply in cooperative networks with respect to deployment flexibility and maintenance charge reduction. However, unlike traditional power source, it is intuitive that the fluctuation of harvested energy flow can degrade the benefit resulted from relay-aided cooperative transmission. To this end, this paper focuses on the performance analysis of an energy harvesting relay-aided cooperative network under slow fading channel from a perspective of outage behavior and attempts deriving the closed-form expression of outage probability of the proposed cooperative protocol. Compared with simple direct transmission protocol, it is proved that a multiplicative gain for improving system performance in terms of minimizing outage probability can be obtained by energy harvesting relay-aided cooperative transmission. In particular, if there are multiple available relay nodes around the environment or the energy-exhausted probability of relay node is small, it is very profitable to employ relay-aided cooperative transmission protocol. [ABSTRACT FROM PUBLISHER]

Widespread deployment of high-speed railways in recent years has created huge demand for high-mobility broadband wireless communications. To provide broadband wireless access for passengers on the train, a well-acknowledged approach is to apply a two-hop architecture, under which passengers communicate with base stations (BSs) via an access point (AP) installed in the train. We consider the uplink transmission from the AP to the BSs on the ground along the railway. The key problem we discuss is how to match user-data arrival process and time-varying channel service process with a delay constraint at the AP. We first assume the constant-rate data arrival and the wireless channel affected only by large-scale fading in high-speed railway scenarios. We present the optimal power-allocation policy given the delay constraint. Based on the policy, we show that there exists a power–delay tradeoff in our system model, with the delay within a limited range. Besides, we find that the average transmit power and the train velocity have a tradeoff, which is similar to the power–delay tradeoff. Besides, given average transmit power constraint, the delay constraint is inversely proportional to the train velocity. Finally, when small-scale fading is considered, we modify the optimal power-allocation policy and provide a convenient approach to counteract Nakagami- $m$ fading in high signal-to-noise ratio (SNR) regimes. The modified power-allocation policy can be well adapted to different fading statistics in different terrestrial environments experienced by the high-speed train. [ABSTRACT FROM PUBLISHER]

In this paper, we investigate a multisource multicast network with the aid of an arbitrary number of relays, where it is assumed that no direct link is available at each S-D pair. The aim is to find the fundamental limit on the maximal common multicast throughput of all source nodes if resource allocations are available. A transmission protocol employing the relaying strategy, i.e., compute-and-forward (CPF), is proposed. We also adjust the methods in the literature to obtain the integer network-constructed coefficient matrix (i.e., a naive method, a local optimal method, and a global optimal method) to fit the general topology with an arbitrary number of relays. Three transmission scenarios are addressed. The first scenario is delay-stringent transmission, where each message must be delivered within one slot. The second scenario is delay-tolerant transmission where no delay constraint is imposed. The associated optimization problems to maximize the short- and long-term common multicast throughputs are formulated and solved, and the optimal allocation of power and time slots are presented. The third case (a general $N$-slot-delay-tolerant scenario) is also discussed, and a suboptimal algorithm is presented. Performance comparisons show that the CPF strategy outperforms conventional decode-and-forward (DF) strategy. It is also shown in the simulation that with more relays, the CPF strategy performs even better due to the increased diversity. Finally, by simulation, it is observed that, with CPF, the $N$-slot-delay-constraint case can perform close to that of the delay-tolerant case in terms of throughput, given that $N$ is relatively large. [ABSTRACT FROM PUBLISHER]

In this paper, we consider one quality-of-service (QoS) criterion, average packet delay (queueing delay plus service time), in a multiaccess system and investigate the basic problem whether a multiaccess system can meet the different average packet delay requirements of all users by combining information theory with queueing theory. Two different cases of the central scheduler with and without queue state information (QSI) are discussed. If the QSI is not available to the central scheduler, we show that static rate allocation policies (SRAPs) can achieve better average packet delay performance than probabilistic rate allocation policies. Based on this conclusion, the delay feasibility checking process reduces to checking whether the required service rate vector lies in the multiaccess capacity region. We find that for users with equal transmit power, only $N$ inequalities are necessary for the checking process, whereas for users with unequal transmit powers, we provide a polynomial-time algorithm for such a decision. Furthermore, if the system cannot satisfy the average packet delay requirements of all users, we prove that as long as the sum power is larger than a threshold, there is always an approach to adjust the transmit powers of different users to satisfy the average packet delay requirements. On the other hand, if the QSI is available to the central scheduler, we propose two dynamic scheduling algorithms to achieve proportional average packet delay and compare their performances with optimal SRAP by simulations. [ABSTRACT FROM AUTHOR]

Unlike the additive white Gaussian noise (AWGN) channel, fading channels suffer from random channel gains, in addition to the additive Gaussian noise. As a result, the instantaneous channel capacity varies randomly along time, which makes it insufficient to characterize the transmission capability of a fading channel using data rate only. In this paper, the transmission capability of a buffer-aided Rayleigh block-fading channel is examined by a constant-rate input data stream and is reflected by several parameters, such as the average queue length, stationary queue length distribution, packet delay, and overflow probability. Both infinite-buffer and finite-buffer models are considered. Taking advantage of the memoryless property of the service provided by the channel in each block in the low-SNR regime, the information transmission over the channel is formulated as a discrete-time discrete-state D/G/\1 queueing problem. The obtained results show that block-fading channels are unable to support a data rate close to their ergodic capacity, no matter how long the buffer is, even when seen from the application layer. For the finite-buffer model, the overflow probability is derived with explicit expression and is shown to decrease exponentially when buffer size is increased, even when the buffer size is very small. [ABSTRACT FROM PUBLISHER]

In this paper, we investigate a three-node two-way relaying network over fading channels. The stability of this system is first discussed. Three protocols are then investigated in this work in terms of throughput, including the opportunistic digital network coding (ODNC) scheduling protocol, the physical network coding (PNC) protocol, and the cooperation protocol of DNC with direct link (CDNC-DL). For the first two protocols, we assume that the direct link is not available. For the third protocol, the direct link is available and can support data rates. The problems of maximizing the long-term totally fair optimal throughput with the three protocols, with respect to the stability constraints, are formulated and solved by allocating power resources with respect to channel fluctuations. To complement this work, optimal time resource allocation for each phase of the three protocols as well as the associated end-to-end throughput are investigated. In the end, it is shown in simulation that ODNC performs better than the conventional DNC and that CDNC-DL outperforms the other two proposed protocols as long as the direct link is in relatively good condition. [ABSTRACT FROM PUBLISHER]

In this paper, we analyze the outage probability of space–time network coding (STNC) in a distributed cooperative relaying system, where multiple sources transmit their information to a common destination with the help of multiple decode-and-forward (DF) relays in timedivision multiple-access (TDMA) mode. We first derive an exact closed-form expression of the outage probability for STNC with an arbitrary number of relays for independent but not necessarily identically distributed (i.n.i.d.) Rayleigh fading channels. With the developed result, we discuss the impact of the transmit SNR, the outage threshold, the number of relays, the nonorthogonal codes, and the relay position on the system performance. [ABSTRACT FROM PUBLISHER]

High-speed railways (HSRs) have been widely deployed all over the world in recent years. Different from traditional cellular communications, the high mobility of HSR communication makes it essential to implement power allocation (PA) along time. In the HSR case, the transmission rate greatly depends on the distance between the base station (BS) and the train. As a result, the train receives a time-varying data rate service when passing by a BS. It is clear that the most efficient PA will spend all the power when the train is nearest the BS, which will cause great unfairness along time. On the other hand, channel inversion allocation achieves the best fairness in terms of constant rate transmission. However, its power efficiency is much lower. Therefore, power efficiency and fairness along time are two incompatible objects. For the HSR cellular system considered in this paper, a tradeoff between the two is achieved by proposing a temporal proportional-fair PA scheme. In addition, a near-optimal closed-form solution and one algorithm finding \epsilon-optimal allocation are presented. [ABSTRACT FROM PUBLISHER]

Soft compression is a lossless image compression method that is committed to eliminating coding redundancy and spatial redundancy simultaneously. To do so, it adopts shapes to encode an image. In this paper, we propose a compressible indicator function with regard to images, which gives a threshold of the average number of bits required to represent a location and can be used for illustrating the working principle. We investigate and analyze soft compression for binary image, gray image and multi-component image with specific algorithms and compressible indicator value. In terms of compression ratio, the soft compression algorithm outperforms the popular classical standards PNG and JPEG2000 in lossless image compression. It is expected that the bandwidth and storage space needed when transmitting and storing the same kind of images (such as medical images) can be greatly reduced with applying soft compression. [ABSTRACT FROM AUTHOR]

In the paper, we investigate the information spread problem in a joint vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication system. A scene is considered where more information centers (or base stations) are allocated along the road so that the information centers are able to broadcast timely messages to vehicles within the range of the broadcast signal of each base station, which we shall refer to as broadcast zone . The seamless information spread is used to guarantee that messages are correctly received by each vehicle, regardless of whether it pulls into broadcast zones or not. We first derive the maximum throughput of the V2I downlink system for both additive white Gaussian noise (AWGN) channels and Rayleigh fading channels with Doppler effects. A measurement-based algorithm to estimate the throughput is also proposed. We then discuss the maximum achievable amount of information that can be relayed forward along a vehicular stream. A network coding technique will then be proposed to cancel the interference caused by relay signals to vehicles that are receiving messages from the corresponding information center. These theoretical results will give more insight into the vehicular communication system design. [ABSTRACT FROM PUBLISHER]

Presents a study on a noncoherent coded 16QAM modulation scheme by modifying the trellis-coded 16QAM scheme. Construction of noncoherent coded-16QAM; Discussion on noncoherent decoding algorithm; Conclusions.

Presents a study which proposed an adaptive multirate encoding method, called as multirate modulation scheme based on the trellis coded modulation (MTCM). Structure and design criterion of MTCM; Implementation method of MTCM encoding; Analysis of MTCM performance.

Presents a study which proposed a joint turbo coding and modulated coding for the intersymbol interference channels with additive white Gaussian noise (AGWN) by simulation. Benefits of turbo coding for AGWN channels; Results; Conclusion.

As autonomous driving may be the most important application scenario of the next generation, the development of wireless access technologies enabling reliable and low-latency vehicle communication becomes crucial. To address this, 3GPP has developed Vehicle-to-Everything (V2X) specifications based on 5G New Radio (NR) technology, where Mode 2 Side-Link (SL) communication resembles Mode 4 in LTE-V2X, allowing direct communication between vehicles. This supplements SL communication in LTE-V2X and represents the latest advancements in cellular V2X (C-V2X) with the improved performance of NR-V2X. However, in NR-V2X Mode 2, resource collisions still occur and thus degrade the age of information (AOI). Therefore, an interference cancellation method is employed to mitigate this impact by combining NR-V2X with Non-Orthogonal multiple access (NOMA) technology. In NR-V2X, when vehicles select smaller resource reservation intervals (RRIs), higher-frequency transmissions use more energy to reduce AoI. Hence, it is important to jointly considerAoI and communication energy consumption based on NR-V2X communication. Then, we formulate such an optimization problem and employ the Deep Reinforcement Learning (DRL) algorithm to compute the optimal transmission RRI and transmission power for each transmitting vehicle to reduce the energy consumption of each transmitting vehicle and the AoI of each receiving vehicle. Extensive simulations demonstrate the performance of our proposed algorithm. [ABSTRACT FROM AUTHOR]

Presents information on a study which proposed a block coded modulation (BCM) technique to reduce the peak to average power ratio (PAPR) in orthogonal frequency division multiplexing (OFDM) systems. Review of the OFDM system; Information on BCM and PAPR; Conclusions.

Platooning is a critical technology to realize autonomous driving. Each vehicle in platoons adopts the IEEE 802.11p standard to exchange information through communications to maintain the string stability of platoons. However, one vehicle in platoons inevitably suffers from a disturbance resulting from the leader vehicle acceleration/deceleration, wind gust and uncertainties in a platoon control system, i.e., aerodynamics drag and rolling resistance moment etc. Disturbances acting on one vehicle may inevitably affect the following vehicles and cause that the spacing error is propagated or even amplified downstream along the platoon, i.e., platoon string instability. In this case, the connectivity among vehicles is dynamic, resulting in the performance of 802.11p in terms of packet delay and packet delivery ratio being time-varying. The effect of the string instability would be further deteriorated once the time-varying performance of 802.11p cannot satisfy the basic communication requirement. Unlike the existing works which only analyze the steady performance of 802.11p in vehicular networks, we will focus on the impact of disturbance and construct models to analyze the time-dependent performance of 802.11p-based platooning communications. The effectiveness of the models is validated through simulation results. Moreover, the time-dependent performance of 802.11p is analyzed through numerical results and it is validated that 802.11p is able to satisfy the communication requirement under disturbance. [ABSTRACT FROM AUTHOR]

Multi-input multi-output and non-orthogonal multiple access (MIMO-NOMA) Internet-of-Things (IoT) systems can improve channel capacity and spectrum efficiency distinctly to support real-time applications. Age of information (AoI) plays a crucial role in real-time applications as it determines the timeliness of the extracted information. In MIMO-NOMA IoT systems, the base station (BS) determines the sample collection commands and allocates the transmit power for each IoT device. Each device determines whether to sample data according to the sample collection commands and adopts the allocated power to transmit the sampled data to the BS over the MIMO-NOMA channel. Afterwards, the BS employs the successive interference cancellation (SIC) technique to decode the signal of the data transmitted by each device. The sample collection commands and power allocation may affect the AoI and energy consumption of the system. Optimizing the sample collection commands and power allocation is essential for minimizing both AoI and energy consumption in MIMO-NOMA IoT systems. In this paper, we propose the optimal power allocation to achieve it based on deep reinforcement learning (DRL). Simulations have demonstrated that the optimal power allocation effectively achieves lower AoI and energy consumption compared to other algorithms. Overall, the reward is reduced by 6.44% and 11.78% compared the to GA algorithm and random algorithm, respectively. [ABSTRACT FROM AUTHOR]

This paper studies the information-energy ($\mathrm{I}$ - $\mathrm{E}$) region for simultaneous wireless information and power transfer (SWIPT) networks in mobility scenarios, where a moving transmitter simultaneously transmits information and power to a fixed receiver, and the receiver adopts power splitting (PS) receiving architecture. In order to characterize the tradeoff between the received information and harvested energy, the $\mathrm{I}$ - $\mathrm{E}$ region is defined, and corresponding optimization problems are formulated to explore the system $\mathrm{I}$ - $\mathrm{E}$ regions by jointly optimizing the transmit power at the transmitter and the PS ratio at the receiver with three popular energy harvesting (EH) models, i.e., traditional linear EH model, the logistic nonlinear EH model and the piecewise nonlinear EH model. Particularly, to efficiently solve the nonconvex optimization problem with the logistic nonlinear EH model, a successive convex approximate (SCA)-based algorithm is proposed, which is able to characterize the lower bound of the $\mathrm{I}$ - $\mathrm{E}$ with low complexity. To solve the optimization problems with the linear and piecewise EH models, as they are convex, some closed and semi-closed solutions are derived by using Lagrange dual method and KKT conditions. Numerical results show that compared with the $\mathrm{I}$ - $\mathrm{E}$ regions under the linear and piecewise EH models, that under the logistic nonlinear EH model is smaller due to the limitations of practical EH circuit features. Moreover, with a fixed moving speed, when the transmit power is relatively large, the logistic nonlinear EH model can be replaced with the piecewise one due to the relatively small gap between their achieved $\mathrm{I}$ - $\mathrm{E}$ regions. Additionally, for a fixed moving track length, the higher moving speed yields the smaller $\mathrm{I}$ - $\mathrm{E}$ region with all three EH models. [ABSTRACT FROM AUTHOR]

As a promising distributed learning paradigm, federated learning (FL) faces the challenge of communication–computation bottlenecks in practical deployments. In this work, we mainly focus on the pruning, quantization, and coding of FL. By adopting a layer-wise operation, we propose an explicit and universal scheme: FedLP-Q (federated learning with layer-wise pruning-quantization). Pruning strategies for homogeneity/heterogeneity scenarios, the stochastic quantization rule, and the corresponding coding scheme were developed. Both theoretical and experimental evaluations suggest that FedLP-Q improves the system efficiency of communication and computation with controllable performance degradation. The key novelty of FedLP-Q is that it serves as a joint pruning-quantization FL framework with layer-wise processing and can easily be applied in practical FL systems. [ABSTRACT FROM AUTHOR]

The storage and the transmission of messages in big data are discussed in this paper, where message importance is taken into account. To this end, we propose to use non-parametric message importance measure (NMIM) as a measure of message importance, which can characterize the uncertainty of random events like Shannon entropy and Rényi entropy. We prove that NMIM sufficiently describes the two key characters of big data, i.e., the rare events finding and the large diversities of events. Based on NMIM, we then propose an effective compressed encoding mode for data storage, and discuss the transmission of messages over some typical channel models with limited message importance loss. Our numerical results show that the proposed strategy occupies less storage space without losing too much important information, and the maximum received entropy rate increases with the increasing of message importance loss until it reaches saturation, which contributes to designing of better practical communication system. [ABSTRACT FROM AUTHOR]

The energy harvesting network in mobile environment is promising for sustainable environment and green communication. How to efficiently use the harvested energy is a key to improve the performance of energy harvesting communication network. This paper considers a point-to-point communication scenario where a static sensor harvests energy and transmits information to a moving agent. From the viewpoint of information theory, we present an optimal power allocation (PA) solution to achieve the maximum channel service. We prove that using a water-filling transmission power, during a single transmission timeslot, the energy depletion time is uniquely determined by the initial energy in the sensor regardless of the energy-harvesting power. On the other hand, the energy depletion time is a determinant of whether the energy is exhausted at the end of the transmission timeslot. Based on the obtained results, an event-triggered transmission strategy is proposed where a sensor harvests energy to a certain amount so as to trigger information transmission towards the moving agent. Some numerical results are provided to confirm the theoretical analysis and our proposed event-triggered implementation approach. [ABSTRACT FROM AUTHOR]

Interference has been a key challenge to wireless networks. As a fundamental transmission unit, the Gaussian interference channel (GIC) provides much insight on understanding the optimal transmission policy and the transmission limit over wireless networks. This paper investigates the multiple-layer power allocation of GIC that maximizes the system sum-rate. First, we derive the optimal signal-layer power allocation and the corresponding sum-rate in closed form for all cases of weak interference GIC based on the rate splitting scheme. Theoretical result indicates that: 1) In low power and asymmetric power regimes, the rate splitting scheme degrades to the pure public or private message transmission at transmitters and simple successive decoding process is efficient enough at receivers and 2) the signal-layer sum-rate is not concave for weak interference GIC and a frequency division scheme brings strict positive sum-rate gain for some power constraints. Second, we specify the relationship between the optimal bandwidth-power allocation of the frequency division scheme and the concave envelope of signal-layer sum-rate in the subband-layer. Finally, considering general GIC with time-varying flat fading, we present an optimal channel-state-layer power allocation associated with rate splitting and frequency division. Numerical results demonstrate that the comprehensive power allocation over the signal-layer, the frequency subbands, and the channel states can largely increase the sum-rate of weak interference GIC in most scenarios. [ABSTRACT FROM PUBLISHER]

This paper investigates the multicell coordinated beamforming (MCBF) design for high-speed railway communications, where a high-mobility train goes through a multicell multiuser system in urban areas. All passengers in the train are aggregated to be a high-mobility “big user.” In order to explore the downlink system performance limit, we formulate an optimization problem to maximize the information rate from the serving base station to the high-mobility train while guaranteeing the minimal required achievable information rates of all low-mobility users outside the train by optimizing MCBF vectors. The interchannel interference (caused by Doppler effect to the train), the intercell interference and the interuser interference are jointly considered. Since the problem is nonconvex and cannot be solved directly, we first introduce an auxiliary variable (AV), and for a given AV, we find the optimal MCBF vectors via solving a second-order cone programming. Then, by updating the AV with the bisection method, the corresponding global optimal MCBF vectors are numerically obtained. Simulation results show that our proposed MCBF design greatly enhances the information throughput of the high-mobility train without degrading the required data rates of low-mobility users. [ABSTRACT FROM PUBLISHER]

This paper investigates the link scheduling for relay-aided high-mobility vehicular networks, where the vehicles with good vehicle-to-infrastructure (V2I) links are employed as cooperative relay nodes to help forward information to the ones with poor V2I links over vehicle-to-vehicle (V2V) links. To overcome the inefficiency of current instantaneous information rate based link scheduling (IIR-LS) method, especially in high-mobility scenarios, we propose a mobile service amount based link scheduling (MSA-LS) for high-mobility vehicular networks. We formulate an optimization problem to maximize the MSA of MSA-LS by jointly scheduling the V2I and V2V links. Since the resulted combinational optimization problem is too complex to solve, we design an efficient low-complexity algorithm, where Sort-then-Select, Hungarian algorithm, and Bisection search are employed. Simulation results demonstrate that our proposed MSA-LS is able to achieve new optimal performance. It is also shown that our proposed MSA-LS is much more efficient for high-mobility vehicular systems, which can improve the system average throughput with increment of about 13% compared with existing IIR-LS and with about 22% increment compared with traditional non-cooperation scheduling. [ABSTRACT FROM PUBLISHER]

We consider channel estimation for high-speed railway communication systems, where both the transmitter and the receiver are equipped with large-scale antenna arrays. It is known that the throughput of conventional training schemes monotonically decreases with the mobility. Assuming that the moving terminal employs a large linear antenna array, this paper proposes a position-aided channel estimation scheme whereby only a portion of the transmit antennas send pilot symbols and the full channel matrix can be well estimated by using these pilots together with the antenna position information based on the joint spatial-temporal correlation. The relationship between mobility and throughput/DoF is established. Furthermore, the optimal selections of transmit power and time interval partition between the training and data phases as well as the antenna size are presented accordingly. Both analytical and simulation results show that the system throughput with the position-aided channel estimator does not deteriorate appreciably as the mobility increases, which is sharply in contrast with the conventional one. [ABSTRACT FROM PUBLISHER]

In vehicular edge computing (VEC), some tasks can be processed either locally or on the mobile edge computing (MEC) server at a base station (BS) or a nearby vehicle. In fact, tasks are offloaded or not, based on the status of vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication. In this paper, device-to-device (D2D)-based V2V communication and multiple-input multiple-output and nonorthogonal multiple access (MIMO-NOMA)-based V2I communication are considered. In actual communication scenarios, the channel conditions for MIMO-NOMA-based V2I communication are uncertain, and the task arrival is random, leading to a highly complex environment for VEC systems. To solve this problem, we propose a power allocation scheme based on decentralized deep reinforcement learning (DRL). Since the action space is continuous, we employ the deep deterministic policy gradient (DDPG) algorithm to obtain the optimal policy. Extensive experiments demonstrate that our proposed approach with DRL and DDPG outperforms existing greedy strategies in terms of power consumption and reward. [ABSTRACT FROM AUTHOR]

This paper first analyzes the symbol error rate (SER) performance of the space-time network coding (STNC) over independent but not necessarily identically distributed (i.n.i.d) Nakagami- $m$ fading channels, where multiple sources transmit information symbols to a destination through multiple helping amplify-and-forward (AF) relays. The exact expressions of the overall end-to-end received signal-to-noise ratio (SNR) via multiple STNC-AF relays and its moment generating function (MGF) are derived. Based on these results, the closed-form expression of STNC-AF for the SER with $M$ -ary phase-shift keying and $M$ -ary quadrature-amplitude modulation (QAM) modulations are then presented by adopting the unified MGF method. Furthermore, an approximate SER expression with low computational complexity is also given. In order to observe the performance limit, the diversity order and the nonorthogonality of STNC codes are discussed. Simulation results demonstrate our analytical results and it is illustrated that the diversity order of the STNC with multiple AF relay nodes is a sum function of the fading index of the direct link and the minimal fading indices of the multiple two-hop links. [ABSTRACT FROM PUBLISHER]

This paper investigates the simultaneous wireless information and power transfer (SWIPT) in cooperative relay networks, where a relay harvests energy from the radio frequency (RF) signals transmitted by a source and then uses the harvested energy to assist the information transmission from the source to its destination. Both source and relay transmissions use rateless codes (RCs), which allow the destination to employ any of the two information receiving strategies, i.e., the mutual information accumulation (IA) and the energy accumulation (EA). The SWIPT-enabled relay employs three different SWIPT receiver architectures, the ideal receiver, and two practical receivers (i.e., the power splitting (PS) receiver and the time switch (TS) receiver). Accordingly, three relaying protocols, namely, the ideal protocol, PS protocol, and TS protocol, are presented. To explore the system performance limits with these three protocols, optimization problems are formulated to maximize their achievable information rates. For the ideal protocol, explicit expressions of the optimal solutions are derived. For the PS protocol, a linear-search algorithm is designed to solve the nonconvex problems. For the TS protocol, two solving methods are presented. Numerical experiments are carried out to validate our analysis and algorithms, which show that, with the same SWIPT receiver, the IA-based system outperforms the EA-based system, whereas with the same information receiving strategy, the PS protocol outperforms the TS protocol. Moreover, compared with nonrateless-coded systems, the proposed protocols exhibit considerable performance gains. Moreover, the effects of the relay position on system performance are also discussed, which provides insights on SWIPT-enabled relay systems. [ABSTRACT FROM AUTHOR]

In 5G heterogeneous networking, it is promising to integrate different wireless networks to provide higher data rate. This paper models the integrated system as a receiver frequency division relay channel (RFDRC) and studies how to improve the transmission rate by combining decode-forward (DF), compress-forward (CF), and amplify-forward (AF) schemes. First, we establish clear criterions on how to select a relay scheme among DF, CF, and AF schemes and prove that the CF outperforms AF for all possible configurations. Based on the scheme selection criterions, we propose a hybrid DF–CF scheme which takes advantage of both DF and CF schemes in RFDRC. A near-optimal resource allocation is presented for the DF–CF-based system, leading to a new achievable rate for RFDRC. For ease of implementation, we further put forward a hybrid DF–AF scheme and reconsider the joint bandwidth and power allocation. Two suboptimal resource allocation solutions are established. In particular, when source frequency band and relay frequency band have equivalent bandwidth, we show that the proposed hybrid DF–AF scheme can achieve the concave envelope of the maximum between DF rate and AF rate. Numerical results show that the proposed schemes bring significant gains for RFDRC. [ABSTRACT FROM AUTHOR]

The diamond relay network is an efficient cooperative networking configuration in which the source node cooperates with two selected neighbors. For such networks, we investigate the impact of the relay positioning on the performance. In particular, considering an opportunistic protocol based on the use of relaying buffers, we provide sets of constraints which, when satisfied, give sufficient conditions for simultaneously guaranteeing network stability and throughput improvement. These results are given for both symmetric and asymmetric relay locations. In contrast to prior work dealing with the diamond relay network, we also break the strong hypothesis of no interlink interference between the source and relay transmissions. Our results provide design guidelines for relay selection by establishing areas of feasible relay locations, whilst also identifying optimal positions which lead to maximum throughput gain. [ABSTRACT FROM AUTHOR]

This paper investigates the relay-assisted wireless information and power transfer enabled hybrid visible light communication (VLC)-radio frequency (RF) network, where a light emitting diode (LED) access point (AP) serves multiple information users (IUs) and multiple energy harvesting users (EHUs). IUs are allowed to receive information from the LED AP through time-division-multiple-access (TDMA) manner by either the single-hop VLC-ONLY mode or the relay-assisted dual-hop VLC-RF mode, while EHUs harvest energy via the VLC links. An optimization problem is formulated to maximize the achievable information rate of IUs by jointly optimizing the access mode selection, the direct current (DC) offset at the LED AP, the peak amplitude of the alternating current (AC) component at the LED AP, the electrical power allocated to the LED AP and the power allocation at relay, subject to the energy harvesting (EH) requirement constraints of EHUs. To tackle the non-convex problem with binary variables, we first decompose it into two subproblems in terms of the two access modes. Then, the subproblems are equivalently transformed and solved by the proposed successive convex approximation (SCA)-based algorithms. Simulation results show that significant performance gain can be achieved by optimizing the DC offset. It is also observed that the area where the VLC-ONLY mode is superior to the VLC-RF mode is enlarged with the decrease of the minimal EH requirement. Besides, the achievable information rate of IUs by the VLC-RF mode first increases and then decreases with the increment of the distance between the relay and the LED AP. [ABSTRACT FROM AUTHOR]

Different probabilities of events attract different attention in many scenarios such as anomaly detection and security systems. To characterize the events' importance from a probabilistic perspective, the message importance measure (MIM) is proposed as a kind of semantics analysis tool. Similar to Shannon entropy, the MIM has its special function in information representation, in which the parameter of MIM plays a vital role. Actually, the parameter dominates the properties of MIM, based on which the MIM has three work regions where this measure can be used flexibly for different goals. When the parameter is positive but not large enough, the MIM not only provides a new viewpoint for information processing but also has some similarities with Shannon entropy in the information compression and transmission. In this regard, this paper first constructs a system model with message importance measure and proposes the message importance loss to enrich the information processing strategies. Moreover, the message importance loss capacity is proposed to measure the information importance harvest in a transmission. Furthermore, the message importance distortion function is discussed to give an upper bound of information compression based on the MIM. Additionally, the bitrate transmission constrained by the message importance loss is investigated to broaden the scope for Shannon information theory. [ABSTRACT FROM AUTHOR]

This paper focuses on the problem of finding a particular data recommendation strategy based on the user preference and a system expected revenue. To this end, we formulate this problem as an optimization by designing the recommendation mechanism as close to the user behavior as possible with a certain revenue constraint. In fact, the optimal recommendation distribution is the one that is the closest to the utility distribution in the sense of relative entropy and satisfies expected revenue. We show that the optimal recommendation distribution follows the same form as the message importance measure (MIM) if the target revenue is reasonable, i.e., neither too small nor too large. Therefore, the optimal recommendation distribution can be regarded as the normalized MIM, where the parameter, called importance coefficient, presents the concern of the system and switches the attention of the system over data sets with different occurring probability. By adjusting the importance coefficient, our MIM based framework of data recommendation can then be applied to systems with various system requirements and data distributions. Therefore, the obtained results illustrate the physical meaning of MIM from the data recommendation perspective and validate the rationality of MIM in one aspect. [ABSTRACT FROM AUTHOR]

Platooning strategy is an important component of autonomous driving technology. Autonomous vehicles in platoons are often equipped with a variety of on-board sensors to detect the surrounding environment. The abundant data collected by autonomous vehicles in platoons can be transmitted to the infrastructure through vehicle-to-infrastructure (V2I) communications using the IEEE 802.11 distributed coordination function (DCF) mechanism and then uploaded to the cloud platform through the Internet. The cloud platform extracts useful information and then sends it back to the autonomous vehicles respectively. In this way, autonomous vehicles in platoons can detect emergency conditions and make a decision in time. The characteristics of platoons would cause a fair-access problem in the V2I communications, i.e., vehicles in the platoons moving on different lanes with different velocities would have different resident time within the infrastructure's coverage and thus successfully send different amounts of data to the infrastructure. In this case, the vehicles with different velocities will receive different amounts of useful information from the cloud. As a result, vehicles with a higher velocity are more likely to suffer from a traffic accident as compared to the vehicles with a lower velocity. Hence, this paper considers the fair-access problem and proposes a fair-access scheme to ensure that vehicles with different velocities successfully transmit the same amount of data by adaptively adjusting the minimum contention window of each vehicle according to its velocity. Moreover, the normalized throughput of the proposed scheme is derived. The validity of the fair-access scheme is demonstrated by simulation. [ABSTRACT FROM AUTHOR]

Multi-platooning is an important management strategy for autonomous driving technology. The backbone vehicles in a multi-platoon adopt the IEEE 802.11 distributed coordination function (DCF) mechanism to transmit vehicles' kinematics information through inter-platoon communications, and then forward the information to the member vehicles through intra-platoon communications. In this case, each vehicle in a multi-platoon can acquire the kinematics information of other vehicles. The parameters of DCF, the hidden terminal problem and the number of neighbors may incur a long and unbalanced one-hop delay of inter-platoon communications, which would further prolong end-to-end delay of inter-platoon communications. In this case, some vehicles within a multi-platoon cannot acquire the emergency changes of other vehicles' kinematics within a limited time duration and take prompt action accordingly to keep a multi-platoon formation. Unlike other related works, this paper proposes a swarming approach to optimize the one-hop delay of inter-platoon communications in a multi-platoon scenario. Specifically, the minimum contention window size of each backbone vehicle is adjusted to enable the one-hop delay of each backbone vehicle to get close to the minimum average one-hop delay. The simulation results indicate that, the one-hop delay of the proposed approach is reduced by 12% as compared to the DCF mechanism with the IEEE standard contention window size. Moreover, the end-to-end delay, one-hop throughput, end-to-end throughput and transmission probability have been significantly improved. [ABSTRACT FROM AUTHOR]

This paper studies a simultaneous wireless information and power transfer (SWIPT)-aware fog computing by using a simple model, where a sensor harvests energy and receives information from a hybrid access point (HAP) through power splitting (PS) receiver architecture. Two information processing modes, local computing and fog offloading modes are investigated. For such a system, two optimization problems are formulated to minimize the sensor's required power for the two modes under the information rate and energy harvesting constraints by jointly optimizing the time assignment and the transmit power, as well as the PS ratio. The closed-form and semi-closed-form solutions to the proposed optimization problems are derived based on convex optimization theory. Simulation results show that neither mode is always superior to the other one. It also shows that when the number of logic operations per bit associated with local computing is less than a certain value, the local computing mode is a better choice; otherwise, the fog offloading mode should be selected. In addition, the mode selection associated with the positions of the user for fixed HAP and fog server (FS) is also discussed. [ABSTRACT FROM AUTHOR]

Multi-robot formation control makes prerequisites for a team of robots to execute complex tasks cooperatively, which has been widely applied in both civilian and military scenarios. However, the limited precision of sensors and controllers may inevitably cause position errors in the finally achieved formation, which will affect the tasks undertaken. In this paper, the formation error is analyzed from the viewpoint of information theory. The desired position and the actually achieved position are viewed as two random variables. By calculating the mutual information between them, a lower bound of the formation error is derived. The results provide insights for the estimation of possible formation errors in the multi-robot system, which can assist designers to choose sensors and controllers with proper precision. [ABSTRACT FROM AUTHOR]

In high-speed railway communication systems, the distributed antenna systems are usually employed to mitigate frequent handover and enhance the signal-to-noise ratio to receivers. In this case, jointly optimizing downlink power allocation with antenna selection (PAWAS) can enhance system energy efficiency, while the channel state and traffic density are taken into account. Besides, two typical kinds of terrains with sparse and rich scatterings and three traffic patterns including delay-sensitive, -insensitive, and hybrid traffics are investigated in this paper. We show that severe small-scale fading decreases the ergodic capacity, which is quantitatively analyzed, and proved to be proportional to the number of selected transmit antennas. In addition, in case of delay-sensitive traffic, we show that the PAWAS can be viewed as generalized channel-inversion associated with transmit antenna selection. In case of delay-insensitive traffic, we show that when multiple antennas are selected, the power allocation can be viewed as channel-inversion, whereas when single antenna is selected, it is traditional waterfilling. In case of hybrid traffic, we prove that the optimal PAWAS method can be given by separately solving the PAWAS of its delay-sensitive and -insensitive parts. Simulation results validate our theoretical results and demonstrate that proposed PAWAS can minimize the average transmit power in cases of arbitrary traffic density and channel states. [ABSTRACT FROM AUTHOR]

Information transfer that characterizes the information feature variation can have a crucial impact on big data analytics and processing. Actually, the measure for information transfer can reflect the system change from the statistics by using the variable distributions, similar to Kullback-Leibler (KL) divergence and Renyi divergence. Furthermore, to some degree, small probability events may carry the most important part of the total message in an information transfer of big data. Therefore, it is significant to propose an information transfer measure with respect to the message importance from the viewpoint of small probability events. In this paper, we present the message importance transfer measure (MITM) and analyze its performance and applications in three aspects. First, we discuss the robustness of MITM by using it to measuring information distance. Then, we present a message importance transfer capacity by resorting to the MITM and give an upper bound for the information transfer process with disturbance. Finally, we apply the MITM to discuss the queue length selection, which is the fundamental problem of caching operation on mobile edge computing. [ABSTRACT FROM AUTHOR]

In the long-term evolution for railway, orthogonal frequency-division multiplexing (OFDM) is adopted to provide seamless connection to existing ground cellular network and support high mobility communication in future 5G. However, due to the high velocity of high-speed railway (HSR), the channel is fast time-varying and serious intercarrier interference (ICI) is introduced, meaning that low-complexity and real-time ICI reduction methods are needed, which has not been well discussed in previous works. In this paper, we focus on the HSR downlinks with distributed transmit antennas and develop two corresponding ICI reduction methods for additive white Gaussian noise (AWGN) and Rician channels, respectively. With the information of relative locations and velocities between corresponding antenna pairs, we show that the ICI matrices in AWGN and Rician channels can be mathematically calculated, and they are approximately unitary. With these results, we propose two corresponding low-complexity ICI reduction methods to avoid matrix inversion and suit fast time-varying nature. Simulation results show our proposed ICI mitigation method can achieve similar service quantity with that obtained on the case without ICI when velocity is about 300 km/h. [ABSTRACT FROM PUBLISHER]

We consider content delivery to users in a system consisting of a macro base station (BS), several energy-harvesting small cells (SCs), and many users. Each SC has a large cache and stores a copy of all contents in the BS. A user’s content request can be either handled by the SC for free if it has enough energy, or by the BS that has a cost. Each user has a finite cache and can store some most popular contents. We propose an efficient coded content caching schemes and an optimal transmission schemes for this system to maximally off-load the data traffic from the macro BS to the energy-harvesting SCs, and therefore minimize the power consumption from the grid. Specifically, the proposed coded caching scheme stores fractions of some most popular contents, such that contents requested from multiple users can be simultaneously delivered by the BS or SC. Moreover, the optimal transmission policy is formulated and solved as a Markov decision process. Extensive simulation results are provided to demonstrate that the proposed coded caching and transmission schemes can provide significantly higher traffic off-loading capability compared with systems with no caching or with uncoded caching, as well as systems that employ heuristic-based transmission schemes. [ABSTRACT FROM PUBLISHER]

The outage performance of a dual-hop mobile ad hoc network (MANET), which employs amplify-and-forward (AF) relaying in a Rayleigh fading environment in the presence of a Poisson field of interferers at the destination, is investigated. All mobile users are assumed to follow the Poisson arrival model along the highway and can only communicate with each other via a stationary base station or a mobile relay station. Under this MANET model, a single-integral expression for the exact outage probability and a tight lower bound are both derived. The impacts of transmitting power, path-loss exponent, and spatial density of interferers on the performance of dual-hop systems are found. It is revealed that the outage probability of each hop, as a function of distance, transmitting power, etc., has a different impact on the end-to-end outage probability. The outage probability floor is derived when the transmission power at the source/relay becomes large. The optimal power allocation for the dual-hop link is obtained. The behavior of the outage probability during the communication process between the moving nodes is investigated. The analytical results are validated with Monte Carlo simulation. [ABSTRACT FROM AUTHOR]

Broadband mobile communication systems experience fading over a wide frequency band, which induces severe intersymbol interference and drastic channel gain fluctuation, resulting in a time–frequency doubly selective fading channel gain. Most existing works on fading channel modeling assume wide-sense stationarity with respect to (w.r.t.) time and uncorrelated scattering w.r.t. delay. However, due to the time-varying movement of mobile terminals, fading is usually nonstationary w.r.t. time, i.e., the Doppler spectrum is nonstationary. In addition, electromagnetic signals reflected/scattered from the same clustter/object are correlated and may induce nonstationarity w.r.t. delay due to time-varying cluster environments, e.g., wind causes the movement of leaves, resulting in time-varying scattering of leaves. To model nonstationary broadband mobile fading channels, this paper introduces a two-dimensional evolutionary spectrum (2-D ES) approach, which is compatible with the power spectral density of a stationary process. Based on the 2-D ES, we also propose an estimation method to estimate the 2-D ES parameters from a trace (sample path) of a fading process. Furthermore, we show how to set up a simulator to generate a 2-D stationary or nonstationary fading process according to our developed 2-D ES framework. Using the 2-D ES approach, we analyze a class of Nakagami- $m$ channels which is nonstationary in both the time and frequency domains. Two examples are used to show the accuracy of our developed 2-D ES-based channel modeling approach. [ABSTRACT FROM AUTHOR]

Applying network coding (NC) in the downlink of two-way relay networks can significantly increase the throughput. However, the fading nature of wireless channels usually causes great asymmetry between two downlink channels, which can degrade the performance of NC. Therefore, it is very necessary to perform an opportunistic scheme that selects between broadcast and unicast at the relay node based on channel side information. This paper focuses on the fundamental limit of the sum rate in the downlink. To solve the maximization problem of the sum rate in the downlink, we first introduce an auxiliary variable, known as marginal capacity income of transmit power, and then derive the corresponding power allocation in closed-form expression and present an opportunistic switching selection strategy between broadcast/unicast. Numerical results also show that our proposed scheme achieves a larger downlink sum throughput compared with other existing known schemes. [ABSTRACT FROM PUBLISHER]