The rapid development of low-altitude economy has placed higher demands on the sensing of small-sized unmanned aerial vehicle (UAV) targets. However, the complex and dynamic low-altitude environment, like the urban and mountainous areas, makes clutter a significant factor affecting the sensing performance. Traditional clutter suppression methods based on Doppler difference or signal strength are inadequate for scenarios with dynamic clutter and slow-moving targets like low-altitude UAVs. In this paper, motivated by the concept of channel knowledge map (CKM), we propose a novel clutter suppression technique for orthogonal frequency division multiplexing (OFDM) integrated sensing and communication (ISAC) system, by leveraging a new type of CKM named clutter angle map (CLAM). CLAM is a site-specific database, containing location-specific primary clutter angles for the coverage area of the ISAC base station (BS). With CLAM, the sensing signal components corresponding to the clutter environment can be effectively removed before target detection and parameter estimation, which greatly enhances the sensing performance. Besides, to take into account the scenarios when the targets and clutters are in close directions so that pure CLAM-based spatial domain clutter suppression is no longer effective, we further propose a two-step CLAM-enabled joint spatial-Doppler domain clutter suppression algorithm. Simulation results demonstrate that the proposed technique effectively suppresses clutter and enhances target sensing performance, achieving accurate parameter estimation for sensing slow-moving low-altitude UAV targets.

Collaborative access points (APs) enabled cell-free networks can provide stable and uniform communication services for all user locations, making them a promising network architecture for the sixth-generation (6G) mobile communication systems. While the performance of pure cell-free networks has been extensively studied, it remains unclear whether deploying large-scale cell-free APs in legacy cellular networks can effectively boost communication performance. Besides, the realization of a cell-free network is considered to be a gradual long-term evolutionary process in which APs will be incrementally introduced and form a hybrid communication network with the existing cellular base stations (BSs). Such a collaboration will bridge the gap between the established cellular network and the innovative cell-free network. Therefore, hybrid cellular and cell-free networks (HCCNs) emerge as a feasible solution for advancing cell-free network development, and it is worthwhile to further explore its performance limits. Different from heterogeneous networks or multipoint coordinated networks, the characterization of HCCNs needs to take both inter- and intra-layer collaboration into account. This paper presents a stochastic geometry-based HCCN model to analyze the distributions of signal and interference and reveal their mutual coupling. Specifically, in order to benefit the user equipments (UEs) from both the cellular BSs and the cell-free APs, a conjugate beamforming design is employed, and the aggregated signal is analyzed using moment matching. Then, the coverage probability of the hybrid network is characterized by deriving the Laplace transforms and their higher-order derivatives of interference components. Furthermore, the average achievable rate of the hybrid network over channel fading is derived based on the interference coupling analysis. Simulation results demonstrate that compared to traditional cellular networks, HCCN effectively narrows communication quality differences between different UEs and improves overall communication performance.

Integrated sensing and communication (ISAC) is a promising technique for expanding the functionalities of wireless networks with enhanced spectral efficiency. The 3rd Generation Partnership Project (3GPP) has defined six basic sensing operation modes in wireless networks. To further enhance the sensing capability of wireless networks, this paper proposes a new sensing operation mode, i.e., the base station (BS) and user equipment (UE) cooperative sensing. Specifically, after decoding the communication data, the UE further processes the received signal to extract the target sensing information. We propose an efficient algorithm for fusing the sensing results obtained by the BS and UE, by exploiting the geometric relationship among BS, UE and targets as well as the expected sensing quality in the BS monostatic and BS-UE bistatic sensing. The results show that the proposed data fusion method for cooperative sensing can effectively improve the position and velocity estimation accuracy of multiple targets, and provide a new approach on the expansion of the sensing pattern.

Integrated sensing and communications (ISAC) in distributed networks presents an efficient solution for multi-target localization. This paper investigates the joint target matching and parameter estimation with multiple cooperative base stations (BSs), where each BS can only estimate the range and radial velocity of a subset of targets. The interdependence of these tasks introduces a mixed measurement-to-target association (MTA) problem, characterized by prohibitively significant computational complexity due to the binary constraints and the growing number of variables with an increasing number of targets and BSs. To address this challenge, we propose an iterative algorithm that alternates between target matching and parameter estimation, supported by a heuristic initialization method to enhance efficiency. Simulation results demonstrate the robustness of the proposed method, achieving high matching accuracy under different system configurations, including scenarios with low sensing resolution and an increasing number of targets.

For orthogonal frequency division multiplexing (OFDM) integrated sensing and communication (ISAC), in order to perform efficient delay and Doppler estimation, the equivalent data symbols on the time-frequency resource element need to be removed, so that the resulting signal obeys the manifold structure with phases linearly increasing across subcarriers/OFDM symbols. However, this task becomes more challenging for multiple-input and multiple-output (MIMO)-OFDM ISAC with spatial multiplexing, since the received equivalent data symbols in time-frequency resource element not only depend on the transmitted information-bearing symbols, but also depend on the transmit steering vectors and the precoding matrix. To address such challenges, this paper proposes an effective sensing signal processing method for MIMO-OFDM ISAC with spatial multiplexing. Specifically, we first estimate the angle of arrival, followed by channel matrix decomposition to obtain transmit steering vector, then reconstruct the equivalent symbol. After removing the equivalent symbol from the received signal, the classical periodogram algorithm can be used to estimate the range and Doppler of targets. Simulation results show that the proposed method achieves effective parameter estimation for MIMO-OFDM ISAC with spatial multiplexing.

Channel knowledge map (CKM) is a novel technique for achieving environment-aware wireless communication and sensing. CKM exchange among different base stations (BSs) is needed to achieve efficient BS cooperations. This paper proposes an efficient CKM exchange method based on semantic communication to facilitate the utilization of CKM at dynamic users. The proposed semantic communication framework achieves the tradeoff between the bandwidth consumption and reconstruction quality of CKM. Compared to traditional image compression and transmission methods, the semantic communication approach demonstrates higher stability and reliability in CKM exchange, especially under low SNR region. Besides, semantic communication based on deep learning can also effectively mitigate the measurement errors associated with CKM reconstruction. Simulation results show that under 4 times compression ratio, the proposed method outperforms traditional methods in terms of stability and can effectively transmit severely corrupted data, recovering CKMs close to the original values.

Channel knowledge map (CKM) is a novel technique for achieving environment awareness, and thereby improving the communication and sensing performance for wireless systems. A fundamental problem associated with CKM is how to construct a complete CKM that provides channel knowledge for a large number of locations based solely on sparse data measurements. This problem bears similarities to the super-resolution (SR) problem in image processing. In this paper, we propose an effective deep learning-based CKM construction method that leverages the image SR network known as SRResNet. Unlike most existing studies, our approach does not require any additional input beyond the sparsely measured data. In addition to the conventional path loss map construction, our approach can also be applied to construct channel angle maps (CAMs), thanks to the use of a new dataset called CKMImageNet. The numerical results demonstrate that our method outperforms interpolation-based methods such as nearest neighbour and bicubic interpolation, as well as the SRGAN method in CKM construction. Furthermore, only 1/16 of the locations need to be measured in order to achieve a root mean square error (RMSE) of 1.4 dB in path loss.

This study addresses the challenges of near-field interference suppression and resource allocation in extremely large-scale multiple-input multiple-output (XL-MIMO) communication systems, particularly under dense-user scenarios. We propose a quality-of-service (QoS)-aware joint user scheduling and power control scheme. Leveraging the spherical wave (SW) characteristics of near field channels, a dual-domain interference suppression strategy is developed by analyzing the spatial correlation of beam focusing vectors in terms of both angular separation and distance constraints. Based on this, a spatial correlation-based scheduling (SCS) algorithm is designed. By integrating this user selection strategy with a dynamic power allocation mechanism, the proposed approach optimizes the sum spectral efficiency while ensuring the user QoS. This framework is further extended to modular XL-MIMO systems. We show how modular deployment can enhance spatial resolution and develop an adapted QoS-aware user scheduling algorithm, called modular SCS (SCS-mod), for this architecture. Simulation results validate that the proposed algorithms significantly outperform existing schemes in terms of sum spectral efficiency and the number of scheduled users, especially under high user density and high transmission power conditions.

Channel knowledge map (CKM) is a promising technology to enable environment-aware wireless communications and sensing. Link state map (LSM) is one particular type of CKM that aims to learn the location-specific line-of-sight (LoS) link probability between the transmitter and the receiver at all possible locations, which provides the prior information to enhance the communication quality of dynamic networks. This paper investigates the LSM construction for cellular-connected unmanned aerial vehicles (UAVs) by utilizing both the expert empirical mathematical model and the measurement data. Specifically, we first model the LSM as a binary spatial random field and its initial distribution is obtained by the empirical model. Then we propose an effective binary Bayesian filter to sequentially update the LSM by using the channel measurement. To efficiently update the LSM, we establish the spatial correlation models of LoS probability on the location pairs in both the distance and angular domains, which are adopted in the Bayesian filter for updating the probabilities at locations without measurements. Simulation results demonstrate the effectiveness of the proposed algorithm for LSM construction, which significantly outperforms the benchmark scheme, especially when the measurements are sparse.

Channel gain map (CGM) is a promising technique that enables the environment-aware communications by providing a priori channel gain information for users at arbitrary locations. The construction of CGM can be viewed as spatial channel prediction from the communication perspective, which motivates the channel model-based construction algorithm. On the other hand, it can also be viewed as spatial data interpolation from the geostatistic perspective, where a bunch of model-free methods can be adopted. This paper compares model-based and model-free approaches for CGM construction. We extend the existing model-based channel prediction by introducing a multi-mode channel model to address the spatial inconsistency issue in conventional models. The model-based methods are then compared with various model-free spatial interpolation methods, including a deep learning method. The results show that the channel model may introduce substantial bias for CGM construction in complex environments.

Cell-free wireless communication is envisioned as one of the most promising network architectures, which can achieve stable and uniform communication performance while improving the system energy and spectrum efficiency. The deployment of cell-free networks is envisioned to be a long-term evolutionary process, in which cell-free access points (APs) will be gradually introduced into the communication network and collaborate with the existing cellular base stations (BSs). To further explore the performance limits of hybrid cellular and cell-free networks, this paper develops a hybrid network model based on stochastic geometric toolkits, which reveals the coupling of the signal and interference from both the cellular and cell-free networks. Specifically, the conjugate beamforming is applied in hybrid cellular and cell-free networks, which enables user equipment (UE) to benefit from both cellular BSs and cell-free APs. The aggregate signal received from the hybrid network is approximated via moment matching, and coverage probability is characterized by deriving the Laplace transform of the interference. The analysis of signal strength and coverage probability is verified by extensive simulations.

This paper considers the age-optimal joint sampling and transmitting scheduling design for wireless sensor networks with energy harvesting. The optimization problem is modeled as a Markov decision process (MDP), and a low-complexity online policy is proposed based on the formulation, which has two main steps, i.e., “separate deciding” and “joint scheduling”. Specifically, each sensor's action is determined by the double-threshold policy first and then the scheduled sensor is chosen from the set of non-idle sensors by comparing the expected reward gain. The proposed policy can be extended to the case of partial state information knowledge, where the state information at the sensors' side can only be delivered through packet updatings. Numerical results show that our proposed policy outperforms the existing benchmarks under general settings.

Integrated super-resolution sensing and communication (ISSAC) is a promising technology to achieve extremely high sensing performance for critical parameters, such as the angles of the wireless channels. In this paper, we propose an ISSAC-based channel estimation method, which requires little or even no pilot, yet still achieves accurate channel state information (CSI) estimation. The key idea is to exploit the fact that subspace-based super-resolution algorithms such as multiple signal classification (MUSIC) do not require a priori known pilots for accurate parameter estimation. Therefore, in the proposed method, the angles of the multi-path channel components are first estimated in a pilot-free manner while communication data symbols are sent. After that, the multi-path channel coefficients are estimated, where very little pilots are needed. The reasons are two folds. First, compared to the conventional channel estimation methods purely relying on channel training, much fewer parameters need to be estimated once the multi-path angles are accurately estimated. Besides, with angles obtained, the beamforming gain is also enjoyed when pilots are sent to estimate the channel path gains. To rigorously study the performance of the proposed method, we first consider the basic line-of-sight (LoS) channel. By analyzing the minimum mean square error (MMSE) of channel estimation and the resulting beamforming gains, we show that our proposed method significantly outperforms the conventional methods purely based on channel training. We then extend the study to the more general multipath channels. Simulation results are provided to demonstrate our theoretical results.

Reliability and latency are two key performance indicators of communications. This paper investigates the tradeoff between them over a random packet erasure channel in the absence of feedback. In contrast to the instant feedback case where guaranteed reliability with low latency can be achieved by simple automatic repeat query (ARQ), we show that in the absence of feedback, the reliability increases with the coding window size, at the cost of the degraded latency performance. Specifically, we propose a sliding window network coding (SWNC) scheme that works in the absence of feedback and achieves various reliability and latency tradeoff by adjusting the coding window size. The tradeoff between the reliability and latency are investigated by deriving the achievable performance of the proposed SWNC scheme as a function of the coding window size. We further show that the proposed scheme degenerates to the existing benchmark schemes that are superior in either latency or reliability, and it has much higher flexibility.

This paper considers the design of age-optimal packet scheduling policies in networks with delayed feedback, long-term resource constraint and various packet arrival models. The problem is formulated as a constrained partial observable Markov decision process (CPOMDP) in general. We first derive the achievable age of information (AoI) by the random and determined transmission policies, which works in the absence of feedback. Then, we propose a greedy policy that traces the expected receiver AoI based on the delayed feedback, and selects the action that minimizes the expected immediate Lagrange cost defined in the formulated CPOMDP. The proposed policy outperforms the existing policies for a wide range of feedback delay, even in the absence of feedback, yet the implementation complexity is still low since the decision threshold is derived in the closed-form, as a function of the network parameters.

Channel knowledge map (CKM) has been recently proposed to enable environment-aware communications by utilizing historical or simulation generated wireless channel data. This paper studies the construction of one particular type of CKM, namely channel gain map (CGM), by using a finite number of measurements or simulation-generated data, with model-based spatial channel prediction. We try to answer the following question: How much data is sufficient for CKM construction? To this end, we first derive the average mean square error (AMSE) of the channel gain prediction as a function of the sample density of data collection in offline CGM construction, as well as the number of data points used in online spatial channel gain prediction. To model the spatial variation of the wireless environment within each cell, we divide the CGM into subregions and estimate the channel parameters from the local data within each subregion. The parameter estimation error and the channel prediction error based on estimated channel parameters are derived as functions of the number of data points within the subregion. The analytical results may guide the CGM construction and utilization by determining the required spatial sample density for offline data collection and the number of data points to be used for online channel prediction, so that the desired level of channel prediction accuracy is guaranteed.

Predictive millimeter-wave (mmWave) beamforming is a promising technique to enable low-latency and high-rate ground-air communications for cellular-connected unmanned aerial vehicles (UAVs). However, the high vulnerability of mmWave to blockages poses practical challenges to the implementation of such a technology. In this paper, we tackle the challenges by proposing a channel knowledge map (CKM)-assisted predictive beamforming approach based on the echoed joint communication and sensing signal, whereby the line-of-sight (LoS) link identification is performed via hypothesis testing using prior information provided by CKM. Depending on the identification result, extended Kalman filtering (EKF) is adopted to reliably track the target UAV. Furthermore, if the non-line-of-sight (NLoS) state is identified, the target UAV will be immediately connected to a candidate base station (BS), namely a handover will be triggered to alleviate the communication outage. The simulation results show that the proposed method can significantly enhance the UAV tracking and mmWave communication performance compared to the benchmarking schemes without using CKM or LoS identification.

Sensing-assisted predictive beamforming is a promising technique for reducing the communication overhead and latency in millimeter wave (mmWave) communication systems. In this paper, we propose a robust sensing-assisted predictive beamforming scheme that performs non-line-of-sight (NLoS) identification based on the reflected joint communication and sensing signal. Specifically, the time delay, Doppler shift and reflection coefficient are estimated from the echo signal, based on which a hypothesis test problem is formulated to determine whether the echo signal is reflected by the target vehicle or obstacles. Besides, based on the estimated channel parameters, extended Kalman filtering (EKF) is employed to estimate and predict the motion parameters of the target vehicle. If the echo signal is reflected by obstacles, it implies that the LoS link between the base station (BS) and the target vehicle is blocked. In this case, the proposed scheme can adjust the communication mode and vehicle tracking mechanism accordingly to enhance link reliability. Numerical results demonstrate that the proposed predictive beamforming scheme with NLoS identification can substantially enhance the achievable communication rate as compared to the existing techniques without taking blockage into account.

The performance of wireless localization critically depends on the actual radio propagation environment. This paper proposes a novel framework towards environment-aware wireless localization, enabled by the emerging concept known as channel knowledge map (CKM). Specifically, we propose a line-of-sight (LoS) map-enabled environment-aware anchor selection scheme to minimize the Bayesian Cramer-Rao lower bound (BCRLB) of the positioning error. As the formulated problem is combinatorial, we propose an efficient greedy-based algorithm, which selects the best anchor node sequentially by ensuring that each newly selected anchor leads to the maximum reduction to the BCRLB. Simulation results show that the proposed environment-aware anchor selection can significantly outperform the benchmarking environment-ignorant schemes, including the min-distance based selection or simply activating all anchors.

This paper investigates the tradeoff between data freshness and transmission efficiency for packet delivery over erasure channels with delayed feedback. First, we show that the transmission efficiency of the preemptive last-generated-first-served (pLGFS) policy degrades drastically with the feedback delay. To address this issue, we propose a new coding scheme by incorporating the pLGFS policy with a selective packet encoding strategy. Specifically, the transmitter estimates the packet reception status based on the delayed feedback information, and then decides whether to remain silent, or to send the latest information packet or a coded packet. The proposed transmission policy can achieve flexible tradeoff between data freshness and transmission efficiency by adjusting the decision and coding parameters. In particular, compared with the pLGFS policy, the proposed policy can greatly enhance the transmission efficiency, with only a slight degradation of data freshness.

This paper considers unmanned aerial vehicle (UAV) trajectory design for post-disaster information collection based on video inspection. Unlike the conventional UAV photogrammetric applications that aims to cover the whole area of interest, we model the critical locations in the disaster area as points of interest (PoIs) with heterogeneous resolution requirements. An efficient algorithm is then proposed to design the three-dimensional (3D) UAV trajectory that minimizes the time required for inspecting all the PoIs, subject to the image quality constraints, in terms of the image resolution, produced blur and the number of shots for each PoI. The proposed modeling and trajectory design can greatly reduce the time required for UAV video inspection, which hence enables fast response and rescue planning after the disaster.

Wireless relay network is a solution for transmitting information from a source node to a sink node far away by installing a relay in between. The broadcasting nature of wireless communication allows the sink node to receive part of the data sent by the source node. In this way, the relay does not need to receive the whole piece of data from the source node and it does not need to forward everything it received. In this paper, we consider the application of batched network coding, a practical form of random linear network coding, for a better utilization of such a network. The amount of innovative information at the relay which is not yet received by the sink node, called the innovative rank, plays a crucial role in various applications including the design of the transmission scheme and the analysis of the throughput. We present a visualization of the innovative rank which allows us to understand and derive formulae related to the innovative rank with ease.

Vehicle-to-Vehicle (V2V) communication is a core technology for enabling safety and non-safety applications in next generation intelligent transportation systems (ITS). However, the reliability of V2V communication may degrade drastically if the communication links are blocked by big vehicles. Message relaying is an effective approach to enhancing the communication reliability, and in this scenario, the big vehicles are the ideal candidates of the relay node due to their height advantages. However, extensive message relaying may consume excessive communication resources and lead to severe network congestion. To this end, we propose a selective message relaying strategy which sorts the messages received by the big vehicles according to their “importance” and the messages in front of the queue are relayed with higher priority. The proposed selective relaying strategy can be incorporated with various metrics of the messages and implemented efficiently at the relay node. As an illustrative use-case, we show that the “importance” of the messages can be simply characterized by the relative position between their generating cars and the relaying nodes when the packet delivery ratio (PDR) is the design objective. Numerical results show that the proposed selective relaying strategy based on relative distance metric can substantially enhance the overall PDR as compared with the random relaying strategy.

Cellular-connected unmanned aerial vehicle (UAV) is a promising technology to unlock the full potential of UAVs in the future by reusing the cellular base stations (BSs) to enable their air-ground communications. However, how to achieve ubiquitous three-dimensional (3D) communication coverage for the UAVs in the sky is a new challenge. In this paper, we tackle this challenge by a new coverage-aware navigation approach, which exploits the UAV's controllable mobility to design its navigation/trajectory to avoid the cellular BSs' coverage holes while accomplishing their missions. To this end, we formulate an UAV trajectory optimization problem to minimize the weighted sum of its mission completion time and expected communication outage duration, which, however, cannot be solved by the standard optimization techniques due to the lack of an accurate and tractable end-to-end communication model in practice. To overcome this difficulty, we propose a new solution approach based on the technique of deep reinforcement learning (DRL). Specifically, by leveraging the state-of-the-art dueling double deep Q network (dueling DDQN) with multi-step learning, we first propose a UAV navigation algorithm based on direct RL, where the signal measurement at the UAV is used to directly train the action-value function of the navigation policy. To further improve the performance, we propose a new framework called simultaneous navigation and radio mapping (SNARM), where the UAV's signal measurement is used not only for training the DQN directly, but also to create a radio map that is able to predict the outage probabilities at all locations in the area of interest. This enables the generation of simulated UAV trajectories and predicting their expected returns, which are then used to further train the DQN via Dyna technique, thus greatly improving the learning efficiency.

Conventional networks are based on layered protocols with intensive cross-layer interactions and complex signal processing at every node, making it difficult to meet the ultra-low latency requirement of mission critical applications in future communication systems. In this paper, we address this issue by proposing the concept of network template, which allows data to flow through it at the transmission symbol level, with minimal node processing. This is achieved by carefully calibrating the inter-connecting links among the nodes and pre-calculating the routing/network coding actions for each node, according to a set of preconfigured flows. In this paper, we focus on the minimum cost network template design to minimize the connections within the template, while ensuring that all the pre-defined configurations are feasible with the guaranteed throughput, latency and reliability. We show that the minimum cost network template design problem is difficult to solve optimally in general. We thus propose an efficient greedy algorithm to find a close-to-optimal solution. Simulation results show that the construction cost of the templates obtained by the proposed algorithm is very close to a lower bound. Furthermore, the construction cost increases only slightly with the number of pre-defined configurations, which confirms the flexibility of the network template design.

In this paper, we consider the packet-level forward error correction (FEC) code design, without feedback or with delayed feedback, for achieving the minimum end-to-end latency, i.e., the latency between the time that packet is generated at the source and its in-order delivery to the application layer of the destination. We first show that the minimum-latency FEC design problem can be modeled as a partially observable Markov decision process (POMDP), and hence the optimal code construction can be obtained by solving the corresponding POMDP. However, solving the POMDP optimally is in general difficult unless its state and action space is very small. To this end, we propose an efficient heuristic algorithm, namely the majority vote policy, for obtaining a high quality approximate solution. We also derive the tight lower and upper bounds of the optimal state values of this POMDP, based on which a more sophisticated D-step search algorithm can be implemented for obtaining near-optimal solutions. The simulation results show that the proposed code designs via solving the POMDP, either with the majority vote policy or the D-step search algorithm, strictly outperform the existing schemes, for both cases, without or with only delayed feedback.

This paper studies the path design problem for cellular-connected unmanned aerial vehicle (UAV), which aims to minimize its mission completion time while maintaining good connectivity with the cellular network. We first argue that the conventional path design approach via formulating and solving optimization problems faces several practical challenges, and then propose a new reinforcement learning-based UAV path design algorithm by applying temporal-difference method to directly learn the state-value function of the corresponding Markov Decision Process. The proposed algorithm is further extended by using linear function approximation with tile coding to deal with large state space. The proposed algorithms only require the raw measured or simulation-generated signal samples as the input and are suitable for both online and offline implementations. Numerical results show that the proposed path designs can successfully avoid the coverage holes of cellular networks even in the complex urban environment.

BATS codes are a class of random linear network coding scheme which have close-to-optimal achievable rates, while adaptive recoding is a packet combining scheme which adds a boost to the throughput by adapting the packet combining at the relay to the random fluctuations in the number of erasures in individual batches. In this paper, we apply BATS code with adaptive recoding for the transmission on wireless relay network. In contrast to the existing adaptive recoding schemes, we propose a model which can make use of the broadcast nature of wireless communication to enhance the achievable rates via overhearing. We also optimize the packet efficiency, i.e., minimizing the average number of channel use for each input packet for a successful decoding.

Vehicle to vehicle (V2V) communications using dedicated short range communications (DSRC) are considered as promising technology for enhancing road safety. However, the V2V communications see a challenge from obstruction of big vehicle such as big bus or truck. Based on our measurement, the big vehicle can cause signal loss from 10 to 15 dB. It makes the communication range shorter and reduces the safety message dissemination capability. In this paper, we analyze the impact of multiple big vehicles shadowing on the V2V communication. We propose a model which takes into account both geometric and stochastic shadowing of multiple big vehicles. Based on the proposed model, we derive the average length of the shadow region and the shadowing loss caused by multiple big vehicles. It is revealed that when the number of big vehicles increases from 1 to 25 vehicle per km, the shadow region increases from 50 m to more than 400 m on the road. Furthermore, the shadowing loss causes the packet delivery ratio of a typical car reducing from 90% to 20% or 50%, depending on whether the big vehicles are on the same or the next lane of this typical car.

In this paper, we investigate the optimal scheduling schemes for multi-hop video streaming with generation-based random linear network coding in vehicular networks. With the proposed scheduling algorithm, each vehicle in the network is guaranteed to decode the video block from the dedicated transmissions and the overheard packets with high probability. We derive explicit expression for the number network coded packets that need to be generated at each vehicle in order to ensure successful decoding at its downstream vehicles, by considering the overheard packets due to wireless broadcasting. Furthermore, the distance beyond which the channel can be reused is optimized to maximize the achievable video generating rate at the source node. Simulation results show that the proposed coding and scheduling scheme achieves higher end-to-end throughput than the conventional packet erasure correction coding scheme that is only optimized for one-hop communications.

Wireless communication enabled by unmanned aerial vehicles (UAVs) has emerged as an appealing technology for many application scenarios in future wireless systems. However, the limited endurance of UAVs greatly hinders the practical implementation of UAV-enabled communications. To overcome this issue, this paper proposes a novel scheme for UAV-enabled communications by utilizing the promising technique of proactive caching at the users. Specifically, we focus on content-centric communication systems, where a UAV is dispatched to serve a group of ground nodes (GNs) with random and asynchronous requests for files drawn from a given set. With the proposed scheme, at the beginning of each operation period, the UAV pro-actively transmits the files to a subset of selected GNs that cooperatively cache all the files. As a result, when requested, a file can be retrieved by each GN either directly from its local cache or from its nearest neighbor that has cached the file via device-to-device communications. It is revealed that there exists a fundamental trade-off between the file caching cost, which is the total time required for the UAV to transmit the files to their designated caching GNs, and the file retrieval cost, which is the average time required for serving one file request. To characterize this trade-off, we formulate an optimization problem to minimize the weighted sum of the two costs, via jointly designing the file caching policy, the UAV trajectory, and communication scheduling. As the formulated problem is NP-hard in general, we propose efficient algorithms to find high-quality approximate solutions for it. Numerical results are provided to corroborate our study and show the great potential of proactive caching for overcoming the endurance issue in UAV-enabled communications.

In this paper, we study the achievable throughput of batched temporal network coding in multi-hop erasure channels, where network coding is applied only within small coding blocks and each communication hop is modeled as a Gilbert-Elliott (GE) packet erasure channel. The GE channel is a 2-state Markov model that is commonly used for channels with memory. While channel memory does not affect the end-to-end capacity of multi-hop erasure channels, we show that it degrades the end-to-end throughput, when batched network coding with finite batch size is applied, due to the higher variance in erasures within one coding block. On the other hand, if the initial channel state information is available, the channel variance can be significantly reduced. We show that this fact can be utilized for improving the efficiency of the recoding operations at the intermediate nodes, and hence improve the end-to-end throughput of batched network coding schemes. Specifically, we propose adaptive recoding operations, where the network coded packets are adaptively generated based on the number of received packets and the initial channel state for each coding block. The simulation results show that the proposed adaptive recoding scheme significantly enhances the end-to-end throughput of batched network coding over multi-hop GE channels.

In this paper we consider scalable video multicasting over erasure networks with heterogeneous video quality requirements. With random linear network coding (RLNC) applied at the intermediate nodes the information received by the destinations is determined by the associated channel rank distributions based on which we obtain the optimal achievable code rate at the source node. We show that although a concatenation of priority encoded transmission (PET) with RLNC achieves the optimal code rate it incurs prohibitive high coding complexity. On the other hand batched sparse (BATS) code has been recently proposed for unicast networks which has low coding complexity with near-optimal overhead. However the existing BATS code design cannot be applied for multicast networks with heterogeneous channel rank distributions at different destinations. To this end we propose a novel expanding window BATS (EW-BATS) code where the input symbols are grouped into overlapped windows according to their importance levels. The more important symbols are encoded with lower rate and hence they can be decoded by more destinations while the less important symbols are encoded with higher rate and are only decoded by the destinations with high throughput for video quality enhancement. Based on asymptotical performance analysis we formulate the linear optimization problems to jointly optimize the degree distributions for each window and the window selection probabilities. Simulation results show that the proposed EW-BATS code satisfies the decoding requirements with much lower transmission overhead compared with separate BATS code where the degree distributions are separately optimized for each destination.

This paper studies an unmanned aerial vehicle (UAV)-enabled multicasting system, where a UAV is dispatched to disseminate a common file to a set of ground terminals (GTs). We aim to design the UAV trajectory to minimize its mission completion time, while ensuring that each GT successfully recovers the file with a desired high probability. The formulated problem is nonconvex and difficult to be solved in its original form. Therefore, we first derive an effective lower bound for the success file recovery probability of each GT. The problem is then reformulated in a more tractable form, where the UAV trajectory only needs to be designed to ensure the minimum connection time constraint with each GT, during which their distance is below a certain threshold. We show that without loss of optimality, the UAV trajectory consists of connected line segments only, which can be obtained by determining the optimal set of waypoints as well as the UAV speed along the path connecting the waypoints. We propose efficient schemes for the waypoint design based on a novel concept of virtual base station placement and by applying convex optimization. Furthermore, for fixed waypoints, the optimal UAV speed is efficiently obtained by solving a linear programming problem. Numerical results show that the proposed UAV-enabled multicasting with optimized trajectory design achieves significant performance gains over other benchmark schemes.

In this paper, we consider the wireless broadcasting scenario with a source node sending some common information to a group of closely located users, where each link is subject to certain packet erasures. To ensure reliable information reception by all users, the conventional approach generally requires repeated transmission by the source until all the users are able to decode the information, which is inefficient in many practical scenarios. In this paper, by exploiting the close proximity among the users, we propose a novel two-phase wireless broadcasting protocol with user cooperations based on an efficient batched network code, known as batched sparse (BATS) code. In the first phase, the information packets are encoded into batches with BATS encoder and sequentially broadcasted by the source node until certain terminating criterion is met. In the second phase, the users cooperate with each other by exchanging the network-coded information via peer-to-peer (P2P) communications based on their respective received packets. A fully distributed and light-weight scheduling algorithm is proposed to improve the efficiency of the P2P communication in the second phase. The performance of the proposed two-phase protocol is analyzed and the channel rank distribution at the instance of decoding is derived, based on which the optimal BATS code is designed. Simulation results show that the proposed protocol outperforms most existing cooperative packet exchange schemes, especially when the inter-user links are not reliable. Lastly, the performance of the proposed scheme is further verified via testbed experiments.

In order to reduce the number of retransmissions and save power for the source node, we propose a two-phase coded scheme to achieve reliable broadcast from the source to a group of users with minimal source transmissions. In the first phase, the information packets are encoded with batched sparse (BATS) code, which are then broadcasted by the source node until the file can be cooperatively decoded by the user group. In the second phase, each user broadcasts the re-encoded packets to its peers based on their respective received packets from the first phase, so that the file can be decoded by each individual user. The performance of the proposed scheme is analyzed and the rank distribution at the moment of decoding is derived, which is used as input for designing the optimal BATS code. Simulation results show that the proposed scheme can reduce the total number of retransmissions compared with the traditional single-phase broadcast with optimal erasure codes. Furthermore, since a large number of transmissions are shifted from the source node to the users, power consumptions at the source node is significantly reduced.

In this paper, we present an achievable rate region for double-unicast networks by assuming that the intermediate nodes perform random linear network coding, and the source and sink nodes optimize their strategies to maximize the achievable region. Such a setup can be modeled as a deterministic interference channel, whose capacity region is known. For the particular class of linear deterministic interference channels of our interest, in which the outputs and interference are linear deterministic functions of the inputs, we show that the known capacity region can be achieved by linear strategies. As a result, for a given set of network coding coefficients chosen by the intermediate nodes, the proposed linear precoding and decoding for the source and sink nodes will give the maximum achievable rate region for double-unicast networks. We further derive a suboptimal but easy-to-compute rate region that is independent of the network coding coefficients used at the intermediate nodes, and is instead specified by the min-cuts of the network. It is found that even this suboptimal region is strictly larger than the existing achievable rate regions in the literature.

We provide the degrees of freedom (DoF) characterization for the three-user MT× MRmultiple-input-multipleoutput (MIMO) interference channel (IC) with rank-deficient channel matrices, where each transmitter is equipped with MTantennas and each receiver with MRantennas, and the interfering channel matrices from each transmitter to the other two receivers are of ranks D1and D2, respectively. One important intermediate step for both the converse and achievability arguments is to convert the fully-connected rank-deficient channel into an equivalent partially-connected full-rank MIMO-IC by invertible linear transformations. As such, existing techniques developed for full-rank MIMO-IC can be incorporated to derive the DoF outer and inner bounds for the rank-deficient case. Our result shows that when the interfering links are weak in terms of the channel ranks, i.e., D1+ D2≤ min(MT, MR), zero forcing is sufficient to achieve the optimal DoF. On the other hand, when D1+ D2> min(MT, MR), a combination of zero forcing and interference alignment is in general required for DoF optimality. The DoF characterization obtained in this paper unifies several existing results in the literature.

We study the achievable degrees of freedom (DoF) of the 3-user multiple-input multiple-output (MIMO) interference channel (IC), with possibly rank-deficient channel matrices. A two-layered linear processing scheme is proposed, with the inner layer zero-forcing part of the interfering links by applying a technique known as change of basis, and the outer layer performing interference alignment for the remaining interfering links. The achievable DoF of the proposed scheme is derived. In many special cases of the problem considered in this paper, our proposed two-layered scheme is DoF optimal.

This paper presents a lower bound on the mutual information between any two sets of source/edge random variables in a general multi-source multi-sink network. This bound is useful to derive a new class of better information-theoretic upper bounds on the network coding capacity given existing edge-cut based bounds. A refined functional dependence bound is characterized from the functional dependence bound using the lower bound. It is demonstrated that the refined versions of the existing edge-cut based outer bounds obtained using the mutual information lower bound are stronger.