Unlike vanilla long-tailed recognition trains on imbalanced data but assumes a uniform test class distribution, test-agnostic long-tailed recognition aims to handle arbitrary test class distributions. Existing methods require prior knowledge of test sets for post-adjustment through multi-stage training, resulting in static decisions at the dataset-level. This pipeline overlooks instance diversity and is impractical in real situations. In this work, we introduce Prototype Alignment with Dedicated Experts (PADE), a one-stage framework for test-agnostic long-tailed recognition. PADE tackles unknown test distributions at the instance-level, without depending on test priors. It reformulates the task as a domain detection problem, dynamically adjusting the model for each instance. PADE comprises three main strategies: 1) parameter customization strategy for multi-experts skilled at different categories; 2) normalized target knowledge distillation for mutual guidance among experts while maintaining diversity; 3) re-balanced compactness learning with momentum prototypes, promoting instance alignment with the corresponding class centroid. We evaluate PADE on various long-tailed recognition benchmarks with diverse test distributions. The results verify its effectiveness in both vanilla and test-agnostic long-tailed recognition.

As the Internet of Things (IoT) continues to expand and reshape our world, new vertical application scenarios have emerged, such as underwater communications, leading to increased interest in academia and industries. The multiple-input–multiple-output (MIMO) technology plays a critical role in enhancing channel capacity for underwater acoustic (UWA) communications, where accurate channel prediction is essential for system performance. In this article, we propose a novel efficient channel impulse response (CIR) prediction model for the UWA MIMO communications with a small adaptive bidirectional gated recurrent unit (ABiGRU) network. The proposed model can capture the channel information without additional knowledge of the internal properties of the channel itself. Moreover, it first utilizes preceding short-term CIR data from the channel estimation for online training, and then exploits the trained model for the CIR prediction, which tracks time-varying UWA channels. To verify the effectiveness of the predicted CIRs, we design a scheme combining a space-time block coding (STBC) and minimum mean square error (MMSE) pre-equalization for the UWA MIMO system. Our proposed STBC-MMSE pre-equalization scheme has demonstrated practical feasibility and low-bit-error rate (BER) in numerical simulations. In addition, we evaluate the prediction error performance of the proposed ABiGRU network through comparison with the widely used MMSE algorithm and two common recurrent neural networks (RNNs) predictors, i.e., the gated recurrent unit and long short term memory (LSTM) network. Finally, we conduct realistic in-field UWA MIMO experiments to demonstrate and justify the superiority of the proposed ABiGRU network, which can lay the solid foundation for cost-effective UWA MIMO communications for building promising underwater IoT sensor networks.

In-network aggregation is a fundamental operation for massive packets in the Internet of Things (IoT). By aggregating ultra-small packets, the energy consumption for data transmission is not related to the packet number, while the average delay performance depends on the delay of all packets even if they are aggregated. In this paper, we propose a low-delay ultra-small packet transmission scheme with in-network aggregation in energy-harvesting multi-hop networks, where each device periodically transmits packets in a collect-wait-forward relaying manner. Considering the resulting extra waiting time during relaying, we first drive the tractable form of the average end-to-end delay by problem transformation. By characterizing the two-dimensional evolution property from the perspective of both hops and time, the delay minimization problem is reformulated as an infinite-horizon average-cost Markov decision process with a two-dimensional optimality equation. To deal with the curse of dimensionality, we decompose the global Bellman equation into several per-device local relay selection problems. Based on the problem decomposition, we propose a distributed ultra-Small Packet Aggregation Relay SElection (SPARSE) algorithm via stochastic learning. The convergence is further proved theoretically and verified by simulation. Simulation results reveal that the proposed scheme achieves significant performance gain over the baselines for ultra-small packets.

Image co-segmentation and co-localization exploit inter-image information to identify and extract foreground objects with a batch mode. However, they remain challenging when confronted with large object variations or complex backgrounds. This paper proposes a multi-view graph embedding (MV-Gem) learning scheme which integrates diversity, robustness and discernibility of object features to alleviate this phenomenon. To encourage the diversity, the deep co-information containing both low-layer general representations and high-layer semantic information is generated to form a multi-view feature pool for comprehensive co-object description. To enhance the robustness, a multi-view adaptive weighted learning is formulated to fuse the deep co-information for feature complementation. To ensure the discernibility, the graph embedding and sparse constraint are embedded into the fusion formulation for feature selection. The former aims to inherit important structures from multiple views, and the latter further selects important features to restrain irrelevant backgrounds. With these techniques, MV-Gem gradually recovers all co-objects through optimization iterations. Extensive experimental results on real-world datasets demonstrate that MV-Gem is capable of locating and delineating co-objects in an image group.

Simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) are gaining great attention for their ability to achieve full-space coverage. In this paper, the queue-aware STAR-RIS assisted non-orthogonal multiple access (NOMA) communication system is investigated to ensure system stability. To tackle the challenge of infinite time periods for stability, the long-term stability-oriented problem is reformulated as a per-slot queue-weighted sum rate (QWSR) maximization problem using Lyapunov drift theory. Particularly, the allocated rate weight for each user is determined by the corresponding data queue at the base station (BS). By jointly optimizing the NOMA decoding order, the active beamforming coefficients at the BS, and the passive transmission and reflection coefficients at the STAR-RIS, three STAR-RIS operating protocols are considered, namely energy splitting (ES), mode switching (MS), and time switching (TS). An equivalent-combined channel gain based scheme is proposed to obtain the desired decoding order. For ES, the highly coupled and non-convex problem is solved iteratively and alternatively by invoking the blocked coordinate descent and the successive convex approximation methods. This approach is further expanded to a penalty-based two-loop algorithm to solve the binary amplitude constrained problem for MS. For TS, the problem is decomposed into two subproblems, each of which is solved similarly as ES. Simulation results show that: i) our proposed STAR-RIS assisted NOMA communication achieves superior performance to the conventional schemes; ii) the reformulated QWSR maximization problem is proven to ensure the system stability; and iii) TS performs best in both the QWSR and the average queue length.

Multi-view data describes an image sample with different modalities of features, thus provides a more comprehensive description of data. Its three basic characteristics, i.e., consensus, complementary and redundancy, determine its performances in computer vision tasks. In this paper, we effectively exploit the above three characteristics to propose a deep learning scheme with joint shared-and-specific information (JSSI) for multi-view clustering. Aiming at facilitating the consensus, JSSI extracts shared information of multi-view data via an adversarial similarity constraint, which is realized by classification and discrimination interactions. Aiming at reducing the redundancy, JSSI separate out view-specific features and prevent them from interfering with the shared features via a difference constraint. Aiming at ensuring the complementary, JSSI aligns the shared features and then concatenates them with the specific features. We examine the effectiveness of JSSI with multi-view clustering on real-world datasets, such as faces and indoor scenes. Extensive experiments and comparisons show that JSSI outperforms other state-of-the-art methods in most of these datasets.

With a significant Rate-Distortion (RD) improvement than H.265/HEVC, Versatile Video Coding (VVC) has set a new milestone in lossy video compression. It also incorporates the emerging$\lambda $-domain rate control technique, aiming at a higher visual quality under a fixed bit constraint. However, the challenge remains how to efficiently allocate bits to all frames and Coding Tree Units (CTUs). In this paper, we propose an effective solution by formulating the above task as a Nash equilibrium problem, where all CTUs are treated as players that bargains with each other. By introducing$\lambda $-domain RD models, a constrained optimization is derived with no closed-form solution. We then propose a two-step strategy to address this issue: a Newton method to iteratively calculate an intermediate variable, and a final solution of Nash equilibrium to obtain an approximately optimal$\lambda $. Finally, we utilize the derived$\lambda $to perform an effective CTU-level bit allocation, which is the very first attempt to introduce Nash equilibrium in$\lambda $-domain rate control. Experimental results with Common Test Conditions (CTC) demonstrate the effectiveness and superiority of our method, which outperforms the state-of-the-art CTU-level rate allocation algorithms for VVC.

The booming of multimedia technologies has promoted the diversity of visual big data. To learn common features across heterogeneous image data, the image co-processing has exhibited its advantages over the separate one. Recently, an active topic of image co-processing is the object co-segmentation, which aims at simultaneously extracting and segmenting shared objects from relevant images. In this paper, we address this problem with a weak-supervision-based probabilistic model. We introduce the weakly supervised priors to alleviate the confusion between common foreground and background, thereby facilitating performance improvement. To ensure the validity of potential background prior knowledge, the nodes on four sides of image are respectively leveraged as the labelled queries. After that, we develop quantitative probabilistic metrics for precisely measuring internal consistencies within single image and correlations between multiple images. Combining the intra-image consistencies with the inter-image correlations, we propose an optimized energy function coupled with binary labeling and graph connectivity to carry out the object co-segmentation. Extensively experimental results on real-world datasets demonstrate that the proposed method achieves superior co-segmentation performance to the state-of-the-arts, with a significantly reduced time consumption.

Caching popular contents in close proximity to the users can effectively reduce the latency in communication systems. Considering the imperfect content fetching from the original server, the caching management at the edge node should be determined according to not only the content popularity but also how difficult to fetch these content objects from their corresponding original servers. In this paper, we study the edge caching for minimizing the long-term average delay, where the fetching delay of the uncached contents is introduced. We construct a decision-theoretic framework for this delay-optimal content-caching problem, where the main obstacle is that the consideration of the imperfect content fetching breaks the Markovian property in the decision-theoretic framework. To overcome this obstacle, by analyzing the queue dynamics within the content fetching delay after the content object is removed from the cache, we transform the problem for meeting the Markovian property, and model it as an infinite horizon semi-Markov decision process (SMDP). Achieving the delay optimality of the caching problem needs to solve the Bellman equation of the SMDP, but it leads to the curse of dimensionality. We decompose the global optimality equation into several per-content optimality equations, and propose a low-complexity delay-optimal content caching algorithm by stochastic learning for each content. Finally, the simulation results show that our proposed algorithm achieves significantly lower delay than conventional caching algorithms.

By caching popular contents at edge nodes, the content can be delivered via device-to-device (D2D) communications to reduce the delay significantly. In this paper, we study cooperative content delivery from multiple edge nodes with duplicate caching, where the content caching and the cooperative transmission algorithms are designed jointly to minimize the average delay. Specifically, we decompose the delay optimization problem as short time-scale transmission subproblems for each time slot and a long time-scale caching master problem by primal decomposition. The transmission subproblem is modelled as a finite horizon Markov decision problem (MDP), and we propose a link selection algorithm based on the value function in the MDP model. For the caching master problem, we propose a caching algorithm by the simultaneous perturbation stochastic approximation (SPSA) method. Simulation results show that our proposed transmission and caching algorithms achieve lower delay than the conventional schemes.

Spectral clustering has been an attractive topic in the field of computer vision due to the extensive growth of applications, such as image segmentation, clustering and representation. In this problem, the construction of the similarity matrix is a vital element affecting clustering performance. In this paper, we propose a multi-view joint learning (MVJL) framework to achieve both a reliable similarity matrix and a latent low-dimensional embedding. Specifically, the similarity matrix to be learned is represented as a convex hull of similarity matrices from different views, where the nuclear norm is imposed to capture the principal information of multiple views and improve robustness against noise/outliers. Moreover, an effective low-dimensional representation is obtained by applying local embedding on the similarity matrix, which preserves the local intrinsic structure of data through dimensionality reduction. With these techniques, we formulate the MVJL as a joint optimization problem and derive its mathematical solution with the alternating direction method of multipliers strategy and the proximal gradient descent method. The solution, which consists of a similarity matrix and a low-dimensional representation, is ultimately integrated with spectral clustering or K-means for multi-view clustering. Extensive experimental results on real-world datasets demonstrate that MVJL achieves superior clustering performance over other state-of-the-art methods.

Classification remains challenging when confronted with the existence of multi-view data with limited labels. In this paper, we propose an embedding regularizer learning scheme for multi-view semi-supervised classification (ERL-MVSC). The proposed framework integrates diversity, sparsity and consensus to dexterously manipulate multi-view data with limited labels. To encourage diversity, ERL-MVSC recasts a linear regression model to derive view-specific embedding regularizers and automatically determines their weights. This is able to tactfully incorporate complementary information of different views. To ensure sparsity, ERL-MVSC imposes$\ell _{2,1}$-norm on a fused embedding regularizer to exploit the sparse local structure of samples, thereby conveying valuable classification information and enhancing the robustness against noise/outliers. To enhance consensus, ERL-MVSC learns a shared predicted label matrix, which serves as the comment target of multi-view classification. With these techniques, we formulate ERL-MVSC as a joint optimization problem of an embedding regularizer and a predicted label matrix, which can be solved by a coordinate descent method. Extensive experimental results on real-world datasets demonstrate the effectiveness and superiority of the proposed algorithm.

Leveraging the content caching technology, popular contents can be in close proximity to the users, which reduces the delay effectively. In this paper, we dedicate to minimizing the long-term average delay for the cache-enabled wireless networks, in which the fetching delay is considered when the uncached contents are retrieved from remote servers. We construct a decision-theoretic framework for this delay-optimal content-caching problem. The most challenging obstacle is that the consideration of imperfect content fetching breaks the Markovian property in the decision-theoretic framework, which makes the problem difficult to solve. To overcome this obstacle, we propose an equivalent transformation by analyzing the queue dynamics during the content fetching delay, so that the transformed problem can be modeled as an infinite horizon semi-Markov decision process (SMDP). To handle the curse of dimensionality for solving the SMDP, we decompose the global optimality equation into several per-content equations, and propose a low-complexity delay-optimal content caching algorithm. Simulation results show that the proposed algorithm achieves significantly lower delay than conventional caching algorithms.

Image clustering remains challenging when handling image data from heterogeneous sources. Fusing the independent and complementary information existing in heterogeneous sources together facilitates to improve the image clustering performance. To this end, we propose a joint learning framework of multi-view image data fusion and clustering based on nuclear norm minimization. Specifically, we first formulate the problem as matrix factorization to a shared clustering indicator matrix and a representative coefficient matrix. The former is constrained with orthogonality and nonnegativity, which ensures the validation of clustering assignments. The latter is imposed with nuclear norm minimization to achieve compression of principal components for performance improvement. Then, an alternating minimization strategy is employed to efficiently decompose the multi-variable optimization problem into several small solvable sub-problems with closed-form solutions. Extensive experimental results on real-world image and video datasets demonstrate the superiority of proposed method over other state-of-the-art methods.

To facilitate the deployment of private industrial Internet-of-Things (IoT), applying long-term-evolution (LTE) over unlicensed spectrum (LTE-U) is a promising technology, which can deal with the licensed spectrum scarcity problem and the stringent quality-of-service (QoS) requirement via centralized control. In this paper, we investigate the computing offloading problem for LTE-U-enabled IoT, where computing tasks on an IoT device are either executed locally or offloaded to the edge server on an LTE-U base station. Considering a constrained edge computing cost (e.g., operation power consumption) for offloaded tasks, the task scheduling problem is formulated as a constrained Markov decision process (CMDP) to maximize the long-term average reward, which integrates both task completion profit and task completion delay. In order to address the uncertainty of task arrivals and channel availability, a constrained deep Q-learning-based task scheduling algorithm with provable convergence is proposed, where an adaptive reward function can appropriately bound the average edge computing cost. Extensive simulation results show that the proposed scheme considerably enhances the system performance.

In order to tackle the spectrum scarcity problem and enhance the spectrum efficiency, deploying LTE in unlicensed band (LTE-U) is an emerging technology for supporting massive connections in future networks. By taking into account of the coexistence between the LTE-U cellular user equipments (CUEs) and the legacy Wi-Fi stations (STAs) in the unlicensed band, a partial non-orthogonal multiple access (NOMA)-based scheme is proposed in this paper. By dividing all UEs into two groups and making the Wi-Fi STA as the UE with the weakest channel gain in its group, we can exploit the multiplexing gain of NOMA by introducing no extra modification to Wi-Fi STAs. Accordingly, a fairness-oriented resource allocation framework is formulated as a max-min problem to jointly optimize the inter-group time occupancy ratio and the intra-group power allocation when the guaranteed bit rate (GBR) requirements for each UE are considered. A modified two-dimensional bisection algorithm is proposed to search the optimal time occupancy ratio and the max-min rate in this coexisting network. Numerical results validate the effectiveness of the partial NOMA scheme and outperform the traditional orthogonal multiple access method, in terms of both efficiency and robustness.

To facilitate the private deployment of industrial Internet-of-Things (IoT), applying LTE in unlicensed spectrum (LTE-U) is a promising approach, which both tackles the problem of lacking licensed spectrum and leverages an LTE protocol to meet stringent quality-of- service (QoS) requirements via centralized control. In this paper, we investigate the computing offloading problem in an LTE-U-enabled network, where the task on an IoT device is carried out either locally or is offloaded to the LTE-U base station (BS). The offloading policy is formulated as an optimization problem to maximize the long term discounted reward, considering both task completion profit and the task completion delay. Due to the stochastic task arrival process at each device and the Wi-Fi's contention-based random access, we reformulate the computing offloading problem into a Q-learning problem and solve it by a deep learning network-based approximation method. Simulation results show that the proposed scheme considerably enhances the system performance.

Dynamic time-division duplex (D-TDD) transmission in small cell networks have been emerged as one of the promising solutions to support the asymmetric traffic requirements in the next generation cellular mobile communication systems. On the other hand, the backhaul, which carries the traffic between base stations (BSs) and the core network, has great influence on providing reliable and timely connectivity. In this work, we study a two-tier D-TDD network taking the random locations of devices, packet arrival process, scheduling, interference, and backhaul into consideration to understand the influence from backhaul to uplink (UL) and downlink (DL) mean packet throughput per UE (MPT for short). We use an approximate method to derive the interference and successful transmission probability via stochastic geometry, and then achieve the mathematical derivation of DL and UL MPT with different kinds of backhaul using queueing theory tools. Based on the simulation results, we verify the accuracy of our analysis and explore the impact of the service parameters and backhaul.

Energy harvesting technology enables the devices to collect the energy from the surrounding environment. In energy harvesting networks, besides the coupling among different devices, the data transmission depends on the available energy of the devices as well, which leads to a complicated coupling and brings new technical challenges for delay optimization. In this paper, we study delay-optimal random access for large-scale energy harvesting networks. To overcome the challenges, we model a two-dimensional Markov decision process (MDP) with reflections to address the coupling between data and energy, and adopt the mean field game (MFG) theory to address the mutual coupling between devices by utilizing the large-scale property. Specifically, we decompose the optimization problem into two parts. First, to obtain the optimal access policy of each device, we derive the Hamilton-Jacobi-Bellman (HJB) equation which needs the statistical information of other devices. Second, to model the evolution of the state distribution in the system, we derive the Fokker-Planck-Kolmogorov (FPK) equation which needs the access policy of the devices. By solving these two coupled equations iteratively, we obtain the delay-optimal access solution by adopting the Lax-Friedrichs scheme and Lagrange relaxation method. Finally, the numerical results show that the proposed algorithm achieves significant performance gain compared to conventional algorithms.

Small cell networks under dynamic time-division duplex (D-TDD) transmission have emerged as a promising solution to accommodate the varied uplink (UL) and downlink (DL) traffic in next generation cellular mobile communication networks. By allowing each cell to individually configure its communication direction, D-TDD allocates resources to accommodate whichever transmission direction needs it most. However, with unaligned transmissions, the interference increases and limits the performance of mean packet throughput (MPT). In this paper, we study the small cell networks under D-TDD with cell clustering being the interference mitigation technique (clustered D-TDD). By leveraging stochastic geometry and queuing theory, we develop an analytical framework that captures both spatial and temporal randomness. We study the MPT whose analytical expression is verified via simulation, and based on the analysis, we explore the impact from different network and service parameters. In particular, numerical results show that there is an optimal cluster size for DL MPT, while UL MPT always benefits from increasing cluster size. By grouping cells into clusters, the clustered D-TDD can provide the flexible service compared with static time-division duplex (S-TDD), and provide significant improvement over a traditional D-TDD in terms of UL MPT at a small cost of DL MPT.

Wireless energy transfer (WET) is emerging as a promising paradigm that provides sustainability for pervasive battery-powered devices to complete various tasks. Due to high attenuation of WET, it is crucial to design new architecture that conserves energy while guaranteeing task completion. In this paper, we propose an energy bank-based crowdsourcing framework and an incentive mechanism for energy conservation in wireless-powered task-oriented networks. An employer device outsources the whole or a part of its task to several worker devices and pays them energy as reward. Through energy-service trading, the employer consumes less energy and workers make energy profits. The virtual energy bank keeps accounts for all devices, authenticates the trading, and settles payments through a lossless bookkeeping-like manner. We analyze the employer's expense-minimized and workers' profit-maximized decisions and prove that the optimal decisions compose a Stackelberg equilibrium. To quantify the potential in energy saving, we further apply the framework to a relay-based sensor network where a source employs relays to forward data with a minimum rate requirement. An algorithm is developed for the NP-hard expense minimization problem. The simulation results reveal that our proposed framework and mechanism improve the energy efficiency by providing a win-win situation for both sides.

In this paper, we study wireless energy transfer-enabled passive communication networks, where passive nodes (PNs) harvest radio frequency (RF) energy emitted by an active node (AN) and/or scatter the RF wave to a receiver for data transfer. The performance of such networks highly depends on channel state information (CSI), but its acquisition is quite challenging since energy-and-hardware constrained PNs are generally unable to estimate or feedback CSI. We propose a harvest-while-scatter protocol, where every PN uses the time when other PNs scatter to harvest RF energy, while only introducing minimum interference. Furthermore, we develop a channel training approach for this protocol to estimate means and (co)variances of channel gains via collecting and utilizing historical data and energy transmissions. To minimize the energy consumed at the AN with limited statistical CSI, we formulate a distributionally robust energy minimization problem involving a non-convex objective function and a quality-of-service chance constraint. In addition, we develop an iterative algorithm to optimally solve it with low complexity. Simulation results show the effectiveness of our proposed protocol and algorithm, and reveal the effect of relative node locations on energy consumption in terms of energy harvesting and data transfer.

LTE over unlicensed spectrum (LTE-U) is one of the promising approaches to further improve LTE network throughput. To maximize the benefit of LTE-U, in this work we study joint resource allocation for LTE over the legacy licensed spectrum and the sharing unlicensed spectrum in a multi-cell scenario. Specifically, we formulate a mixed-integer power-channel allocation problem aiming at maximizing the network throughput, with the constraints of protecting the coexisting Wi-Fi networks and hardware limitation of user equipments in the LTE-U networks. To solve the resource allocation problem efficiently, we exploit delay column generation approach to decompose the original optimization problem and then propose a novel algorithm based KKT conditions. Simulation results show the advantage of LTE-U networking and the effectiveness of the proposed algorithm in terms of convergence speed and network throughput.

In vehicular networks, exchanging beacons among neighboring vehicles is a promising solution to guarantee vehicle safety. However, frequent beaconing under high vehicle density will cause collisions, which is harmful to safety and tracking accuracy. In this work, we propose an adaptive beaconing method for vehicle safety and tracking accuracy. Each vehicle broadcasts beacon interval requests, including the intervals needed for safety and for tracking accuracy. The road side unit allocates resources for vehicle's beaconing according to the requests from all vehicles. We formulate the resource allocation problem for maximizing the sum utility which measures the satisfaction of vehicles. We transform the optimization problem into a maximum weighted independent set problem, and propose an algorithm to solve it efficiently. Simulation results show that the proposed method outperforms the benchmark in terms of beacon reception ratio, safety guarantee, and tracking accuracy.

Wireless energy transfer (WET) has received considerable attention for green communications. Unbalanced energy distribution and performance outages bring difficulties to the application of WET. This paper considers a network consisting of a WET-enabled access point (AP) and several energy-harvesting and source-powered user equipments (UEs). We investigate time-frequency resource allocation of downlink (DL) and uplink (UL) wireless information transfer (WIT) and DL WET. To improve energy efficiency and resource utilization, WET and WIT are orthogonally assigned at a low-frequency narrowband and a high-frequency wideband, respectively. A practical energy harvesting model is adopted, where power conversion efficiency depends on the received power instead of being a constant. To meet UEs' different energy demands with the highest conversion efficiency, we develop beam switching, where the AP employs beamforming to charge UEs by jointly controlling power and time based on energy distributions and channel conditions. We formulate the overall energy minimization as a non-convex stochastic optimization problem, which is decomposed by fixing DL-UL time allocation ratio, transformed through Markov decision processes, and finally solved via linear programming. Simulation results verify the effectiveness of our proposed scheme on energy conservation and reveal the tradeoff of time allocation between DL and UL.

In this paper, we investigate energy cooperation among sustainable cooperative relay nodes, which adopt decode- and-forward (DF) relaying. The relay nodes work on either information decoding (ID) mode or energy harvesting (EH) mode when receiving the signal from the source. Due to different harvested energy at the relay nodes, effective energy cooperation can benefit the energy efficiency, but suffer from practical energy loss when achieving energy cooperation. By solving the problem using Lagrangian duality, we obtain the optimal energy cooperation policy for the relay nodes and further discuss its two-level waterfilling structure. Then we derive the optimal and low-complexity EH/ID mode selecting algorithm with the two-level waterfilling energy cooperation policy. Finally, we compare the throughput between the systems with and without energy cooperation to show the benefit of energy cooperation.

The employment of Long-Term Evolution (LTE) in unlicensed spectrum, known as LTE-U, can alleviate the spectrum scarcity problem in the 5G networks. With this new technique, the traditional mobile data offloading schemes, which generally offload LTE users to the WiFi network, should be revisited. In this paper, we propose to transfer WiFi users to the LTE-U network and simultaneously allocate some unlicensed spectrum to LTE-U. In this way, a win-win situation could be generated since LTE can achieve better spectrum efficiency than WiFi in the unlicensed spectrum. To facilitate it, three important challenges are addressed in the paper: which WiFi users should be transferred; how many WiFi users need to be transferred; and how much unlicensed resource should be allocated to the LTE network. We utilize the Nash bargaining solution to design fair unlicensed spectrum allocation between WiFi and LTE-U and thereby a win-win strategy is developed, whose performance is demonstrated by numerical simulation.

To improve system capacity, licensed-assisted access (LAA) has been proposed for long-term evolution (LTE) systems to use unlicensed bands. However, the energy efficiency (EE) of the LTE system may be degraded by LAA since unlicensed bands are generally less energy-efficient than licensed bands. In this paper, we investigate the EE optimization of LAA systems. We first develop a criterion to determine whether unlicensed bands can be leveraged to improve the EE of LAA systems. We prove that unlicensed bands can be used to improve the EE only when the allocated licensed resource blocks (RBs) are not enough. We then investigate joint licensed and unlicensed RB allocation to maximize the EE of each small cell base station (SBS) in a multi-SBS scenario, taking into account fair resource sharing between LTE and WiFi networks. The complete Pareto optimal EE set can be obtained by the weighted Tchebycheff method. We also develop an algorithm to provide fair EE among different SBSs based on the Nash bargaining solution. Numerical results are presented to confirm our analysis and to demonstrate the effectiveness of the proposed algorithms.

With the foreseeable explosive growth of small cell deployment, backhaul has become the next big challenge in the next generation wireless networks. Heterogeneous backhaul deployment using different wired and wireless technologies may be a potential solution to meet this challenge. Therefore, it is of cardinal importance to evaluate and compare the performance characteristics of various backhaul technologies to understand their effect on the network aggregate performance. In this paper, we propose relevant backhaul models and study the delay performance of various backhaul technologies with different capabilities and characteristics, including fiber, xDSL, millimeter wave (mmWave), and sub-6 GHz. Using these models, we aim at optimizing the base station (BS) association so as to minimize the mean network packet delay in a macrocell network overlaid with small cells. Numerical results are presented to show the delay performance characteristics of different backhaul solutions. Comparisons between the proposed and traditional BS association policies show the significant effect of backhaul on the network performance, which demonstrates the importance of joint system design for radio access and backhaul networks.

Traditional mobile data offloading transfers cellular users to WiFi networks to relieve the cellular system from the pressure of the ever-increasing data traffic load. However, the spectrum utilization of the WiFi network is bound to suffer from potential packet collisions due to its contention-based access protocol, especially when the number of competing WiFi users grows large. To tackle this problem, we propose transferring some WiFi users to be served by the LTE system, in contrast to the traditional mobile data offloading which effectively offloads LTE traffic to the WiFi network. Meanwhile, leveraging the emerging LTE in unlicensed spectrum (LTE-U) technology, some unlicensed spectrum resources may be allocated to the LTE system in compensation for handling more WiFi users. In this way, a win-win situation would be generated since LTE can generally achieve better performance than WiFi due to its capability of centralized co-ordination. To facilitate it, three important challenging issues are addressed in the paper: which WiFi users should be transferred; how many WiFi users need to be transferred; and how much unlicensed resources should be relinquished to the LTE-U network. We investigate three different user transfer schemes according to the availability of channel state information (CSI): the random transfer, the distance-based transfer, and the CSI-based transfer. In each scheme, the minimum required amount of unlicensed resources under a given transferred user number is analyzed. Furthermore, we utilize the Nash bargaining solution (NBS) to develop joint user transfer and unlicensed resource allocation strategy to fulfill the win-win situation for both networks, whose performance is demonstrated by numerical simulation.

Traffic offloading and resource sharing are two common methods for delivering cellular data traffic over unlicensed bands. In this paper, we first develop a hybrid method to take full advantages of both traffic offloading and resource sharing methods, where cellular base stations (BSs) offload traffic to WiFi networks and simultaneously occupy certain number of time slots on unlicensed bands. Then, we analytically compare the cellular throughput of the three methods with the guarantee of WiFi per-user throughput in the single-BS scenario. We find that traffic offloading can achieve better performance than resource sharing when existing WiFi user number is below a threshold and the hybrid method achieves the same performance as the resource sharing method when existing WiFi user number is large enough. In the multi-BS scenario where the coverage of small cells and WiFi access points are mutually overlapped, we consider to maximize the minimum average per-user throughput of each small cell and derive a closed-form expression for the throughput upper bound in each method. Meanwhile, practical traffic offloading and resource sharing algorithms are also developed for the three methods, respectively. Numerical results validate our theoretical analysis and demonstrate the effectiveness of the proposed algorithms as well.

In a wireless powered communication network, where user equipments (UEs) harvest radio frequency energy from an access point (AP) and send data to the AP, there exists the near-far problem with respect to energy harvesting efficiency due to UEs' random locations. In this paper, we introduce the concept of delay-aware energy balancing by minimizing the average transmission delay while taking into account the issue of unbalanced harvested energy distribution. In particular, we propose an adaptive harvest-then-cooperate protocol, where every UE first harvests the energy emitted by the AP and then sends data to the AP directly or via other UEs acting as relays in a time-division multiplexing manner. In this protocol, the AP selects the combination of transmission power and routing topology by matching load and energy distributions in the network while minimizing the average transmission delay. Furthermore, we develop a method generating scheduling schemes for this protocol to avoid data overflow in the UE relay. To determine the combination with minimum delay, we approximate the average delay as a Markov decision process and propose a low-complexity sample path-based algorithm to obtain a near-optimal solution. Simulation results demonstrate that the proposed protocol is able to balance the energy distribution while minimizing the transmission delay.

Network densification is one of the dominant evolutions to increase network capacity toward future cellular networks. However, the complex and random interference in the resultant interference-limited heterogeneous cellular networks (HCN) may deteriorate packet transmission reliability and increase transmission delay, which are essential performance metrics for system design in HCN. By modeling the locations of base stations (BSs) as superimposed of independent Poisson point process, we propose an analytical framework to investigate delay and reliability tradeoffs in HCN in terms of timely throughput and local delay. In our analysis, we take the BS activity and temporal correlation of transmissions into consideration, both having significant effects on the performances. The effects of mobility, BS density, and association bias factor are evaluated through numerical results.

As the number of vehicles increases significantly, traffic congestion has become a major social problem in recent years. Such a situation can be alleviated effectively with the emerging of vehicular ad hoc network (VANET)-based real-time path planning systems. However, existing systems face the challenges of poor anti-congestion capability and high complexity. To address the related issues, an road side unit (RSU)-based architecture is proposed in this paper, in which a city is divided into different areas, each with an RSU. Based on the architecture, a decentralized and hierarchical real-time path planning algorithm is proposed. The path planning problem is formulated from two layers, i.e., area path selection in upper layer and intra-area routing in bottom layer, both of which target to minimize the average travel time. Numerical results show that, our proposed algorithm inherits the anti-congestion capability and owns the advantage of low complexity, as compared with the shortest path algorithm and centralized algorithm.

In this paper, two novel mechanisms are developed for the coexistence of cellular and WiFi systems in unlicensed spectrum. In the opportunistic method, the small cell base station opportunistically selects traffic offloading or resource sharing on each WiFi access point (AP). In the hybrid method, the base station simultaneously offloads users and shares the unlicensed spectrum of each AP. The performances of the proposed methods are analyzed and compared. We find that traffic offloading can achieve better performance than resource sharing when the number of existing WiFi users is below a threshold and the hybrid method achieves the same performance as the resource sharing method when existing WiFi user number is large enough. Numerical results are presented to demonstrate the effectiveness of the proposed methods.

In this paper, we study the data offloading via device-to-device (D2D) communications with proactive caching in mobile cellular networks. The problem is formulated as the optimal data caching problem, where mobile nodes have different mobility and limited cache capacities and data files have different popularity and content sizes, and is proved to be NP-hard. To deal with the problem, the contacts between mobile nodes are used to adjust the cached data files with a well-designed (1 + α)-approximation algorithm. Based on the analysis, we first propose a distributed infrastructure-assisted data offloading algorithm (IADOA), where the base station (BS) needs to provide information to mobile nodes. To further increase the flexibility, a fully-distributed data offloading algorithm (FDDOA) is designed, where mobile nodes exchange control information via D2D communications and make parameter estimations. Finally, simulation results show that our proposed algorithms have significant performance gains compared with two conventional baseline algorithms.

Time-frequency resource conversion (TFRC) is a recently proposed network resource allocation strategy. By exploiting user behavior, it withdraws spectrum resources strategically from connection(s) not focused on by the user, to relieve network congestion effectively. Aiming at supporting the exponentially increasing traffic volume, especially on-demand data services, in this work we propose TFRC-based scheduling techniques. Considering an LTE-type cellular network, we formulate the problem of service scheduling as a joint request, channel, and slot allocation problem, which is a mixed integer nonlinear programming (MINLP) problem. A deflation and sequential fixing based algorithm with only polynomial-time complexity is proposed to solve the MINLP problem. Simulation results not only demonstrate the efficiency of the proposed algorithm in terms of quality-of-service (QoS) provisioning and network resource utilization, but also show the effectiveness of the proposed TFRC-based scheduling techniques when integrating with the existing scheduling strategies such as first in first served (FIFS) and earliest deadline first (EDF).

Network densification via deploying dense small cells is one of the dominant evolutions towards future cellular network to increase spectrum efficiency. Packet transmission delay and reliability in the resultant interference-limited heterogeneous cellular network (HCN) are essential performance metrics for system design. By modeling the locations of base stations (BSs) in HCN as superimposed of independent Poisson point processes, we propose an analytical framework to derive the timely throughput of HCN, which captures both the delay and reliability performance. In the analysis, the BS activity and temporal correlation of transmissions are taken into consideration, both of which have significant effect on network performance. The effect of mobility, BS density, and association bias factor is investigated through numerical results, which shows that network performance derived ignoring the temporal correlation of transmissions is optimistic.

Licensed-assisted access (LAA) has been developed to improve LTE system capacity by using unlicensed bands. However, the energy efficiency (EE) of the LTE system may be degraded by LAA since unlicensed bands are generally less energy-efficient than licensed bands. In this paper, we investigate the EE optimization of LAA systems. We first develop a criterion to determine whether unlicensed bands can be leveraged to improve the EE of LAA systems. We prove that unlicensed bands can be used to improve the EE only when the allocated licensed resource blocks (RBs) are not enough. We then investigate how to jointly allocate licensed and unlicensed RBs to achieve EE fairness among small cell base stations (SBSs), based on the Nash bargaining solution. Numerical results are presented to confirm our analysis and to demonstrate the effectiveness of the proposed algorithm.

With the foreseeable explosive growth of small cell deployment, backhaul has become the next big challenge in the next generation wireless networks in terms of capacity and latency, especially for delay-sensitive services and network functionalities. Heterogeneous backhaul deployment using different wired and wireless technologies may be a potential solution to meet this challenge. Therefore, it is cardinal to evaluate and compare the performance characteristics of various backhaul technologies as a means to understand the effect of backhaul on the total network performance. In this paper, we propose relevant backhaul models and study the delay performance of promising technologies, including fiber, xDSL, millimeter wave (mmWave), and sub-6 GHz, which have different characteristics. Numerical results are presented to show the delay performance characteristics of different backhaul solutions.

In this paper, we investigate the random access for an energy harvesting secondary user (SU) in a cognitive radio system, in which the SU harvests energy from radio frequency (RF) radiation of the primary user (PU). With multipacket reception channel model, the SU can increase its throughput through not only utilizing the idle periods of the PU, but also opportunistically sharing the PU spectrum with some probability when the PU is active. By choosing the appropriate random access probability, we maximize the throughput of the SU under the constraint of the primary queue stability. We also define the energy-limited region and spectrum-limited region to specify the tradeoff between the SU performance and the PU activity. Furthermore, we investigate the effect of the primary queueing delay constraint on the throughput performance of the SU.

Distributive service data storage at the caching-enabled base stations (BSs) can reduce the traffic load in future cellular networks. Taking the limited caching space into account, it is necessary for the BSs to adjust their caching data based on service popularity in order to achieve better caching efficiency. In this paper, we investigate the cache replacement strategy for BSs to minimize the transmission cost between BSs in cellular networks. The cache replacement problem is modelled as a Markov Decision Process (MDP). Without extra information exchange about caching data between the BSs, we propose a distributed cache replacement strategy based on Q-learning. Especially, we calculate the transmission cost for possible cache replacement actions according to the previous data request and transmission between BSs. The convergence of the proposed distributed cache replacement strategy is proved by sequential stage game model. Simulation results verify the convergence of the proposed cache replacement strategy and show its performance gain compared to conventional strategies.

Opportunistic forwarding assisted by mobile relays is an effective way of improving network capacity and packet delivery ratio in delay tolerant networks (DTNs). However, such performance gain comes at the price of increased energy consumption due to the duplicated transmissions at relays. In this paper, we investigate how energy harvesting, a promising technique of enabling sustainable communications, can be exploited to improve the performance of opportunistic forwarding in mobile DTNs. Specifically, we formulate the problem using a Markov Decision Process (MDP) framework in which each source should strike a balance between exploitation, by forwarding the packet to the relay currently in contact, and exploration, by waiting for possible better relays in the future, given the harvested energy constraint. The formulated MDP having exponential complexity, we devise a heuristic relay-assisted opportunistic forwarding scheme, termed as adaptive M-step lookahead scheme, to alleviate the computation complexity, where M can be adjusted adaptively according to both the current energy and the energy that might be harvested in the future. Simulation results show that our proposed algorithm can use the harvested energy more efficiently, especially for the circumstance where the energy harvesting rate is low.

Mobile relay-assisted forwarding can improve the network capacity, but meanwhile increase the energy consumption. In this paper, we propose two distance-based energy-efficient opportunistic forwarding (DEEOF) schemes in mobile delay tolerant networks (DTNs). The proposed schemes strike a balance between energy consumption and network performance by maximizing the energy efficiency while maintaining a high packet delivery ratio from two different angles. Specifically, in the developed algorithms, we introduce the forwarding equivalent energy-efficiency distance (FEED) to quantify the transmission distances achieving the same energy efficiency at different time instances. The expected energy efficiency can thus be estimated based on the FEED. Furthermore, the distribution of the greatest forwarding energy efficiency in the predicted period is investigated to provide more accurate prediction for the energy efficiency. The forwarding decision in the algorithms is made by comparing the current energy efficiency and the estimated future expectation. The performance improvement of the proposed algorithms is also demonstrated by simulation, especially for systems where the source has very limited battery reserves.

Rapid increase of large-scale high-rise structures results in three-dimensional distribution of wireless traffic, and thus raises a new demand of three-dimensional coverage of the common control signals in cellular mobile communication networks. The control approach of three-dimensional coverage is investigated in this paper, based on planar array of macro basestation and three-dimensional beamforming technology. A novel algorithm is proposed for obtaining the excitation weight matrix of planar array. Coverage requirement is extracted according to the target coverage distances and the actual landforms in different directions, and then mapped into an anisotropic desired array pattern. The optimization model is established as a joint minimization of array pattern design error and transmission power. An approximately optimal tradeoff coefficient and the corresponding weight matrix are solved through iteration. Numerical results validate that the proposed algorithm has the advantages of excellent coverage performance, considerable saving of transmission power and effective suppression of inter-cell interference.

Instant Messaging (IM) Service is an integrated service composed by multiple types of subservices, which have diverse performance requirements and evaluation criteria. IM service has drawn a lot of attention, yet a suitable Quality of Experience (QoE) model for IM service can rarely be found. In this paper, we propose a Representative Service (RS) based QoE modeling criterion for IM service. We discover that the QoE of IM service users mainly depends on the Representative Service Quality (RSQ) by exploiting the patterns of the IM service user behaviors. In the proposed RS based QoE model, the Normalized Quality (NQ) is defined for different subservices with various performance evaluation criteria to unify their qualities, and the Attention Factor (AF) is proposed to estimate the subservice that a user is focusing on. By performing a small-scale but illustrative subjective test, we verify the proposed QoE model and indicate the effectiveness of the model in tracing the IM service user behaviors.

In cognitive vehicular ad hoc networks (CVANETs), channel conditions are highly dynamic due to both vehicle mobility and primary user activity. In this paper, to support high-quality video playback in such a challenging scenario, an adaptive video streaming algorithm built on scalable video coding (SVC) is proposed for reducing interruption ratio and improving visual quality. The proposed streaming algorithm is capable of deciding the proper number of video layers for vehicle users, by taking into account several important factors including vehicle position, velocity, the activity of primary users. Simulation results demonstrate the superiority of the proposed algorithm on playback interruption ratio and visual quality over the compared algorithm.

To support delay-sensitive traffic in multi-channel cognitive radio systems, designing a channel access scheme faces two major challenges, namely, the long waiting time due to continuous channel occupancy of primary users (PUs) and the performance degradation due to transmission collisions among secondary users (SUs). To address both issues, we propose a two-phase channel access scheme, which consists of a distributed channel negotiation phase and a hopping-based channel access phase for each SU. Specifically, in its first phase, an SU attempts to negotiate a specific initial slot/channel differing from the ones chosen by other SUs. Then, in its second phase, the SU chooses a channel in each time slot in a hopping-based manner to transmit data, where the hopping starts from its initial channel and follows a common hopping sequence. Virtual channels are introduced to accommodate the situation when the number of SUs is larger than that of actual channels. The average maximal waiting time due to the channel negotiation phase is derived, and the effective capacity of the service process for each SU in the channel access phase is analyzed. Numerical results show that the proposed scheme can support a higher traffic load under the statistical delay constraint, as compared with fixed or random channel access schemes.

In this paper, to address network congestion stemmed from traffic generated by advanced user equipment, we propose a novel network resource allocation strategy, i.e., time-frequency resource conversion (TFRC), via exploiting user behavior, a specific kind of context information. The key idea is to use radio resources mainly on the traffic/connection to which a user pays attention. The TFRC withdraws spectrum resources strategically from connection(s) not focused on by the user, providing reuseable spectrum called “virtual spectrum hole”. Considering an LTE-type cellular network, a double-threshold guard channel policy is proposed to facilitate the implementation of TFRC. An analytical model is established to study benefits of exploiting TFRC in terms of call-level performance, including new call blocking, handoff call dropping, and recovering call dropping probabilities. Numerical results demonstrate the effectiveness of the proposed approach, in increasing the cell capacity (maximum user number per cell) while limiting potential service quality degradation introduced by the newly proposed technique.

Heterogeneous networks consisting of a macrocell tier and a small cell tier are considered an attractive solution to cope with the fierce increase of mobile traffic demand. Nevertheless, a massive deployment of small cell access points (SAPs) leads also to a considerable increase in energy consumption. Motivated by growing environmental awareness and the high price of energy, the design of energy efficient wireless systems for both macrocells and small cells becomes crucial. In this work, we analyze the trade-off between traffic offloading from the macrocell and the energy consumption of the small cell. Using tools from stochastic geometry, we define the user detection performance of the SAP and derive the small cell capacity accounting for the uncertainties associated with the random position of the user, the propagation channel, activity of the users, and the aggregate network interference. The proposed framework yields design guidelines for energy efficient small cells.

In this paper, we propose a novel power control scheme for small cells deployed within macro cells. Our aim is to find the optimal power level for each small cell according to the energy consumption tradeoff between network and User Equipments (UEs). Two different small cell deployment scenarios are considered: the non-dense scenario and the dense scenario. The multiagent decentralized Reinforcement Learning (RL) technique is applied to deal with the dense deployment scenario where the coverage of different small cells are overlapped. In the proposed multiagent RL algorithm, each small cell is modeled as an agent to learn the optimal policy from interaction with environment to dynamically change its transmit power. Simulation results are presented to validate the proposed method and show that the RL based algorithm could provide a satisfactory performance.

In cellular networks, the proactive storage at caching-enable BSs is an efficient way to reduce traffic load of backhaul links. In this paper, we investigate the storage allocation problem with network coding. By decomposing the NP-hard problem, we propose a low-complexity storage allocation to solve two subproblems in an iterative way. Combining a heuristic initial allocation scheme and the iterative process, we can obtain the storage allocation result very close to the optimal solution. The convergence of the proposed algorithm is proved and its computation complexity is analyzed. The simulation results evaluate the performance of the proposed algorithm, which has graceful performance degradation on total storage with much lower complexity.

Coverage optimization is an important task, which directly affects the performance of cellular networks. It is difficult to correct the weak coverage and the over coverage simultaneously using existing methods such as adjusting antenna downtilts and/or transmit powers of sectors. In this paper, a novel coverage optimization method based on multi-sector joint beamforming is proposed, so that weak coverage area and over coverage area can be decreased simultaneously. A heuristic algorithm based on the semidefinite relaxation is then developed to obtain antenna array excitation weights with low computational complexity. Simulation results show that the performance of our algorithm is superior to that of antenna downtilt optimization algorithm.

In multi-channel multi-user cognitive radio systems, there are two main challenges to support traffic with delaysensitive QoS requirements, namely, the coordination among the secondary users (SUs) and the long channel occupancy time of the primary users (PUs). In this paper, we propose a hopping-based channel access scheme for SUs to improve their performance. Each SU chooses one of the channels allocated to PUs in a hopping-based manner in each time slot to opportunistically sense and transmit. This scheme not only avoids collisions among SUs, but also reduces the effect of the long channel occupancy time of PUs on the QoS performance of the SUs' delay-sensitive applications. We analyze the effective capacity of SU's service process with our proposed scheme. Numerical results show that our proposed scheme can achieve a higher effective capacity compared to random or fixed channel allocation.

Although small cell networks are environmentally friendly and can potentially improve the coverage and capacity of cellular layers, it is imperative to control the interference in such networks before overlaying them in a macrocell network on a large-scale basis. In recent work, we developed the joint admission and power control algorithm for two-tier small cell networks in which the number of small cell users that can be admitted at their quality-of-service (QoS) constraints is maximized without violating the macrocell users' QoS constraints. The QoS metric adopted is outage probability. In this paper, we investigate the distributed implementation of the joint admission and power control problem where the small cells can determine jointly their admissibility and transmit powers autonomously.

Heterogeneous networks consisting of a macrocell tier and a small cell tier are foreseen as one of the solutions to meet the ever increasing mobile data demand. Since a massive deployment of small cell access points (SAPs) leads also to a considerable increase in energy consumption, the energy efficient design of those SAPs is crucial. Sleep mode techniques are a promising strategy to reduce the energy consumption, yet they require cognitive capabilities to detect the presence of a macrocell user. In this work, we define a fundamental limit on the interference density that allows robust user detection. Beyond this limit, which we call the interference wall, an energy efficient SAP design is impossible. In addition, we elucidate the relation between energy efficiency and sensing time using large deviations theory.

This paper presents an analytical investigation on the outage performance of dual-hop multiple antenna amplify-and-forward relaying systems in the presence of interference. For both the fixed-gain and variable-gain relaying schemes, exact analytical expressions for the outage probability of the systems are derived. Moreover, simple outage probability approximations at the high signal-to-noise-ratio regime are provided, and the diversity order achieved by the systems are characterized. Our results suggest that variable-gain relaying systems always outperform the corresponding fixed-gain relaying systems. In addition, the fixed-gain relaying schemes only achieve diversity order of one, while the achievable diversity order of the variable-gain relaying scheme depends on the location of the multiple antennas.

Cooperative multicast is an effective solution for the bottleneck problem of single-hop broadcast in wireless networks. By incorporating with the random linear network coding technique, the previously proposed schemes can reduce the number of retransmissions significantly. However, these schemes may incur a large decoding delay at the receivers, In addition, the centralized scheduling methods of these schemes depend on the explicit feedback mechanism, which is not practical for a large size network. In this paper, we address the decoding delay and feedback storm problems in cooperative multicast. A cooperative multicast protocol named MWNCast is proposed based on a novel moving window network coding technique. Theoretical models are developed for analyzing the performance of the proposed scheme. Simulation results show that MWNCast outperforms the existing schemes by achieving better tradeoff between the throughput and decoding delay, meanwhile keeping the packet loss probability and decoding complexity at a very low level without explicit feedback.

In this paper, we investigate the TCP throughput performance enhancement for cognitive radio networks (CRNs) through lower-layer configurations. There is an interaction between TCP and the lower-layer operations. The TCP sending rate at the transport layer determines the packet arrival rate of the lower-layer buffer, and meanwhile it is influenced by the average round trip time and packet loss rate, which are determined by the lower-layer mechanisms and configurations. Therefore, an iteration process is employed to investigate the TCP throughput under given channel condition and lower-layer configurations. For each iteration, queueing analysis is done to derive the packet loss rate and average delay on wireless link, which are then used to calculated the TCP throughput. Through derivations and numerical evaluations, the impacts of lower-layer parameters, primary user (PU) activities and channel conditions on the TCP throughput are discussed. Moreover, the way how these factors influence the TCP throughput is tracked. By using the proposed analytical method, TCP throughput enhancement can be achieved through appropriately setting lower-layer configurations.

Network coding techniques have absorbed much attention for providing reliable broadcasting services in wireless networks. However, the intrinsic tradeoff among throughput, decoding delay, and feedback overhead has obstructed the application of the previously proposed schemes in practice. In this paper, we firstly propose a rate-controlled network coding scheme (RANC), which can effectively reduce the decoding delay of the receiver suffering from a poor channel condition, without compromising the system throughput. Based on RANC, we further propose a moving window network coding scheme together with an early loss alarm mechanism (MWNC-ELA), which achieves similar decoding delay performance to RANC, but greatly simplifies its feedback mechanism. As a benchmark of MWNC-ELA, we analyze the decoding delay performance of RANC using the random walk theory. Simulation results show that the proposed schemes outperform the existing solutions in terms of throughput, decoding delay, and feedback overhead.

In this paper, the queue dynamics of secondary users (SUs) in a multi-SU and multi-channel cognitive radio network is analyzed to obtain the expressions of quality of service (QoS) metrics. Specially, in the analysis, we take several lower-layer mechanisms and settings into account, including automatic repeat request (ARQ), finite-size buffer, adaptive modulation and coding (AMC) and non-ignorable spectrum sensing errors. By modeling the queue dynamics as a Markov chain, we derive the analytical expressions of queue length, packet dropping rate and packet collision rate. Based on these expressions, the QoS metrics including delay, packet loss rate and throughput are calculated further. Through simulation, our queueing analysis is verified and the QoS metrics are investigated.

We investigate the ergodic sum rate of multiple-input multiple-output Nakagami-m fading channels with linear receivers. In particular, both mean square error and zero-forcing receivers are considered. For dual transmit antenna configurations, we present new, closed-form upper bounds on the ergodic sum rate of both receivers. Moreover, we derive exact expressions for the two key parameters dictating the sum rate behavior in the low signal to noise ratio regime, namely the minimum energy per information bit to reliably convey any positive rate and the wideband slope. By doing so, we are able to explicitly demonstrate the sub-optimality of linear receivers compared to optimal receivers, and draw useful insights into the impact of model parameters (e.g., number of antennas, fading parameters).

Abstract One of the most challenging issues in wireless sensor networks is to meet the requirements of coverage and connectivity under given energy constraints. Most existing coverage and connectivity algorithms work to form tree networks when sensor nodes do not have location information of themselves. However, a tree topology network does not perform well in terms of energy efficiency and scalability if compared with a cluster network. In this paper, a novel topology control algorithm called Adaptive Random Clustering (ARC) is proposed to form a cluster network with required coverage and connectivity without location information. The performance of its coverage intensity and connectivity is analyzed based on the characteristics of cluster topology, and their proper parameters are determined. ARC inherits an excellent energy efficiency from cluster topology and avoids the collisions and overhearing of data packets. A good scalability can be achieved as only a limited number of channels are needed in ARC for a large‐scale network. Furthermore, ARC can adjust the number of active nodes adaptively according to the required coverage to balance the energy consumption. Simulation results demonstrate that required coverage and connectivity can be satisfied and network lifetime is prolonged significantly. Copyright © 2010 John Wiley & Sons, Ltd.

This paper considers the time and frequency synchronization based on the interspersed pilots. The shortcoming of an existing algorithm is observed, and a new channel estimation assisted time and frequency algorithm is proposed. Since the channel estimation is always needed by the data transmission, our newly proposed algorithm can improve the synchronization performance without too much increase in the complexity. The simulations prove the effectiveness of the newly proposed algorithm.

In densely deployed IEEE 802.11 wireless networks, the transmission delay experienced by a user depends not only on the traffic load of the associated AP, but also the contention level of other APs operating on the same channel. However, due to the random distribution of users and inappropriate allocation of AP channels, the traffic loads of different APs are often uneven, leading to unfair delay experience to different users. In this paper, we consider the problem of channel assignment and user association for balancing the traffic load of APs operating on different channels, which is modeled as a non-cooperative game. We prove the existence of Nash equilibrium (NE) for this game, and derive the price of anarchy and the fairness index at NE. Simulation results are provided to compare the performance of the proposed algorithm with the theoretical bounds.

In this paper, we propose a link outage probability based resource allocation scheme for multi-radio multi-channel wireless mesh networks. The objective is to optimize the link outage probability under the effect of channel variation and external interference while preserving the end-to-end traffic demand requirements. The problem is formulated as a mixed-integer nonlinear programming problem, which is solved by decomposing it into a feasibility- checking problem and an outage probability search problem so that the original problem can be solved iteratively with reduced complexity. Numerical results are provided to show that the proposed algorithm is superior to the existing scheme in reducing the outage probability.

In the multi-cell IEEE 802.11 wireless networks, the traffic loads of access points(APs) are often uneven, which leads to inefficient use of network resources and unfair service to users. To alleviate such imbalance, different user association schemes have been proposed that use different metrics for measuring the congestion level of APs. In this paper, we propose a game theoretical model for the user association problem using the airtime cost as the congestion metric. The centralized and localized algorithms are designed for achieving the airtime- balancing Nash equilibrium. Simulation results show that the proposed algorithms outperform the existing scheme in terms of fairness and load balance.

In this paper, we consider the robust resource provisioning problem targeting for end-to-end rate guarantee under moderate channel variations and external interference. We formulate the robustness optimization problem that explicitly takes into account practical radio switching, co-channel contention, and multi-path routing and traffic splitting constraints. By exploiting the special property of the problem, we propose a scheme to decompose the problem into a feasibility-checking problem and an interference margin search problem, which is guaranteed to converge to the optimal value with reduced complexity. Using traces collected from an indoor wireless network testbed, we evaluate the performance of the proposed algorithm, and show that the algorithm is superior to existing scheme in providing larger interference margin and reducing packet loss probability.

For mitigating inter-symbol interference (ISI) and inter-carrier interference (ICI) caused by insufficient cyclic prefix (CP), per-tone equalizer (PTEQ) outperforms traditional time domain equalizer (TEQ) since each tone is equalized independently. However, PTEQ with equal number of taps (equal length) for all the tones can not achieve the optimal performance because channel conditions of different tones between different transmit and receive antennas are not the same. Variable-length PTEQ can make full use of each tap, however it is difficult to design for MIMO-OFDM because its design needs exhaustive search and results in extremely high complexity. In this paper, a novel algorithm of designing variable-length PTEQ is proposed. The design is realized in two steps--tap number allocation and tap coefficient calculation, so that the complexity is low. The tap number allocation and the optimal coefficient calculation are based on channel conditions, so that better system performance can be achieved than using equal-length PTEQ. Simulation is carried out and main parameters are investigated.

In this paper, we address the problem of subchannel, rate and power allocation in OFDM-based two-tier femtocell networks, which comprise a conventional macrocell and multiple femtocells. Our objective is maximizing the multiple femtocell users' weighted rate sum by jointly adjusting their subchannel, rate and power allocation, under the constraints of cross-tier interference (CTI) between macrocell and multiple femtocells. Then we propose an optimal distributed resource allocation algorithm based on Lagrangian dual method, and interpret it from economics angle. In order to illustrate the benefit of allowing femtocells to share the subchannels occupied by macrocell, we compare our system with another one (macrocell guard system), in which the femtocells can only use the subchannels unoccupied by macrocell. Simulation results validate the proposed algorithm, and show that our system can obtain better performance than the macrocell guard system.

In this paper, the problem of the multi-user uplink scheduling in cognitive radio cellular network (CogCell) is investigated. The objective is to maximize the system throughput, while protecting the QoS of primary user (PU) from being affected by secondary user (SU). Here, PU's QoS is represented by its signal-to-interference-plus-noise (SINR) outage probability. It is equivalent to say that SU can increase its transmit power to enhance the system performance as long as PU's SINR outage probability does not exceed the predefined threshold. So the first scheduling algorithm is proposed to maximize the system throughput through utilizing the multi-user diversity. Different from the first algorithm which does not take the fairness among SUs into account, the second scheduling algorithm with considering proportional fairness among SUs is proposed. It is shown to provide a satisfactory tradeoff between maximizing the system throughput and achieving fairness among SUs. Finally, these proposed algorithms are validated through extensive simulations.

In cognitive radio systems, the design of spectrum sensing has to face the challenges of radio sensitivity and wide-band frequency agility. It is difficult for a single cognitive user to achieve timely and accurate wide-band spectrum sensing because of hardware limitations. However, cooperation among cognitive users may provide a way to do so. In this paper, we consider such a cooperative wide-band spectrum sensing problem with each of the cognitive users able to imperfectly sense only a small portion of spectrum at a time. The goal is to maximize the average throughput of the cognitive network, given the primary network’s collision probability thresholds in each spectrum sub-band. The solution answers the essential questions: to what extent should each cognitive user cooperate with others and which part of the spectrum should the user choose to sense? An exhaustive search is used to find the optimal solution and a heuristic cooperative sensing algorithm is proposed to simplify the computational complexity. Inspired by this optimization problem, two practical cooperative sensing strategies are then presented for the centralized and distributed cognitive network respectively. Simulation results are given to demonstrate the promising performance of our proposed algorithm and strategies.

In this paper, a distributed spectrum access algorithm for Cognitive Wireless Network (CogWN) is proposed. The objective is to maximize the number of admitted Secondary Users (SUs) under the constraint of Interference Temperature (IT) at Measurement Point (MP), while providing AQP (Active users' Quality of service Protection) at the same time. Here AQP means that the Signal to Interference plus Noise Ratios (SINRs) of all active SUs will not fluctuate below their predetermined thresholds during the process of new SUs' spectrum access. In addition, an alarm mechanism is introduced into CogWN, which ensures the IT at MP is always below the predefined threshold, so that the communication of primary system is protected from being affected by SUs. Finally, the proposed algorithm is evaluated through extensive simulations, and the results show that it has a better performance than the traditional algorithm.

Multi-channel spectrum sensing is prevailing but also very challenging in wideband cognitive radio systems. Conventional multi-channel spectrum detection such as channel-by-channel scan costs much time and energy. This paper aims to show a novel multi-user cooperative spectrum sensing method which can reduce the sensing ability requirement for secondary users while still guaranteeing the sensing accuracy and effectiveness in a multi-channel cognitive radio context. In our proposed method, each cognitive user chooses an Ideal-Soliton-Distributed number of channels to sense, and the partial detection results are then passed to a confusion center which uses a specially designed Belief Propagation (BP) algorithm to infer the spectrum activities of all the channels. A heuristic method to release the detected channels from the whole spectrum bands is also proposed to reduce the sensing complexity further. Simulation results show that the proposed sensing methods can obtain excellent performance.

Cognitive radio (CR) has the potential to substantially improve the system capacity and adaptability of wireless mesh network (WMN). In this paper we investigate the achievable performance gain of cognitive wireless mesh network (CWMN), in which all nodes are equipped with CRs, by jointly optimizing spectrum allocation, routing and time scheduling. The formulated optimization problem aims to minimize the system activation time to satisfy the given traffic demands, under the constraint of multiple access interference and the limited available spectrum bands at each node. Then we develop a column generation (CG) approach to solve this problem. Our analytical model is validated by the simulation results, which provide a better performance compared with fixed bandwidth allocation.

Spreading code plays an extremely important role on the overall performance of a CDMA system. The correlation properties and available number of spreading codes determine the interference-resist capability as well as system capacity. In this paper, we analyze the characteristics and limitations of traditional and recently reported spreading codes. Based on the analysis, we propose a new code design approach which will be used to generate inter-group complementary (IGC) codes. The correlation functions of the IGC codes possess definite and bi-valued interference-free windows. In addition, a corresponding code assignment algorithm and spreading scheme will be introduced to take advantage of the desirable properties of the IGC codes for their applications in CDMA systems. Both theoretical analysis and simulation results show that an IGC-CDMA system is interference-resistant and capable to offer a high spectral efficiency if compared with the ones based on other spreading codes.

An analytical model is proposed to describe the enhanced distributed channel access (EDCA) mechanism of IEEE 802.11e, and to evaluate the performance of wireless local area network (WLAN) in practical conditions. Compared with the existing models, the proposed model takes several new factors into consideration: error-prone channel, wireless station supporting several access categories (ACs), different treatments of internal collision and external collision, retry limit, error-correction ability provided by channel coding. This makes the proposed model much more suitable for practical use than the existing models. The Markov chain model, state transfer and probabilities were presented; parameters and solution were derived; expressions of throughput and MAC delay were obtained. Simulation results validate the accuracy of the model. The proposed model has wide applicability, enabling evaluation of system performances and optimization of system parameter values.

In this paper we generalize the traditional Walsh code to a novel spreading code, whose ideal spectral correlation properties can be utilized in MC-CDMA system to resist multiuser interference (MUI) under multipath channel conditions. We also propose the application style of the new spreading code in MC-CDMA, so that the system not only provides MUI-free performance, but also supports multi-rate services flexibly by using spreading codes with variable-spreading-factor. These advantages make the proposed MC-CDMA system a promising candidate for future WLANs. Both theoretical analysis and simulation results indicate that the proposed system has better BER performance than traditional Walsh code based MC-CDMA, and has higher capacity than the MUI-free MC-CDMA in Ref. [6] under most channel conditions.

The technology of space-time coded continuous phase modulation (STC-CPM) has aroused considerable attention recently in wireless communication systems for improving the capacity and data rate without bandwidth expansion. Symbol timing synchronization is an important issue in such systems. In this paper, a data aided symbol timing estimation algorithm was proposed for burst-mode STC-CPM systems over Rayleigh fading channels. A training sequence is embedded before transmission of each packet. The receiver first adds the signals from different receive antennas and then calculates the digital Fourier transformation (DFT) of the phase of the one-symbol differential signal of the sum. The initial symbol timing offset can be estimated from the phase of the tone component of the DFT outputs. MATLAB simulation results show that the variance of the timing estimation error is very small in slow Rayleigh flat fading channels and frequency selective fading channels and the degradation of frame error rate (FER) is significantly small under the condition of non-ideal synchronization. This algorithm is suitable for any numbers of transmit and receive antennas and can estimate the timing offset rapidly and accurately in STC-CPM systems.

To transmit real-time traffic in mobile ad hoc networks, a combined design of TDMA MAC and routing protocol was proposed. Most approaches proposed recently applied schemes in the routing protocol to provide QoS and paid no attention to the MAC design. Actually, the ability to provide QoS heavily depends on how well the resources are managed in the MAC layer. In our work, we try to focus on the design of MAC protocol and combine it with AODV to provide QoS. The QoS requirement of a certain session can be satisfied if all nodes in the route reserve slots with the corresponding frame length. It is convenient to manage slot assignment information and select a slot with a certain frame length in a binary tree structure. Simulation results are presented to verify the performance of the combined design.

Traditional admission control algorithms are based on bandwidth or channel reservation policy, which may be incompetent in IEEE 802.16e OFDMA systems for two reasons: (1) WiMAX system supports dynamic and flexible resource allocation, and (2) there exists a fundamental tradeoff between bandwidth resource and power resource. In this paper, we propose a novel admission control scheme based on power reservation. Foremost, we introduce a joint subchannel and power allocation algorithm for WiMAX system, which achieves high power efficiency by minimizing the overall downlink transmit power and utilizing all available subchannels. Based on this, we propose two power reservation schemes for inter-cell handoff calls and intra-cell handoff calls, respectively. Correspondingly, two reservation factors are introduced, the values of which are determined by optimizing the metric of grade of service(GoS). Computer simulation is carried out to evaluate the performance of the proposed admission control scheme.

When the time dispersive channel deteriorates, the excellent interference-free performance of CDMA system spread with loosely synchronized code (LS-CDMA) may not be guaranteed any more. The goal of our study is to improve the robustness for LS-CDMA system against channel deterioration. Based on theoretical and experimental analysis, it is found that code allocation algorithm, which is seldom mentioned for LS-CDMA before, has great impact on the interference-resist ability of the system. We define a new parameter to reflect system robustness and propose a novel code allocation algorithm aiming to maximize this parameter. Simulation results show that system employing the proposed algorithm has superior interference-resist performance compared with system based on random code allocation algorithm, especially when the system load is not very heavy

CDMA based third generation mobile communication adopts Rake receiver to get diversity gain for multipath signals. This paper first analyzes why traditional CDMA delay tracker fails when multipaths are closely spaced (1-2 chips apart). Then we propose a novel adaptive coherent CDMA tracking loop. This new structure uses an adaptive delay detector which is able to suppress the disturbance from neighboring multipath components. Experiments show our method can track multipath profiles with 1-2 chips apart. The proposed structure is also low in complexity. It's well suited for the downlink tracking in WCDMA/cdma2000 standard

This paper introduces an approach to generate generalized pairwise complementary (GPC) codes, which offer a uniform interference free windows (IFWs) across the entire code set. The GPC codes work in pairs and can fit extremely power efficient quadrature carrier modems. The characteristic features of the GPC codes include: the set size is 2K, the processing gain is 4NK, and the IFW's width is 8N identically for all codes in a set, where K is the times to perform Walsh-Hadamard expansions and N is element code length of seed complementary codes. Therefore, by using different N, the IFW width of a GPC code set can be adjusted with its set size unchanged. Each GPC code set consists of two code groups, with each having K codes, and they have sparsely and uniformly distributed autocorrelation side lobes and cross-correlation levels outside the IFWs.

Abstract The main objective of this paper is to present new receiver structures to improve the applicability of a recently developed multicarrier CDMA architecture based on complete complementary codes. A mathematical description of the new system is presented in this paper. It is found that though it is the offset‐stack scheme that guarantees high spectral efficiency and scalable rate‐matching, error propagation under multipath fading noisy channel conditions is generated due to such special spreading modulation scheme and the corresponding recursive receiver. In order to avoid error propagation, the precondition of multipath replica separation is derived mathematically so that a non‐recursive receiver can be employed. The range of the channel delay spread is discussed and the impact of the time shift in offset‐stack spreading modulation on spectral efficiency is analyzed. Then, a RAKE receiver is proposed to further improve the performance through multipath diversity. Simulations are carried out under multipath fading noisy channel conditions to further verify the theoretical analysis. Performance loss caused by error propagation is observed by comparing the performances of the recursive receiver and the non‐recursive receiver. With the same spectral efficiency, the proposed offset‐stack scheme and the RAKE receiver gain obvious performance improvement compared to the existing offset‐stack scheme and the recursive receiver. Copyright © 2005 John Wiley & Sons, Ltd.