Visible-Infrared (RGB-IR) Unmanned Aerial Vehicle (UAV) object detection integrates complementary cues from visible and infrared sensors, offering broad application potential. However, due to sensor parallax, it still faces the challenge of weak spatial misalignment, which significantly limits its performance in UAV-based object detection. Existing methods emphasize strict alignment, overlooking spectral heterogeneity under varying illumination. To address these issues, we propose the Illumination Guided Implicit Alignment Network (IGIANet) to mitigate modality heterogeneity without explicit alignment. Specifically, we integrate three novel modules. First, we propose an illumination-guided frequency modulation module that adaptively allocates fusion weights to visible and infrared features based on global illumination estimation, effectively alleviating modality imbalance under varying lighting conditions. Second, we introduce a frequency-guided cross-modality differential enhancement module, which computes differential cues across frequency domains to enhance complementary information and highlight weakly aligned and low-contrast regions. Finally, we introduce an implicit alignment-driven dynamic fusion module that actively estimates offsets and generates dynamic, position-adaptive fusion kernels to align and fuse modalities. Extensive experiments demonstrate that IGIANet outperforms state-of-the-art models on various benchmarks, achieving 80.9% mAP on DroneVehicle, 57.1% mAP on VEDAI, and 49.4% mAP on FLIR.

Neural textures have emerged as pivotal assets in next-generation neural rendering pipelines. However, hardware limitations and programming interface constraints lead to suboptimal performance in multi-instance real-time rendering scenarios. This bottleneck becomes particularly acute for texture-intensive tasks such as font rendering. To address this, we propose Neural Outline Cache (NOC), a novel neural font texture supporting real-time anti-aliased rendering and procedural editing within modern neural graphics pipelines. NOC's lightweight network leverages multi-resolution hash encoding to cache spline-derived SDFs, delivering anti-aliased rendering via standard graphics pipelines. For massive-instance scalability, our cache buffer layout (CBL) and batch-fused inference (BFI), tailored for NOC, mitigate neural texture streaming bottlenecks. We constructed an evaluation dataset using five font styles. In offline rendering, our proposed method achieves overall average results of 57.35 dB PSNR, 0.998 SSIM, and 1.1584e-3 pixel RMSE, while maintaining approximately 0.5ms frame latency with 500 real-time instances. To demonstrate its versatility, we integrated a procedural editor for visual effects editing of NOC textures. These results all prove that NOC is a reliable, production-ready neural asset.

Despite the progress made through deep learning, existing Visual Object Tracking (VOT) frameworks struggle with real-world challenges. Recent approaches incorporate additional modalities like Depth, Thermal Infrared, and Language to enhance the robustness of VOT, particularly with the improvement of the depth sensor precision, facilitating RGB-D tracking. However, current RGB-D trackers often copy RGB tracking paradigms, leading to inefficiency due to two-stream architectures that fail to exploit heterogeneous features, and reliance on simplistic or large-parameter fusion methods. To address these challenges, we propose AMTrack, a one-stream RGB-D tracker leveraging Mamba's linear complexity for simultaneous feature extraction and two-stage cross-modal feature fusion. Our innovation also includes a low-parameter Multimodal Mix Mamba (3M) module, which optimizes deep feature fusion and reduces computational overhead. The advantage of the 3M module stems from our Multimodal State Space Model (MSSM), a multimodal feature interaction component reconstructed based on SSM. Experiments across multiple RGB-D tracking datasets indicate that AMTrack achieves superior performance with lower parameters and memory demands compared to state-of-the-arts.

Reconfigurable intelligent surfaces (RIS) are emerging as a promising technology for next-generation wireless communications, capable of mitigating severe propagation attenuation, enhancing spectral efficiency, and expanding signal coverage. This paper focuses on online millimeter-wave (mmWave) beam tracking for multi-RIS-assisted hybrid beamforming systems. We develop two novel beam tracking algorithms based on multi-agent deep reinforcement learning (DRL): a multi-agent deep deterministic policy gradient (MADDPG)-based algorithm for continuous-domain beam angle tracking and a multi-agent deep Q-network (MADQN)-based algorithm for codebook-based discrete-domain beam angle tracking. Both algorithms are designed to maximize the sum rate by jointly optimizing analog beamforming for the base station (BS) and reflection coefficients for multiple RISs in dynamic environments, leveraging historical information and without requiring current user position or channel information. After determining analog beamforming and RIS reflection coefficients, digital beamforming for the BS is constructed by estimating the end-to-end effective channel, which significantly reduces the overhead of channel estimation. Experimental results demonstrate that the proposed algorithms effectively adapt the analog beamformer and RIS reflection coefficients to account for user mobility, significantly outperforming existing benchmark schemes.

Multi-radio access technology (multi-RAT) enabled mobile edge computing (MEC) has emerged as a promising paradigm for supporting heterogeneous applications. However, efficiently managing resources for both ultra-reliable low-latency communications (URLLC) and enhanced mobile broadband (eMBB) services in large-scale networks remains challenging. In this paper, we investigate a joint optimization problem involving user association and bandwidth allocation in multi-RAT-enabled MEC systems. We propose a novel cooperative evolutionary framework operated based on the interplay between inner and outer agents to efficiently optimize large-scale networks. Extensive simulation results demonstrate that the proposed approach significantly outperforms the conventional single-RAT MEC system and several representative evolutionary computation algorithms.

UAVs are increasingly essential in wireless communication applications, such as internet of things (IoT) and sensor networks, due to their agile mobility. However, planning three-dimensional (3D) UAV trajectories over a continuous temporal-spatial domain remains challenging due to the computational complexity of non-convex optimization. This paper addresses UAV-assisted IoT data collection, aiming to minimize total energy consumption while considering UAV capabilities, heterogeneous IoT data demands, and 3D terrain. We propose a matrix-based differential evolution with constraint handling (MDE-CH), a computationally efficient algorithm for solving constrained non-convex optimization problems. Numerical results show that MDE-CH efficiently generates continuous 3D UAV trajectories, significantly reducing energy consumption and outperforming the conventional fly-hover-fly model for 2D and 3D trajectory planning.

Federated learning aims to construct a global model that fits the dataset distributed across local devices without direct access to private data, leveraging communication between a server and the local devices. In the context of a practical communication scheme, we study the completion time required to achieve a target performance. Specifically, we analyze the number of iterations required for federated learning to reach a specific optimality gap from a minimum global loss. Subsequently, we characterize the time required for each iteration under two fundamental multiple access schemes: 1) time-division multiple access (TDMA) and 2) random access (RA). We propose a step-wise batch allocation, demonstrated to be optimal for TDMA-based federated learning systems. Additionally, we show that the nonzero batch gap between devices provided by the proposed step-wise batch allocation significantly reduces the completion time for RA-based learning systems. Numerical evaluations validate these analytical results through real-data experiments, highlighting the remarkable potential for substantial completion time reduction.

The freshness of information is crucial for IoT applications, such as remote sensing systems and real-time status updates. The overabundance of stale information at the destination can potentially compromise the accuracy and reliability of system decision-making processes. To address this concern, a new metric termed the Age of Information (AoI) has been proposed to capture the freshness of status updates. In this article, we present an analytical framework to establish a dual-action guideline for minimizing the average AoI in random access networks. We then utilize this guideline to propose two online activation control protocols: 1) the age-dependent activation control (ADAC) algorithm and 2) the threshold-based ADAC (T-ADAC) algorithm. The former prioritizes the activation of devices with higher instantaneous AoI, while the latter enables a device to be active with a dynamic probability only when its instantaneous AoI is beyond a predetermined threshold. Extensive simulations demonstrate the effectiveness of the proposed ADAC and T-ADAC, showing that our proposed algorithms outperform the state-of-the-art approaches in random access networks. Specifically, the proposed methods can achieve maximum throughput and minimum average AoI, showcasing their superior performance in real-world scenarios.

The influence maximization (IM) problem in large-scale social networks has attracted great attention. Considering the interactions among multiple online social platforms, the multilayer IM problem poses further challenges ($\rm i.e.,$high-simulation burden and low-optimization quality). To solve these problems, this article proposes a susceptible-exposed-infected1-infected2-infected12-vigilant (SE3IV) model to simulate the information spreading process in multilayer networks. The spreading dynamic is modeled by mean-field equations considering the effect of cross-layer propagation. To optimize the multilayer information maximization modeled by SE3IV, an asynchronous distributed cooperative coevolutionary algorithm (ADCA) is proposed. To improve the efficiency of the algorithm in multilayer networks, the multilayer community detection first decompresses the network into a single layer by dimension-based method. Then, the Louvain method is adopted to decompose the problems into subcomponents with lower dimensionality. The populations with the same size evolve corresponding subcomponents in an asynchronous and distributed way based on the pool model. Besides, an asynchronous communication mechanism is devised to manage the communication among the shared pool. An adaptive seeds regulation strategy is designed to adjust the number of seeds of subcomponents. Numerous experiments on different networks show that ADCA possesses good scalability and efficiency, especially in large-scale networks.

This paper presents a novel approach that leverages two models to integrate features from numerous unlabeled images, addressing the challenge of semi-supervised salient object detection (SSOD). Unlike conventional methods that rely on selecting high-quality pseudo labels, our method identifies the model that produces consistent predictions for original images and their color transformation versions from two models to infer reliable pseudo labels for all unlabeled images, improving the diversity of the training set. Specifically, we propose adaptive selection indicators to quantify prediction differences and guide the updates of the two models using the unlabeled set alternatively. Initially, two models used in our framework are trained on the labeled set. Once the adaptive selection indicator conditions are satisfied, one model is designated as the proxy, generating pseudo labels, while the other serves as the saliency model, which is further trained using these pseudo labels. Subsequently, the updated saliency model optimizes the proxy model’s parameters according to another adaptive selection indicator. Experimental results and ablation studies on six benchmark salient object detection datasets confirm the effectiveness and robustness of our method. Our approach achieves performance comparable to recent fully supervised methods while using only one eighth of the labeled data, demonstrating its potential for efficient and scalable SSOD. This paper is publicly available athttps://github.com/Liyuan0905/CATNet.

Uncrewed aerial vehicles (UAVs) are increasingly employed for data collection in wireless sensor networks (WSNs) owing to their flexibility and real-time operational capabilities. However, effective UAV path planning remains a critical research challenge, requiring the design of optimal routes to efficiently complete data collection in WSNs. This paper introduces a novel constrained UAV data collection model tailored to address real-world challenges in this domain. Traditional mathematical optimization methods often face significant difficulties in derivation and computational complexity. Similarly, classical evolutionary computation (EC) algorithms are limited by their dependence on serial computations, resulting in substantial time costs. To address these issues, we propose a matrix-based differential evolution algorithm (MDE), leveraging matrix index operations to facilitate parallel computation and solve the problem efficiently. Given that existing matrix-based evolutionary computation (MEC) algorithms have limited applications in constrained optimization problems, we further introduce a constraint-guided optimization (CGO) method, enabling the MDE algorithm to inherently support constrained optimization. Experimental results demonstrate that the proposed MDE-CGO outperforms other representative EC methods in optimizing the model of constrained UAV data collection from WSNs. Only our proposed approach successfully optimizes the model to generate feasible UAV paths in all the experiments. Moreover, a computational speed comparison highlights that the MDE-CGO not only delivers superior optimization performance but also achieves high computational efficiency.

Learning multi-scale feature representations is essential for dense prediction tasks in autonomous driving. Most existing works are based on U-shaped architectures, where high-resolution representations are progressively recovered by connecting different levels of the decoder with low-resolution representations from the encoder. We observed that rich details from low-level representation and high semantic information from high-level representations are not fully utilized in the cross-stage fusion process. Additionally, current architectures often struggle to extract efficient discriminative feature along object boundaries. To address this issue, we propose CSNet, a generic cross-stage subtraction network that extracts spatial and semantic multi-scale representations through guided contextual feature. This approach allows fine-grained features to refine deeper layers, capturing discriminative high-resolution features while filtering out redundant information. Specifically, we introduce a cross-stage subtraction module (CSM), which consists of three sub-modules: 1) a Short Path Unit, focusing on capturing complementary adjacent information; 2) Medium Path Unit for effective middle-stages features aggregation; and 3) Long Path Unit for redundant information masking and long-range context modeling. Additionally, we propose the Semantic Guided Context Reasoning (SGCR) module to reason and model contextual relations between different subtraction units. CSNet demonstrates consistent performance gains across various semantic segmentation datasets. Our model, CSNet-M, achieves 82.2% mIoU on the Camvid dataset, while CSNet-S and CSNet-M attain 79.6% and 80.5% mIoU accuracy, respectively, on the Cityscapes dataset. These results show that the proposed CSNet has the potential for enhancing real-time semantic segmentation in autonomous driving applications, offering improved accuracy and efficiency in diverse urban scenarios. The source code for this work will be published athttps://github.com/mohamedac29/CSNet.

Open-vocabulary multi-object tracking (OVMOT) is a cutting research direction within the multi-object tracking field. It employs large multi-modal models to effectively address the challenge of tracking unseen objects within dynamic visual scenes. While models require robust domain generalization and temporal adaptability, OVTrack, the only existing open-vocabulary multi-object tracker, relies solely on static appearance information and lacks these crucial adaptive capabilities. In this paper, we propose OVSORT, a new framework designed to improve domain generalization and temporal information processing. Specifically, we first propose the Adaptive Contextual Normalization (ACN) technique in OVSORT, which dynamically adjusts the feature maps based on the dataset's statistical properties, thereby fine-tuning our model's to improve domain generalization. Then, we introduce motion cues for the first time. Using our Joint Motion and Appearance Tracking (JMAT) strategy, we obtain a joint similarity measure and subsequently apply the Hungarian algorithm for data association. Finally, our Hierarchical Adaptive Feature Update (HAFU) strategy adaptively adjusts feature updates according to the current state of each trajectory, which greatly improves the utilization of temporal information. Extensive experiments on the TAO validation set and test set confirm the superiority of OVSORT, which significantly improves the handling of novel and base classes. It surpasses existing methods in terms of accuracy and generalization, setting a new state-of-the-art for OVMOT.

This paper studies a cooperative relaying communication scheme that employs a single passive reconfigurable intelligent surface (RIS), utilizing integer forcing (IF) as a multiple-input multiple-output (MIMO) technique. In the case of IF-based transceivers, the transmitter sends independently encoded data streams using the same lattice code, and the receiver decodes integer-linear combinations of codewords instead of decoding each codeword separately. Although the flexible decoding provided by IF improves achievable rates compared to conventional separate decoding, the integer-linear combinations observed at the IF-based receiver must remain unchanged throughout the codeword’s duration, even in the presence of channel variations, to enable the decoding of summed codewords. Motivated by this fact, we introduce a novel strategy tailored for IF that involves adjusting the reflection matrix of the RIS to reduce fluctuations in the resulting end-to-end channel between the transmitter and receiver throughout codeword transmission, which we refer to aschannel stabilization. Furthermore, we develop a novel IF-based transceiver scheme calledsuccessive cancellation IF (SC-IF), which effectively integrates successive IF (S-IF) sum decoding with minimum mean square error-successive interference cancellation (MMSE-SIC) individual decoding within a unified MIMO framework to achieve improved performance. Simulation results demonstrate that when a large number of reflective elements are employed in the RIS, the proposed channel stabilization scheme significantly outperforms benchmark schemes that aim to individually optimize the reflection matrix for each sub-block, and the proposed SC-IF can achieve a rate comparable to the theoretical upper bound represented by the joint maximal likelihood (ML) receiver.

Multi-radio access technology (multi-RAT) enabled mobile edge computing (MEC) has emerged as a promising paradigm for supporting diverse applications with heterogeneous service requirements. However, efficiently managing resources to accommodate both ultra-reliable low-latency communications (URLLC) and enhanced mobile broadband (eMBB) services remains challenging, especially in large-scale networks. In this paper, we investigate a joint optimization problem involving user association, task offloading, power and bandwidth allocation, and scheduling policies within a multi-RAT-enabled MEC system to efficiently address the heterogeneous demands of URLLC and eMBB services. We first formulate a generalized optimization problem and mathematically derive optimal power and task offloading strategies to reduce the search space. We then propose improved scheduling algorithms that sequentially update scheduling decisions based on arrival times at the edge server. Furthermore, we develop a matrix-based cooperative evolutionary computation framework with inner and outer agents to efficiently handle the large-scale optimization problem. Extensive simulation results demonstrate that our proposed approach significantly outperforms conventional scheduling methods and representative evolutionary algorithms.

The widespread adoption of social networks facilitates the dissemination of authentic information while also accelerating the spread of misinformation, such as rumors. The propagation of positive information can enhance user awareness and mitigate the hazards of misinformation. The misinformation containment (MC) problem aims to identify a set o$k$nodes that initiate the spread of positive information, maximizing its influence while minimizing the hazards of misinformation. The greedy approach, which employs extensive Monte Carlo simulations to estimate influence, is time-consuming and can only prioritize either propagation or containment, but not both. This paper studies the MC problem under competitive activation models. Based on geometric models of probability, we calculate the approximate probabilities of nodes being activated by positive information and misinformation at various times. Taking into account the two-hop theory, we propose a consistent and efficient computational method to assess node influence distribution from the perspectives of propagation and containment. This method strikes a balance between propagation and containment, surpassing degree centrality, further informing a heuristic solution to the MC problem. The heuristic solution's overall performance surpasses that of greedy approaches, which can only prioritize one aspect. Experiments on real-world networks demonstrate that our approach effectively balances the propagation of positive information and misinformation containment with low time complexity.

Traffic signal control (TSC) problems have received increasing attention with the development of the smart city. Reinforcement learning (RL) models TSC as a Markov decision process and learns the timing relationship of traffic scheduling from massive historical data. Due to the uncertainty and mutability of TSC problems, existing RL methods face bottlenecks in diversity and are easy to be trapped into local optima. To alleviate this predicament, this paper combines evolutionary optimization and RL to propose an evolutionary algorithm-assisted reinforcement learning (EARL-Light) method for TSC problems. EARL-Light is a population-based algorithm, in which one individual represents a policy and a population of individuals are evolved to search for near-optimal policies. The diversified search ability of evolutionary optimization can help the algorithm get rid of local optima for global optimization and the rapid learning based on the gradient of RL can achieve fast convergence. Extensive experiments on seven real-world traffic datasets demonstrates that EARL-Light achieves shorter travel time with fast convergence.

Target tracking has broad applications like disaster relief, and unmanned aerial vehicles (UAVs) have been universally applied in target tracking in recent years. Due to the strong responsiveness to deceptive reward signals and diverse exploration, evolutionary reinforcement learning (ERL) is a more noteworthy option for training UAVs than common reinforcement learning. However, for ERL contains too many neural networks, its training efficiency is not satisfactory enough. To address this shortcoming, this paper proposes an evolutionary reinforcement learning with double replay buffers (ERLDRB) for UAV online target tracking problem. Firstly, considering the energy consumption and the possible delay of feedback signals to the UAV, a more realistic model of UAV online target tracking problem is designed. Then based on the problem formulation, ERLDRB utilizes a double experience replay buffers technique to increase learning efficiency in the training stage, which can better solve real-world UAV online target tracking problem. Simulation results show that ERLDRB outperforms multiple contrasting algorithms on the designed model.

Sudoku puzzles are not only popular intellectual games but also NP-hard combinatorial problems related to various real-world applications, which have attracted much attention worldwide. Although many efficient tools, such as evolutionary computation (EC) algorithms, have been proposed for solving Sudoku puzzles, they still face great challenges with regard to hard and large instances of Sudoku puzzles. Therefore, to efficiently solve Sudoku puzzles, this paper proposes a genetic algorithm (GA)-based method with a novel local search technology called local search-based GA (LSGA). The LSGA includes three novel design aspects. First, it adopts a matrix coding scheme to represent individuals and designs the corresponding crossover and mutation operations. Second, a novel local search strategy based on column search and sub-block search is proposed to increase the convergence speed of the GA. Third, an elite population learning mechanism is proposed to let the population evolve by learning the historical optimal solution. Based on the above technologies, LSGA can greatly improve the search ability for solving complex Sudoku puzzles. LSGA is compared with some state-of-the-art algorithms at Sudoku puzzles of different difficulty levels and the results show that LSGA performs well in terms of both convergence speed and success rates on the tested Sudoku puzzle instances.

We study a new rate splitting (RS)-based hybrid beamforming scheme for multi-user downlink cellular networks in which the base station having a hybrid beamforming structure serves multiple users each having conventional multiple antennas. To maximize the potential of RS, we propose a general RS-enabled hybrid beamforming framework that can be applied to both fully-connected and sub-array hybrid beamforming structures, allowing for the assignment of a flexible number of streams for each user. Our proposed RS method divides each stream into an arbitrary number of common and private sub-streams, where private sub-streams are only recoverable by a dedicated user, whereas common streams can be recovered by all users. We propose a low-complexity analog and digital beamforming design suitable for the proposed RS and optimize the number of allocated common and private sub-streams for all users through a low-complexity genetic algorithm to maximize the achievable sum rate or minimum rate over multiple users. Numerical results demonstrate that the proposed scheme achieves a near-optimal sum rate close to that of dirty paper coding with low computational complexity and outperforms the benchmark approaches without considering RS.

Collective decision-making problems consisting of individual decisions are commonly seen in social applications. In this article, the vehicle energy station distribution problem (VESDP) is considered, which is modeled as a network-based collective decision-making problem fulfilling consumers’ requirements by arranging the distribution of energy stations rationally. This problem involves the game among the government and energy station investors. The government intends to maximize the satisfaction of both gas and electric vehicle (EV) customers through policy guidance, while investors aim to maximize their own profits. To solve this problem, we propose an individual evolutionary game model guided by global evolutionary optimization with the following three features. From the individual perspective, we use a network-based evolutionary game with a confidence mechanism to describe the behavior of investors. From the global perspective, we design a genetic algorithm to find out the global-optimized program, which considers the satisfaction of all customers. To heal the divergence between these two perspectives, we design a policy formulation method for the government to motivate selfish investors to adopt strategies in accordance with the overall interests of all customers by using subsidies and taxation. Experiments are performed on both square grid and real-world networks. Experimental results demonstrate the effectiveness of the proposed model.

In high dimensional environment, the interaction among particles significantly affects their movements in searching the vast solution space and thus plays a vital role in assisting particle swarm optimization (PSO) to attain good performance. To this end, this paper designs a random contrastive interaction (RCI) strategy for PSO, resulting in RCI-PSO, to tackle large-scale optimization problems (LSOPs) effectively and efficiently. Unlike existing interaction mechanisms for low-dimensional problems, RCI randomly chooses several different peers from the current swarm to construct a random interaction topology for each particle. Then, it lets the particle interact with the selected peers based on their current evolutionary information instead of their historical evolutionary information. Within the topology, RCI only propagates the evolutionary information of two contrastive dominators with the largest difference in fitness to direct the evolution of the particle. Therefore, particles with no more than two dominators in their topologies are not updated. Furthermore, a dynamic topology size adjustment scheme is devised to gradually enlarge the interaction topology. In this way, the swarm gradually switches from exploring the immense search space dispersedly to exploiting the found optimal regions intensively as the evolution continues. With these two strategies, RCI-PSO expectedly compromises search diversity and search convergence well at the swarm level and the particle level. At last, extensive experiments executed on two public LSOP suites verify that RCI-PSO performs competitively with or even much better than totally 40 state-of-theart large-scale approaches and preserves a good capability and scalability in tackling complex LSOPs.

Tourism is an important industry sector that requires tour companies to plan multiple routes for different tour groups, which is called tour multi-route planning. This paper focuses on tour multi-route planning, which can improve the economic benefit and allocation efficiency of tour resources. The main contributions of this paper are threefold. First, we propose a novel multiple routes planning model that captures the real-world tourism scenario and practical constraints. We also define four typical constraints for tourism planning and classify them into soft and hard constraints. Second, we develop a matrix-based differential evolution algorithm to jointly optimize multiple routes that can efficiently handle the high-dimensional optimization under various constraints. Third, we collect real-world data to construct problem instances and compare the performance of our algorithm with the conventional differential evolution algorithms in terms of runtimes. The experimental results show that our algorithm can effectively solve tour multi-route planning problems and achieve excellent runtimes performance, suitable for large-scale transportation network optimization.

Crowdsourcing utilizes the crowd intelligence for pervasive data sensing and processing. When the processing task is a decision-making and optimization problem, the objective is evaluated based on sensed data, which is defined as crowdsourcing-based distributed optimization (CrowdDO). As evolutionary computation (EC) is a powerful technique for black-box and data-driven optimization problems, this paper combines crowdsourcing and EC to propose crowdsourcing-based EC (CrowdEC) for CrowdDO. CrowdEC performs optimization based on a server and a crowd of workers. Once receiving a CrowdDO request, the server posts the problem to workers. Each worker senses its own data and makes local decisions by local EC optimizer. Due to the heterogeneity of worker behaviors and devices, the sensed data are partial with noises, and thus the server needs to coordinate global optimization based on workers information. To avoid the leakage of worker privacy, workers only compare optimization results with adjacent workers and report comparison results to the server. With partial comparison results, the server adopts the competitive ranking to guide workers cooperation and develop the reliability detection to distinguish unreliable workers. A crowdsourcing-based level-based learning swarm optimizer is implemented as an example. Comparison experiments on benchmark testsuites and distributed clustering optimization demonstrate the potential applications of CrowdEC.

We consider reconfigurable intelligent surface (RIS) aided sixth-generation (6G) terahertz (THz) communications for indoor environment in which a base station (BS) wishes to send independent messages to its serving users with the help of multiple RISs. For indoor environment, various obstacles such as pillars, walls, and other objects can result in no line-of-sight signal path between the BS and a user, which can significantly degrade performance. To overcome such limitation of indoor THz communication, we firstly optimize the placement of RISs to maximize the coverage area. Under the optimized RIS placement, we propose 3D hybrid beamforming at the BS and phase adjustment at RISs, which are jointly performed at the BS and RISs via codebook-based 3D beam scanning with low complexity. Numerical simulations demonstrate that the proposed scheme significantly improves the average sum rate compared to the cases of no RIS and randomly deployed RISs and also outperforms several benchmark schemes. It is further shown that the proposed codebook-based 3D beam scanning efficiently aligns analog beams between BS–user links or BS–RIS–user links and, as a consequence, achieves the average sum rate close to that of coherent beam alignment requiring global channel state information.

We consider a multi-user multi-server mobile edge computing (MEC) system, in which users arrive on a network randomly over time and generate computation tasks, which will be computed either locally on their own computing devices or be offloaded to one of the MEC servers. Under such a dynamic network environment, we propose a novel task offloading policy based on hybrid online–offline learning, which can efficiently reduce the overall computation delay and energy consumption only with information available at nearest MEC servers from each user. We provide a practical signaling and learning framework that can train deep neural networks for both online and offline learning and can adjust its offloading policy based on the queuing status of each MEC server and network dynamics. Numerical results demonstrate that the proposed scheme significantly reduces the average computation delay for a broad class of network environments compared to the conventional offloading methods. It is further shown that the proposed hybrid online–offline learning framework can be extended to a general cost function reflecting both delay- and energy-dependent metrics.

Due to flexibility, ad-hoc networking is an attractive structure for future emerging networks, such as Internet of Things (IoT), wireless sensor networks and vehicular networks. However, due to infrastructure-less, optimizing transmission control to avoid collision in ad-hoc networks is challenging. In this paper, we proposed an adaptive slotted-contention-based media access control (A-SCMAC) protocol which can adapt to network dynamics so that it can reduce collision and improve throughput. With A-SCMAC, each time slot consists of two periods: reservation period (RP) and transmission period (TP). By observing the channel outcomes in the RP the nodes learn the network status such as the number of nodes who have packets to transmit and control the transmission optimally for reserving the TP. Simulation results show that the proposed A-SCMAC can achieve the maximum throughput even the network traffic load changes.

Internet of Things (IoT) represents one of the most significant paradigm shifts recently, with several heterogeneous services and applications to the ultimate realization of connected living. In many status-sensitive IoT services, information usually has a higher value when it is fresher. A new metric, termed the age of information (AoI), was proposed to capture the freshness of status updates. In this paper, we propose a novel online control weight-based age-dependent activation control (W-ADAC) algorithm that prioritizes the activation of the devices that are divided into different classes possibly according to different AoI requirements. With the proposed W-ADAC, we introduce the concept of weighted AoI which is applied to control the activation probability of each device. In particular, higher weights are given to the devices that manifest higher priority requirements. As it is hard to obtain the system weighted AoI due to a lack of information on the distributed devices, we further introduce the capability of estimating the system weighted AoI into the proposed W-ADAC. Extensive simulations show the effectiveness of the proposed W-ADAC over multiple priorities and confirm that the proposed algorithm not only improves the overall system throughput but also provides the near-minimum AoI.

Different from existing studies where low-dimensional optimizers are utilized to optimize the path of an unmanned aerial vehicle (UAV), this paper attempts to employ large-scale swarm optimizers to solve the path planning problem of UAV, such that the path can be subtler and smoother. To this end, a variation encoding scheme is devised to encode particles. Specifically, each dimension of a particle is encoded by a triad consisting of the relative movements of UAV along the three coordinate axes. With this encoding scheme, a large number of anchor points can be optimized to form the path and repetitive anchor points can be avoided. Subsequently, this paper embeds this encoding scheme into four representative and well-performed large-scale swarm optimizers, namely the stochastic dominant learning swarm optimizer (SDLSO), the level-based learning swarm optimizer (LLSO), the competitive swarm optimizer (CSO), and the social learning particle swarm optimizer (SL-PSO), to optimize the path of UAV. Experiments have been conducted on 16 scenes with 4 different numbers of peaks in the landscapes. Experimental results have demonstrated that the devised encoding scheme is effective to cooperate with the four large-scale swarm optimizers to solve the path planning problem of UAV and SDLSO achieves the best performance.

We consider reconfigurable intelligent surface (RIS) aided sixth-generation (6G) terahertz (THz) communications for indoor environment in which a base station (BS) wishes to send independent messages to its serving users with the help of multiple RISs. For indoor environment, various obstacles such as pillars, walls, and other objects can result in no line-of-sight signal path between the BS and a user, which can significantly degrade performance. To overcome such limitation of indoor THz communication, we firstly optimize the placement of RISs to maximize the coverage area. Under the optimized RIS placement, we propose 3D hybrid beamforming at the BS and phase adjustment at RISs, which are jointly performed at the BS and RISs via codebook-based 3D beam scanning with low complexity. Numerical simulations demonstrate that the proposed scheme significantly improves the average sum rate compared to the cases of no RIS and randomly deployed RISs and also achieves the average sum rate close to that of coherent beam alignment requiring global channel state information.

Feature selection, which aims to improve the classification accuracy and reduce the size of the selected feature subset, is an important but challenging optimization problem in data mining. Particle swarm optimization (PSO) has shown promising performance in tackling feature selection problems, but still faces challenges in dealing with large-scale feature selection in Big Data environment because of the large search space. Hence, this paper proposes a bi-directional feature fixation (BDFF) framework for PSO and provides a novel idea to reduce the search space in large-scale feature selection. BDFF uses two opposite search directions to guide particles to adequately search for feature subsets with different sizes. Based on the two different search directions, BDFF can fix the selection states of some features and then focus on the others when updating particles, thus narrowing the large search space. Besides, a self-adaptive strategy is designed to help the swarm concentrate on a more promising direction for search in different stages of evolution and achieve a balance between exploration and exploitation. Experimental results on 12 widely-used public datasets show that BDFF can improve the performance of PSO on large-scale feature selection and obtain smaller feature subsets with higher classification accuracy.

The job-shop scheduling problem (JSSP) is a challenging scheduling and optimization problem in the industry and engineering, which relates to the work efficiency and operational costs of factories. The completion time of all jobs is the most commonly considered optimization objective in the existing work. However, factories focus on both time and cost objectives, including completion time, total tardiness, advance time, production cost, and machine loss. Therefore, this article first time proposes a many-objective JSSP that considers all these five objectives to make the model more practical to reflect the various demands of factories. To optimize these five objectives simultaneously, a novel multiple populations for multiple objectives (MPMO) framework-based genetic algorithm (GA) approach, called MPMOGA, is proposed. First, MPMOGA employs five populations to optimize the five objectives, respectively. Second, to avoid each population only focusing on its corresponding single objective, an archive sharing technique (AST) is proposed to store the elite solutions collected from the five populations so that the populations can obtain optimization information about the other objectives from the archive. This way, MPMOGA can approximate different parts of the entire Pareto front (PF). Third, an archive update strategy (AUS) is proposed to further improve the quality of the solutions in the archive. The test instances in the widely used test sets are adopted to evaluate the performance of MPMOGA. The experimental results show that MPMOGA outperforms the compared state-of-the-art algorithms on most of the test instances.

Expensive optimization problems (EOPs) are common in industry and surrogate-assisted evolutionary algorithms (SAEAs) have been developed for solving them. However, many EOPs have not only expensive objective but also expensive constraints, which are evaluated through distributed ways. We define this kind of EOPs as distributed expensive constrained optimization problems (DECOPs). The distributed characteristic of DECOPs leads to the asynchronous evaluation of both objective and constraints. Though some researchers have studied the asynchronous evaluation of objectives, the asynchronous evaluation of constraints has not gained much attention. Therefore, this article gives a formal formulation of DECOPs and proposes a distributed evolutionary constrained optimization algorithm with on-demand evaluation (DEAOE). DEAOE can adaptively evolve different constraints in an asynchronous way through the on-demand evaluation strategy. The on-demand evaluation works from two aspects to improve the population convergence and diversity. From the aspect of individual selection, a joint sample selection strategy is adopted to determine which candidates are promising. From the aspect of constraint selection, an infeasible-first evaluation strategy is devised to judge which constraints need to be further evolved. Extensive experiments and analyses on benchmark functions and engineering problems demonstrate that DEAOE has better performance and higher efficiency compared to centralized state-of-the-art SAEAs.

Trajectory planning is a crucial task in designing the navigation systems of automatic underwater vehicles (AUVs). Due to the complexity of underwater environments, decision makers may hope to obtain multiple alternative trajectories in order to select the best. This paper focuses on the multiple-trajectory planning (MTP) problem, which is a new topic in this field. First, we establish a comprehensive MTP model for AUVs, by taking into account the complex underwater environments, the efficiency of each trajectory, and the diversity among different trajectories, simultaneously. Then, to solve the MTP, we develop an ant colony-based trajectory optimizer, which is characterized by a niching strategy, a decayed alarm pheromone measure, and a diversified heuristic measure. The niching strategy assists in identifying and maintaining a diverse set of high-quality solutions. The use of decayed alarm pheromone and diversified heuristic further improves the search effectiveness and efficiency of the algorithm. Experimental results on practical datasets show that our proposed algorithm not only provides multiple AUV trajectories for a flexible choice, but it also outperforms the state-of-the-art algorithms in terms of the single trajectory efficiency.

Travel time estimation is a crucial task in practical transportation applications, while providing the reliability of estimation is important in many working scenarios. Most existing studies do not consider the dynamics of traffic status for different road segments in real time, thus yielding unsatisfactory results. To address the problem, we propose to formulate the traffic network as a temporal attributed graph and perform node representation learning on it. The learned representation is capable of jointly exploiting the dynamic traffic conditions and the topology of the road network, which is then fed into a route-based spatio-temporal dependence learning module to estimate the travel time. By incorporating a distribution loss function, our proposed model is able to predict the distribution of travel time. In the meantime, we design an auxiliary local task of predicting the congestion status of each road segment, which further enhances the generalization performance of the representation learning. Extensive experiments on real-world large-scale datasets demonstrated the superiority of our method compared with the state-of-the-arts.

In slotted random access systems, when multiple users transmit packets simultaneously, owing to the successive interference cancellation (SIC) technique, the access point (AP) is able to decode them through SIC-enabled resolution procedures (SRPs), which may occupy multiple consequent slots. While such an SRP could potentially improve the system throughput, how to fully exploit it in practical systems is still questionable when SIC capability is limited. Moreover, the number of active users contending for the channel varies over time which complicates the random access algorithm design. In order to fully exploit the potential of such limited SIC capability and maximize the system throughput, a novel online estimation based on Bayesian approach is introduced to estimate the number of active users in real-time and controls each user's transmission accordingly. It is shown that the throughput of the proposed algorithm can reach up to 0.693 packets/slot under practical assumptions, which is the first result achieving the throughput limit proved by Yu-Giannakis. It is further shown that the system throughput of 0.594 packets/slot (85.7% of the throughput limit) is achievable even when the SIC capability is restricted by two. It is also shown that the proposed online estimation and adaptation framework can be extended to further exploit collision information and the imperfect SIC.

A new interference management scheme based on integer forcing (IF) receivers is studied for the two-user multiple-input and multiple-output (MIMO) interference channel. The proposed scheme employs a message splitting method that divides each data stream into common and private sub-streams, in which the private stream is recovered by the dedicated receiver only while the common stream is required to be recovered by both receivers. Specifically, to enable IF sum decoding at the receiver side, all streams are encoded using the same lattice code. Additionally, the number of common and private streams of each user is carefully determined by considering the number of antennas at transmitters and receivers, the channel matrices, and the effective signal-to- noise ratio (SNR) at each receiver to maximize the achievable rate. Furthermore, we consider various assumptions of channel state information at the transmitter side (CSIT) and propose low-complexity linear transmit beamforming suitable for each CSIT assumption. The achievable sum rate and rate region are analytically derived and extensively evaluated by simulation for various environments, demonstrating that the proposed interference management scheme strictly outperforms the previous benchmark schemes in a wide range of channel parameters due to the gain from IF sum decoding.

Cold chain logistics (CCL) scheduling is important for smart cities as it directly affects the service quality and operating profits of logistics companies. However, traditional CCL models seldom reflect the real transportation environment, making the solutions hardly applicable to the real CCL scenes. Hence, this paper attempts to establish a multisource data-associated CCL model oriented to the real transportation environment. This environment is considered by employing the real-captured driving duration and distance between any two places. Three scheduling objectives (namely, quality losses, personnel and vehicle costs, and transportation costs) are taken into account. To efficiently solve the proposed multisource data-associated multiobjective CCL model, a multiple population-based multiobjective ant colony system (MPMOACS) approach is proposed. Based on the multiple populations for multiple objectives framework, the MPMOACS approach can optimize multiple objectives sufficiently, and thus obtain promising solutions distributed along the entire Pareto front. To further enhance the performance of the MPMOACS, a ranking-based local search strategy is also designed. Experiments are conducted on not only the existing benchmark instances but also a real environment-aware multisource dataset that is built based on real-captured transportation data of Guangzhou and Shenzhen, China. Compared with six state-of-the-art and very recent well-performing multiobjective optimization approaches, the proposed MPMOACS approach exhibits the overall best performance.

We consider a multichannel random access system in which each user accesses a single channel at each time slot to communicate with an access point (AP). Users arrive to the system at random and be activated for a certain period of time slots and then disappear from the system. Under such dynamic network environment, we propose a distributed multichannel access protocol based on multi-agent reinforcement learning (RL) to improve both throughput and fairness between active users. Unlike the previous approaches adjusting channel access probabilities at each time slot, the proposed RL algorithm deterministically selects a set of channel access policies for several consecutive time slots. To effectively reduce the complexity of the proposed RL algorithm, we adopt a branching dueling Q-network architecture and propose an efficient training methodology for producing proper Q-values over time-varying user sets. We perform extensive simulations on realistic traffic environments and demonstrate that the proposed online learning improves both throughput and fairness compared to the conventional RL approaches and centralized scheduling policies.

A multichannel random access system is considered in which each user accesses a single channel among multiple orthogonal channels to communicate with an access point (AP). Users arrive to the system at random and be activated for a certain period of time slots and then disappear from the system. Under such dynamic network environment, we propose a distributed multichannel access protocol based on multi-agent reinforcement learning (RL) to improve both throughput and fairness between users. Unlike the previous approaches adjusting channel access probabilities at each time slot, the proposed RL algorithm deterministically selects a set of channel access policies for several consecutive time slots. To effectively reduce the complexity of the proposed RL algorithm, we adopt a branching dueling Q-network architecture and propose a training methodology for producing proper Q-values under time-varying user sets. Numerical results demonstrate that the proposed scheme significantly improve both throughput and fairness.

In this paper, we consider the problem of handover decision making in the context of a dense heterogeneous network with a macro base station and multiple small base stations. We propose a distributed deep Q-learning based algorithm that minimizes the overall energy consumption by taking into account both the energy consumption from transmission and hand over overheads. The proposed algorithm is performed in a distributed and interactive manner in which a centralized training agent manages the replay buffer for training its deep Q-network, by gathering state, action, and reward information reported from distributed handover agents. We perform several numerical evaluations and demonstrate that the proposed algorithm provides 10% to 30% energy savings over other contemporary handover mechanisms depending on handover overhead costs.

In this paper, we consider a joint beam tracking and pattern optimization problem for massive multiple input multiple output (MIMO) systems in which the base station (BS) selects a beamforming codebook and performs adaptive beam tracking taking into account the user mobility. A joint adaptation scheme is developed in a two-phase reinforcement learning framework which utilizes practical signaling and feedback information. In particular, an inner agent adjusts the transmission beam index for a given beamforming codebook based on short-term instantaneous signal-to-noise ratio (SNR) rewards. In addition, an outer agent selects the beamforming codebook based on long-term SNR rewards. Simulation results demonstrate that the proposed online learning outperforms conventional codebook-based beamforming schemes using the same number of feedback information. It is further shown that joint beam tracking and beam pattern adaptation provides a significant SNR gain compared to the beam tracking only schemes, especially as the user mobility increases.

This paper proposes an adaptive transmission algorithm for slotted random access systems supporting the successive interference cancellation (SIC) at the access point (AP). When multiple users transmit packets simultaneously in a slot, owing to the SIC technique, the AP is able to decode them through SIC resolve procedures (SRPs), which may occupy multiple consequent slots. While such an SRP could potentially improve the system throughput, how to fully exploit this capability in practical systems is still questionable. In particular, the number of active users contending for the channel varies over time which complicates the algorithm design. By fully exploiting the potential of SIC, the proposed algorithm is designed to maximize the system throughput and minimize the access delay. For this purpose, an online estimation is introduced to estimate the number of active users in real-time to control their transmissions accordingly. It is shown that the throughput of the proposed algorithm can reach up to 0.693 packets/slot under such practical assumptions, which is the first result achieving the throughput limit proved by Yu-Giannakis. It is further shown that the system throughput of 0.559 packets/slot (80.6% of the throughput limit) is still achievable when the SIC capability is restricted by two.

An Internet-of-Things (IoT) cellular network is considered in which IoT devices communicate with an IoT base station using IoT sub-bands placed between long-term evolution (LTE) bands. Due to spectral leakage, inter-band interference exists among IoT sub-bands and also between LTE and IoT bands. It is assumed that the IoT cellular network is responsible for reducing its interference to the LTE network to a certain threshold level. Under such interference regulation to LTE bands, we establish a joint sub-band assignment and power allocation optimization in order to maximize the sum rate of the IoT cellular network. A novel two-stage suboptimal algorithm that sequentially performs sub-band assignment and power control is proposed, reflecting the impact of spectral leakage in its optimization procedure. Simulation results demonstrate that the proposed algorithm considering the impact of spectral leakage outperforms the conventional optimization algorithms without considering spectral leakage. It is further shown that it provides almost the same sum rate achievable for a stand-alone network as if there were no LTE networks.

A new practical multiple-input multiple-output (MIMO) transceiver technique calledspatially modulated integer-forcing(SM-IF) is proposed for hybrid beamforming array systems, which combines generalized spatial modulation (GSM) with integer-forcing (IF) MIMO multiplexing transmission to achieve improved spectral efficiency with low system complexity. In the proposed scheme, the transmitter first activates a subset of antennas using analog beamforming and delivers an SM information stream via the index of the activated antenna group. Then MIMO digital multiplexing is applied to send multiple MIMO information streams via multilevel coding (MLC) with binary channel codes through the activated antennas. At the receiver side, the receiver first estimates the index of the activated antenna group to recover the SM stream and then recovers the MIMO streams by IF sum decoding in conjunction with multi-stage decoding. The proposed scheme provides a unified coding framework to achieve a synergistic gain by integrating off-the-shelf practical binary codes and low complexity IF decoding into hybrid beamforming array systems. By extensive link-level numerical simulations, the proposed scheme is shown to outperform both the conventional plain IF MIMO and plain SM approaches. The results demonstrate that the proposed SM-IF can be a promising transceiver technique implementable in practical hybrid beamforming array systems.

An internet of things (IoT) cellular network is considered in which IoT devices communicate with an IoT base station using IoT sub-bands placed between long-term evolution (LTE) bands. Due to spectral leakage, inter-band interference exists among IoT subbands and also between LTE and IoT bands. It is assumed that the IoT cellular network is responsible for reducing its interference to the LTE network to a certain threshold level. Under such interference regulation to LTE bands, we establish a joint sub-band assignment and power allocation optimization in order to maximize the sum rate of the IoT cellular network. A novel two-stage suboptimal algorithm that sequentially performs sub-band assignment and power control is proposed, reflecting the impact of spectral leakage in its optimization procedure. Simulation results demonstrate that the proposed algorithm considering the impact of spectral leakage outperforms the conventional algorithms without considering spectral leakage.

Simultaneous wireless information and power transfer (SWIPT) is investigated in amplify-and- forward (AF) relay systems. Each relay node facilitates communication between a source node and a destination node. The relay nodes are equipped with radio frequency (RF) energy harvesters to supply power for retransmission of information signals from the source node to the destination node. In this paper, both power splitting (PS) ratio and adaptive power control are jointly optimized for optimal SWIPT multiple- relay systems performance. We demonstrate that the optimal power allocation strategy in joint optimization problem is to use full harvested power. We propose both an optimal centralized PS scheme and a distributed suboptimal scheme suitable for practical implementation. Simulation results demonstrate that the proposed schemes outperform naive PS schemes in terms of the average rate and the bit-error rate.

We study Please be advised that per instructions from the Communications Society this proof was formatted in Times Roman font and therefore some of the fonts will appear different from the fonts in your originally submitted manuscript. For instance, the math calligraphy font may appear different due to usage of the usepackage[mathcal]euscript. The Communications Society has decided not to use Computer Modern fonts in their publications. the degrees of freedom (DoF) of cellular networks in which a full duplex (FD) base station (BS) equipped with multiple transmit and receive antennas communicates with multiple mobile users. We consider two different scenarios. In the first scenario, we study the case when half duplex (HD) users, partitioned to either the uplink (UL) set or the downlink (DL) set, simultaneously communicate with the FD BS. In the second scenario, we study the case when FD users simultaneously communicate UL and DL data with the FD BS. Unlike conventional HD only systems, inter-user interference (within the cell) may severely limit the DoF, and must be carefully taken into account. With the goal of providing theoretical guidelines for designing such FD systems, we completely characterize the sum DoFs for both FD cellular networks. The key idea of the proposed scheme is to carefully allocate UL and DL streams using interference alignment and beam forming techniques. By comparing the DoFs of the FD systems with those of the conventional HD systems, we show that the DoF can approach the two-fold gain over the HD systems, when the number of users becomes large enough compared with the number of antennas at the BS.

We consider a wireless device-to-device network, where n nodes are uniformly distributed at random over the network area. We let each node caches M files from a library of size m ≥ M. Each node in the network requests a file from the library independently at random, according to a popularity distribution, and is served by other nodes having the requested file in their local cache via (possibly) multihop transmissions. Under the classical “protocol model” of wireless networks, we characterize the optimal per-node capacity scaling law for a broad class of heavy-tailed popularity distributions, including Zipf distributions with exponent less than one. In the parameter regime of interest, i.e., m=o(nM), we show that a decentralized random caching strategy with uniform probability over the library yields the optimal per-node capacity scaling of Θ(√M/m) for heavy-tailed popularity distributions. This scaling is constant with n , thus yielding throughput scalability with the network size. Furthermore, the multihop capacity scaling can be significantly better than for the case of single-hop caching networks, for which the per-node capacity is Θ (M/m). The multihop capacity scaling law can be further improved for a Zipf distribution with exponent larger than some threshold > 1, by using a decentralized random caching uniformly across a subset of most popular files in the library. Namely, ignoring a subset of less popular files (i.e., effectively reducing the size of the library) can significantly improve the throughput scaling while guaranteeing that all nodes will be served with high probability as n increases.

The K-user multiple-input and multiple-output (MIMO) broadcast channel (BC) with no channel state information at the transmitter is considered, where each receiver is assumed to be equipped with either conventional antennas or reconfigurable antennas in which each reconfigurable antenna is capable of choosing a subset of receiving modes from several preset modes. Under general antenna configurations, the sum linear degrees of freedom (DoFs) of the K-user MIMO BC with reconfigurable antennas are completely characterized, which corresponds to the maximum sum DoF achievable by linear coding strategies.

Throughput scaling laws of an ad hoc network equipping directional antennas at each node are analyzed. More specifically, this paper considers a general framework in which the beam width of each node can scale at an arbitrary rate relative to the number of nodes. We introduce an elastic routing protocol, which enables to increase per-hop distance elastically according to the beam width, while maintaining an average signal-to-interference-and-noise ratio at each receiver as a constant. We then identify fundamental operating regimes characterized according to the beam width scaling and analyze throughput scaling laws for each of the regimes. The elastic routing is shown to achieve a much better throughput scaling law than that of the conventional nearest-neighbor multihop for all operating regimes. The gain comes from the fact that more source-destination pairs can be simultaneously activated as the beam width becomes narrower, which eventually leads to a linear throughput scaling law. In addition, our framework is applied to a hybrid network consisting of both wireless ad hoc nodes and infrastructure nodes. As a result, in the hybrid network, we analyze a further improved throughput scaling law and identify the operating regime where the use of directional antennas is beneficial. In addition, we perform numerical evaluation in both ad hoc and hybrid networks, which completely validates our analytical results.

The feasibility conditions of interference alignment (IA) are analyzed for interfering broadcast-multiple-access channels in which one cell operates as downlink but the other cell operates as uplink. Under a general multiple-input and multiple-output antenna configuration, a necessary condition and a sufficient condition for one-shot linear IA are established, i.e., linear IA without symbol or time extension. In various example networks of interest, the optimal sum degrees of freedom (DoFs) are characterized by the derived necessary condition and sufficient condition. For symmetric DoF within each cell, a sufficient condition is established in a more compact expression, which yields the necessary and sufficient condition for a class of symmetric DoFs. An iterative construction of transmit and receive beamforming vectors is further proposed, which provides a specific beamforming design satisfying one-shot IA. Simulation results demonstrate that the proposed IA not only achieves larger DoF, but also significantly improves the sum rate over the single-cell operation in the practical signal-to-noise ratio regime.

Full-duplex (FD) cellular networks are considered in which an FD base station (BS) simultaneously supports a set of half-duplex (HD) downlink (DL) users and a set of HD uplink (UL) users. The transmitter and the receiver of the BS are equipped with reconfigurable antennas, each of which can choose its transmit or receive mode from several preset modes. Under the no self-interference assumption arisen from an FD operation at the BS, the sum degrees of freedom (DoF) of FD cellular networks is investigated for both no channel state information at the transmit side (CSIT) and the partial CSIT. In particular, the sum DoF is completely characterized for the no CSIT model and an achievable sum DoF is established for the partial CSIT model, which improves the sum DoF of the conventional HD cellular networks. For both no CSIT and partial CSIT models, the results show that the FD BS with reconfigurable antennas can double the sum DoF even in the presence of user-to-user interference as both the numbers of DL and UL users and preset modes increase. It is further demonstrated that such DoF improvement indeed yields the sum rate improvement at the finite and operational signal-to-noise ratio regime.

We analyze the optimal throughput-delay trade-off in content-centric mobile heterogeneous networks (HetNets), where each node moves according to the random walk mobility model and requests a content object from the library independently at random, according to a Zipf popularity distribution. Instead of allowing access to all content objects at base stations (BSs) via costly backhaul, we consider a more practical scenario where mobile nodes and BSs, each having a finite-size cache space, are able to cache a subset of content objects so that each request is served by other mobile nodes or BSs via multihop transmissions. Under the protocol model, we characterize a fundamental throughput-delay trade-off in terms of scaling laws by introducing our content delivery routing protocol and the corresponding optimal caching allocation strategy.

This paper studies the degrees of freedom (DoFs) of full-duplex (FD) multicell networks that share the spectrum among multiple cells. In the considered network, we assume that FD base stations (BSs) with multiple transmit and receive antennas communicate with multiple single-antenna uplink (UL) and downlink (DL) mobile users. By spectrum sharing among multiple cells, and with FD radio, the network can utilize the spectrum more efficiently. However, since spectrum sharing and FD induce additional inter-cell and intra-cell interferences, interference management is crucial to take advantage of these features. In this paper, we propose novel interference management strategies which take into account the new sources of interferences caused by spectrum sharing and FD to establish a general achievability result on the sum DoFs. The key ideas in our proposed scheme are to minimize the dimension of UL inter-cell and user-to-user interferences using interference alignment at the UL users. On the BS side, we propose an interference management strategy to align or null out DL intra-cell and inter-cell interferences, and BS-to-BS interferences. We further establish an upper bound on the sum DoFs and show that our lower and upper bounds match under certain conditions. Several numerical evaluations are shown which demonstrate that spectrum sharing and FD can significantly improve the throughput over conventional cellular networks, especially for a network with large number of users and/or cells.

An uplink-downlink two-cell cellular network is studied in which the first base station (BS) with M1antennas receives independent messages from its N1serving users, while the second BS with M2antennas transmits independent messages to its N2serving users. That is, the first and second cells operate as uplink and downlink, respectively. Each user is assumed to have a single antenna. Under this uplink-downlink setting, the sum degrees of freedom (DoFs) is completely characterized as the minimum of (N1N2+ min(M1, N1)(N1- N2)++ min(M2, N2)(N2- N1)+)/ max(N1, N2), M1+ N2, M2+ N1, max(M1, M2), and max(N1, N2), where a+denotes max(0, a). The result demonstrates that, for a broad class of network configurations, operating one of the two cells as uplink and the other cell as downlink can strictly improve the sum DoF compared with the conventional uplink or downlink operation, in which both cells operate as either uplink or downlink. The DoF gain from such uplink-downlink operation is further shown to be achievable for heterogeneous cellular networks having hotspots and with delayed channel state information.

Function computation over wireless sensor networks is investigated, where a set of K sensors observe their sensor readings and a fusion center wishes to learn a predefined function of the sensor readings via fading multiple access channels (MACs). In this paper, the arithmetic sum and type functions are considered since they can yield various fundamental sample statistics such as mean, variance, maximum, and minimum. We propose a novel opportunistic in-network computation (INC) framework in which a subset of sensors with large channel gains opportunistically participate in the transmission at each time, while all sensors simultaneously send their observations or only a single sensor sends its observation in the conventional schemes. We mathematically analyze the long-term average computation rate of the proposed INC, and prove that it achieves a nonvanishing computation rate even when the number of sensors K tends to infinity, which is in fact a significant improvement and the first theoretical result in fading MACs. Note that the computation rates of the conventional schemes become zero as K increases. We further show that a similar multiuser diversity gain is still achievable under delay constraints, which implies that the proposed INC is restricted to exploit a fixed and finite number of time slots (or fading instances) for the function computation.

We consider a wireless device-to-device (D2D) network in which the nodes are uniformly distributed at random over the network area and can cache information from a library of possible messages (files). Each node requests a file in the library independently at random, according to a given popularity distribution, and downloads from other nodes having the requested file in their local cache via multihop transmission. Under the classical “protocol model” of wireless ad hoc networks, we characterize the optimal throughput scaling law by presenting a feasible scheme formed by a decentralized caching policy for the parameter regimes of interest and a local multihop transmission protocol. The scaling law optimality of the proposed strategy is shown by deriving a new throughput upper bound. Surprisingly, we show that decentralized uniform random caching yields optimal scaling in most of the system interesting regimes. We also observe that caching improves the throughput scaling law of classical ad hoc networks, and that multihop improves the previously derived scaling law of caching wireless networks under one-hop transmission.

We study the degrees of freedom (DoF) of cellular networks in which a full duplex (FD) base station (BS) equipped with multiple transmit and receive antennas communicates with multiple mobile users. We consider two different scenarios. In the first scenario, we study the case when half duplex (HD) users, partitioned to either the uplink (UL) set or the downlink (DL) set, simultaneously communicate with the FD BS. In the second scenario, we study the case when FD users simultaneously communicate UL and DL data with the FD BS. For both network models, we completely characterize the sum DoFs by developing achievable schemes and obtaining matching upper bounds. The key idea of the proposed scheme is to carefully allocate UL and DL information streams using interference alignment and beamforming techniques. As a consequence of the result, we show that the DoF can approach the two-fold gain over the HD systems when the number of users becomes large enough as compared to the number of antennas at the BS.

Function computation over wireless sensor networks is investigated, where K sensors measure their observations and a fusion center wishes to estimate a pre-defined function of the observations via fading multiple access channels (MACs). The arithmetic sum and type functions are considered since they yield various fundamental sample statistics such as mean, variance, maximum, minimum, etc. We propose a novel opportunistic in-network computation (INC) scheme in which a subset of sensors with large channel gains opportunistically participate in the transmission at each time slot, while all sensors in a network simultaneously send their observations or only a single sensor sends its observation in the conventional INC schemes. We analyze the ergodic computation rate of the proposed INC scheme and prove that it achieves a non-vanishing computation rate even when the number of sensors K tends to infinity, which provides a significant rate improvement compared to the conventional INC schemes whose computation rates converge to zero as K increases.

We consider a wireless device-to-device (D2D) network where n nodes are uniformly distributed at random over the network area. We let each node with storage capacity M cache files from a library of size m. Each node in the network requests a file from the library independently at random, according to a popularity distribution, and is served by other nodes having the requested file in their local cache via (possibly) multihop transmissions. Under the classical “protocol model” of wireless networks, we characterize the optimal per-node capacity scaling law for a broad class of heavy-tailed popularity distributions including the Zipf distribution with Zipf exponent less than one. Surprisingly, in the parameter regimes of interest, we show that decentralized random caching uniformly across the library yields optimal per-node capacity scaling of Θ(√M/m), which outperforms the single-hop caching networks whose capacity scales as Θ (M/m) [1], [2].

In wireless sensor networks, various applications involve learning one or multiple functions of the measurements observed by sensors, rather than the measurements themselves. This paper focuses on the class of type-threshold functions, e.g., the maximum and the indicator functions. A simple network model capturing both the broadcast and superposition properties of wireless channels is considered: the collocated Gaussian network. A general multiround coding scheme exploiting superposition and interaction (through broadcast) is developed. Through careful scheduling of concurrent transmissions to reduce redundancy, it is shown that given any independent measurement distribution, all type-threshold functions can be computed reliably with a nonvanishing rate in the collocated Gaussian network, even if the number of sensors tends to infinity.

The parallel relay network is studied, in which a single source node sends a message to a single destination node with the help of N parallel relays. Channel coefficients are assumed to vary over time and channel state information (CSI) is causally available only at the receiver side (CSIR). Opportunistic noisy network coding is proposed for intelligently exploiting CSIR at each relay in a distributed manner by operating the noisy network coding scheme with adaptive compression. More specifically, each relay opportunistically vector-quantizes the collection of received symbols that is received with channel gains larger than a certain threshold. It then forwards the digital compression information to the destination node using independently generated Gaussian codes. For independent and identically distributed (i.i.d.) Rayleigh fading, the proposed scheme is shown to achieve the ergodic capacity in the large number of relays regime. Furthermore, the proposed scheme is extensively compared with several alternative schemes, the decode-forward scheme, the adaptive amplify-forward scheme, and the non-adaptive noisy network coding scheme over geometric models. We show that the new proposed scheme provides significant gain over these schemes in various cases.

An uplink-downlink cellular network is studied in which the first base station (BS) with M1antennas receives independent messages from its N1serving users, while the second BS with M2antennas transmits independent messages to its N2serving users. Each user is assumed to have a single antenna. Under this uplink-downlink setting, the sum degrees of freedom (DoF) is completely characterized as the minimum of (N1N2+ min(M1,N1)(N1- N2)++ min(M2,N2)(N2-N1)+)/ max(N1,N2), M1+ N2,N1+ M2, max(M1,M2), and max(N1,N2), where a+denotes max(0, a). The result demonstrates that, depending on the network configuration, operating one of the cells as uplink and the other cell as downlink can improve DoF compared to the conventional uplink or downlink operation, in which both cells operate as either uplink or downlink.

Throughput scaling law of a large wireless network equipping directional antennas at each node is analyzed based on the information-theoretic approach. More specifically, this paper considers a general framework in which the beamwidth of each node can scale at an arbitrary rate relative to the number of nodes in the network. We introduce an elastic routing protocol, which enables to increase per-hop distance elastically according to the beamwidth, while maintaining an average signal-to-interference-and-noise ratio at each receiver as a constant. This elastic routing is shown to achieve a much better throughput scaling law than that of the conventional nearest-neighbor multihop routing. The gain comes from the fact that more source-destination pairs can be activated simultaneously as the beamwidth becomes narrower, which eventually leads to a linear throughput scaling law.

The capacity scaling laws of two overlaid networks are investigated, which are located in the same area sharing the same wireless resources with different priorities. The primary network can be regarded as an existing communication system operated in a licensed band and, therefore, is assumed to operate in an order-optimal fashion to achieve its standalone capacity scaling law. The secondary cognitive network must keep its interference to the primary network below a certain threshold while at the same time maximizing its own throughput scaling law based on cognition information. The existing scaling results for cognitive networks inherently assume multihop communication, which is a restricted coding model. By contrast, in this paper, a general coding model is considered without any specific physical layer coding assumptions. The capacity scaling exponents for both networks are analyzed when the numbers of primary nodes n, primary base stations l, which support the communication between primary nodes, and secondary nodes m increase with the relations m = nβ, β > 1, and l = nγ, 0 ≤ γ ≤ 1. For the extended network model, the capacity scaling exponents are completely characterized as max {2-α/2, 1/2, γ} and max{2-α/2, 1/2} for the primary and secondary networks respectively, where α > 2 denotes the path-loss exponent. That is, the capacity scaling laws for the primary and secondary networks are represented, respectively, by nmax{2-α/2,1/2,γ}±∈and mmax{2-α/2,1/2}±∈for α > 0 arbitrarily small. For the dense network model, when the primary network achieves its standalone capacity scaling exponent of 1, the secondary network is shown to achieve a scaling exponent of 1 - 1/(2β), which improves the previous scaling exponent of 1/2 achieved by multihop. For both models, it turns out that the conventional multihop approach is in general quite suboptimal.

The fading AWGN two-user two-hop network is considered where the channel coefficients are independent and identically distributed (i.i.d.) according to a continuous distribution and vary over time. For a broad class of channel distributions, the ergodic sum capacity is characterized to within a constant number of bits/second/hertz, independent of the signal-to-noise ratio. The achievability follows from the analysis of an interference neutralization scheme where the relays are partitioned into M pairs, and interference is neutralized separately by each pair of relays. When M = 1, the proposed ergodic interference neutralization characterizes the ergodic sum capacity to within 4 bits/sec/Hz for i.i.d. uniform phase fading and approximately 4.7 bits/sec/Hz for i.i.d. Rayleigh fading. It is further shown that this gap can be tightened to 4 log π-4 bits/sec/Hz (approximately 2.6) for i.i.d. uniform phase fading and 4-4 log(3π/8) bits/sec/Hz (approximately 3.1) for i.i.d. Rayleigh fading in the limit of large M1.

Function computation over Gaussian networks with orthogonal components is studied for arbitrarily correlated discrete memoryless sources. Two classes of functions are considered: 1) the arithmetic sum function and 2) the type function. The arithmetic sum function in this paper is defined as a set of multiple weighted arithmetic sums, which includes averaging of the sources and estimating each of the sources as special cases. The type or frequency histogram function counts the number of occurrences of each argument, which yields various fundamental statistics, such as mean, variance, maximum, minimum, median, and so on. The proposed computation coding first abstracts Gaussian networks into the corresponding modulo sum multiple-access channels via nested lattice codes and linear network coding and then computes the desired function using linear Slepian-Wolf source coding. For orthogonal Gaussian networks (with no broadcast and multiple-access components), the computation capacity is characterized for a class of networks. For Gaussian networks with multiple-access components (but no broadcast), an approximate computation capacity is characterized for a class of networks.

We study a minimum delay routing problem in the context of distributed networks with and without partial load information. Even though a general minimum delay routing problem is NP hard, assuming uniformly distributed K source-destination (SD) pairs at random, we provide a lower bound on the average delay and demonstrate by simulation that it is tight for a certain classes of regularly deployed networks. We also show that some routing in a distributed manner is enough to achieve asymptotically optimal load balancing with high probability as K tends to infinity. In order to set such routing, however, each SD pair should know global load information, which is unrealistic for most networks. We propose novel predetermined path routing algorithms in which each SD pair chooses its routing path only among a set of predetermined paths. We then propose an efficient way of distributed construction for predetermined paths that are able to distribute traffic over a network. Our predetermined path routing algorithms work in a fully distributed manner with very limited load information or without any load information. In various network models, we demonstrate by simulation that the delay of the predetermined path routing algorithms quickly converges to that of the distributed routing with global load information.

The capacity scaling laws of two overlaid networks sharing the same wireless resources with different priorities are investigated. The primary network is assumed to operate in an order-optimal fashion to achieve its standalone capacity scaling law. The secondary “cognitive” network must keep its interference to the primary network below a certain threshold while at the same time maximizing its own throughput scaling law based on cognition information. The existing scaling results for cognitive networks inherently assume multihop communication treating all other signals except from a single intended transmitter as noise. By contrast, in this paper, a general coding model is considered without any specific physical layer coding assumptions. Therefore, this paper provides a general framework for comprehensive understanding of fundamental limits on the capacity scaling laws of cognitive networks. For the extended network model, the capacity scaling laws of both the primary and secondary networks are completely characterized. For the dense network model, an improved throughput scaling law is achieved by inducing cooperation within the secondary network. In both cases, it turns out that the conventional multihop approach is in general quite suboptimal.

Function computation of arbitrarily correlated discrete sources over Gaussian networks with multiple access components but no broadcast is studied. Two classes of functions are considered: the arithmetic sum function and the frequency histogram function. The arithmetic sum function in this paper is defined as a set of multiple weighted arithmetic sums, which includes averaging of sources and estimating each of the sources as special cases. The frequency histogram function counts the number of occurrences of each argument, which yields many important statistics such as mean, variance, maximum, minimum, median, and so on. For a class of networks, an approximate computation capacity is characterized. The proposed approach first abstracts Gaussian networks into the corresponding modulo-sum multiple-access channels via lattice codes and linear network coding and then computes the desired function by using linear Slepian-Wolf source coding.

In wireless sensor networks, various applications involve learning one or multiple functions of the measurements observed by sensors, rather than the measurements themselves. This paper focuses on the computation of type-threshold functions which include the maximum, minimum, and indicator functions as special cases. Previous work studied this problem under the collocated collision network model and showed that under many probabilistic models for the measurements, the achievable computation rates tend to zero as the number of sensors increases. In this paper, wireless sensor networks are modeled as fully connected Gaussian networks with equal channel gains, which are termed collocated Gaussian networks. A general multi-round coding scheme exploiting not only the broadcast property but also the superposition property of Gaussian networks is developed. Through careful scheduling of concurrent transmissions to reduce redundancy, it is shown that given any independent measurement distribution, all type-threshold functions can be computed reliably with a non-vanishing rate even if the number of sensors tends to infinity.

In this paper, we study a layered linear binary field network with time-varying channels, which is a simplified model reflecting broadcast, interference, and fading natures of wireless communications. We observe that fading can play an important role in mitigating interuser interference effectively for both single-hop and multihop networks. We propose new coding schemes with randomized ergodic channel pairing, which exploit such channel variations, and derive their achievable ergodic rates. By comparing them with the cut-set upper bound, the capacity region of single-hop networks and the sum capacity of multihop networks are characterized for some classes of channel distributions and network topologies.

We investigate how the network connectivity can affect the degrees of freedom (DoF) of wireless networks. We consider a network of n source-destination (SD) pairs and assume that any two nodes are connected with a positive probability p, independent of other node pairs. We show that, for any arbitrarily small p, a constant DoF is achievable for every SD pair with probability approaching one as n tends to infinity. The achievability is based on the two-hop transmission with decode-and-forward relaying and over each-hop we adopt interference alignment. Considering that an achievable per-user DoF for direct or one-hop transmission can be arbitrarily small as the connectivity probability p decreases, our result shows that, somewhat surprisingly, two-hop transmission is enough to guarantee non-vanishing per-user DoF for any p showing that sparsely connected networks can still provide non-vanishing per-user DoF.

We consider a fading AWGN 2-user 2-hop network in which the channel coefficients are independently and identically distributed (i.i.d.) drawn from a continuous distribution and vary over time. For a broad class of channel distributions, we characterize the ergodic sum capacity within a constant number of bits/sec/Hz, independent of signal-to-noise ratio. The achievability follows from the analysis of an interference neutralization scheme where the relays are partitioned into K pairs, and interference is neutralized separately by each pair of relays. For K = 1, we previously proved a gap of 4 bits/sec/Hz for i.i.d. uniform phase fading and approximately 4.7 bits/sec/Hz for i.i.d. Rayleigh fading. In this paper, we give a result for general K. In the limit of large K, we characterize the ergodic sum capacity within 4((log π) - 1) ≃ 2.6 bits/sec/Hz for i.i.d. uniform phase fading and 4(4 - log3π) ≃ 3.1 bits/sec/Hz for i.i.d. Rayleigh fading.

We show that the 2 × 2 × 2 interference network, i.e., the multihop interference network formed by concatenation of two two-user interference channels achieves the min-cut outer bound value of 2 DoF, for almost all values of channel coefficients, for both time-varying or fixed-channel coefficients. The key to this result is a new idea, called aligned interference neutralization, that provides a way to align interference terms over each hop in a manner that allows them to be canceled over the air at the last hop.

We consider a wireless network in which a single-source node havingmantennas transmits independent messages tondestination nodes, where each destination node has a single antenna. We assume that the source is located at the center of a unit area and the destinations are located uniformly at random in the same area. By applying transmit beamforming at the source, an achievable sum rate can scale as min{m,n}, which is defined by the aggregate rate of all messages. For transmit beamforming, however, channel state information (CSI) is essentially required at the transmitter (CSIT), which is hard to acquire in practice because of the time-varying nature of wireless channels and feedback overhead. We show that, even without CSIT, almost the same sum rate scaling law assuming CSIT is achievable by inducing cooperation between destinations. Specifically, we study the sum rate scaling law when both the numbermof the source antennas and the numbernof destinations increase such thatn=mβfor β >; 0. If β >; 1 the optimal sum rate scales asmlogmand if 0mβ(1-ε)andmβlogm, where ε >; 0 is an arbitrarily small constant, which shows significant improvement compared to the case of no cooperation between destinations. Our result is of particular interest because we did not assume any additional bandwidth for cooperation.

The recently developed noisy network coding naturally extends compress-forward coding for the relay channel by Cover and El Gamal to arbitrary relay networks. In particular, the noisy network coding scheme achieves the best known capacity lower bound for general Gaussian networks. Motivated by the recent development of noisy network coding, we propose a novel extension of noisy network coding specialized for the fading parallel relay network. In the new scheme, the relay observation is opportunistically compressed by adapting on the local channel state information of the source-relay link. More specifically, each relay node opportunistically compresses the collection of output symbols with channel gains above a certain threshold, and forwards the digital compression to the destination node using independent Gaussian codes. To present the potential of the new scheme, we focus on the symmetric setting in which the channel coefficients within each hop are identically and independently distributed. We show that in the large number of relays regime, our scheme achieves the capacity while outperforming other schemes such as amplify-forward and decode-forward. Our result demonstrates that adaptation using channel state information at the receiver side can be beneficial.

We show that the 2 × 2 × 2 interference network, i.e., the multihop interference network formed by concatenation of two 2-user interference channels achieves the min-cut outer bound value of 2 DoF, for almost all values of channel coefficients, for both time-varying or fixed channel coefficients. The key to this result is a new idea, called aligned interference neutralization, that provides a way to align interference terms over each hop in a manner that allows them to be cancelled over the air at the last hop.

We study a layeredK-userM-hop Gaussian relay network consisting ofKmnodes in themthlayer, whereM≥ 2 andK=K1=KM+1. We observe that the time-varying nature of wireless channels or fading can be exploited to mitigate the interuser interference. The proposed amplify-and-forward relaying scheme exploits such channel variations and works for a wide class of channel distributions including Rayleigh fading. We show a general achievable degrees of freedom (DoF) region for this class of Gaussian relay networks. Specifically, the set of all (d1,...,dK) such thatdi≤ 1 for alliand Σi=1K di≤KΣis achievable, wherediis the DoF of theithsource-destination pair andKΣis the maximum integer such thatKΣ≤ minm{Km} andM/KΣis an integer. We show that surprisingly the achievable DoF region coincides with the cut-set outer bound ifM/ minm{Km} is an integer; thus, interference-free communication is possible in terms of DoF. We further characterize an achievable DoF region assuming multi-antenna nodes and general message set, which again coincides with the cut-set outer bound for a certain class of networks.

Two distinct, but overlapping, networks that operate at the same time, space, and frequency is considered. The first network consists ofnrandomly distributed primary users, which form an ad hoc network. The second network again consists ofmrandomly distributed ad hoc secondary users or cognitive users. The primary users have priority access to the spectrum and do not need to change their communication protocol in the presence of the secondary users. The secondary users, however, need to adjust their protocol based on knowledge about the locations of the primary users to bring little loss to the primary network's throughput. By introducing preservation regions around primary receivers, a modified multihop routing protocol is proposed for the cognitive users. Assumingm=nβwith β >; 1, it is shown that the secondary network achieves almost the same throughput scaling law as a stand-alone network while the primary network throughput is subject to only a vanishingly small fractional loss. Specifically, the primary network achieves the sum throughput of ordern1/2and, for any δ >; 0, the secondary network achieves the sum throughput of orderm1/2-δwith an arbitrarily small fraction of outage. Thus, almost all secondary source-destination pairs can communicate at a rate of orderm-1/2-δ.

This paper analyzes the impact and benefits of infrastructure support in improving the throughput scaling in networks ofnrandomly located wireless nodes. The infrastructure uses multiantenna base stations (BSs), in which the number of BSs and the number of antennas at each BS can scale at arbitrary rates relative ton. Under the model, capacity scaling laws are analyzed for both dense and extended networks. Two BS-based routing schemes are first introduced in this study: an infrastructure-supported single-hop (ISH) routing protocol with multiple-access uplink and broadcast downlink and an infrastructure-supported multihop (IMH) routing protocol. Then, their achievable throughput scalings are analyzed. These schemes are compared against two conventional schemes without BSs: the multihop (MH) transmission and hierarchical cooperation (HC) schemes. It is shown that a linear throughput scaling is achieved in dense networks, as in the case without help of BSs. In contrast, the proposed BS-based routing schemes can, under realistic network conditions, improve the throughput scaling significantly in extended networks. The gain comes from the following advantages of these BS-based protocols. First, more nodes can transmit simultaneously in the proposed scheme than in the MH scheme if the number of BSs and the number of antennas are large enough. Second, by improving the long-distance signal-to-noise ratio (SNR), the received signal power can be larger than that of the HC, enabling a better throughput scaling under extended networks. Furthermore, by deriving the corresponding information-theoretic cut-set upper bounds, it is shown under extended networks that a combination of four schemes IMH, ISH, MH, and HC is order-optimal in all operating regimes.

We study K-user M-hop Gaussian relay networks with Kmnodes in the m-th layer, where M is even and K = K1= KM+1. We observe that the time-varying nature of wireless channels (fading) can be exploited to mitigate the inter-user interference. The proposed block Markov encoding and relaying scheme exploits such channel variations and works for any isotropically distributed channels including Rayleigh fading. We show a general achievable degrees of freedom (DoF) region of this class of Gaussian relay networks, which coincides with the cut-set outer bound if M/Kminis an integer, where Kmin= minm{Km}. Therefore, we completely characterize the DoF region for the case where M/Kminis an integer.

We study a fading linear finite-field relay network having multiple source-destination pairs. Because of the interference created by different unicast sessions, the problem of finding its capacity region is in general difficult. We observe that, since channels are time-varying, relays can deliver their received signals by waiting for appropriate channel realizations such that the destinations can decode their messages without interference. We propose a block Markov encoding and relaying scheme that exploits such channel variations. By deriving a general cut-set upper bound and an achievable rate region, we characterize the sum capacity for some classes of channel distributions and network topologies. For example, when the channels are uniformly distributed, the sum capacity is given by the minimum average rank of the channel matrices constructed by all cuts that separate the entire sources and destinations. We also describe other cases where the capacity is characterized.

We study two distinct, but overlapping, networks which operate at the same time, space and frequency. The first network consists of n randomly distributed primary users, which form either an ad hoc network, or an infrastructure supported ad hoc network in which l additional base stations support the primary users. The second network consists of m randomly distributed secondary or cognitive users. The primary users have priority access to the spectrum and do not change their communication protocol in the presence of secondary users. The secondary users, however, need to adjust their protocol based on knowledge about the locations of the primary users so as not to harm the primary network's scaling law. Base on percolation theory, we show that surprisingly, when the secondary network is denser than the primary network, both networks can simultaneously achieve the same throughput scaling as a standalone ad hoc network.

We analyze the benefits of infrastructure support in improving the throughput scaling in networks of n randomly located wireless nodes. The infrastructure uses multi-antenna base stations (BSs), in which the number of BSs and the number of antennas at each BS can scale at arbtrary rates relative to n. We introduce two multi-antenna BS-based routing protocols and analyze their throughput scaling laws. Two conventional schemes not using BSs are also shown for comparison. In dense networks, we show that the BS-based routing schemes do not improve the throughput scaling. In contrast, in extended networks, we show what our BS-based routing schemes can, under certain network conditions, improve the throughput scaling significantly.

We study how fast a broadcast message can be propagated through large wireless networks. A two-phase opportunistic broadcasting is proposed in this paper. At the first phase, all nodes having the message broadcast it simultaneously with random phases, which gives a chance for remote nodes to receive the message through opportunistic beamforming. At the second phase, each node having the message transmits it to its neighbor nodes. By performing this two phases repeatedly, the message propagates through the network. It is shown that the two- phase opportunistic broadcasting achieves a linear increase of the propagation distance. By comparing it with an upper-bound, we show it is asymptotically order optimal in the high attenuation regime. Furthermore, our scheme can have a potentially huge gain compared to naive multihop broadcasting.