Autonomous aerial vehicles (AAVs) have the potential to greatly aid Internet of Things (IoT) networks in mission-critical data collection, thanks to their flexibility and cost-effectiveness. However, challenges arise due to the AAV’s limited onboard energy and the unpredictable status updates from sensor nodes (SNs), which impact the freshness of collected data. In this paper, we investigate the energy-efficient and timely data collection in IoT networks through the use of a solar-powered AAV. Each SN generates status updates at stochastic intervals, while the AAV collects and subsequently transmits these status updates to a central data center. Furthermore, the AAV harnesses solar energy from the environment to maintain its energy level above a predetermined threshold. To minimize both the average age of information (AoI) for SNs and the energy consumption of the AAV, we jointly optimize the AAV trajectory, SN scheduling, and offloading strategy. Then, we formulate this problem as a Markov decision process (MDP) and propose a meta-reinforcement learning algorithm to enhance the generalization capability. Specifically, the compound-action deep reinforcement learning (CADRL) algorithm is proposed to handle the discrete decisions related to SN scheduling and the AAV’s offloading policy, as well as the continuous control of AAV flight. Moreover, we incorporate meta-learning into CADRL to improve the adaptability of the learned policy to new tasks. To validate the effectiveness of our proposed algorithms, we conduct extensive simulations and demonstrate their superiority over other baseline algorithms.

Utilizing UAVs and satellites can offer an effective means to collect data for the Internet of remote things (IoRT) networks. However, due to the limited energy of UAVs and the high cost of satellite communication, ensuring the reduction of UAV energy consumption and communication costs while collecting fresh data poses a significant challenge. In this paper, we explore the issue of data gathering in IoRT networks with the assistance of UAVs and satellites. The UAV gathers data from sensor nodes (SNs) and decides whether to relay the collected data via satellite or send it directly to the data processing center. We handle this problem by formulating it as a Markov decision process to minimize the combined weighted sum of the average age of information, the energy consumption of the UAV, and communication costs through the implementation of a compound-action proximal policy optimization (CPPO) method. It can handle the compound actions of the UAV. This approach simultaneously optimizes the UAV's path, SN scheduling, and transmission decisions. Simulation results demonstrate that our algorithm can achieve better performance compared to baseline methods.

In this paper, we explore the integration of coordinated multipoint (CoMP) transmission and non-orthogonal multiple access (NOMA) in downlink cellular-connected UAV networks, which include both aerial users (AUs) and terrestrial users (TUs). AUs are categorized into CoMP-AUs and Non-CoMP AUs based on a comparison of the desired signal strength and the dominant interference strength. CoMP-AUs receive transmissions from two cooperative Base Stations (BSs) and form two exclusive NOMA clusters with two TUs, respectively. A Non-CoMP AU forms a NOMA cluster with a TU served by the same BS. Leveraging the tools of stochastic geometry, we propose an analytical framework to assess the performance of the CoMP-NOMA-based cellular-connected UAV network in terms of coverage probability and average ergodic rate. We demonstrate the superiority of the proposed CoMP-NOMA scheme by comparing it with three benchmark schemes, and further quantify the impacts of key system parameters on network performance. By harnessing the benefits of both CoMP and NOMA, we prove that the proposed scheme can provide a reliable connection for AUs using CoMP and enhance the average ergodic rate through the application of NOMA technique.

The unmanned aerial vehicle (UAV) can act as the edge server in delay-sensitive monitoring for data collection and processing in the Internet of things (IoT) networks due to its flexibility and low operational cost. One of its major disadvantages is the limited battery level. This paper focuses on a problem with the rechargeable UAV-assisted energy-efficient and fresh data collection in the IoT networks. In particular, the UAV takes off from the initial position to collect data packets from sensor nodes (SNs) in the IoT networks and needs to reach the final position at a given time. Some charging stations (CSs) are in the IoT networks, which can recharge the UAV by the wireless power transfer technique to keep the UAV’s energy level from falling below the threshold energy. To minimize the weighted sum of the average age of information (AoI) and the average recharging price, we design a Markov Decision Process (MDP) to determine the UAV’s flight trajectory, the scheduling of SNs, and energy recharging. The MDP is then solved using a rechargeable UAV-assisted data collection algorithm based on dueling double deep Q-networks (D3QN). Numerous simulations show that the proposed D3QN algorithm can reduce the weighted sum of the average AoI and the average recharging price more effectively than the baseline algorithms.

This paper focuses on spectrum sharing in heterogenous wireless networks, where different nodes utilize various Media Access Control (MAC) protocols to transmit data packets to a common access point on a shared wireless channel. Previous studies have developed Deep Reinforcement Learning (DRL) based multiple access protocols for specific scenarios within heterogeneous wireless networks. However, there exists a wide range of coexisting scenarios, characterized by varying numbers of nodes and the use of different MAC protocols. Existing approaches require training new models from scratch when encountering unseen scenarios, resulting in significant training time. To address this issue, we propose a novel MAC protocol called Generalized Multiple Access (GMA), which employs the Meta-Reinforcement Learning (meta-RL) algorithm. By learning a meta-policy during training, GMA enable the fast adaptation of the agent node to different and previously unknown heterogeneous network environments, without prior knowledge of the specific MAC protocols used in those environments. We conduct a performance comparison between the proposed GMA protocol and existing DRL-based protocols. Simulation results demonstrate that while the GMA protocol experiences a slight performance loss compared to baseline methods in training environments, it demonstrates faster convergence and higher performance in new environments compared to baseline methods.

Thanks to their high-flexibility and low-operational cost, unmanned aerial vehicles (UAVs) can be used to support mission-critical applications in the Internet of Things (IoT). However, due to the limited onboard energy, it is difficult for UAVs to provide continuous data collection. In this article, we study the problem of rechargeable-UAV-aided timely data collection in IoT networks, where the UAV collects status updates from multiple sensors and gets recharged from the charging stations (CSs) to keep its energy level above a threshold. To tradeoff the information freshness and energy consumption, we formulate a Markov decision process (MDP) with the objective of minimizing the weighted sum of the average total Age of Information and average recharging price. Under the dynamics and uncertainty of the environment, we propose a multitask transfer deep reinforcement learning method to jointly optimize the UAV ’ s flight trajectory, transmission scheduling, and battery recharging. To enable the application of the learned policy to new environments with similar settings and avoid starting from scratch, we develop a multitask network made up of common knowledge layers and task-specific knowledge layers. It specifically makes it possible for the transfer of common knowledge between environments with different network scales (e.g., different numbers of sensors/CSs) and/or topologies (e.g., different locations of sensors/CSs). Simulation results demonstrate that the proposed algorithm can adapt to new environments and achieve superior performance compared to the baseline algorithms.

Emerged as a promising solution for future wireless communication systems, intelligent reflecting surface (IRS) is capable of reconfiguring the wireless propagation environment by adjusting the phase-shift of a large number of reflecting elements. To quantify the gain achieved by IRSs in the radio frequency (RF) powered Internet of Things (IoT) networks, in this work, we consider an IRS-assisted cellular-based RF-powered IoT network, where the cellular base stations (BSs) broadcast energy signal to IoT devices for energy harvesting (EH) in the charging stage, which is utilized to support the uplink (UL) transmissions in the subsequent UL stage. With tools from stochastic geometry, we first derive the distributions of the average signal power and interference power which are then used to obtain the energy coverage probability, UL coverage probability, overall coverage probability, spatial throughput and power efficiency, respectively. With the proposed analytical framework, we finally evaluate the effect on network performance of key system parameters, such as IRS density, IRS reflecting element number, charging stage ratio, etc. Compared with the conventional RF-powered IoT network, IRS passive beamforming brings the same level of enhancement in both energy coverage and UL coverage, leading to the unchanged optimal charging stage ratio when maximizing spatial throughput.

Maintaining the freshness of information in the Internet of Things (IoT) is a critical yet challenging problem. In this paper, we study cooperative data collection using multiple Unmanned Aerial Vehicles (UAVs) with the objective of minimizing the total average Age of Information (AoI). We consider various constraints of the UAVs, including kinematic, energy, trajectory, and collision avoidance, in order to optimize the data collection process. Specifically, each UAV, which has limited on-board energy, takes off from its initial location and flies over sensor nodes to collect update packets in cooperation with the other UAVs. The UAVs must land at their final destinations with non-negative residual energy after the specified time duration to ensure they have enough energy to complete their missions. It is crucial to design the trajectories of the UAVs and the transmission scheduling of the sensor nodes to enhance information freshness. We model the multi-UAV data collection problem as a Decentralized Partially Observable Markov Decision Process (Dec-POMDP), as each UAV is unaware of the dynamics of the environment and can only observe a part of the sensors. To address the challenges of this problem, we propose a multi-agent Deep Reinforcement Learning (DRL)-based algorithm with centralized learning and decentralized execution. In addition to the reward shaping, we use action masks to filter out invalid actions and ensure that the constraints are met. Simulation results demonstrate that the proposed algorithms can significantly reduce the total average AoI compared to the baseline algorithms, and the use of the action mask method can improve the convergence speed of the proposed algorithm.

The dilemma between the ever-increasing demands for data processing, and the limited capabilities of mobile devices in a wireless communication system calls for the appearance of federated learning (FL). As a distributed machine learning (ML) method, FL executes in an iterative manner by distributing the global model parameters and aggregating the local model parameters, which avoids the transmission of huge raw data and preserves data privacy during the training process. However, since FL cannot control the local training and transmission process, this gives malicious users the opportunity to deteriorate the global aggregation. We adopt a reputation model based on beta distribution function to measure the credibility of local users, and propose a reputation-based scheduling policy with user fairness constraint. By taking into account the impact of wireless channel conditions and malicious attack features, we derive tractable expressions for the convergence rate of FL in a wireless setting. Moreover, we validate the superiority of the proposed reputation-based scheduling policy via numerical analysis and empirical simulations. The results show that the proposed secure wireless FL framework can not only distinguish malicious users from normal users but also effectively defend against several typical attack types featured in attack intensity and attack frequency. The analysis also reveals that the effect of average attack intensity on the convergence performance of FL is dominated by the percentage of malicious user equipments (UEs), and imposes even greater negative effect on the convergence performance of FL as the percentage of malicious UEs increases.

Statistical priority-based multiple access (SPMA) protocol has attracted much attention in virtue of its support for multi-priority traffic, and the guarantee of low-latency and high-reliability transmissions for high-priority. In this work, we propose an analytical framework to study the performance of SPMA from spatial perspective with tools from the stochastic geometry. We consider two kinds of priority traffic, including high-priority traffic and low-priority traffic. In SPMA, a packet is split into multiple bursts to reduce the collision probability, and the turbo coding, frequency hopping, and time hopping are employed to further decrease the packet loss rate. We first derive the analytical expressions for the medium access probability (MAP) and burst success probability of two priority users in closed form, taking into account the potential transmitters (PTs) density, ratio of different traffic users, amount of orthogonal resources, channel occupancy statistics (COS) threshold, and statistical sliding window (SSW). Based on the derived MAP and burst success probability, we further obtain the packet success probability and spatial throughput. After evaluating the effect of key parameters on the above performance metrics, we provide guidelines on optimal design of several key system parameters, such as the COS threshold and PTs density, to guarantee the high-priority user a 99% packet success probability.

For the physical (PHY) layer in the 5th generation (5G) networks, there may be only time division multiplexing (TDM) and orthogonal frequency division multiplexing (OFDM) as candidates to serve multiple information flows via a single wireless link. Such two multiplexing schemes require the timing/ frequency synchronizations strictly, which is not suitable for power division based medium access control (MAC) protocols, i.e., non-orthogonal multiple access (NOMA). To address this, associating with multiple-input multiple-output (MIMO) techniques, a power division multiplexing (PDM) scheme is proposed as an alternative option to MIMO-TDM/MIMO-OFDM. The proposed MIMO-PDM directly utilizes the division of transmit power to replace the conventional time-slot/sub-band for the different information flows. Thus, it owns high compatibility with NOMA which is a promising multiple access protocol in 5G. With regarding the quality of service (QoS) required by the information flows, this paper derives the optimum power division of MIMO-PDM in conditions of multiple-input single-output (MISO), single-input multiple-output (SIMO), and MIMO, respectively. Additionally, we study the optimum pre- and post-coding of MIMO when serving arbitrary number of information flows. The proposed optimization algorithms of MIMO-PDM own reasonable computational complexity which is not higher than the classic water-filling algorithms. Consequently, the proposed MIMO-PDM could efficiently achieve the optimum performance as well as ensure the QoS of multiple information flows.

In the smart grid, the application of information and communication technology (ICT) significantly promotes the efficiency of energy generation and consumption system. However, the integration of intelligence and cyber system to a smart grid can result in serious cyber security concerns and presents the entire power system more vulnerable to be attacked. In this paper, a new cyber attack model with the alternating direction multiplier method (ADMM) based optimal power flow (OPF) problem is introduced and exploited by malicious attackers. To deal with this challenge, a defence mechanism is presented to not only detect the existence of data injection attack, but also mitigate the potential impact to improve the stability of the smart grid. This scheme takes the power measurements between neighbouring nodes into account, and distinguishes the time-delay attack and bad data injection attack by monitoring the measurement variations between received data and predicted data based on the artificial neural network (ANN) algorithm. The simulation results demonstrate that the proposed scheme has the capability of detecting and mitigating the stealthy attacks significantly by using a 33-bus power system. Consequently, it is a significant study in the actual smart grid and can minimise the impact of the cyber attack.

Low latency and high reliability are significant trends in the development of wireless communications. Both the new generation of data link system, Tactical Targeting Network Technology (TTNT), and the fifth-generation mobile networks (5G) have put forward higher requirements for low latency and high reliability. Statistical priority-based multiple access protocol (SPMA) can be applied to these systems by virtue of its excellent performance. It is very important to model and analyze the performance of SPMA. In this paper, we establish the node state model by discrete-time Markov renewal process under a saturated network. We construct a fixed-point equation to solve the medium access probability. Then the average packet success rate and throughput are obtained. We evaluate the performance of the saturated SPMA system through the three variables. We also analyze the impact of system parameters on protocol performance. Extensive simulations demonstrate that our theoretical results closely match the simulation results. Moreover, the throughput limit can be obtained by adjusting the system parameters.

The dual connectivity is emerging as a promising solution to boost capacity in heterogeneous networks. However, it is challenging to obtain an optimal user association in heterogeneous networks with dual connectivity, due to its non-convex and combinatorial nature. In this paper, we propose a user association scheme based on deep reinforcement learning to maximize the overall network utility, which takes both throughput and user fairness into account, in the downlink of a heterogeneous network. Particularly, each user associates with the macro base station (BS) and a micro BS. We apply a deep Q-network (DQN) to obtain the nearly optimal policy to associate the users and micro BSs. Simulation results demonstrate that DQN-based user association performs better compared to the conventional user association schemes in heterogeneous networks with dual connectivity, and it behaves good scalability when the environment changes.

Drone small cells (DSCs) are served as aerial base stations to complement the terrestrial cellular networks, in order to provide seamless wireless coverage and increased network capacity. In this paper, we study a drone-assisted downlink heterogeneous network (HetNet) consisting of a first tier of DSCs overlaid with a second tier of terrestrial small cells (TSCs), both of which operate on the same frequency band. By considering a flexible biased association policy, we develop an analytical framework to evaluate the network performance. After deriving the association probabilities, and the probability distribution functions (PDFs) of typical link length, we derive exact expressions for the per-tier and overall coverage probabilities. The proposed framework allows to quantify the impact on network performance of most important system parameters, such as the height of DSCs, the bias factor, and the base station density. In absence of interference management, our results show that very limited gains can be obtained in the dense network scenario and the improper deployment of DSCs can only degrade the coverage probability achieved by the single-tier TSC network. What's more, the unbiased cell association is shown to be optimal for the overall coverage probability in the interference-limited network regime.

In recent years, the dramatically rising mobile data traffic has required an ever-increasing data rates. Ultra-dense network (UDN) is a promising technique to significantly enhance the network capacity by densely deploying small cells. The large amount of traffic also has been characterized by asymmetry and variations in both time and space. Dynamic time-division duplex (DTDD) has been taken into account to accommodate the traffic due to its advantage in the dynamic adjustment of UL/DL configuration. In this work, we propose an analytical framework to investigate the coverage performance of a UDN operating DTDD scheme, where impacts of line-of-sight (LOS)/non-line-of-sight (NLOS) propagation in large-scale fading and small-scale fading are both incorporated. Our results show that the LOS propagation in small-scale fading can substantially improve the DL and UL coverage probabilities in the low-to-middle density region, while the enhancement vanishes in the ultra-dense region where the network coverage is dominated by the LOS propagation in large-scale fading.

To satisfy the growing capacity demand of explosive traffic, ultra-dense network (UDN) is proposed as a key technology, where small cells are densified to fully exploit the spatial spectrum reuse gain. The decreasing coverage area of a small cell also leads to the obviously increasing traffic asymmetry in UL and DL within the same cell and among different cells. To adapt to the dynamic and asymmetric traffic within UDN, dynamic time-division duplex (DTDD) can be seen as a promising scheme due to its capability in the flexible adjustment of ratio of UL to DL subframes. In this paper, we propose a load-aware analytical framework to accurately characterize the network performance of a DTDD UDN with the emphasis on the effect of idle mode cells. With a practical multi-slope path loss model, we first derive the void probability of a random SAP and obtain the coverage probability and area spectral efficiency (ASE). Then we evaluate how the SAP void probability, UL/DL configuration and network density affect the network performance. Our numerical results show that the idle mode cells have significant effect on the network performance, which alters the variation tendency of coverage probability and ASE with the increasing network density.

Orbital angular momentum (OAM) provides extra degrees of freedom to improve spectrum efficiency. However, the harsh requirement that transmit antenna(s) and receive antenna(s) need aligning throws out a great challenge to its application. To investigate the reason of performance degradation caused by misalignment, we first consider uniform circular array (UCA) based OAM system and set up two free space channel models respectively for two misalignment case, i.e., non- parallel and off-axis. Then, the capacities of single- mode and multi-mode OAM system are analyzed and compared under different oblique angles and axis deviations. Analysis and simulation results show that OAM transmission especially the multi-mode OAM is quite sensitive to the misalignment between transmitter and receiver. At last, we propose a misalignment-robust receiving method through extracting the phases of channel state information (CSI) and implementing joint phase compensation and signal detection. Simulation results show that proposed receiving scheme is more robust to misalignment than conventional receiving scheme.

In this paper, we study the uplink spectrum sharing in a device-to-device (D2D) underlaid LTE-Advanced network. The fractional power control (FPC) policy is proposed for both cellular user equipments (CUEs) and D2D users to reduce the cross-tier interference. We present an analytical framework to evaluate the effect of FPC on the network performance in terms of coverage probability and D2D user transmission capacity. By exploiting the CUE's location information, we further propose a location-aware spectrum sharing (LoSS) policy which can increase the D2D user's transmission opportunities while satisfying the CUE's QoS constraint. The proposed framework allows to determine the optimal FPC parameter for a CUE at any given location in the macrocell, and provides a more accurate analysis on the spectrum sharing between CUEs and D2D users. From the perspective of D2D transmission capacity, we provide guidelines for the optimal design of FPC in the D2D underlaid LTE-Advanced network.

In cellular networks, since Media Access Control (MAC) layer plays a key role in every access equipment, it fascinates that little progress on multiple access protocol could save considerable energy. Accordingly, this paper studies a novel MAC protocol, i.e., the power division multiple access (PDMA) protocol, with the purpose of green communication. As a fundamental study of PDMA, we first propose a power division multiplexing (PDM) scheme, analogous to the time division multiplexing and frequency division multiplexing. It is proved that the transmit power could be divided into multiple regular power segments (PSs) to simultaneously transmit multiple independent information/data streams in peer to peer communications. Based on our fundamental studies of PDM, an orthogonal PDMA (OPDMA) protocol is proposed to utilize multiplexing and degraded channel gains for energy saving. By adopting the orthogonal PSs proposed in OPDMA, multiple information streams in different channels could be transmitted efficiently and concurrently with quality of service guarantee. This paper shows that the proposed OPDMA not only has low computational complexity as the conventional Time Division Multiple Access (TDMA) and Frequency Division Multiple Access (FDMA) protocols but also gains better energy efficiency, which consists with the energy saving requirement in green communications.

In this paper, a joint power and resource block (RB) allocation (JPRBA) algorithm with low complexity is proposed, which addresses the intra-and-inter-cell interference management problem for a multicell device-to-device (D2D) communication underlaying LTE-Advanced network. We first introduce a power control and resource allocation vector (PORAVdm) to each D2D transmitter, and the set of all PORAVdmhas two functions: one is to select appropriate reused RBs for each D2D link, whereas the other is to determine the optimal power for D2D transmitters on each selected RB. To obtain the appropriate PORAVdms, we exploit the group sparse structure to formulate a sum rate maximization problem (referred to as the group least absolute shrinkage and selection operator programming). Then we derive the stationary solution by solving its equivalent sparse weighted mean square error minimization problem. Finally, simulation results show that the proposed JPRBA algorithm can efficiently improve the total throughput.

The advent of simultaneous wireless information and power transfer (SWIPT) offers a promising approach to providing cost-effective and perpetual power supplies for energy-constrained mobile devices in heterogeneous cellular networks (HCNs). As energy efficiency (EE) has been envisioned as a key performance metric in 5G wireless networks, we consider a multiple-input single-output (MISO) femtocell cochannel overlaid with a Macrocell to exploit the advantages of SWIPT while promoting the EE. The femto base station sends information to information decoding (ID) femto users (FUs) and transfers energy to energy harvesting (EH) FUs simultaneously, and also suppresses its interference to Macro users. We maximize the information transmission efficiency (ITE) of ID FUs and energy harvesting efficiency (EHE) of EH FUs, respectively, with the QoS of all users, and investigate their relationship. We formulate these problems as fractional programming, which are nontrivial to solve due to the nonconvexity of ITE and EHE. To tackle these problems, we devise two beamformers namely zero-forcing (ZF) and mixed beamforming (MBF), and then propose an efficient algorithm to obtain the optimal power under both beamformers. Simulation results demonstrate that MBF provides better ITE and EHE than ZF, and there exists a tradeoff between ITE and EHE in general.

Abstract In this paper, we consider the distributed opportunistic scheduling problem for the Orthogonal Frequency Division Multiplexing OFDM‐based device‐to‐device (D2D) communications, where D2D links contend for access to the dedicated spectrum with limited assistance from cellular infrastructures. Particularly, a synchronous distributed opportunistic scheduling protocol under fairness constraints (DO‐Fast) is prompted. In DO‐Fast, a round‐robin strategy is integrated with the opportunistic scheduling to tackle the trade‐off between system throughput and access fairness. Moreover, without instantaneous channel state information at receivers, we incorporate a priority allocation scheme, where access priorities are assigned randomly in a local fashion. Consequently, DO‐Fast is robust against imperfect channel estimates and inaccurate channel state information ordering. In addition, the opportunistic strategy in DO‐Fast is distinguished from the existing ones in that efficient spatial reuse is exploited by allowing concurrent transmissions based on the signal‐to‐interference ratio scheduling criterion. Meanwhile, access opportunities are moderately granted for poor quality links by the round‐robin strategy for fairness considerations. We analyze and compare three practical scheduling strategies in terms of the access probability. We also evaluate access fairness through Jain's Index. It is shown via numerical and simulation results that DO‐Fast could achieve efficient spectrum utilization and guarantee the short‐term fairness. Copyright © 2014 John Wiley & Sons, Ltd.

In heterogeneous cellular networks (HCNs), the interference received at a user is correlated over time slots since it comes from the same set of randomly located base stations (BSs). This results in the correlations of link successes, thus affecting network performance. Under the assumptions of a K-tier Poisson network, strongest long-term averaged biased-received- power based BS association, and independent Rayleigh fading, we first quantify the correlation coefficients of interference. We observe that the interference correlation is independent of the number of tiers, BS density, signal-to-interference-ratio (SIR) threshold, and transmit power. Then, we study the correlations of link successes in terms of the joint success probability over multiple time slots.We show that analysis without considering the temporal interference correlation underestimates the joint success probability. Moreover, we explore the effects of BS density, transmit power and user association bias on the joint success probability. In particular, BS density and transmit power affect the joint success probability of the overall network by influencing the association probability of each tier. We also reveal that the unbiased cell association outperforms the biased cell association in terms of the joint success probability. Finally, we conduct simulations to validate our analysis.

The integration of Device-to-Device (D2D) communications into cellular networks, albeit improving spectrum efficiency, may inevitably lead to cross-tier interference between cellular users and D2D users. In this paper, we endow D2D users with the capability of power control to address the cross-tier interference and theoretically analyze the benefits of power control in enhancing the transmission capacity region (TCR). In particular, based on transmission capacity, the TCR is defined as the enclosure of all feasible combinations of transmitter intensities in cellular and D2D networks. We first employ the stochastic geometry framework to derive closed-form expressions of the TCR for two prevalent spectrum sharing modes, i.e., reuse mode and dedicated mode. As for the reuse mode, we then study how to enlarge the TCR through initializing the power levels of cellular users and D2D users. Finally, the dedicated mode is compared with the reuse mode through TCR. Specifically, given the same target rate for cellular users and D2D users, the reuse mode is shown to outperform the dedicated mode in terms of the TCR when 2α/2≤ θ + 2, where α and θ are, respectively, the path loss exponent and decoding threshold. The analysis provides useful guidance for spectrum regulation and design of efficient power control techniques in D2D integrated cellular networks.

An important consideration for LEO satellite networks is choosing suitable routes to prolong the network lifetime while stringently guarantee the throughput requirement. However, both the highly dynamic network topology and intrinsically time-varying renewable energy availability pose great constraints and challenges in designing such routing schemes. To solve the problem, we resort to Capacity Region Evolving Graph (CREG) and formulate the throughput constrained maximum lifetime routing problem. Unfortunately, solving the problem without exploiting its special structure is indeed time-consuming, since multiple time intervals must be jointly handled. Two efficient routing algorithms, namely, Maximum Lifetime Routing (MLR) and Shortest Path-based Progressive Routing (SPPR), are thus proposed to reduce the execution time of solving the routing problem. Specifically, MLR decomposes the problem into multiple independent subproblems without trading its optimality, while SPPR exploits the deterministic mobility of satellite networks without solving the optimization problem. Simulation results verify that prolonged network lifetime and balanced traffic distribution can be obtained for both the routing algorithms.

A Multilayered Satellite Network (MLSN) tends to be a promising architecture in facilitating global ubiquitous broadband communication. However, unbalanced traffic distribution among its satellite layers should frequently occur, where the lower layers could get relatively congested while the upper layers remain underutilized. This unfair distribution of network traffic can lead to large end-to-end delay and severe throughput degradation. To cope with the above issue, we propose a Tailored Load-Aware Routing (TLAR) strategy to optimally distribute traffic load among the multiple satellite layers, so that the overall traffic congestion in the MLSN is minimized. In TLAR, an optimal portion of network load, which is decided based upon the newly arrived traffic estimation and theoretical analysis of traffic congestion rate in each layer, is detoured through the upper layer. The performance of the proposed routing method has been validated through extensive simulations, which demonstrate that TLAR can significantly alleviate traffic congestion, achieve low end-to-end delay and sustain improved throughput.

In next-generation wireless networks, energy efficiency optimization needs to take individual link fairness into account. In this paper, we investigate a max-min energy efficiency-optimal problem (MEP) to ensure fairness among links in terms of energy efficiency in OFDMA systems. In particular, we maximize the energy efficiency of the worst-case link subject to the rate requirements, transmit power, and subcarrier assignment constraints. Due to the nonsmooth and mixed combinatorial features of the formulation, we focus on low-complexity suboptimal algorithms design. Using a generalized fractional programming theory and the Lagrangian dual decomposition, we first propose an iterative algorithm to solve the problem. We then devise algorithms to separate the subcarrier assignment and power allocation to further reduce the computational cost. Our simulation results verify the convergence performance and the fairness achieved among links, and particularly reveal a new tradeoff between the network energy efficiency and fairness by comparing the MEP with the existing algorithms.

In this paper, we characterize the transmission capacity region (TCR) in D2D integrated cellular networks when two prevalent interference management techniques, power control and Successive Interference Cancellation (SIC) are utilized. The TCR is defined as the enclosure of all feasible sets of active transmitter intensities in cellular and D2D systems. Closed-form approximate expressions of TCR are derived for two spectrum sharing modes, i.e., reuse mode and dedicated mode. The analysis provides insights into the impact of network parameters, interference management methods, as well as bandwidth allocation policy on the TCR. Moreover, we compare the reuse mode and dedicated mode in terms of TCR. Specifically, with power control, given the same target rate for cellular users and D2D users, the TCR of the dedicated mode is shown to be entirely enclosed by that of the reuse mode when 2α/2 ≤ θ+2, where α and θ are, respectively, the path loss exponent and decoding threshold. However, with SIC utilized, numerical results show that when θ > 1, better performance can always be achieved by the reuse mode in terms of TCR. The results can serve as a guideline for the design of efficient interference management techniques and spectrum regulation in D2D integrated cellular networks.

Energy efficiency (EE) is very crucial for future wireless communication systems, especially for cognitive radio networks (CRNs). The EE performance relies on channel state information (CSI) of channels. Besides, the interference from secondary users (SUs) to primary users (PUs) also closely depends on CSI in underlay CRNs. However, available works on EE usually assume that CSI is perfect, which is often inaccurate in practical systems. Thus, in this paper we investigate the robust EE maximization problem in underlay CRNs with multiple SUs and PUs. Assuming CSI error to be bounded, we consider that all channels lie in some bounded uncertainty regions. From the perspective of worst-case optimization, we formulate it as the max-min problem with infinite constraint, which is nontrivial even without this constraint. This is because that the outer-maximization problem is non-convex and the inner-minimization problem is a concave minimization problem known as NP-hard in general. We propose a scheme to handle this problem via the fractional programming and global optimization techniques. Particularly, we efficiently solve this problem in two special cases. Simulation results validate that our proposed scheme can improve the worst-case EE of SUs distinctly and strictly guarantee the quality-of-service (QoS) of PUs under all parameters' uncertainties.

Green communications are becoming an inevitable trend for future wireless network design, meanwhile, as a promising technique, distributed antenna systems (DAS) cater for this evolution. In this paper, we focus on the problem of devising globally optimal antenna selection and power allocation algorithm in downlink DAS to achieve energy efficiency (EE) maximization. We formulate it as a mixed-integer nonlinear programming (MINLP), which maximizes EE subject to rate requirements, transmit power, and antenna selection constraints. By equivalent transformation, an iterative antenna selection and power allocation algorithm is proposed based on nonlinear fractional programming theory, and branch and bound methods. Our algorithm ensures global optimality and thus, it provides an important benchmark for performance evaluation of other heuristic algorithms targeting the same problem. Simulation results show that the computation complexity can be dramatically reduced comparing with exhaustive search, as well as demonstrate that a significant gain can be obtained in terms of EE against the schemes without antenna selection.

We investigate the local connectivity of cognitive radio ad hoc networks (CRAHNs), i.e., node degree and probability of node isolation. The local connectivity of CRAHNs depends on not only its own network parameters but also the primary networks. To analyze the local connectivity, we use stochastic geometry and probability theory to derive the distribution of node degree, probability of available spectrum and probability of node isolation of the Secondary Users (SUs). The relation between the local connectivity of CRAHNs and the parameters of both primary and secondary networks is given. Theoretical analysis and simulation results indicate that the average node degree of SUs scales linearly for increases in the density of SUs with the slope determined by the density of Primary Users (PUs). It also indicates that the SUs' node isolation probability is largely determined by the density of PUs.

In this paper, by jointly considering antenna selection and power allocation, we address the energy efficiency (EE) maximization problem with delay performance taken into account in downlink distributed antenna systems (DAS). To characterise system EE, we first define a revenue-cost (RC) function as the weighted difference between sum transmit rate and total energy consumption. We then formulate the problem as a stochastic optimization model, which maximizes the long-term average RC value subject to network stability (used to depict delay performance) and average power constraints. An Energy-Efficient Antenna selection and Power allocation Algorithm (EE-APA) is proposed based on Lyapunov optimization technique. The EE-APA adapts to time-varying channel conditions and stochastic traffic arrivals without requiring any corresponding prior-knowledge. Moreover, the theoretical analysis shows that the EE-APA can not only push the EE arbitrarily close to the optimal at the cost of delay performance, but also quantitatively control the EE-delay performance. Numerical results validate the adaptiveness of the EE-APA and the correctness of the theoretical analysis.

In this paper, by jointly considering subchannels, power, and Modulation and Coding Scheme (MCS) allocation, we address the sum-rate maximization problem in OFDMA downlink systems. We formulate the problem as an integer linear programming (ILP), which maximizes the system sum-rate subject to the minimum rate requirements of users and total transmit power constraint of base station. To solve the formulation with low complexity, we propose a two-level iterative Subchannels, Power, and MCS allocation Algorithm (SPMA) by exploiting Tabu Search (TS). At each iteration, the SPMA firstly assigns MCS to users and then allocates subchannels and power based on a SubChannels and Power allocation Algorithm (SCPA). Particularly, the SCPA maximizes the system sum-rate by first satisfying the minimum rate requirements with the least transmit power. Simulation results show that the SPMA outperforms the existing algorithms in terms of sum-rate and average rate per user, as well as demonstrate that the sum-rate is distributed flexibly among users in instantaneous channel conditions with the SPMA.

In this paper, we address the energy conservation problem with the quality of service (QoS) requirements taken into account for the physical downlink shared channel (PDSCH) in LTE/LTE-Advanced networks. By jointly allocating the modulation and coding schemes (MCS), resource blocks (RB), and power, we first propose a standards-compliant QoS-oriented power control algorithm (SQPC) for realistic systems to save energy. Specifically, with an appropriate MCS allocation, the proposed algorithm can tailor the power to match the QoS requirements. However, the SQPC saves energy at the cost of RB utilization. To this end, we further devise an Enhanced SQPC algorithm (ESQPC) to strike a balance between RB allocation and energy consumption. Finally, simulation results show that the proposed algorithms have the advantage of reducing nearly half of energy consumption compared to the existing algorithm, as well as demonstrate that the ESQPC can improve RB utilization against the SQPC.

In cognitive radio networks (CRNs), secondary users (SUs) must vacate the spectrum when it is reclaimed by the primary users (PUs). As such, multiple SUs that operate on the same channel requested by the PUs will be affected, resulting in a possible network partition. Therefore, how to maintain the connectivity of CRNs when PU appears is a critical problem. In this paper, we propose a topology control algorithm to address this problem. Particularly, we combine power control and channel assignment to construct a bi-channel-connected and conflict-free topology using minimum number of channels. Theoretical analysis shows that the CRN can maintain connectivity upon any single channel interruption by PUs. The simulation results demonstrate that the proposed algorithm can reduce the number of required channels efficiently and preserve energy spanner property.

In cognitive radio networks (CRNs), secondary users (SUs) must vacate the spectrum when it is reclaimed by the primary users (PUs). As such, multiple SUs transmitting on the same channel will be affected when the channel is requested by the PUs, thereby resulting in a possible network partition of CRNs. Therefore, how to maintain the connectivity of CRNs considering the activity of PUs is a critical problem. In this paper, we propose a centralized and a distributed topology control algorithm respectively to address this problem. Particularly, we combine power control and channel assignment to construct a bi-channel-connected and conflict-free topology using the minimum number of channels. In the power control phase, we tailor the topology for the channel assignment in the second phase. In the channel assignment phase, we utilize the graph coloring algorithm to achieve conflict-free transmission by assigning a channel to each SU. Theoretical analysis and simulation study show that the derived topology can maintain connectivity in the event of any single channel interruption by PUs. Simulation results also demonstrate that the proposed algorithms can efficiently reduce the average number of required channels for achieving bi-channel-connectivity and conflict-free transmission and ensure that the minimum power paths in the original network preserved in the final topology.

We aim to maximize system throughput subject to constraints on both short-term and long-term fairness in terms of Jain's index in single cell downlink OFDMA systems, where the transmission power is fixed. While it is accepted that short-term fairness implies long-term fairness, we find that this is not always true. Noting that long-term performance metric is the average of short-term ones, we point out that it depends on the averaging method and the fairness definition. We prove that short-term throughput Jain's index implies long-term throughput Jain's index. Therefore, we can remove the long-term fairness constraint if it is looser than the short-term constraint. Otherwise, we heuristically replace the long-term fairness constraint by a cumulative fairness constraint. We relax the considered discrete subchannel and slot allocation problem into a continuous convex problem, which can be efficiently solved. Then, the discrete resource allocation is derived by rounding the optimal solution. The analysis indicates that the rounding error is small. Simulation results show that we obtain a good suboptimal solution with small deviations from the optimal relaxed system throughput and the Jain's index constraints. Moreover, comparing with the strategies that take into account only long-term fairness, we guarantee both long-term and short-term fairness.

The architecture of decentralization makes future wireless networks more flexible and scalable. However, due to the lack of the central authority (e.g., BS or AP), the limitation of spectrum resource, and the coupling among different users, designing efficient resource allocation strategies for decentralized networks faces a great challenge. In this paper, we address the distributed channel selection and power control problem for a decentralized network consisting of multiple users, i.e., transmit-receiver pairs. Particularly, we first take the users' interactions into account and formulate the distributed resource allocation problem as a non-cooperative transmission control game (NTCG). Then, a utility-based transmission control algorithm (UTC) is developed based on the formulated game. Our proposed algorithm is completely distributed as there is no information exchange among different users and hence, is especially appropriate for this decentralized network. Furthermore, we prove that the global optimal solution can be asymptotically obtained with the devised algorithm, and more importantly, in contrast to existing utility-based algorithms, our method does not require that the converging point is one Nash equilibrium (NE) of the formulated game. In this light, our algorithm can be adopted to achieve efficient resource allocation in more general use cases.

In wireless resource allocation, improving system throughput and simultaneously enhancing user fairness are two fundamental but contradictory objectives. As for fairness, both short-term and long-term fairness are of significant importance. However, less effort has been dedicated to explore the optimal tradeoff between system throughput and the two mentioned fairness in terms of widely used Jain's index. In this context, we aim to maximize system throughput subject to constraints on both short-term and long-term fairness in single cell downlink OFDMA systems. The difficulty of this issue lies in that the considered subchannel and slot allocation problem is a nonlinear integer programming problem, and furthermore seems to be non-causal. To overcome these challenges, we first relax the integer variables. Second, we prove that short-term fairness ensures long-term fairness so that the long-term fairness constraint is redundant and can be removed. Third, the problem is decomposed into a sequence of short-term convex optimization problems that can be easily solved. Numerical results show that the proposed method achieves a good suboptimal solution with small deviations from the optimal relaxed system throughput and the Jain's index constraint.

Enhancing system throughput and improving user fairness are two basic but contradictory objectives for resource allocation in wireless cellular networks. To obtain an efficient tradeoff between these two goals, Network Utility Maximization (NUM) framework has been adopted with log-utility to obtain proportional fairness among all the users in the network. However, such tradeoff can not control the bias towards throughput or fairness. In this paper, we focus on α-fairness NUM in Soft Frequency Reuse (SFR) based cellular networks, where SFR is an attractive frequency reuse technique to mitigate Inter-Cell-Interference (ICI) and α can be utilized to adjust the tradeoff. The difficulty of the considered issue comes from that it is a Mixed Integer Programming (MIP) problem taking into account both intra-cell user scheduling and inter-cell user association. To overcome this challenge, the α-fairness NUM problem is decomposed into two subproblems, which are dealt with one by one. First, maximize intra-cell utility by user scheduling and second, maximize network utility by distributed user association. Numerical results show that the proposed algorithm approaches the optimal solution of the α-fairness NUM problem. Also, we get a better tradeoff between throughput and fairness, where fairness is measured by Jain's index. Particularly, we improve the maximum Jain's index from about 0.3 to about 1.

In this paper, we consider the distributed scheduling problem for the OFDM based device-to-device (D2D) communications. In order to fully exploit the spatial diversity of the channel variation as well as provide access fairness for all D2D links, we propose a synchronous Distributed Opportunistic scheduling protocol under Fairness constraints (DO-Fast). DO-Fast incorporates the opportunistic scheduling with a round-robin strategy. By exchanging local Channel State Information (CSI) in a distributed way, the opportunistic scheduling strategy enables the links with better channel conditions to take precedence for higher access priorities. It leads to more concurrent transmissions and higher system throughput than the random scheduling strategy, where links are allocated with priorities in a random manner regardless of channel conditions. Meanwhile, we prompt a round-robin strategy so that the D2D links would take high priorities alternately, which guarantees the short-term fairness requirements of the links with poor channel conditions. We show via simulations that DO-Fast achieves throughput improvement over the existing scheduling protocol from the network perspective with acceptable delay performance.

Interference management is a hot issue in Heterogeneous Networks (HetNets), which is very crucial for the performance promotion in heterogeneous cellular networks with full frequency reuse. Focusing on mitigating the interference between Macrocell and Femtocell, we propose the IM-Torch (Interference Mitigation via Traffic Offloading in Macro/Femtocell + WiFi Heterogeneous Networks) scheme to handle this problem via traffic offloading. We formulate it as a Mixed Integer Nonlinear Program which is hard to solve, and design a two-step heuristic algorithm to solve this problem. In the first self-scheduling step, Femtocell tries to reallocate the power and PRBs (Physical Resource Blocks) for HUEs (Home User Equipment) to mitigate the interference. After the failure of the first step, Femtocell offloads some necessary HUEs with data services to WiFi and re-adjusts the resources for HUEs to alleviate the interference. The analysis and simulations validate that IM-Torch scheme can greatly alleviate the interference in HetNets and thus improve the system total throughput of Femtocell while guarantee the QoS of Macrocell and HUEs with real time applications.

Traditional load balancing schemes only considered two cells cooperation that is less likely to succeed. A novel scheme, load balancing by cells- cooperation-chains (C$^3$LB), is proposed. C$^3$LB establishes multi-level cells-cooperation-chains (C$^3$) to transfer traffic and extents the conditions of traffic transfer. We formulate a minimum-level C$^3$ selection problem and propose a simple algorithm to solve it. In addition, we present a C$^3$LB protocol to execute the found C$^3$. Numerical results show that as the maximum allowed levels of C$^3$ increase, the system call blocking probability decreases. Finally, we give the proposed value of the maximum allowed levels.

End-to-end delay analysis is an important element of network performance analysis in multi-hop wireless networks. In this paper, we analyze the end-to-end delay of wireless networks with general traffic model and capacity-varying channel. A new concept of residual effective capacity using Effective Bandwidth theory and Effective Capacity theory is presented, which allows us to calculate the cumulative distribution function of queuing delay. We derive a formula to calculate the average end-to-end delay for multi-hop wireless networks and validate our analysis through simulations.

Most of previous topology control algorithms that aim to extend the network lifetime focus only on the energy consumption of transmissions, and thus construct a static topology without adaptation to the varying energy consumption rates at different nodes. As a result, the network lifetime has not been prolonged to the most extent as expected. However, other topology control algorithms that consider the residual energy levels of nodes have not addressed the problem of fault tolerance. In this paper, we propose an adaptive topology control algorithm, Residual Energy-aware Shortest Path (RESP), which not only balances the energy consumption of different nodes but also provides fault tolerance. Particularly, RESP is able to ensure k-edge connectivity and preserve the minimum-weight path. Simulation results show that RESP extends the network lifetime and is superior to other existing localized fault-tolerant algorithms.

With the development of heterogeneous networks, concurrent transmission of Multimode-User Equipment (MUE) within multiple Radio Access Technologies (RATs) can improve transmission reliability and boost system performance. In this paper, some novel splitting strategies combining with different queuing management architectures are presented to obtain the multi-radio transmission diversity gain effectively. Two- dimensional discrete-state continuous-time Markov process is used to analyze our strategies and closed-form solutions have been made. Simulation results demonstrate that our proposed flow splitting strategies utilize the system resources efficiently and outperform current strategies.

For cognitive radio (CR) networks with user hierarchy, the sensing strategy with "listen-before-talk" (LBT) policy plays a key role in spectrum utilization and primary user (PU) protection. However, existing sensing strategies do not handle satisfactorily the randomness of both user locations and channel conditions in the network environment, resulting in inefficient spectrum utilization. To cope with such randomness, this paper develops three dynamic sensing strategies that can adaptively schedule the sensing slots/cycles according to the online link conditions without assuming knowledge of the PU traffic model. The proposed strategies can improve the efficiency of spectrum utilization while being robust with respect to the uncertainty in the PU traffic pattern. To maximize spectrum utilization, the proposed strategies are formulated through closed-form expressions. Efficient methods are introduced to compute the optimal values of the parameters used in the strategies, such as sensing time and sensing threshold. Simulations verify that the proposed sensing strategies offer an evident improvement on the spectrum utilization of the CR network.

Cooperative communications achieve tremendous improvements in system performance. Meanwhile, it tends to change the conventional access and transmission schemes in wireless networks. Thus, CoopMAC is proposed and analyzed for fully connected WLANs. It improves the performance of the network dramatically by enabling additional collaboration from other nodes. However, how does it work in multi-hop Ad Hoc networks is not well-understood yet. In this paper, we first propose an analytical model to evaluate the performance of CoopMAC for multi-hop Ad Hoc networks with consideration of hidden node problem. Our analysis results show that the cooperative transmissions are frequently interrupted by hidden nodes' interference. To mitigate the effect of hidden node, an enhanced CoopMAC (ECoopMAC) is further proposed without introducing any overhead and complexity, which jointly optimizes the order of handshake and the helper selection metric. By increasing the probability of successful cooperative transmission and decreasing the interference to other flows, the saturated throughput and access delay of multi-hop Ad Hoc networks are improved by ECoopMAC remarkably. Extensive simulations evaluate the performance of our analytical model and protocol, the results of analysis and simulation match well. Compared with CoopMAC, the saturated throughput is improved up to 12% on average.

Through sensing and utilizing available network resources, cognitive network can obviously increase network performance. In this paper, a distributed on-demand routing protocol with traffic awareness (TACR) is proposed for cognitive wireless network. This routing protocol establishes the path based on the cognition and reasoning of traffic loads in a network and it also meet quality of service (QoS) requirements by introducing autonomous intelligence-Q-learning. Simulation results show that the TACR routing protocol shortens the average end-to-end delay significantly and also improves the average throughput.

Network-assisted diversity multiple access (NDMA) was recently developed to resolve collision and achieve high throughput. The blind NDMA method using independent component analysis (ICA), named ICA-NDMA, has been shown to overcome the difficulties of synchronization and orthogonal identification codes required by the training-based NDMA protocols. In this paper, we propose a novel blind collision resolution strategy by employing cooperative transmission in ICA-NDMA. When K-node collision happened, a cooperative transmission mechanism is adopted to collect different superposition of the K nodes' packets in the following slots. Moreover, a cooperative blind detection algorithm is intro- duced to determine the number of collision nodes. Finally, with ICA, the destination can retrieve the original packets through processing the collided packet and the signals forwarded by relays. Simulation results show that, the proposed method can work effectively under both fast-varying and slow-varying channels, the performances of average packet delay and maximum stable throughput are better than ICA-NDMA.

This letter studies cooperative spectrum sensing (CSS) in which secondary users efficiently cooperate to achieve superior detection accuracy with minimum cooperation overhead. We consider each cooperative user only spends "1 bit" on reporting its own sensing decision to data fusion center as the total cooperation overhead. However, this "1 bit" CSS could not gain better sensing outcomes in current data fusion rules (DFRs). To ameliorate this issue, we derive theorems to reveal the optimum threshold of general DFR. Then we propose three novel DFRs and related three algorithms to efficiently obtain the optimum decision threshold for different objectives. By simulations, the proposed DFRs indicate evident improvement on CSS performance.

Topology control with per-node transmission power adjustment is an effective way to extend network lifetime. However, due to the commonly used assumption of homogeneous wireless networks with uniform maximal transmission power, most topology control algorithms suffer from performance degradations in practical applications where physical characteristics of each node may be different. Hence, it is valuable to take heterogeneous networks into consideration. In such an environment, however, most of existing algorithms mainly consider the energy consumption caused by transmitting, meanwhile ignore the residual energy of network nodes, thus in fact they can not balance energy consumption efficiently. In this paper, a localized distributed topology control algorithms ESATC (Energy-aware Self-Adjust Topology Control) is proposed for extending network lifetime of heterogeneous wireless Ad Hoc networks. Based on overall consideration of power consumption and residual energy of two end nodes, ESATC builds a dynamic network topology that changes with the variation of node energy. Without location information, each node self-adjusts its transmission power according to the network information collected locally, which makes our algorithm suit for large scale networks. Theoretic analysis and experiment results show that ESATC provides routing with an underlying topology with bi-directional reachability and minimum-cost property. Compared with other algorithms, it can extend the lifetime of networks dramatically.

In this paper, we propose the optimal resource allocation strategies for energy constrained coded cooperative networks. By combining power control and multi-relays selection with LOC adjustment, our schemes aim at providing higher QoS and extending network lifetime. We consider the TDMA based scenario where one node acts as the source and the other nodes can be selected as relay nodes in a time slot and each node is limited by separate power constraint. Firstly, we build an optimization model for minimizing and balancing the energy consumption under QoS constrains. After that, when the LOC is constant, a closed form optimal solution is got by KKT conditions, which results in a dynamic resource allocation strategy. Moreover, based on solution for the fixed LOC, a suboptimal scheme is further proposed for a variable LOC via one dimension searching. The simulation results show that our schemes can improve network QoS and prolong network lifetime dramatically comparing with other existing cooperative resource allocation schemes.

Topology control via per-node transmission power adjustment has been shown effective in extending network lifetime. However, most of existing algorithms construct static topologies which fail to consider the residual energy of network nodes, thus in fact they can not balance energy consumption efficiently. To address this problem, a lightweight distributed topology control algorithm EDTC (Energy-aware Dynamic Topology Control) is proposed in this paper. Based on the link metric reflecting both the energy consumption rates and residual energy levels at the two end nodes, EDTC generates a dynamic network topology that changes with the variation of node energy. In addition, without the aid of location information, each node determines its transmission power according to local network information, which reduces the complexity and overhead of EDTC greatly. Theoretic analysis and experiment results show that EDTC preserves network connectivity and minimum-cost property and compared with other algorithms, it can extend network lifetime more remarkably.

Biconnectivity is the baseline graph theoretic metric of fault tolerance to node failures which can keep network connectivity while allowing unexpected node failures. In this paper we analyze this property for one-dimensional wireless Ad Hoc networks with finite nodes, which finds many applications in the real world, such as bus networks built along freeways and sensor networks deployed along frontiers. With common adopted assumption of uniform node distribution for static networks, the formula for the critical transmitting range that realizes network biconnectivity with certain probability is derived. Simulation results validate the accuracy of our conclusion and confirm its efficiency in practical network design.