Satellite mega-constellations (SMCs), comprising thousands of interconnected satellites, have emerged as critical infrastructure for 6G networks to achieve seamless global coverage. This paper addresses two fundamental challenges in SMC operation: 1) the inherent spatial-temporal traffic heterogeneity with continuously escalating demand, and 2) the diverging quality-of-service (QoS) requirements for diverse traffic types requiring robust end-to-end performance guarantees. To enhance resource utilization while ensuring service differentiation, we propose a novel dual-scale traffic management framework encompassing macroscopic network-level coordination and microscopic node-level adaptation. The macroscopic component formulates a multi-objective optimization framework that strategically allocates transmission paths by simultaneously minimizing inter-satellite link load disparities and end-to-end queuing delays. The microscopic component introduces an adaptive resource allocation mechanism that decomposes end-to-end QoS requirements into per-node service level agreements, employing federated learning based traffic prediction to enable dynamic resource pre-allocation based on real-time load conditions. This hybrid approach achieves load-aware resource provisioning that maximizes traffic completion rates while minimizing inefficient transmissions. Simulation results show our scheme outperforms the on-demand multi-objective optimization approach, improving traffic completion rates by 17.0-27.5% and resource utilization by 23.29-62.34% across varying loads, while reducing latency and enhancing fairness.

Low Earth Orbit (LEO) satellite networks are increasingly central to wide-area communication services, yet their capacity is highly sensitive to the inherent vulnerability of satellite and inter-satellite link (ISL) failures. This paper develops an analytical framework for characterizing network capacity under arbitrary failure patterns. Building on bisection cut-set analysis, we formalize capacity bottlenecks through graph partitioning and derive rigorous upper bounds on the network capacity. Within this framework, we further identify and characterize the maximum failure cut set, i.e., a critical subset of ISLs whose removal yields the most severe capacity degradation, and establish its necessity in achieving the worst-case capacity bound. Furthermore, the analytical framework has been shown to be applicable to general LEO networks, and a closed-form expression for capacity degradation has been derived using a 2D torus topology as an example, which takes into account both the number and spatial distribution of the satellite or ISL failure. The results indicate that maximizing the minimum bisection cut set capacity is fundamental to strengthening the intrinsic robustness of the LEO network. This paper provides a theoretical methodology for capacity assessment and offers principled guidelines for topology design in satellite constellations.

Satellite computing networks leverage the placement of computing resources on low-Earth orbit (LEO) satellite communication network to bring computing power closer to users, enabling robust and scalable solutions for emerging applications such as Internet of thing, edge computing and real-time analysis. However, with the explosion of multi-layer LEO mega-constellations(MLMCs), how to use the least number of computing nodes to achieve computing power resource coverage? This paper proposes a computing node deployment algorithm for MLMCs to construct satellite computing network with the minimum number of computing node. Specifically, we analyze the existence conditions of optimal computing node deployment, and derive the expression of the relationship between the number of computing nodes needed on the LEO, the multi-layer satellite network structure and the number of accessible hops of computing nodes. The expression can determine the minimum number of computing nodes needed to be placed on the MLMCs to realize optimal computing node deployment under a certain number of accessible hops. Then, according to the limitation of optimal computing node deployment, a multi-layer satellite network computing node deployment algorithm is proposed to determine the position of the computing resource in MLMCs, which can form a stable computing structure in satellite network. Finally, through simulation, the influence of network scale and computing node deployment on the number of required computing nodes and the effectiveness of signaling delay is analyzed. The simulation results show that this method can achieve better delay with the least number of computing nodes, balance the number of computing nodes and delay, and meet the specific network requirements.

Mega satellite constellations (MSCs) based on low Earth orbit (LEO) satellites and inter-satellite links (ISLs) have become increasingly important due to the seamless coverage and high throughput. Unfortunately, the communication components of satellites are susceptible to radiation-induced single event upsets (SEUs), which lead to the failure of ISLs and the decline in network throughput. In this paper, we study the impact of SEUs on network throughput and propose MSC design algorithms to enhance the throughput. To mitigate the impact of SEUs, each satellite is equipped with low-cost mitigation techniques, under which ISLs experience different levels of impairment. Furthermore, we derive the expressions of network throughput and observe the mismatch between the traffic pattern and the network topology. Based on the expressions, we develop the MSC design algorithm to address the gap for throughput enhancement. Simulation results validate the accuracy of the theoretical results, and demonstrate that the proposed algorithm can effectively enhance the network throughput by 8.42% compared to the classical topology under the impact of SEUs.

In data relay satellite networks (DRSNs), user satellite (US) data is transmitted to ground stations via geostationary (GEO) relay satellites (RSs). As the number of USs increases to enable real-time observation, the limited relay capacity becomes a critical bottleneck. On-board caching and processing at USs before transmission are promising approaches to alleviate relay pressure. However, constrained storage, computation and transmission (SCT) capacities pose significant challenges in meeting stringent delay and reliability requirements. This paper investigates the user capacity of a typical DRSN with integrated SCT processes, which is defined as the maximum number of USs that can be supported under both delay and reliability constraints. These constraints are quantified by delay violation probability (DVP) and data loss probability (DLP), whose expressions are difficult to derive directly due to the inherent coupling of SCT processes. To this end, tight upper bounds of DVP and DLP are derived based on a tandem queuing model with martingale-based analysis, and these bounds demonstrate exponential decay with increasing delay threshold and storage capacity. Based on these insights, a bi-level optimization problem is formulated and a two-step user capacity algorithm is proposed to efficiently obtain the user capacity under joint DVP and DLP constraints. The proposed analysis is conducted using representative DRSN parameters, and the numerical results show that the proposed methods can enhance user capacity by up to 38.2% and reduce computational complexity by around 90%. The results can provide guidance for future DRSN configuration, including satellites deployment and resources allocation.

Driven by diverse applications in the emerging low-altitude economy, modern aerial networks must inherently cater for highly heterogeneous environments, characterized by communication services under mixed service delay constraints and diverse user equipment (UE) mobility. However, such heterogeneity leads to resource allocation conflicts and imbalances, which undermine communication reliability and may result in network unavailability. To address this, we investigate resource management in uplink low-altitude heterogeneous networks. Specifically, we propose a flying access point (FAP)-coordinated multi-point packet delivery mechanism with a unified resource allocation (URA) scheme to efficiently manage spatial, frequency, and temporal resources. This includes subchannel allocation, time slot partitioning, and pilot length design. Then, we derive a lower bound (LB) on network availability (NA) and reveal that extended heterogeneity significantly degrades the LB due to: (a) resource reduction under URA and (b) the independence in ensuring services under heterogeneity. To mitigate this degradation, we derive a closed-form condition on the required number of FAPs by relaxing the LB, thereby ensuring sufficient spatial resources to achieve the target NA. Meanwhile, we derive closed-form expressions for jointly approximating the optimal number of UEs sharing time-frequency resources and the pilot length. This optimization improves resource efficiency for NA by balancing the post-processing signal-to-noise ratio and its associated thresholds to satisfy reliability requirements under heterogeneous conditions. Numerical results validate the analysis and demonstrate that the proposed resource management strategy achieves the target NA under increased heterogeneity, thereby outperforming existing approaches.

Reusing existing terrestrial base stations (TBSs) for ground-to-air (G2A) coverage has emerged as a promising method to enhance communication service for aerial users (AUs). However, due to distinct coverage areas, G2A coverage cannot achieve seamless coverage of ground-to-ground coverage, necessitating flexible TBS beam adjustment to satisfy communication demands of different areas. Moreover, reusing TBSs for G2A coverage inevitably sacrifices coverage performance for ground users (GUs). In this paper, we propose a transmissive reconfigurable intelligent surface (RIS)-enabled coverage method and a time-division beam switching (TDBS) strategy, which allows flexible beam adjustment and simultaneous coverage for AUs and GUs. Specifically, coverage probability (CP) for AUs and GUs is analyzed to evaluate the simultaneous coverage performance. Results show that increasing G2A TBSs enhances CP for AU, which, however, comes at the cost of severely degrading CP for GUs. Moreover, an optimal G2A TBS ratio exists, indicating that simply increasing G2A TBSs is not always effective in coverage improvement. Thus, the TDBS strategy is proposed, where part of G2A TBSs alternately serve AUs and GUs. Simulations demonstrate that the proposed strategy significantly enhances CP for GUs and AUs at high altitudes, with only a slight trade-off in CP for AUs at low altitudes.

In the realm of fifth-generation new radio (5G-NR) communication technology, how to use limited resources and low-complexity algorithms to provide high transmission rates for massive multiple-input multiple-output (mMIMO) systems with differentiated service demands remains a challenge. This paper explores a novel scenario-specific beamforming design tailored to user distributions and propagation environments. The proposed method dynamically adjusts beam orientations and widths, enabling precise coverage of users’ active regions. To mitigate inter-beam interference in spatial multiplexing, we derive a strict orthogonality condition and propose a corresponding beam grouping strategy to enforce it. Furthermore, to address the nonconvex optimization problem of time-frequency-space resource allocation (RA), we build upon the idea of the greedy algorithm to balance transmission rate and computational complexity. Building on this foundation, we propose a traffic-aware RA strategy and introduce a novel beam selection criterion by incorporating both channel characteristics and user-specific traffic demands. These improvements jointly enhance the system’s utilization of time-frequency resources and spatial reuse capability. Simulation results validate the proposed algorithms, demonstrating notable improvements in computational efficiency, system adaptability, spectral efficiency, and average achievable rate.

In this paper, we aim to improve the carbon efficiency (CE) of hybrid energy-supplied cellular networks by jointly optimizing communication and energy resources. The network is powered by both renewable and conventional grid energy. However, the stochastic and intermittent nature of renewable energy causes spatiotemporal mismatches between energy supply and traffic demand, thereby posing a challenge to CE improvement. Moreover, due to the nonlinearity of power amplifiers (PAs) at base stations (BSs), energy dissipation nonlinearly increases with transmit power. As a result, the existing static PA efficiency-based resource allocation may lead to energy inefficiency and CE degradation. On this basis, we formulate a stochastic long-term CE optimization problem that considers PA nonlinearity. Aided by Lyapunov optimization theory, the problem is equivalently transformed into three short-term deterministic subproblems, i.e., traffic flow control, resource allocation, and energy sharing. Leveraging this insight, we propose a queue-aware traffic flow control policy and a second-order cone programming-based resource allocation method to align traffic with PA characteristics. Additionally, a many-to-many stable matching-based energy sharing scheme is developed, where energy-deficient BSs are matched with energy-excessive BSs based on energy loss coefficients. Consequently, energy waste due to energy sharing is reduced, thereby improving CE.

Satellite networks with wide coverage are considered natural extensions to terrestrial networks for their long-distance end-to-end (E2E) service provisioning. However, the inherent topology dynamics of low earth orbit satellite networks and the uncertain network scales bring an inevitable requirement that resource chains for E2E service provisioning must be efficiently re-planned. Therefore, achieving highly adaptive resource management is of great significance in practical deployment applications. This paper first designs a regional resource management (RRM) mode and further formulates the RRM problem that can provide a unified decision space independent of the network scale. Subsequently, leveraging the RRM mode and deep reinforcement learning framework, we develop a topology feature-based dynamic and adaptive resource management algorithm to combat the varying network scales. The proposed algorithm successfully takes into account the fixed output dimension of the neural network and the changing resource chains for E2E service provisioning. The matched design of the service orientation information and phased reward function effectively improves the service performance of the algorithm under the RRM mode. The numerical results demonstrate that the proposed algorithm with the best convergence performance and fastest convergence rate significantly improves service performance for varying network scales, with gains over compared algorithms of more than 2.7%, 11.9%, and 10.2%, respectively.

The image task scheduling demand exhibits a thriving trend since China leverages heterogeneous low earth orbit (LEO) satellites to empower the earth observation field for Belt and Road Initiative (BRI) countries and regions. However, the high-dynamic mobility and intermittent inter-satellite links (ISLs) exacerbate communication, caching, and computing (3C) resources scarcity, while inefficient scheduling significantly deteriorates the successful transmission ratio (STR). To tackle this challenge, this paper optimizes the task scheduling strategy. To be specific, we first propose a dynamic resource mapping model (DRMM) that captures the dynamic characteristics of 3C resources through discontinuous ISLs and quantifies resources over continuous time. Based on the DRMM, we formulate an STR maximization problem under heterogeneous resource capacity constraints. A computing resources priority allocation algorithm (CRPAA) is designed to preferentially allocate onboard computing resources for task processing, thereby freeing up transmission and caching resources for additional tasks. Furthermore, the CRPAA leverages an incrementally searching slots range to reduce computational complexity and optimize the scheduling strategy, planning each task individually and clearing it promptly to prevent redundant scheduling while enhancing execution efficiency. The extensive simulation results validate that the proposed algorithm outperforms benchmark approaches.

Mega-constellation networks (MCNs) based on low Earth orbit (LEO) satellites have become increasingly important due to the high throughput and seamless coverage. However, due to single-event upsets (SEUs) caused by cosmic radiation, satellites will suffer failure, which deteriorates the network throughput. This paper aims to elucidate the relationship between network throughput and the impacts of SEUs. Taking into account the long-term impacts caused by SEUs, we present the availability of satellites based on the reliability theory. Furthermore, we model the effective data rate of inter-satellite links (ISLs) and find that the upper bound of network throughput $C \propto {\left( {\frac{{1 - {e^{ - \kappa {T_p}}}}}{{\kappa \left( {{T_p} + \gamma } \right)}}} \right)^2}\sqrt {{R_o}{R_h}} $, where κ denotes the SEU rate, and Tpis the scrubbing period for SEU mitigation. Roand Rhdenote the data rates of intra-plane ISLs and inter-plane ISLs, respectively. Consequently, the throughput decline caused by SEUs can be mitigated by adjusting the structure of MCNs. Guided by the throughput upper bound, we propose an optimal throughput constellation design algorithm (OTCDA) to enhance the network throughput considering the impact of SEUs. Experimental results illustrate that the proposed OTCDA can achieve the throughput that is only 6.49% lower than the upper bound.

The aerospace integrated networks (AINs), leveraging satellites and unmanned aerial vehicles (UAVs), offers a promising solution for large-scale Internet of Remote Things (IoRT), effectively ensuring information freshness, i.e., low Age of Information (AoI). However, in resource-constrained and dynamic AIN environment, a key challenge is how to achieve fresh data by efficiently resolving mission conflicts across multiple IoRT devices, which requires advanced scheduling design for collaborative UAVs monitoring and UAVs-satellites data transmission. In this paper, we first construct a mission scheduling framework for collaborative monitoring and transmission utilizing the wide coverage of low earth orbit (LEO) satellites and the mobility of UAVs. Then, by considering constraints such as mission conflicts, energy consumption, and motion characteristics, we characterize the relationship between UAVs trajectories and IoRT demands. Based on this, we propose a multi-agent deep reinforcement learning (MADRL) algorithm that jointly optimizes UAV trajectories and transmission scheduling. The algorithm incorporates a filter layer to optimize UAV cooperation, preventing redundant device IoRT selection and resolving mission conflicts. Simulation results indicate that the proposed algorithm can reduce 22.9% AoI compared to the benchmark.

High-reliability and low-latency multipoint-to-multipoint (MP2MP) broadcast is essential for disaster rescue. Multi-connectivity (MC) is a promising high-reliable and low-latency communication technique that can effectively exploit diversity gains for reliability by transmitting duplicate data over independent links. However, MP2MP broadcast may significantly increase communication service loads and burstiness, which impedes the fulfillment of low delay and high reliability. In this work, we first analyze the overall delay-constrained reliability over MC. We find that the overall delay-constrained reliability over MC is ineffectively improved by increasing the independent links to exploit more diversity gains as service loads and burstiness increase, which is the drawback that restricts the potential of MC. Then, we propose a traffic shaping-based MC to unlock its potential in enabling high-reliable and low-latency MP2MP broadcast. Specifically, we introduce a traffic shaping factor Λ ∈ (0,1), and reduce service loads and burstiness per link before MC transmission by evenly dividing service packets across independent links to make the delay-constrained reliability per link exceed 1−Λ. Moreover, we optimize Λ to maximize the overall delay-constrained reliability over MC under a given total independent links by balancing reliability per link with diversity gain. Finally, simulations validate our analysis and show that the proposed traffic shaping-based MC is of great potential to enable high-reliable and low-latency MP2MP broadcast.

In multiple-input multiple-output (MIMO) systems, inverse discrete Fourier transform processing induces phase-dependent subcarrier signal aliasing, thereby elevating time-domain peak-to-average power ratio (PAPR). After being amplified by nonlinear power amplifiers (PAs), these signals suffer from severe in-band distortion, which leads to a deterioration in both energy efficiency (EE) and spectral efficiency (SE). To evaluate the impact, we explore the trade-off between SE and EE in MIMO systems with such distortion. Analytical results reveal that increasing input power causes distortion to dominate over the useful signal, thereby compressing the line integral area of the EE-SE trade-off function and leading to a ring-shaped EE-SE trade-off function. It indicates an exponential increase in energy consumption of MIMO systems. To mitigate distortion-induced performance degradation, a signal-to-leakage-plus-noise ratio (SLNR) precoding method is proposed to solve the signal-to-interference-plus-distortion-noise ratio maximization subproblem. We prove that the optimal SLNR-precoding matrix could be obtained when the PAPR on per antenna is equal. Simulation results show that the proposed method expands the integral area of the EE-SE trade-off curve by more than one-fold and improves the system EE by more than 50% compared to the benchmark.

Cell-free massive multiple-input multiple-output (CF-mMIMO) systems have received a lot of attention due to its ability to effectively eliminate inter cell interference, thus increasing the downlink data rates. However, the limited number of the pilot sequences leads to imperfect channel state information (CSI), which significantly degrades the performance in CF-mMIMO systems. Rate-Splitting Multiple Access (RSMA) is widely used to address imperfect CSI due to its ability to flexibly manage interference. Nevertheless, it is still unclear how RSMA affects system energy consumption through which parameters when the number of the pilot sequences is limited in CF-mMIMO systems. This uncertainty hinders the design of systems aimed at reducing energy consumption under pilot constraints. In this paper, we derive the closed-form expression for the data rates with imperfect CSI. Specifically, we can find the energy consumption is related to a quadratic function with the number of pilot sequences as the independent variable. Based on the analysis above, a nonconvex optimization problem is formulated to minimize energy consumption. We use pathfollowing algorithm to approximate the data rates as its concave lower bound, obtaining suboptimal solutions through iterative algorithms. Finally, the simulation results demonstrate that using RSMA can save about 20% of energy consumption compared to the baseline solution when the pilot contamination is serious.

With the rise of low-altitude economy (LAE), the incorporation of unmanned aerial vehicles (UAVs) with ground networks can assist integrated localization and communication (ILAC) services for ground user equipment (UE). However, the UAV location and number significantly affect communication coverage and localization performance for ground UEs. Additionally, UAV energy consumption and operational lifespan constrain the number of UEs each UAV serves. In this paper, we investigate the UAV deployment and access control assisting with ground stations to provide both communication and localization service to potential UEs. We decompose the UAV cooperation problem into two subproblems, namely minimizing UAV problem and the UAV assignment problem. To minimize the UAV number, we design an iterative greedy search algorithm that utilizes criteria importance through intercriteria correlation (CRITIC) method to dynamically evaluate each candidate UAV selection. After that, we assign the UAV access for each UE and reduce the number of UE served by each UAV to extend the lifespan of the drones. The communication and localization assignment methods are proposed by analysis of the number of communication links per UAV and the redundancy of anchor nodes. Further, we adopt a probability-based search algorithm to solve the assignment problem. Numerical studies are conducted to verify our proposed approach compared to other benchmark methods.

Integrated sensing and communication (ISAC) has emerged as a cornerstone technology for achieving seamless coverage in next-generation networks. Moreover, the advent of extremely large-scale multiple-input-multiple-output significantly enhances ISAC’s potential, facilitating innovative applications in near-field (NF) ISAC. Nonetheless, ISAC networks face numerous challenges, with power consumption being one of the most critical concerns. To address this issue, we propose a novel low-power beamforming approach within the coordinated multipoint (CoMP) ISAC framework. Specifically, our approach involves orchestrating base station (BS) cooperation for seamless coverage and synergistically augmenting the sensing beam with the communication beam to reduce power consumption. By utilizing the NF communication theory, we accurately model signal propagation dynamics and formulate a beamforming optimization problem aimed at minimizing transmit power while adhering to transmission rate and object detection constraints, which is a nonconvex second-order cone programming (SOCP) problem. To overcome the nonconvexity of this problem, we propose a successive convex approximation (SCA)-based beamforming optimization algorithm that ensures convergence. Moreover, we propose a fast-converging algorithm that leverages the unique characteristics of both communication channel and sensing array response vector. Simulation results validate the effectiveness of the proposed scheme for the power minimization problem and yield essential design insights.

Immersive communication, including emerging augmented reality, virtual reality, and holographic telepresence, has been identified as a key service for enabling next-generation wireless applications. To align with legacy wireless applications, such as enhanced mobile broadband or ultra-reliable low-latency communication, network slicing has been widely adopted. However, attempting to statistically isolate the above types of wireless applications through different network slices may lead to throughput degradation and increased queue backlog. To address these challenges, we establish a unified QoS-aware framework that supports immersive communication and legacy wireless applications simultaneously. Based on the Lyapunov drift theorem, we transform the original long-term throughput maximization problem into an equivalent short-term throughput maximization weighted by virtual queue length. Moreover, to cope with the challenges introduced by the interaction between large-timescale network slicing and short-timescale resource allocation, we propose an adaptive adversarial slicing (Ad2S) scheme for networks with invarying channel statistics. To track the network channel variations, we also propose a measurement extrapolation-Kalman filter (ME-KF)-based method and refine our scheme into Ad2S-non-stationary refinement (Ad2S-NR). Through extended numerical examples, we demonstrate that our proposed schemes achieve 3.86 Mbps throughput improvement and 63.96% latency reduction with 24.36% convergence time reduction. Within our framework, the trade-off between total throughput and user service experience can be achieved by tuning systematic parameters.

Simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) offers a promising solution to conquer the challenge of severe signal attenuation through buildings for seamless indoor-outdoor connectivity in 6G networks. This paper focuses on hybrid active-passive STAR-RIS assisted indoor-outdoor communications and delves into the comparative impact of diverse non-ideal factors on system performance, such as transceiver hardware impairments, imperfect channel state information (CSI), and STAR-RIS phase noise. Under these system imperfections, our objective is to maximize the minimum user rate by optimizing user grouping, non-orthogonal multiple access (NOMA) decoding order, power allocation coefficients, active beamforming, and transmission and reflection beamforming, thereby enhancing system robustness and user fairness. Given the complexity of the formulated problem, characterized by interwoven optimization variables, we propose a collaborative convex optimization iterative (CCOI) algorithm by blending successive convex approximation (SCA), semidefinite relaxation (SDR), Gaussian randomization, and convex upper bound approximation. This methodology is capable of adeptly striking a balance between system performance and computational efficiency. Simulation results demonstrate the significant advantages of our proposed CCOI algorithm and provides guiding insights on system design for various defects. Additionally, the hybrid active-passive STAR-RIS structure offers enhanced flexibility and effectively balances the performance trade-offs between conventional active and passive models.

The rapid and substantial fluctuations in wireless network capacity and traffic demand, driven by the emergence of 6G technologies, have exacerbated the issue of traffic-capacity mismatch, raising concerns about wireless network energy consumption. To address this challenge, we propose a model-data dual-driven resource allocation (MDDRA) algorithm aimed at maximizing the integrated relative energy efficiency (IREE) metric under dynamic traffic conditions. Unlike conventional model-driven or data-driven schemes, the proposed MDDRA framework employs a model-driven Lyapunov queue to accumulate long-term historical mismatch information and a data-driven Graph Radial bAsis Fourier (GRAF) network to predict the traffic variations under incomplete data, and hence eliminates the reliance on high-precision models and complete spatial-temporal traffic data. We establish the universal approximation property of the proposed GRAF network and provide convergence and complexity analysis for the MDDRA algorithm. Numerical experiments validate the performance gains achieved through the data-driven and model-driven components. By analyzing IREE and EE curves under diverse traffic conditions, we recommend that network operators shall spend more efforts to balance the traffic demand and the network capacity distribution to ensure the network performance, particularly in scenarios with large speed limits and higher driving visibility.

Due to the high dynamic of unmanned aerial vehicle (UAV), the beam of UAV-mounted aerial base station (ABS) is difficult to align with ground users (GUs) and macro-cell base stations (MBSs), thereby reducing the communication rate. Towards this end, the channel state information of communication is used to assist onboard radar of ABS to sense the locations of GUs and MBSs for beam alignment to increase communication rate. To clarify the mechanism of mutual assistance between sensing and communication, we first derive the fundamental communication rate lower bound of integrated sensing and communication by utilizing the Cramér-Rao Bound. We find that the sensing power, sensing time, and transmit power between GU-ABS and ABS-MBS mutually influence the bounds of their communication rates with the shared frequency between sensing and communication. Accordingly, the maximizing communication rate problem is established by jointly optimizing transmit power, sensing power, and sensing dwell time allocation, which is decoupled into GU-ABS and ABS-MBS resource allocation subproblems. To reduce the computation complexity, a deep reinforcement learning based algorithm is proposed to solve this problem to replace the successive convex approximation technique. The simulation results demonstrate that the proposed approach is effective in maximizing the communication rate.

In UAV ad hoc networks (UANETs), transmission range of transmitters is a crucial factor in ensuring network capacity. Inadequate adjustment of transmission range may lead to link disconnection or excessive interference when network topology changes, worsening network capacity. In this paper, we study the impact of transmission range on network capacity characterized by spatial throughput (ST) in UANETs under external jamming and design a power control strategy to achieve optimal transmission range (OTR). Specifically, transmitters and jammers are modeled by a three-dimensional Poisson cluster process and a three-dimensional Poisson point process, respectively. Analysis of ST is accordingly given to illustrate the impact of topology changes and jamming. Afterwards, we analyze ST under transmission range and find that ST scales with the transmission range R as$e^{\kappa _{1}R^{3}}\left ({{1-e^{\kappa _{2}R^{3}}}}\right),\left ({{\kappa _{1},\kappa _{2}\lt 0}}\right)$. This indicates that ST first increases and then decreases with the transmission range. In other words, an OTR exists, which maximizes ST, and it is proved that the OTR scales with node density$\lambda $as$\Theta {\left ({{\lambda ^{-\frac {1}{3}}}}\right)}$. Accordingly, we propose a power control strategy implemented at each transmitter to achieve OTR and enhance ST. Simulation results show that ST adopting OTR linearly increases with$\lambda $, and the proposed strategy shows superiority in enhancing ST under intense jamming among other strategies.

The low-earth-orbit (LEO) satellite constellation networks play a vital role due to their potential to provide high throughput in response to the escalating demands of future communication networks. However, inadequate matching between the constellation topology and traffic distribution would result in network congestion, which degrades the throughput of the LEO network. In this paper, we aim to enhance the throughput of LEO networks through constellation design. Especially, to provide a theoretical guideline for topology design, we prove that the achievable throughput capacity upper bound equals$3\sqrt {2N}\left ({{W_{lx}+W_{ly}}}\right)$, where N represents the constellation size, and$W_{lx}$and$W_{ly}$represent the inter- and intra-plane data rates of the inter-satellite link (ISL), respectively. Aided by this, a throughput capacity maximum topology design (TCMTD) algorithm is proposed to determine the constellation parameters and connection relationships. Consequently, the average path length of the data packet transmission can be minimized and the utilization rate of each ISL can be maximized, thereby achieving the throughput capacity upper bound. Furthermore, a throughput capacity enhanced topology design (TCETD) algorithm is proposed to achieve a near-optimal throughput when some ISLs cannot be constructed due to LoS constraints. Both algorithms take into account the constraints including phase factors and LoS constraints. Simulation results conducted using OPNET demonstrate the effectiveness of the proposed algorithms.

Equipping satellites with computing resources to ensure efficient mission completion has become a pivotal trend in multi-layer satellite networks (MLSNs). The uneven spatial distribution of missions and computing resources across satellites necessitates advanced scheduling of communication and computing resources through satellite collaboration. However, the intricate interactions between communication and computing resources, the dynamic mission arrivals and computing resources, and the difficulty of collaboration across different layers in MLSNs present significant challenges for effective scheduling. This paper proposes a collaborative scheduling framework for low Earth orbit (LEO) and medium Earth orbit (MEO) satellites to support communication-computing integration in the MLSN. To adapt to network dynamics, we introduce a federated aggregation matrix and propose an intelligent MEO-LEO collaborative scheduling algorithm that optimizes the decision-making process under uncertain mission arrivals. Additionally, we design a distributed LEO-LEO collaborative scheduling algorithm that leverages the synergy between inter-satellite communication and computing resources to enhance scheduling capabilities and create communication-computing resource chains that meet mission requirements. Extensive simulations demonstrate that our proposed collaborative scheduling framework significantly enhances the scheduling capability of the MLSN.

The network topology is a typical element that affects the network capacity of low Earth orbit (LEO) mega-constellations. Considering the relative relationship between satellites, most mega-constellations with inclined orbits adopt the classic torus-like topology in which each satellite establishes four inter-satellite links (ISLs) with neighboring satellites. However, do we need so many ISLs to achieve the required capacity? In this paper, we provide an in-depth capacity analysis for quasi-torus topologies in which each satellite is connected by fewer than four ISLs. Based on the topological regularity and constellation scale, we first introduce the models of typical quasi-torus topologies. Considering that all the satellites are not symmetrical, we divide a quasi-torus topology into the same regions exhibiting axial symmetry. Combining the uniform traffic distribution and the symmetry of each region, we derive the closed-form formulae of network capacity by determining the maximum traffic load among ISLs. The formulae provide insights into the impact of ISL distribution and the constellation scale. Simulation results validate the theoretical analysis and show that the quasi-torus topology with fewer ISLs can achieve high capacity comparable to the torus-like topology. The theoretical analysis obtained is instrumental to the topology design of mega-constellations.

Coordinated multi-point (CoMP) is a promising technique to ensure timely and reliable communications. However, flying access point (FAP) CoMP in aerial disaster emergency networks (ADENs) relies on wireless fronthaul, which is unreliable due to performance loss compared to wired fronthaul. Moreover, FAP movement and the existence of no-fly regions (NFRs) may cause dynamic transmission distances between ground users (GUs) and FAPs. Thus, how to robustly ensure timely and reliable communications via FAP CoMP is an urgent problem in ADENs. In this paper, we introduce wireless-fronthaul-effective-factor (WFEF) to reflect the unreliability of FAP CoMP and analyze network availability (NA), which is defined as the probability that delay and reliability requirements of each GU are satisfied. Through analysis, we first reveal WFEF constraints that prevent CoMP from underperforming non-CoMP. Then, givenKGUs served byLFAPs over the same time-frequency resource, we provide conditionC : P(L−K+1)≫ 1 to ensure timely and reliable communications, where P is a function of system parameters including WFEF and NFRs, etc., and delay and reliability requirements. Finally, we derive that adoptingK= [L/2] alleviates NA degradation due to NFRs by balancing the bandwidth of GUs with spatial diversity and multiplexing gains of CoMP, which enhances the robustness of ADENs.

In this paper, we study the robust throughput capacity of multi-connectivity wireless networks when the network encounters zone node failures. In order to reveal the inherent relationship between the robustness of the network structure and the capability of wireless networks to carry information, robust throughput capacity, which is the product of the fraction of served source and destination (S-D) pairs, the number of S-D pairs and feasible throughput, is defined. It is shown that the robust throughput capacity is$\Theta \left ({{\sqrt {\frac {n}{k\log n}}}}\right)$for$\beta \gt 2$and$\Theta \left ({{\frac {1}{k\log n}\sqrt {\frac {n}{k\log n}}}}\right)$for$1 {\lt }\beta \leq 2$, where n is the number of nodes,$\beta $is the failure exponent and$k\:(\geq 1)$is the connectivity parameter representing the number of disjoint data paths between any two nodes. To balance the tradeoff between the throughput capacity and the robustness of the network structure, the feasible regions of connectivity parameters, which are limited by the failure exponent, are given for$\beta \gt 2$and$1\lt \beta \leq 2$, respectively. Correspondingly, the robust throughput capacity is$\Theta \left ({{\sqrt {\frac {n^{1-\gamma }}{\log n}}}}\right)$and$\Theta \left ({{\sqrt {\frac {n^{1-3\gamma }}{\log n}}}}\right)$, respectively, where$\gamma \in [0,1$) is the robustness exponent. These results can provide guidance for designing network protocols with fault tolerance in large-scale wireless networks.

Satellite-terrestrial networks (STNs) are emerging as a promising solution for provisioning comprehensive services, such as the Internet of Remote Things (IoRT) and remote sensing, within the realm of 6G wireless networks. Nonetheless, resource failures and the exigencies of diverse mission urgencies exacerbate the intricacies of resource scheduling in STNs, thus impeding the effective alignment of distinct mission requirements with dynamic resources. In light of these challenges, we first mathematically formulate the complex resource scheduling problem in STNs as a stochastic optimization paradigm, endeavoring to maximize the number of successfully accomplished missions. Subsequently, we conceptualize the resource evolution to delineate scheduling dynamics, encompassing potential contingencies of resource discontinuities. Next, we propose an innovative hierarchical deep learning-based mission-driven resource scheduling (HDL-MDRS) algorithm, aimed at optimizing resource collaboration and reconfiguration to amplify network performance within the dynamic ambits characterized by resource disruptions. The HDL-MDRS algorithm achieves a coarse-grained alignment of diverse mission requirements with multidimensional resources. It enhances overall mission fulfillment and network resource utilization efficiency through fine-grained collaboration and reconfiguration among satellites, both within and across different clusters. Notably, the simulation findings substantiate the effectiveness of the HDL-MDRS algorithm, effectively ensuring the requirements of different types of missions in case of the unforeseen resource failures, orchestrated through efficient resource collaboration and on-demand reconfiguration.

The channel characteristics of immersed tunnels play a crucial role in the design and optimization of electronic toll collection (ETC) communication systems in such environments. Due to the lack of channel characteristics for all-metal immersed tunnel environments, this paper conducts an in-depth study on channel measurement, channel modeling, and optimization of ETC systems within all-metal immersed tunnels. Using a channel measurement system based on the universal software radio peripheral (USRP), this study extracts multidimensional channel characteristic parameters from the measured data, such as path loss (PL), shadow fading (SF), K-factor (KF), root mean square delay spread (RMS DS), power angular spectrum (PAS), and azimuth angle spread (ASA). The unique characteristics of the all-metal tunnel are analyzed in comparison to tunnels made of other materials. To develop a comprehensive all-metal immersed tunnel channel model meeting the quasi-deterministic radio channel generator (QuaDRiGa) requirements, we employed ray tracing (RT) methods to supplement angle domain parameters. Subsequently, we used this channel model to aid in the design and optimization of ETC systems, addressing transaction failures and erroneous transactions within all-metal immersed tunnels by establishing optimization targets and constraints, resulting in optimal height, tilt angle, transmission power, and beamforming. The findings of this research not only enrich the channel scenarios in QuaDRiGa but also provide practical guidance for improving the performance of ETC systems.

The inter-layer connection planning strategy of the multi-layer satellite mega constellations is a key technology to guarantee reliable and rapid transmission for various traffic demands. However, satellites deployed at different orbital heights cause more complicated layer-relative motions and frequently intermittent satellite connections. How to plan inter-layer links (ILLs) to guarantee high-throughput communication and provide reliable satellite relays is extremely challenging. In this paper, we leverage the satellite regular trajectories of each layer to design a hierarchical planning architecture, which consists of two phases, critical satellite selection and dynamic ILL planning. In the first phase, critical satellites are selected by traffic load and delay requirements, and they can connect with other layers to search for efficient relays. Considering the high dynamic motion between layers, we further propose an ILL planning problem between critical satellites and relays to maximize throughput and obtain robust ILLs. To tackle the proposed problem in large spatio-temporal scales, we propose a multi-agent learning-based ILL planning strategy, which can adjust switching directions based on the orbit relative position from critical satellites to relay satellites of various layers and obtain efficient ILLs. Simulation results illustrate that the optimal ILL number is less than 1/3 of its layer scale, and the proposed strategy can enhance throughput by 14.6%, and reduce the switching rate by 60.9% compared to state-of-the-art baseline algorithms.

Disasters severely damage cellular network infrastructures, weakening network communication service capability (CSC) and impeding post-disaster efforts. Therefore, evaluating network ability to resist disasters and recovering CSC are crucial. In this paper, we introduce a novel metric, network capacity scalability (NCS), defined by spatial throughput (ST) and its standard deviation to characterize CSC in disasters. By revealing the impact of disasters on NCS, network resistance to disasters can be reflected. Specifically, a critical disaster intensity (CDI) is derived, below which the effect of disasters on NCS is negligible and networks are disaster-resistant. However, NCS rapidly deteriorates once CDI is exceeded, necessitating recovery strategies. In response, we design a CSC compensation strategy where uncrewed aerial vehicle access points (UAPs) are supplemented, and a critical UAP density maximizing NCS is provided. Notably, compared to ST, NCS can more promptly reflect CSC deterioration, enabling rapider strategy implementation. Moreover, we also demonstrate that fluctuating burst service superlinearly worsens NCS, revealing the network’s poor tolerance to burst service. In this light, we propose a coverage adjustment strategy for terrestrial base stations and UAPs. Simulation results show that the negative effect of burst service can be significantly mitigated, which verifies the effectiveness of the proposed strategy.

Integrating aerial base stations (ABSs) with terrestrial base stations (TBSs) represents a promising architecture for future networks. However, challenges arise from the ABS mobility and complex interference, leading to degradation in the coverage performance, including both the average coverage quality and coverage stability. To address these challenges, we investigate the average coverage quality and coverage stability via the first- and second-order statistical properties of network spatial throughput, respectively. Our findings reveal that the inappropriate ABS deployment, especially the antenna beamwidth, causes the average coverage quality deterioration due to the co- and cross-layer interference surge induced by the overlapping coverage between ABSs and TBSs. Additionally, coverage stability experiences degradation due to the ABS mobility, particularly exacerbated by factors such as the large deployment density and circling radius as well as the small flight height and antenna beamwidth of ABSs. Moreover, it is demonstrated that there exists an optimal ABS antenna beamwidth maximizing the coverage performance. On this account, we propose an antenna beamwidth optimization algorithm as well as a time-efficient but low performance loss alternative antenna beamwidth optimization strategy, aimed at mitigating the overlapping coverage-induced interference and reducing the ABS mobility-induced impact, both of which are validated through numerical results.

Network control, including mobility management, resource management and e.t.c., plays a vital role in maintain the effectiveness and reliability of multi-layer low-earth orbit megaconstellations (MLMCs). One of the critical issues in satellite network control is how to choose the position of controller to achieve the optimal state of satellite network control structure with the least number of control nodes, so that any access satellite in the network can reach the control satellites in J hops at most and realize seamless coverage of the MLMCs by the control satellites without overlapping. This paper proposes a seamless coverage control structure to improve the temporal effectiveness of controlling signaling distribution in MLMCs. Specifically, this paper firstly analyzes the influence of the shape and size of satellite beam coverage on the network control structure. Based on the above analysis, the existence conditions of seamless network control structure are deduced through the design of satellite beam inclination angle, where any access satellite can reach the control satellites in J hops at most. Finally, taking access and mobility management function (AMF) as an example, we apply the placement of control units to the analysis of intersatellite handover strategy, and find that compared with AMF placed on ground stations and on middle-earth orbit satellites, the handover delay of the proposed scheme is reduced by 40.78% and 13.24%, respectively.

Aerial disaster emergency networks (DENs) are of great potential to provide instant and long-duration emergency services in disasters by deploying fixed-wing unmanned aerial vehicle (UAV)-mounted flying access points (FAPs). However, the inherent hovering feature of fixed-wing UAVs may lead to frequent handovers and unstable wireless links between ground users (GUs) and FAPs, which degrades network availability (NA). Especially, NA is the performance metric for DENs, which is defined as the probability of satisfying each GU’s delay and reliability requirements. This study evaluates NA in aerial DENs with FAP coordinated multi-point (CoMP) and GU grouping, where no-fly regions (NFRs) exist in disasters. Specifically, FAP CoMP eliminates frequent handovers, stabilizes wireless links, and provides spatial diversity and multiplexing gains. GU grouping provides frequency multiplexing gain by scheduling bandwidth for GUs. Consequently, the interference among GUs can be managed, and GUs’ delay-constrained reliability can be improved in disasters, where the number of GUs is much greater than that of FAPs. Our results reveal that there exists an optimal number of GU groups K∗, which can maximize NA, given the number of FAPs L. The reason is that K∗balances GUs’ bandwidth with spatial diversity and multiplexing gains. Moreover, NFRs are shown to seriously degrade NA without K∗. Therefore, we optimize the number of GU groups and obtain an approximate optimal number of GU groups ${\hat K^{\ast}} = \left\lceil {\frac{L}{2}} \right\rceil $. Simulation results show that adopting ${\hat K^{\ast}}$ can enhance NA by more than one fold and alleviate NA degradation due to NFRs.

In this paper, we aim to minimize the energy consumption of cellular networks by optimizing communication resources. However, due to the nonlinear characteristic of power amplifier (PA) during the process of wireless signal amplification at base stations (BSs), energy will be dissipated exponentially as the transmit power increases. Consequently, the existing static PA efficiency-based resource allocation method will lead to extra energy consumption in cellular networks. To address the problem, a stochastic long-term energy consumption minimization problem considering nonlinear PA is established. Aided by Lyapunov optimization theory, we prove that the original stochastic long-term energy consumption minimization problem can be equivalently transformed into two short-term deterministic subproblems, i.e., traffic flow control subproblem and resource allocation subproblem. Leveraging this insight, a traffic flow control rule determined by energy queue and traffic data queue is proposed, which could reduce energy dissipation by matching traffic flow with transmit power in nonlinear PA. Then, a second-order cone programming-based resource allocation method is proposed to optimize transmit power further, in which a time-sharing formulation and a first-order Taylor expansion are used to relax discrete subcarrier allocation variables and approximate nonlinear power consumption as linearity, respectively. Simulation results show that our proposed algorithm can effectively reduce average energy consumption of the cellular network, especially when BS density is large.

In a low earth orbit (LEO) mega-constellation, each satellite establishes four inter-satellite links (ISLs) with its neighboring satellites to provide high capacity. However, is it necessary to establish so many ISLs in every mega-constellation? This paper investigates redundant ISLs caused by the structure asymmetry. We illustrate the existence of redundant ISLs in a mega-constellation with$P$orbital planes and$S$satellites per plane when$P\neq S$. According to the relative relationship between$P$and$S$, we determine redundant ISLs in mega-constellations with different scales. Then we prove a proposition ensuring that the capacity experiences only a minor decrease after removing redundant ISLs. Guided by the proposition, we propose an asymmetric redundant ISL removal (ARIR) algorithm to remove the maximum number of redundant ISLs while achieving the expected capacity of the complete four- Islconnecting mode. Simulation results validate that our proposed approach can remove at most 25% ISLs and achieve the expected capacity in the classical Starlink constellation.

Mega satellite networks consisting of hybrid orbit satellites play an important role in the sixth generation (6G) wireless networks. Location management (LM) can ensure service continuity for mobile users and is one of the key technologies in mega satellite networks. Moving satellites (e.g., LEO, MEO)1 and users require repeated location updates, which creates the challenge problem of significant LM overhead. In this paper, we propose a novel dynamic hierarchical LM scheme based on the virtual attachment point (VAP). The approach utilizes MEO satellites to provide LM, and divides the direct association between the user and the satellite into two independent steps, including the association between the user and the VAP, and the association between the satellite and the VAP. With this mechanism, the user’s location no longer needs to be updated due to satellite movement. Besides, a dynamic adaptive location area (LA) scheme is proposed to update the user’s location. The scheme can reduce the update frequency of high-speed mobile users, keep the number of paging satellites controllable, and not increase drastically with the constellation scale, thus reducing the paging overhead. The simulation results demonstrate that the proposed LM technology solution can reduce the total LM overhead by at least 40.6%.

The limited energy and computing resources of unmanned aerial vehicles (UAVs) hinder the application of aerial artificial intelligence. The utilization of split inference in UAVs garners attention due to its effectiveness in mitigating computing and energy requirements. However, achieving energy-efficient split inference in UAVs remains complex considering of various crucial parameters such as energy level and delay constraints, especially involving multiple tasks. In this paper, we present a two-timescale approach for energy minimization in split inference, where discrete and continuous variables are segregated into two timescales to reduce the size of action space and computational complexity. This segregation enables the utilization of tiny reinforcement learning (TRL) for selecting discrete transmission modes for sequential tasks. Moreover, optimization programming (OP) is embedded between TRL’s output and reward function to optimize the continuous transmit power. Specifically, we replace the optimization of transmit power with that of transmission time to decrease the computational complexity of OP since we reveal that the energy consumption monotonically decreases with increasing transmission time. The replacement significantly reduces the feasible region and enables a fast solution according to the closed-form expression for optimal transmit power. Simulation results show that the proposed algorithm can achieve a higher probability of successful task completion with lower energy consumption.

Precise parameter estimation is essential for developing effective channel models. However, jointly estimating the various channel parameters is a challenging problem as these parameters can only be extracted from comprehensive data collected during massive measurement activities. Traditional algorithms suffer from limited estimation accuracy due to high mobility and signal correlation. In this paper, we propose a low-complexity full-dimension parameter extraction algorithm. The method extends the forward-backward spatial smoothing technique to estimate two-dimensional (2-D) angle information in the uniform planar array (UPA), enhancing aperture size and providing higher resolution for coherent signals. Then, we provide a Doppler compensation technique to prevent the deterioration in complex amplitude estimation quality caused by large Doppler frequency in high-speed mobile environments. Moreover, the proposed algorithm is also applied to the fifth-generation new radio (5G-NR) system. To remedy power leakage in the channel impulse response (CIR), we propose an eigenvalue-assisted sampling point selection scheme. This scheme preserves sampling positions abundant in channel information to reduce computational complexity and eliminate most noise. Simulation and field tests show that the proposed algorithm achieves better performance with lower computational complexity compared to the existing algorithm.

In this paper, for a general full-duplex wireless MapReduce distributed computing network, we investigate the minimization of the communication overhead for a given computation overhead. The wireless MapReduce framework consists of three phases: Map phase, Shuffle phase and Reduce phase. Specifically, we model the Shuffle phase into a cooperative X network based on a more general file assignment strategy. Furthermore, for this cooperative X network, we derive an information-theoretic upper bound on the sum degree of freedom (SDoF). Moreover, we propose a joint interference alignment and neutralization (IAN) scheme to characterize the achievable SDoF. Especially, in some cases, the achievable SDoF coincides with the upper bound on the SDoF, hence, the IAN scheme provides the optimal SDoF. Finally, based on the SDoF, we present an information-theoretic lower bound on the normalized delivery time (NDT) and achievable NDT of the wireless distributed computing network, which are less than or equal to those of the existing networks. The lower bound on the NDT shows that 1) there is a tradeoff between the computation load and the NDT; 2) the achievable NDT is optimal in some cases, hence, the proposed IAN scheme can reduce the communication overhead effectively.

We study the data offloading problem in space-air-ground integrated networks (SAGINs) by jointly optimizing task scheduling and power control to balance the total energy consumption and mean makespan. We consider a mixed integer nonlinear programming problem to minimize a normalized weighted combination of these two conflicting objectives. We first propose an approximation algorithm to find a high-quality solution, which is shown to be at most$\frac{1}{2}$from the optimum to this problem for given power allocation. We further show that optimal power allocation can be obtained in closed form under the assumption that satellite-ground links have low signal-to-noise ratio (SNR). Thus, the proposed approximation algorithm can be directly utilized to obtain a constant-factor solution to the studied problem in low-SNR scenarios. To extend our solution to more general scenarios, we further propose an efficient hybird algorithm based on a genetic framework. Our simulation results demonstrate the near-optimality and correctness of the proposed algorithms, and they unveil the interplay between total energy consumption and mean makespan in SAGINs as well.

To deal with the challenges in mobility management of mega low-Earth-orbit (LEO) satellite constellations with long management delays and high signaling overheads, especially under the existing fixed and limited deployments of ground mobility management entities, the cooperative mobility management mode of medium-Earth-orbit (MEO) satellites and ground stations (GSs) has become an attractive tendency. In this paper, considering with the global non-uniform user distribution and constrained satellite storage resources, we propose a dynamic satellite-ground integrated mobility management strategy (DSG-MMS) to cope with the relative mobility among users, GSs, and satellites, which can dynamically decide the optimal GS/MEO management node with the minimal handover and migration delays. Specifically, the DSG-MMS optimization problem is modeled as distributed Markov decision processes, and a reinforcement learning (RL)-based management node selection method is presented to solve them, where each LEO satellite agent dynamically decides its own management node. To further implement the RL algorithm on the resource-limited LEO satellite agents, a novel tensor-based RL algorithm for DSG-MMS is proposed by means of the streamed low-rank tensor decomposition, where only the small-sized core tensor and factor matrices are kept and updated in the strategic optimization so as to realize low storage and computing overheads as well as fast convergence. We perform simulations for the proposed DSG-MMS with parameter configurations of actual Telesat, Kuiper, and Starlink satellite systems to evaluate the mobility management delay and overhead performances as well as the required satellite storage size for mobility management. Moreover, a case study and an architectural comparison are given to demonstrate the superiority of the proposed DSG-MMS than existing methods.

Unmanned aerial vehicles mounted aerial base stations (ABSs) are capable of providing on-demand coverage in next-generation mobile communication system. However, resource allocation for ABSs to provide continuous coverage is challenging, since the high dynamic of ABSs and time-varying air-to-ground channel would result in channel state information (CSI) mismatch between resource allocation decision and implementation. In consequence, the coverage of ABSs is discontinuous in spatial-temporal dimensions, i.e., the variance of user rate between adjacent time slots is large. To ensure the coverage continuity, we design a resource allocation method based on deep reinforcement learning (RDRL). Capable of adaptively tuning neural network structures, RDRL could satisfy coverage requirements by jointly allocating subchannels and power for ground users. Meanwhile, the temporal channel correlation is taken into account in the design of reward function in RDRL, which aims to alleviate the influence of CSI mismatch between method decision and implementation. Moreover, RDRL can apply a pre-trained model of previous coverage requirement to current requirement to reduce computation complexity. Experimental results show that the rate variance of RDRL can be reduced by 66.7% and spectral efficiency of RDRL can be increased by 34.7% compared with benchmark algorithms, which ensures the coverage continuity.

Aerial base stations (ABSs) flying in the air inject wireless networks more flexibility and agility beyond ground base stations (GBSs) to respond to spatio-temporal coverage demand. To fully unlock the potential of ABSs, a high-capacity, flexible and dynamic wireless backhaul provision is necessitated and integrated access and backhaul (IAB) architecture comes into the picture. In this paper, we investigate the availability of IAB architecture in dynamic aerial-terrestrial networks in terms of coverage probability (CP) and further explore the feasible region of IAB to promote aerial-terrestrial coverage enhancement. Specifically, the results show that the capability of IAB to promote aerial-terrestrial coverage enhancement would be diminished with the increases of ABSs flight speed and GBS density. The reason is found that relying on fixed GBSs to provide dynamic backhaul for flying ABSs would come with frequent handovers, which degrades network CP. On this account, to make IAB adapt to dynamic aerial-terrestrial networks, a mobility-adaptable IAB scheme is proposed where a distance thresholdLpis set to alleviate the negative effect caused by handovers. WithLpoptimized, the coverage performance of dynamic aerial-terrestrial IAB network is shown to be increased, especially in dense GBS regime.

This paper focuses on leveraging coordinated multi-point (CoMP) to improve the sum downlink rate in air-terrestrial integrated networks. Considering the CoMP transmission, the problem of maximizing the sum downlink rate possesses two-timescale characteristics, i.e., trajectory planning varies of aerial base station in a long-timescale manner whereas CoMP resource allocation varies in a short-timescale manner. To solve it, a two-timescale parallel framework is proposed. Specifically, the initial two-timescale problem is decomposed into multiple single-timescale subproblems via the alternating direction method of multipliers and then all subproblems are parallelly solved. Furthermore, to resolve the high complexity arising from continuous-discrete hybrid variables in each single-timescale subproblem, we propose an online algorithm that embeds optimization programming (OP) into deep reinforcement learning (DRL). Particularly, discrete CoMP cluster variables in sequential time slots are optimized with DRL to eliminate the need for large-scale combinatorial optimization. Then, the continuous resource allocation variables in each time slot are solved by OP, which is embedded between the output and reward of DRL, to speed up the convergence. Simulation results show that the proposed algorithm achieves less than a 7% total downlink data gap while decreasing the computation time by more than an order of magnitude compared with numerical optimization.

In this paper, we consider an unmanned aerial vehicle (UAV)-assisted post-disaster rescue scenario, where UAV-mounted aerial base stations (ABSs) compute tasks related to post-disaster rescue operations while also providing communication services to ground users (GUs). With the limited computation capacity of ABSs, we aim to minimize the task computation queuing delay and ensure the GU communication rate by jointly optimizing ABS-GU association, task offloading, and ABS trajectory. The problem is formulated as a mixed-integer nonlinear program, and a solution is proposed by integrating Lyapunov optimization and actor-critic based deep reinforcement learning. We utilize a model-based successive convex approximation technique in a critic module to acquire an accurate evaluation of actor module output. Simulation results demonstrate the effectiveness of the proposed approach in reducing the task computation queuing delay.

Satellite telemetry, tracking, and command (TT &C) operations are critical to ensuring the normal operation of mega satellite networks. However, the distribution and number of ground stations are limited, making that the existing TT &C mission scheduling methods are difficult to satisfy the TT &C requirements in mega satellite networks, resulting in low TT &C mission completion rates. In this paper, we first construct a space-ground integrated distributed TT &C mission scheduling frame-work utilizing the broad coverage characteristics of geostationary earth orbit (GEO) satellites. Then, we explore the similarity in the TT &C mission scheduling process among adjacent ground stations or GEO satellites, that the TT &C missions execute within the visible time window between satellites and TT &C antennas. Building on this, we share similar features of mission scheduling between the stations through federated learning (FL) and capture the temporal features of TT &C mission scheduling using deep reinforcement learning (DRL) at each station. Therefore, we propose a federated deep reinforcement learning (FDRL) assisting TT &C mission scheduling algorithm in mega satellite networks to enhance TT &C mission completion rates. Finally, the effectiveness of the FDRL algorithm is verified through simulation experiments. Compare to the traditional space-ground integrated algorithm, the FDRL algorithm improves the TT&C mission completion rate by about 26.5 %.

Satellite telemetry, tracking, and command (TT&C) technology plays a critical role in maintaining stable operation and emergency scheduling in mega-satellite networks. However, due to the high-speed movement of satellites, the intermittent connection between the satellite and the ground station creates a dynamic and complex visibility period, leading to temporal resource utilization conflicts during the TT&C mission scheduling process. These conflicts can further exacerbate real-time response for emergency TT&C missions in large-scale satellite networks. To address this issue, we explore the time-aware dynamic characteristics of emergency TT&C missions and their impact on the scheduling time of routine missions. Then we propose an efficient method for reducing resource utilization conflicts based on Dynamic Priority and Minimum Disturbance (DPMD). Building on this method, we formulate the scheduling process of emergency TT&C missions as a reinforcement learning decision problem suitable for dynamic real-time environments. Additionally, we introduce the DRLETMS algorithm (Emergency TT&C Missions Scheduling Algorithm based on Deep Reinforcement Learning) to achieve faster mission scheduling strategies in highly dynamic environments. Simulations demonstrate the superior efficiency of our proposed algorithm in large-scale scenarios compared to typical heuristic algorithms. Furthermore, we examine the impact of various ground station distributions on the real-time TT&C performance of satellite networks under the same satellite scale, which can provide theoretical guidance for designing mega-satellite TT&C networks in the future.

As the type and volume of traffic increase in mega satellite constellations (MSCs), it is still a challenge to use limited resources to improve the traffic completion rate while providing differentiated services for different traffic. Rational allocation of resources depends on the satellite's accurate prediction of non-uniform traffic from the ground. Considering that centralized training methods require high resource occupancy of traffic data transmission and that numerous satellites need to adjust the prediction network frequently when the service area changes, we propose a federated learning-based traffic management strategy (FLTM). We first classify the traffic according to performance requirements such as delay and connectivity, and create network slices to provide differentiated services. Then we train a unified prediction network between multiple service areas in a distributed manner based on federated learning, reducing the frequency of satellites adjusting the prediction network while sharing the traffic information of these areas. To capture spatial and temporal features of traffic, we also propose a prediction framework combining the densely connected convolutional neural network with the Long Short-Term Memory network. Finally, we adopt traffic prediction results to more reasonably allocate resources among slices. Real traffic data verify the accuracy of the proposed prediction framework and simulation results validate that FLTM can provide differentiated services for different traffic and improve the traffic completion rate of MSCs.

Space-air-ground integrated network (SAGIN) aided multi-tier computing network can be modelled as a dynamic and predictable network. For the SAGIN aided multi-tier computing network, the traditional time expanded graph (TEG) can only jointly model communication and storage capability, as well as one computing function for one mission flow within one same node. However, for multiple computing functions for one mission flow in one same node, TEG is not applicable. In this paper, for SAGIN aided multi-tier computing networks, we propose an multi-functional time expanded graph (MF-TEG) to jointly model the communication, storage, and computation capability of nodes where multiple computing functions for one mission flow in one same node can be characterized. Specifically, based on TEG, for each node having computation functions, we adopt the virtual network graph (VNG) to virtually decompose it into three virtual components: sub-virtual node, virtual computing nodes, and virtual transmission links, where the virtual computing node provides the computing function. We characterize the amount of data flow on each link and also present four kinds of fundamental constraints for the data flow in the MF-TEG for joint communication, storage, and computing function: computation capacity constraints, communication capacity constraints, storage capacity constraints, and flow conservation constraints. We provide one example of using MF-TEG to model the SAGIN aided multi-tier computing network with a service function chain (SFC), where satellite nodes could provide communication, storage, and multiple computing functions for one mission flow in one same node, where TEG is not valid. Furthermore, simulation results show that for SAGIN aided multi-tier computing network, the proposed MF-TEG model significantly outperforms the snapshot graph-aided VNG (SSG-aided VNG) model. The reason for that is only communication and computation capability is considered by the SSG-aided VNG model, while storage capability is not exploited.

Space information network (SIN) can provide global coverage in 6G network. Furthermore, SIN with network function virtualization (NFV) can achieve flexible deployment of network functions and improve the utilization of resources. In SIN with NFV, network functions can be virtualized into virtual network functions (VNFs). However, in SIN with NFV, the mission flow must satisfy the service function chain (SFC) constraint, i.e., the mission flow must be processed by all VNFs in the predefined order. Furthermore, each VNF can be deployed on multiple physical nodes. Moreover, different kinds of services may have the diverse quality of service (QoS) requirements. In this paper, we investigate the joint VNFs deployment and flow routing strategy (VNF-R) to maximize the number of completed missions with the guaranteed end-to-end latency under SFC constraints in time-varying SINs. Specifically, the problem can be formulated as a mixed integer linear programming (MILP) problem, which is proved to be NP-hard. In order to effectively solve the problem, we propose a novel low-complexity near-optimal penalty successive upper bound minimization rounding LP relaxation iterative rounding (PSUM-R-LRIR) algorithm. The simulation results show that the PSUM-R-LRIR algorithm can achieve near-optimal performance, and our proposed VNF-R scheme significantly outperforms the fixed VNF deployment scheme.

The expansion of satellite applications induces the formation of the multi-domain satellite system (MDSS) containing multiple domains with specific applications such as earth resource remote sensing and the Internet of remote things. Resource management is pivotal in enhancing the scheduling capability of the MDSS. However, this is challenging since the dynamic buffer space and communication opportunity, as well as the uncertain data traffic, exacerbate the difficulty of matching satellite resources with data traffic. Moreover, the coexistence of resource competition and collaboration across domains aggravates the dilemma of cross-domain collaboration. In this paper, we propose a hierarchical cross-domain collaborative resource management framework that can flexibly allocate the mission data through local intra-domain and global cross-domain scheduling. Then, to match the uncertain demands of missions with dynamic and limited resources, we propose a multi-agent reinforcement learning-based resource management method to guide collaboration for multi-satellite data carry-forward in a domain. Further, considering resource competition and collaboration in MDSS, we propose a domain-satellite nested matching game data scheduling algorithm to achieve pair-wise stable collaboration of cross-domain satellites. The simulation results indicate that the proposed algorithm improves the amount of offloaded data by 64.4% and 12.7% compared to the non-collaborative and the non-cross-domain schemes, respectively.

The new generation satellite network is a comprehensive service system that can provide communication, observation, navigation, and other functions. Typically, a system with a specific service function is defined as a domain and the supply and demand relationship of resources, such as communication, storage, and energy resources, are unbalanced among domains. Therefore, it is nontrivial to accurately characterize the cross-domain resource state and coordinate the data transmission policy of interrelated satellites from different domains to make full use of the resources in each domain aiming at improving the resource utilization ratio (RUR) of the whole network. To this end, this paper investigates the cross-domain resource scheduling (CDRS) problem in satellite networks aiming at maximizing the total amount of downloaded transmitted data. We start with the orbit motion law of satellites and construct the satellite orbit feature matrix of each domain to design a hierarchical sparse resource representation (HSRR) scheme to accurately characterize the resource state of each domain in real-time with low complexity. Further, based on the HSRR, we develop a cross-domain dynamic multi-resource scheduling algorithm to solve the CDRS problem by introducing the advantage factor and policy-oriented hyper-parameter. The algorithm can make full use of the resources in each domain to achieve efficient CDRS by dynamically adjusting the data transmission policy of satellites among domains. Simulation results show that the greater the service demand and resource difference among domains, the more significant the performance improvement brought by CDRS, and compared with the existing algorithms, the RUR and resource utilization efficiency have been significantly improved.

Terahertz (THz) wireless communications are commonly regarded as one of the key technologies of 6G communication. Combined with THz, the newly exploited physical layer transmission dimension orbital angular momentum (OAM) that multiplexes a set of orthogonal modes on the same frequency channel, can unleash its potential in achieving high spectrum efficiency. Currently, most of the research on radio OAM communications focus on the point-to-point scenario in microwave and millimeter-wave (mmWave) bands. In this paper, we propose a uniform circular array (UCA)-based THz multi-user OAM (MU-OAM) communication system including downlink and uplink transmission schemes that simplifies the signal detection to the despiralization and amplitude detection (AD). A salient feature of the proposed MU-OAM communication scheme is lower computational complexity than the traditional MU-MIMO scheme without sacrifacing bit error rate (BER) and achievable sum rate performances, which is validated by mathematical analysis and numerical simulations.

Orbital angular momentum (OAM) radars are able to estimate the azimuth angle and the rotation velocity of multiple targets without relative motion or beam scanning. Moreover, OAM wireless communications can achieve high spectral efficiency (SE) by utilizing a set of information-bearing modes on the same frequency channel. Benefitting from the above advantages, in this paper, we design a novel radar-centric joint OAM radar-communication (RadCom) scheme based on uniform circular arrays (UCAs), which modulates information signals on the existing OAM radar waveform. In details, we first propose an OAM-based three-dimensional (3-D) super-resolution position estimation and rotation velocity detection method, which can accurately estimate the 3-D position and rotation velocity of multiple targets without beam scanning. Then, we derive the posterior Cramér-Rao bound (PCRB) of the OAM-based estimates and, finally, we analyze SE of the integrated system. To achieve the best trade-off between imaging and communication, the transmitted integrated OAM beams are optimized by means of an exhaustive search method. Both mathematical analysis and simulation results show that the proposed radar-centric joint OAM RadCom scheme can accurately estimate the 3-D position and rotation velocity of multiple targets while ensuring the SE of the communication receiver, which can be regarded as an effective supplement to existing joint RadCom schemes.

For the space information network (SIN) with network function virtualization (NFV), a large number of active nodes deployed with virtual network functions (VNFs) impose heavy coordination overhead. In this paper, we investigate the trade-off between the network maximum flow and coordination overhead under the service function chain (SFC) constraints. Specifically, we propose the group sparse joint VNFs deployment and flow routing strategy (GS-VNF-R) to strike the trade-off between the network maximum flow and coordination overhead. Although the GS-VNF-R scheme can be formulated as a convex problem, for a large-scale SIN, solving the GS-VNF-R problem by traditional convex optimizations imposes a heavy computation burden. In order to reduce the time complexity, we propose a novel optimal low-complexity block-successive upper-bound minimization method of multipliers based group sparse (BSUM-M-GS) algorithm, which can converge to the global optimal with much less complexity. Simulation results show that for some scenarios, 60% of active nodes can be saved by using the proposed GS-VNF-R scheme without any performance loss compared to the full cooperation scheme, which results in significant cooperation overhead reduction. Moreover, simulation results demonstrate that our proposed BSUM-M-GS algorithm can significantly reduce the complexity to the extent of 7 orders of magnitude for some scenarios.

In this paper, we study the impact of node cascading failures and network structure robustness on the capacity of wireless networks. Especially, in order to quantify how much information can be conveyed by wireless networks under node cascading failures, robust capacity is defined to capture the influence of the intensity of the initial failure nodes$m$and the connectivity parameter$k$on capacity. Note that increasing$k$could provide$k$disjoint data paths for any two nodes, thereby combating the cascading failure. Denoting the intensity of the nodes as$n$and$m=n^{\frac{1}{\beta}}$, it is shown that robust capacity$O\left(\sqrt{\frac{n}{k\log n}}\right)$can be achieved when the initial failure exponent$\beta > 2$. In contrast, robust capacity would converge to zero with increasing$n$when$1 < \beta\leq 2$since the data paths of most source-destination pairs are interrupted due to the cascading failures. Moreover, increasing the connectivity parameter$k$, although capable of enhancing the network structure robustness, is shown to degrade cascading failures and robust capacity.

Satellite-terrestrial networks (STNs) consisting of satellite segment and ground segment have been regarded as a desirable solution for 6G. Efficient cooperative resource scheduling strategies, which cover the cooperation in satellite segment for data relay and the cooperation between satellite segment and ground segment for data downloading, play a pivotal role in enhancing the system performance in STNs. Since the dynamic channel condition and energy feeding greatly influence the network status, cooperative resource scheduling should be adaptive to the future environmental fluctuation. In this paper, we model the cooperative resource scheduling problem in STNs as a resource limited Markov Decision Process (MDP). Considering the fact that satellites are unaware of future environmental status, the traditional static optimization solution is infeasible. Therefore, we propose a Deep Reinforcement Learning (DRL) based Cooperative Store-and-Relay Resource Scheduling Algorithm (CSR-RSA), where inter-satellite links are utilized to coordinate with intermittent satellite-ground links for improving the transmission performance of the network. By exploiting the proposed CSR-RSA, the well-trained neural networks can be obtained to generate the adaptive and cooperative resource scheduling strategy without the knowledge of future environmental status. Simulation results verify the effectiveness of the proposed algorithm compared with traditional algorithms.

As the spaceborne resources are heterogeneous and the satellite network topology changes constantly, various time-varying derivative graphs are designed to represent the data acquisition and delivery process in satellite-terrestrial integrated networks (STNs). However, the time complexity of resource allocation approaches based on assorted time-varying graphs is remaining obstinately exponential. Derived from the satellite vertical coverage feature, we design a more general relationship to involve a group of nodes in a hyperedge rather than the bilateral relationship between two nodes. In this paper, we propose a time-expanded hypergraph (TEH) to contract the adjacency matrix of the network topology. Based on the proposed TEH, the problem is formulated to minimize the consumption of the communication resource in the resource-limited STN while completing the same number of tasks. Since the problem is intractable by exhaustive search, we further propose a hybrid hyperedge and Lagrangian relaxation algorithm to perform optimal resource allocation through an oriented search for the feasible hyperedges to reduce the scale of searching. The simulation results validate that the proposed algorithm can effectively complete the tasks with lower time complexity.

Beamforming (BF) often requires the base station (BS) to master precise angle information from the uplink channel impulse response (CIR). Based on the three-dimensional (3D) wireless multipath channel in the fifth generation (5G) new radio (NR) system, we can easily obtain the channel frequency response (CFR) of the orthogonal frequency division multiplexing (OFDM) structure using the sounding reference signal (SRS). However, when CFR is converted into CIR by the inverse fast Fourier transform (IFFT), the phenomenon of leakage will occur. In this paper, we combine the time-delay and spatial domains to design an algorithm for analyzing ultra-high-resolution paths in the presence of coherent sources and energy leakage. And we theoretically expound the advantages of using leaked CIR instead of CFR to extract angle information. The experimental results show that the proposed algorithm can perfectly construct the power angle spectrum (PAS) and reconstruct the CIR in beam domain based on the data received by the uplink at the BS. The original CIR in beam domain received by the BS further verifies the correctness of our results.

The sixth-generation (6G) wireless communication system is expected to integrate communication, intelligence, sensing, positioning, control, and calculation to adapt to time critical, ultrareliable, and energy-saving data delivery, as well as accurate positioning of personnel and equipment, serving the Internet of Things (IoT). On the one hand, reconfigurable intelligent surface (RIS) can intelligently manipulate radio waves and is considered to be one of the candidate technologies for the 6G wireless communication. Hence, there are more and more surveys on RIS-assisted communications. On the other hand, the potential of RIS in positioning has attracted growing attention, and articles on RIS-assisted positioning have been blown out. Therefore, it is time to review this literature to understand the potential of RIS positioning, research status, and point out the direction for future research. This article first explains the working principle and channel model of RIS and summarizes some characteristics of RIS suitable for positioning. Then, we give a concise review and classification of existing RIS positioning research. Finally, we put forward our views on the future research challenges and attractive directions for RIS-aided wireless positioning technology.

The space–air–ground integrated network (SAGIN)-related issues are attractive in the sixth generation (6G) technologies, which facilitate the global coverage and seamless service. The cooperation of high altitude platforms (HAPs) and low-Earth orbit (LEO) satellites provides the remote area users with comprehensive coverage and service. In this work, we consider the cooperation of HAPs and LEO satellites in SAGIN to serve terrestrial users in the remote area for data collection and transmission. To deal with the periodical motion of LEO satellites, we employ the time expanding graph (TEG) to represent the multiple resources in SAGIN and depict task flow transmission processes. Based on TEG, we aim to maximize the total data received by the ground data processing center in a time horizon, considering multiple resource constraints and flow restrictions. The original problem is formulated in the form of mixed-integer linear programming, which is intractable to obtain the optimal solution by brute-force searching. To alleviate this intractability, we propose the Benders decomposition-based algorithm to obtain the optimal solution within an acceptable time complexity via iterations between the master problem and subproblem. Moreover, to further expedite the solution in large-scale systems, an acceleration algorithm is proposed by handling the master problem with an approximation algorithm and the subproblem with a unit-flow-based algorithm. Finally, simulations are conducted and the numerical results verify the efficiency of the proposed algorithms, and the effects of various network parameters are analyzed as well.

Low Earth orbit (LEO) satellite constellations have become a promising architecture to integrate with terrestrial networks for facilitating communications and Internet of Remote Things (IoRT) services through gateways. Nevertheless, different gateway locations may have various channel conditions and service demands due to differentiated atmospheric conditions, populations, and number of terminal devices required by IoRT services in different areas. Besides, the gateway placement scheme further affects the service coverage performance and the access performance of the network to service demands. Therefore, gateway placement plays a pivotal role in improving network capabilities in the integrated system of LEO satellites and terrestrial networks (ISoLS-TNs). Motivated by the aforementioned facts, in this article, we first formulate the gateway placement problem in ISoLS-TNs as a multiobjective optimization problem to maximize the total revenue of service data demand within coverage while minimizing the average access distance and the number of deployed gateways. In order to enhance network resource utilization and assure local information confidentiality, a distributed resource allocation (DRA) mechanism based on the alternating direction method of multipliers (ADMMs) algorithm is designed to calculate the total revenue of service data demand within coverage. Furthermore, we propose a genetic-based gateway placement algorithm with the ADMM for DRA. Finally, through massive simulations based on real data, we validate the effectiveness of the proposed algorithm in improving resource utilization and coverage performance of the network. In addition, the results also bring lights on the relationship between service data demand distribution and gateway location preference.

Compared with traditional observation satellites, agile earth observation satellites are capable of prolonging observation time windows (OTWs) for targets, which significantly alleviates observation conflicts, thereby facilitating imaging data collection. However, it also leads to more uncertainties in determining the start time to image targets within these longer OTWs for an agile satellite network (ASN) to collect imaging data. Furthermore, these collected data are offloaded only within short transmission time windows between data collectors and data sinks, thus resulting in a transmission scheduling problem. Toward this end, this paper investigates joint observation and transmission scheduling in ASNs, aiming at accommodating more imaging data to be collected and offloaded successfully. Specifically, we formulate the studied problem as integer linear programming (ILP) to maximize the weighted sum of scheduled imaging tasks. Then, we explore the hidden structure of this ILP and transform it into a special framework, which can be solved efficiently through semidefinite relaxation (SDR). To reduce computation complexity, we further propose a fast yet efficient algorithm by combining the advantages of the devised SDR method and a genetic algorithm with special population initialization. Finally, simulation results demonstrate that the proposed algorithm can significantly increase the weighted sum of scheduled tasks.

The network control plays a vital role in the mega satellite constellation (MSC) to coordinate massive network nodes to ensure the effectiveness and reliability of operations and services for future space wireless communications networks. One of the critical issues in satellite network control is how to design an optimal network control structure (ONCS) by configuring the least number of controllers to achieve efficient control interaction within a limited number of hops. Considering the wide coverage, rising capacity, and no geographical constraints of space platforms, this paper contributes to designing the ONCS by constructing an optimal space control network (SCN) to improve the temporal effectiveness of network control. Specifically, we formulate the optimal SCN construction problem from the perspective of satellite coverage factors, and apply geometric topology analysis to derive both the conditions for constructing the optimal SCN and the formulaic conclusions for SCN and MSC configurations (i.e., scale and structure). From numerical results, we investigate the tradeoff between network scale, the number of controllers, and control delays in several satellite network control scenarios, to provide guidelines for the MSC control. We also design the optimal SCN for an existing MSC system to demonstrate the effectiveness of the proposed ONCS.

In this paper, we investigate the maximum flow routing strategy with the service function chain (SFC) constraints in the space information networks (SINs), where a SFC consists of a specific ordered sequence of service functions, and the mission flow must go through these functions in a predefined order. The time-varying SIN is modeled by the time-expanded graph (TEG). We formulate the maximum flow routing strategy problem with the SFC constraints as a linear programming (LP) problem. Furthermore, for a large-scale SIN, as the complexity of solving the LP problem is still very high, we propose a novel low-complexity SFC-constrained graph theory based (SFC-GT) algorithm. Specifically, we formulate this problem as one special single commodity maximum flow problem, where this flow must satisfy the SFC constraints. We first define the SFC-constrained residual network and the SFC-constrained augmenting path. Afterwards, we iteratively search the SFC-constrained augmenting path and update the SFC-constrained residual network. Simulation results demonstrate our proposed SFC-GT algorithm can achieve near-optimal performance with much less complexity.

With the growth of global communication service demand, constructing large-scale satellite networks has become the future development trend for improved system performance. However, due to the high-speed orbit motion of satellites, the connection relationship of network topology (CRNT) is complex and changeable. This phenomenon is particularly pronounced in large-scale satellite networks and the existing representation schemes of CRNT for large-scale satellite networks have high space complexity. Therefore, we explore the sparse characterization of the inter-satellite visibility matrix and propose an integrated sparse space-time resource representation (ISST-RR) scheme to efficiently characterize the satellite network communication resources with low complexity from the dimension of time and space. On the basis of the proposed ISST-RR scheme, we further propose a multi-agent reinforcement learning with sparse representation based resource scheduling (MARLSR-RS) algorithm to obtain the optimal resource scheduling policy. Simulations demonstrate the efficiency of the proposed MARLSR-RS algorithm in terms of communication resource utilization. In addition, we investigate the impact of several typical netwrok parameters, e.g., transmission rate of observation satellites on network performance, which can provide a theoretical guidance for system design.

The aerial access network (AAN) is a significant issue in the sixth generation (6G) technologies. In this work, we focus on the terrestrial data collection and transmission by AAN. In detail, high altitude platforms (HAPs) are considered as the aerial access devices and low earth orbit (LEO) satellites assist the data collected by HAPs to complete transmission. To deal with the intractable dynamic topology of AAN, the time expanding graph (TEG) is employed to represent the multiple resources and depict the data flow transmission process. Based on TEG, we aim to maximize the total data received at the ground data processing center, considering the multiple resource restrictions of HAPs and LEO satellites, as well as the flow conservation constraints in TEG. The problem is in the form of mixed integer programming, and it is intractable to obtain the optimal solution, especially in large-scale AAN. To alleviate the intractability, we propose the Benders decomposition based algorithm to obtain the optimal solution within an acceptable time complexity. Simulations are conducted and numerical results verify the effectiveness and efficiency of the proposed algorithm.

An efficient resource allocation algorithm plays a pivotal role in the performance improvement of space information networks (SIN). The dynamic of resources and mission requirements in the network has a great influence on resource allocation. Guiding rapid satellite resource allocation to adapt to dynamic changes of the network by discovering the similarity in the structure of changing missions is a key technology to improve SIN performance. In this paper, we firstly formulate satellite resource allocation as a problem aiming to maximize the total network benefits. Then, we propose a satellite resource allocation algorithm based on Hopfield to solve resource allocation problems in SIN. To accommodate the dynamics of satellite network missions and quickly solve resource allocation problems, we further propose a satellite resource allocation algorithm based on transfer learning to learn the node selection strategy in the Hopfield network. Specifically, we use a small number of additional training samples to better adapt to the changes in mission structure. Simulation results validate that the proposed method can achieve high performance while reducing computational complexity.

Non-terrestrial network (NTN) communication is included in the 3GPP standard because of its excellent features such as resistance to ground physical attacks and wide coverage. Since the available power and storage resources are limited on high throughput satellites (HTSs), optimizing the resource allocation can greatly improve the performance of the HTS based communication system. Notably, weather and other factors make the channel status between the satellite and the terrestrial base stations constantly change. In this paper, we first exploit the model-free feature of reinforcement learning to formulate the aforementioned dynamic and unpredictable channel conditions into the power allocation problem in HTS systems. Due to the complexity of environment state and power allocation, a deep neural network is introduced to replace the Q table, and a deep reinforcement learning framework is built. Furthermore, a power allocation algorithm based on a deep reinforcement learning framework is presented. Finally, the simulation results show that the proposed algorithm is superior to existing algorithms in terms of the long-term system throughput performance, and has better adaptability to dynamic channel environment, thereby improving network performance.

In satellite networks, information can be transmitted either directly by inter-satellite links or the movement of satellites carrying. Consequently, how to quantify network capacity, considering both the carrying and transmission capability of satellites is crucial to the deployment of satellite networks. In this paper, we define the capacity of satellite networks consisting of both, and propose a strategy to exploit the mobile carrying of satellites under the constraint of service requirements. Then, we reveal the theoretical relationship between satellite carrying and the network capacity. The theoretical analysis and simulated results show that 1) satellite carrying can improve the network capacity when the service delay constraints could be released; 2) The capacity gain from satellite carrying is influenced by network parameters, such as orbital altitude, number of satellites, and storage capacity.

As the sixth generation (6G) network is under research, and one important issue is the aerial access network and terrestrial-space integration. The Internet of Remote Things (IoRT) sensors can access the unmanned aerial vehicles (UAVs) in the air, and low Earth orbit (LEO) satellite networks in the space help to provide lower transmission delay for delay-sensitive IoRT data. Therefore, in this article, we consider the LEO satellite-assisted UAV data collection for the IoRT sensors. Specifically, a UAV collects the data from the IoRT sensors, then two transmission modes for the collected data back to Earth: 1) the delay-tolerant data leveraging the carry-store mode of UAVs to Earth and 2) the delay-sensitive data utilizing the UAV-satellite network transmission to Earth. Considering the limited payloads of UAVs, we focus on minimizing the total energy cost (trajectory and transmission) of UAVs while satisfying the IoRT demands. Due to the intractability of direct solution, we deal with the problem using the Dantzig-Wolfe decomposition and design the column generation-based algorithms to efficiently solve the problem. Moreover, we present a heuristic algorithm for the subproblem to further reduce the complexity of large-scale networks. Finally, numerical results verify the efficiency of the proposed algorithms and the advantage of LEO satellite-assisted UAV trajectory design combined with the data transmission is also analyzed.

With the growing demand for communications in remote and dispersed areas, Internet-of-Remote Things (IoRT) networks with joint unmanned aerial vehicle (UAV) access and geostationary orbit (GEO) satellite backhaul hold great promise to provide sufficient access services to Internet-of-Things (IoT) users and devices. As the fundamental of the performance optimization of IoRT networks, the performance analysis sheds light on the relationship between the network performance (i.e., backlog, delay, and throughput) and access scale (i.e., the numbers of UAVs and UAV users). Aiming at the challenges brought by the complex network structure (i.e., two-level queuing network along with the converged traffic), we introduce the stochastic network calculus-based min-plus convolution and the leftover service to mathematically describe the complex structure. For the analytical challenges of the continuous-time arrival process and heterogeneous two-level link capacities, we innovatively prove their supermartingale features and further derive the closed-form expressions of the network backlog and delay bounds based on the martingale theory. To pursue higher throughput while guaranteeing delay performance, we formulate a mixed-integer optimization problem of the access scale that contains a nondifferentiable variable derived from a transcendental equation. For the tractability, we propose a three-directional iterative (TDI) algorithm to search the optimal solution of the optimization problem. Simulation results verify the tightness of our performance bounds in contrast to the standard bound and the effectiveness of the proposed algorithm.

Space-air-ground networks play important roles in both fifth generation (5G) and sixth generation (6G) techniques. Low earth orbit (LEO) satellites and high altitude platforms (HAPs) are key components in space-air-ground networks to provide access services for the massive mobile and Internet of Things (IoT) users, especially in remote areas short of ground base station coverage. LEO satellite networks provide global coverage, while HAPs provide terrestrial users with closer, stable massive access service. In this work, we consider the cooperation of LEO satellites and HAPs for the massive access and data backhaul of remote area users. The problem is formulated to maximize the revenue in LEO satellites, which is in the form of mixed integer nonlinear programming. Since finding the optimal solution by exhaustive search is extremely complicated with a large scale of network, we propose a satellite-oriented restricted three-sided matching algorithm to deal with the matching among users, HAPs, and satellites. Furthermore, to tackle the dynamic connections between satellites and HAPs caused by the periodic motion of satellites, we present a two-tier matching algorithm, composed of the Gale-Shapley-based matching algorithm between users and HAPs, and the random path to pairwise-stable matching algorithm between HAPs and satellites. Numerical results show the effectiveness of the proposed algorithms.

In next-generation wireless networks, high-mobility unmanned aerial vehicles (UAVs) are promising to provide content coverage, where users can receive sufficient requested content within a given time. However, trajectory planning for multiple UAVs to provide content coverage is challenging since 1) UAVs cannot provide content coverage for all users due to the limited energy and caching storage, and 2) the trajectory planning of UAV is coupled with each other. Moreover, the complete information based trajectory planning methods are unusable since UAVs cannot obtain prior information on the rapidly changing environment. In this paper, we investigate the multi-UAV trajectory planning for energy-efficient content coverage. We first formulate an energy efficiency maximization problem considering recharging scheduling, which aims to reduce the total length of trajectories of UAVs under the quality of service (QoS) constraints. To settle environment uncertainty, the trajectory planning problem is modeled as two coupled multi-agent stochastic games, whose equilibrium constitute the optimal trajectory. To obtain the equilibrium, we propose a decentralized reinforcement learning algorithm, which can decouple the two games. We prove that the proposed algorithm can converge to the optimal solution of the Bellman equation with a higher rate compared to the centralized one. Moreover, simulation results show that the energy efficiency of the proposed algorithm is smaller than 5% compared the optimal, which is obtained with the prior information of environments.

Low earth orbit (LEO) satellite networks will play important roles in the sixth generation (6G) communication system. Software defined network technique is a novel approach introduced to the LEO satellite networks to improve the resource flexibility and efficiency, forming the software defined LEO satellite networks (SDLSNs). How to efficiently allocate the resources of SDLSN to provide services for the terrestrial users is a key issue. Hence, in this work, we explore the service provision for SDLSN via virtual network functions (VNFs) orchestration on the software defined time-evolving graph. In view of the scarce, intermittent and unstable satellite-to-satellite (S2S) links, the problem is formulated to minimize the S2S resource consumption while satisfying the terrestrial tasks, which is in the form of integer linear programming. Since the problem is intractable by exhaustive search, we design a branch-and-price algorithm based on the coupling of Dantzig-Wolfe decomposition, column generation, and branch-and-bound to efficiently acquire the optimal solution. Further, to obtain a faster solution for practical usage, we further design an approximation algorithm for the subproblem and leverage the beam search to accelerate the pruning for the search tree. Finally, extensive simulations are conducted and the numerical results validate the effectiveness of the proposed schemes.

Content caching has limitations on achieving cache gains (e.g., cache hit ratio) in small cell networks, due to limited storages of small base stations (SBSs) and inherent user demand patterns (i.e., initial content preferences). Two effective approaches have been proposed to exploit the potential of content caching: SBS cooperation to utilize cache storage, and proactive content recommendation to shape user demand. In this paper, we investigate cooperative content caching and recommendation to maximize cache gains, while guaranteeing users' satisfaction by recommending appealing content items. We propose a generic framework for cooperative content caching and recommendation, based on which we propose an online and distributed scheme by designing a continuous-time Markov chain (CTMC). In particular, online content caching (a.k.a., content replacement) is implemented by hopping from one cache state to another in the CTMC, while content recommendation is performed heuristically through sequential fixing at each cache state. Besides, we characterize the performance gap between our proposed scheme and the theoretical optimum in terms of cache hit ratio. Simulation results demonstrate that the proposed scheme achieves better cache hit ratios than other schemes in single-BS scenarios, and provides a competitive solution in multiple-BS scenarios.

Satellite networks have been regarded as a promising architecture for supporting the Internet of remote things (IoRT) due to their advantages of wide coverage and high communication capacity in remote areas, which further promotes the development of the satellites for IoRT networks (SIoRTNs). The effectiveness of multi-dimensional resource collaboration has significant impacts on the IoRT data downloading performance. However, the environment ’ s dynamics, e.g., channel conditions and solar infeed process, are unknown in practical scenarios, which poses daunting challenges in making efficient utilization of multi-dimensional resources. Motivated by this fact, we model the joint resource scheduling and IoRT data scheduling problem with the aim of maximizing the amount of the IoRT data of the overall network by applying the model-free reinforcement learning framework. To overcome the limitations of traditional reinforcement learning algorithms, we propose several feature functions by investigating the natural attributes of the multi-dimensional resources of the SIoRTNs, and further exploit the concept of function approximation to approximate the expected downloaded IoRT data given the network state. Furthermore, we propose a state-action-reward-state-action (SARSA) based actor-critic reinforcement learning (SACRL) resource allocation strategy to achieve the optimal resource allocation and IoRT data scheduling with casual information at LEO satellites. Simulations validate the convergence property and the effectiveness of the proposed SACRL algorithm in terms of the amount of the downloaded IoRT data. Particularly, we investigate the impact of typical network parameters on network performance to further provide guidance for future SIoRTN system design.

The satellite data relay networks (SDRNs) hold great promise in 6G communications for the timely offloading of the global traffic. Since the delay performance is regarded as one of the most important metrics reflecting the offloading efficiency, studying its relationship with network parameters becomes really essential to the development and application of the SDRN. However, the complex data offloading process and heterogeneity of traffic arrivals and transmission links pose many challenges to the stochastic delay analysis. To accurately model the data offloading process in SDRNs, we build a series-parallel queuing model with through and cross traffic while considering the propagation delay. On this basis, we respectively propose a propagation delay embedded min-plus convolution method based on stochastic network calculus and a Markov chain method based on Monte Carlo to depict the leftover services of the heterogeneous links received by the per-flow traffic in an aggregate. To eliminate the impacts of the heterogeneity, we uniformly characterize the arrivals and leftover services by their moment generating functions (MGFs) which contain the full moment information, and shield the heterogeneity by deriving the envelopes of the arrivals and leftover services with the help of MGFs, Chernoff bound and union bound. Then, in the light of the geometric relationship between the envelopes of the arrivals and leftover services, we analyze the upper bounds of the stochastic delay, which provides the guidance to the network configuration. Eventually, simulation results verify the effectiveness of the theoretical analysis and further reveal maximum four times the delay difference between the heterogeneous links influenced by traffic type, burstiness, and access number.

In cache-enabled wireless networks (CWN), the unbalanced traffic distribution due to the node's sociality may lead to local congestion, which significantly degrades system throughput. Especially, nodes prefer to share content with those that have social relationships with them, which may result in heavy traffic load in the nodes with great social relationships. Therefore, it is crucial to capture the interplay among sociality, content caching and traffic distribution. In this paper, we design a caching strategy through jointly considering sociality and load balance to maximize the throughput capacity. To this end, efficient betweenness (EB) is adopted to quantify the traffic distribution, where EB is the number of content delivery paths through a node. Aided by EB, the impacts of key system parameters including sociality and caching strategy on throughput capacity are elaborated. According to the critical condition of the steady state in CWN, we formulate an optimization problem aiming to maximize throughput capacity. Due to the non-convexity of the initial problem, we propose an effective heuristic algorithm to solve it, which can balance traffic load according to the node's sociality and transmission capacity. Simulation results show that the proposed algorithm can increase the throughput capacity by 35.7% against benchmark approaches.

In this work, we investigate the location error of a fingerprint-based indoor system with the application of hybrid received signal strength (RSS) and channel state information (CSI) fingerprints. It manifests that exploiting correlation between RSS and CSI could effectively reduce location error. On this basis, we propose a hybrid RSS/CSI localization algorithm (HRCL), which is designed based on the deep learning. The HRCL fully exploits quick construction of fingerprint database with the coarse-grained RSS and rich multipath information of the fine-grained CSI. The RSS and CSI with high correlation are selected to construct fingerprint database, aiming to improve localization accuracy. Moreover, the deep neural network is trained for location estimation. Especially, experimental results validate that the location error of HRCL can be reduced by 64.4%, compared with the existing localization method. Moreover, the location error of HRCL can be reduced by 29.1 %, compared with HRCL without RSS/CSI selection by correlation coefficient.

Software defined network technique is a novel approach introduced to manage low earth orbit (LEO) small satellite networks. One important challenge is the allocation of the scarce virtualized satellite network resources in space environment. We devise a virtual network functions orchestration based model to implement the virtualized resources management for LEO satellite networks. This model is formulated as an integer linear programming (ILP) problem. Further, we propose a method combining Dantzig-Wolfe decomposition, column generation and branch-and-bound algorithm for the ILP problem to attain the optimal solution. Finally, simulation results demonstrate the effectiveness and efficiency of the proposed algorithm.

Although the joint design of content caching and retrieving has the potential of relieving the backhaul pressure, either caching or retrieving would significantly influence the distribution of interference in caching-enabled small cell network (CSCN). The complicated interference, if not properly managed, would inversely deteriorate the network performance. In this work, we further investigate the tradeoff of content caching and retrieving in term of network spatial throughput (ST). Specifically, the analysis manifests that, compared to the condition of caching less content, ST is more likely to be reduced with increasing amount of retrieving content when more content is pre-cached by small cell base station (SBS). To maximize the ST, we formulate an optimization problem, which is solved by the joint design of content caching and retrieving. Moreover, a critical SBS density is derived from the optimization result, beyond which less content should be retrieving if more content is pre-cached by SBS. Therefore, the tradeoff between content caching and retrieving could be captured. More importantly, it is shown that a backhaul-free region exists, where the maximization of ST under joint optimization is identical to that under caching optimization. This indicates that the backhaul pressure can be significantly relieved through caching optimization in dense CSCN.

Fixed-wing unmanned aerial vehicles (UAVs) are of great potential to serve as aerial access points (APs) owing to better aerodynamic performance and longer flight endurance. However, the inherent hovering feature of fixed-wing UAVs may result in discontinuity of connections and frequent handover of ground users (GUs). In this work, we model and evaluate the performance of a fixed-wing UAV network, where UAV APs provide coverage to GUs with millimeter wave backhaul. Firstly, it reveals that network spatial throughput (ST) is independent of the hover radius under real-time closest-UAV association, while linearly decreases with the hover radius if GUs are associated with the UAVs, whose hover center is the closest. Secondly, network ST is shown to be greatly degraded with the over-deployment of UAV APs due to the growing air-to-ground interference under excessive overlap of UAV cells. Finally, aiming to alleviate the interference, a projection area equivalence (PAE) rule is designed to tune the UAV beamwidth. Especially, network ST can be sustainably increased with growing UAV density and independent of UAV flight altitude if UAV beamwidth inversely grows with the square of UAV density under PAE.

Mobile-Edge Computing (MEC) could relieve computing pressure and save energy of resource-constrained Smart Mobile Devices (SMDs) via computation offloading. Nevertheless, offloading strategy design for multiuser MEC systems is challenging. Specifically, offloading operations (i.e., terminal execution strategy, access rate, and cloud execution strategy) are not only inner-coupled for each SMD due to parallel local and cloud execution, but also inter-coupled among SMDs due to competition for radio and computation resources. Worse still, the inner- and inter-coupling interplay each other. However, existing works generally weaken this inner-inter-coupling, resulting in an inability to adapt to network differences, terminal capacity differences, and application requirements differences. Hence, only suboptimal performance could be achieved. As motivated, we jointly optimizes terminal execution strategy, radio resource allocation, and MEC computation resource allocation to minimize weighted sum of terminal energy consumption. Additionally, via dynamically matching individual offloading behavior and group's competitive resources allocation, our proposed algorithm could not only reflect mechanism of interaction between inner- and inter-coupling relationship, but also well adopt to diversities of network conditions, terminal capacity, and application requirements to further harvest MEC gain. Finally, simulation results demonstrate that our algorithm significantly outperforms existing schemes, more specifically up to 73.8% less energy consumption.

Exploiting the time-varying feature of wireless channels, retransmission could enable reliable data transmission. However, the growing deployment of small cell base stations (BSs) would induce significant temporal interference correlation. In consequence, once the current transmission fails, the subsequent ones are likely to fail as well. In this light, we investigate the retransmission performance in dense networks with temporally correlated interference in terms of network spatial throughput (ST). Our results reveal that the impact of temporal interference correlation on the retransmission performance critically depends on the BS density. Specifically, temporal interference correlation would cause a greater network ST attenuation when the BS density is closer to the critical density, under which network ST is maximized. Moreover, the impact of temporal interference correlation is shown to be cumulative as the number of retransmission attempts increases. Furthermore, towards efficient retransmission, we adopt and optimize a P-Activation strategy (PAS). It is shown that the optimized PAS is able to effectively mitigate the overwhelming strength and temporal correlation of interference. As a result, the retransmission performance in improving network ST is significantly enhanced in dense networks, while the variation of network ST with BS density exhibits sigmoid trend instead of the previous near-bell shape.

Orbital angular momentum (OAM) at radio frequency (RF) provides a novel approach of multiplexing a set of orthogonal modes on the same frequency channel to achieve high spectrum efficiencies. However, there are still big challenges in the multi-mode OAM generation, OAM antenna alignment and OAM signal reception. To solve these problems, we propose an overall scheme of the line-of-sight multi-carrier and multi-mode OAM (LoS MCMM-OAM) communication based on uniform circular arrays (UCAs). First, we verify that UCA can generate multi-mode OAM radio beam with both the RF analog synthesis method and the baseband digital synthesis method. Then, for the considered UCA-based LoS MCMM-OAM communication system, a distance and AoA estimation method is proposed based on the two-dimensional ESPRIT (2-D ESPRIT) algorithm. A salient feature of the proposed LoS MCMM-OAM and LoS MCMM-OAM-MIMO systems is that the channel matrices are completely characterized by three parameters, namely, the azimuth angle, the elevation angle and the distance, independent of the numbers of subcarriers and antennas, which significantly reduces the burden by avoiding estimating large channel matrices, as traditional MIMO-OFDM systems. After that, we propose an OAM reception scheme including the beam steering with the estimated AoA and the amplitude detection with the estimated distance. At last, the proposed methods are extended to the LoS MCMM-OAM-MIMO system equipped with uniform concentric circular arrays (UCCAs). Both mathematical analysis and simulation results validate that the proposed OAM reception scheme can eliminate the effect of the misalignment error of a practical OAM channel and approaches the performance of an ideally aligned OAM channel.

Satellite Data Relay Networks (SDRNs) play an important role in the data relay from User Satellites (USs) to ground stations by Tracking Data Relay Satellites (TDRSs). For better exploitation of SDRNs, the development of the systematic model and accurate system analysis is essential. To describe the end-to-end data transmission in SDRNs, we construct an MMOO/MMSP/1/K-G/G/1 tandem queuing model where the two parts depict the traffic arrival and transmission service of USs and TDRSs, respectively. Because the active and inactive periods of the data transmission are determined by the visibility between two satellites, classical buffer state based vacation policies become imprecise. Moreover, these two kinds of periods appear alternatively and their duration varies over time so that it is hard to model such intermittent transmission by existing service models. To overcome these difficulties, we propose a Markov Chain Monte Carlo based Markov Modulated Service Process (MMSP) which can tightly match the distributions of the active and inactive periods. In this process, we propose two algorithms to calculate the service state transition probability and the number of the sub-states in each service state, respectively, which guarantees the alternative transition between the active and inactive states as well as the sojourn time spent in each state. For the quality of service analysis, we find the different features of the queue variation under different arrival and service rate conditions. By separately calculating the related mean queue lengths and emergence probabilities, we first derive the expressions of the system loss probability, mean queue length, and mean delay. Finally, we conduct numerous simulations to verify the accuracy of our system model and performance evaluation, which provides the guidance to the buffer design and transmission resource allocation.

Orbital angular momentum (OAM) at radio frequency (RF) provides a novel approach of multiplexing a set of orthogonal modes on the same frequency channel to achieve high spectral efficiencies. However, OAM communication system requires perfect alignment of the transmit and the receive antennas and this harsh precondition greatly challenges practical multiplexing gain of OAM system. In this paper, we propose a UCA-based multi-mode OAM reception method including the beam steering with the estimated angle of arrival (AoA) and the amplitude detection with the estimated distance. Both mathematical analysis and simulation results validate that the proposed OAM reception method can completely eliminate the effect of the misalignment error of a practical OAM channel and approaches the performance of an ideally aligned OAM channel.

Orbital angular momentum (OAM) multiplexing is considered to have the potential of further improving the spectral efficiency of communication systems. However, a common uniform circular array (UCA)-based multi-mode OAM communication system using discrete Fourier transform (DFT) and inverse DFT (IDFT) has high peak-to-average power ratio (PAPR), which reduces the radio frequency power amplifier (PA) efficiency and degrades system performance. In this paper, a constant envelope OAM (CE-OAM) system is proposed to achieve the minimum signal PAPR to maximize the PA efficiency. The transmitter using a phase modulator converts the high PAPR signal into constant envelope signal with $0$dB PAPR, and the receiver performs inverse transform after OAM symbol detection, followed by the conventional OAM demodulation. Simulation results validate the feasibility of the proposed CE-OAM system and shows that CE-OAM system with appropriate modulation coefficient outperforms conventional OAM system in both PAPR and bit error rate (BER) performances.

Constructing energy-efficient data center networks (DCNs) is becoming increasingly significant. In the hybrid DCNs with both wired and wireless links, reconfigurable wireless links can effectively reduce the routing path length and the usage of the switch, thereby greatly reduce the energy consumption DCNs. In this paper, we propose an energy-efficient hybrid flow routing and antenna scheduling scheme for tree-based hybrid DCNs. Firstly, the original problem of hybrid routing and scheduling is decomposed into two subproblems by taking advantage of the wireless energy-saving features. Then, a novel weight-relaxing-rounding algorithm is developed to solve the first subproblem. Specifically, the topology characteristics of the tree-based DCNs is used to perform weight transformation and link remapping, and then to find energy-efficient wired subnet. After that, the relax-and-rounding technology is adopted to obtain energy-efficient wireless links scheduling solution. Finally, the numerical results show that the proposed scheme can achieve a near optimal solution when the network scale is small. As the network scale increases, the proposed scheme is still able to save more energy than the existing algorithm.

With the growing deployment of small cell base stations (BSs), the impact of backhaul congestion on the performance of small cell networks becomes dominant. To capture this effect, we present a conjoint framework integrating backhaul architecture with the access network. In particular, we evaluate the performance of ultra-dense networks (UDNs) in terms of network spatial throughput (ST), supposing that backhaul is conveyed to BSs through millimeter wave (mmWave) links by gateways. Notably, in contrast to diminishing to zero in previous work, network ST is shown to converge into a saturation under the given gateway density since the limited backhaul capability of gateways would stabilize the interference distribution of access network. Moreover, despite the benefit of enhancing backhaul capacity, over-deployed gateways would result in the potential intercell interference, which degrades network ST of UDN. On this account, we further study the optimization of gateway density to balance the tradeoff between enhancing backhaul capacity and mitigating intercell interference. It indicates that the application of mmWave beamforming at gateways leads to an increase in optimal gateway density, under which network ST could be further improved.

Interference serves as the most dominant factor that quantitatively and qualitatively impacts the performance of ultra-dense networks (UDN). Especially, since interference of different time slots basically comes from the same set of interfering base stations (BSs), the temporal interference correlation becomes more significant with the growing deployment of network infrastructures. In this light, we develop an analytical framework to investigate the impact of temporal interference correlation on UDN in terms of network spatial throughput (ST) in this work. In contrast to the available research, which indicates that the interference correlation is independent of BS density, we show that a growing BS density would exacerbate the influence of interference correlation on the performance of UDN. In particular, when the BS density is closer to the critical density, under which network ST could be maximized, the ST attenuation caused by temporally correlated interference is more significant. Moreover, the effect of temporal interference correlation on network ST is additive over time slots. For instance, a greater number of transmissions of hybrid automatic repeat request (HARQ) would result in a more significant effect of temporally correlated interference on network ST.

Tracking and Data Relay Satellite System (TDRSS) is playing an important role in data relay for user satellites. Subject to the finite number of antenna, the non-negligible antenna slewing time, and the time-varying connectivity of inter-satellite links (ISLs), it is significantly challenging to improve the selection of antenna scheduling sequence to improve performances (e.g., higher throughput, shorter mean queue length, smaller mean scheduling number, etc). To overcome above challenges, we utilize the antenna slewing model, the track model, and the multi-queue single-server queuing model to calculate the satellite-specific attributes such as the practical antenna slewing time, the link availability period and the buffer state. Furthermore, we propose a Heuristic Algorithm based on Optimal Weight (HAOW) considering the obtained satellite-specific attributes to optimize the antenna scheduling sequence. With the optimized scheduling sequence, the network performances are analyzed by the proposed queuing model. Finally, we conduct numerous simulations for the performance comparisons of the proposed HAOW with classical scheduling algorithms.

Recently, satellite networks have played an increasingly important role in both military and civilian fields. With the continual growth of the network size, the assessment of link criticality is of great significance to protect or attack satellite networks. With regard to the dynamic topologies and store-carry-forward transmission paradigm in satellite networks, detecting critical links should fully consider the relationship of consecutive snapshots and the key performance of the traffic, which raises great challenges. In this paper, we explore critical link sequence of satellite networks from the perspective of delay. We first formulate the problem based on the time-expanded graph model and discuss its convexity. Then, by exploring the space-time relationship between the criticality of different link at different slots, a heuristic critical link sequence detection algorithm (CLSD) is proposed. The simulation proves that deleting the critical link sequence given by the algorithm can effectively prolong the minimum transmission delay of the network and verifies the importance of network vulnerability assessment from the perspective of delay.

Orbital angular momentum (OAM) at radio frequency (RF) provides a novel approach of multiplexing a set of orthogonal modes on the same frequency channel to achieve high spectral efficiencies. However, OAM communication system requires perfect alignment of the transmit and the receive antennas and this harsh precondition greatly challenges practical application of OAM mode multiplexing gain. In this paper, we first investigate the effect of non- parallel misalignment on the inter-mode interference of the RF-OAM communication system equipped with uniform circular array (UCA). Then, large-scale system analysis shows that increasing the number of antennas has little effect on the inter-mode interference induced by oblique angle, but beam steering approach could eliminate the inter-mode interference completely. Numerical simulations validate our conclusion on the effect of beam steering.

The mobile telecom carriers have deployed massive infrastructures that support or carry the data signal transmission. Most of them are passive devices lacking the ability to actively monitor and automatically report information, which are called “dumb devices.” At present, the dumb device management has problems, such as information incomplete or inaccurate, and lack of dynamic update mechanism. For catering to the construction of smart cities, we design an information management system considering the full lifecycle management for dumb devices to realize real-time or periodical context awareness and information transmission based on narrow band Internet of Things (NB-IoT). Compared to the existing radio frequency identification (RFID)-based solutions, which require RFID readers and electronic tags and have a limited sensing distance, the NB-IoT-based solution for dumb device management has advantages in transmission distance and communication stability. The NB-IoT terminal is attached to the dumb device, and a global positioning system module is installed on it to obtain the positioning information. The NB-IoT terminal is controlled by an real-time clock (RTC) alarm to periodically enter the low power mode and then wake up to automatically collect the location and battery information and upload it to the server. The application objects of this information management system can be extended to dumb devices in other industries.

Unmanned aerial vehicles (UAVs) have been extensively applied as aerial access points to assist the terrestrial wireless network. Despite the inherent potential, nevertheless, it still remains to explore whether the gain of UAV access points (UAPs) could be fully harvested, which is critically dependent on the factors including the flight altitude and deployment density of UAPs. In this light, we investigate the performance of a downlink UAV integrated terrestrial cellular network (UTCN) and analytically study the influence of varying UAP altitude and density on the spatial throughput (ST) of UTCN. In particular, we obtain the UAP altitude upper bound, below which more line-of-sight (LOS) connections could be provided to improve network ST. Otherwise, cross-layer interference over the LOS paths becomes dominant, which results in significant degradation of network ST. More importantly, we reveal the limitation of the application of UAPs by showing that there exists a critical UAP density, beyond which network ST would encounter a rapid decrease. To fully exploit the potential of UAPs, we further tailor a probabilistic interference avoidance scheme and study the optimization of the UAP activated probability. Remarkably, network ST could be substantially improved using the optimized activated probability, i.e., network ST could increase with the growing UAP density and converge to a positive constant instead of zero in the dense UAP regime. Therefore, the results of this paper could provide insight on the deployment and optimization of UTCN.

As the typical delay tolerant networks, the satellite networks possess the intermittent connections, the large-scale time delays and the time-varying topologies. Obviously, such features seriously affect the delivery of mission data with certain requirements on the traffic or latency. To decrease the delivery time, existing relay-based contact graph routing (CGR) method selects the earliest reachable path to forward mission data. However, this method cannot guarantee the transmission of a large amount data and wastes the rare contact opportunities. Therefore, in this paper, we focus on the transmission QoS problem of multiple missions over the satellite networks, and design a QoS support routing strategy to achieve multiple flow-maximizing paths with acceptable delivery delays. Especially, with the storage time aggregated graph (STAG), we construct an on-demand mission model to depict both the network dynamic characteristics and the different mission requirements, and then reduce the QoS support problem as a graph-based maximum flow problem. To solve this problem, one STAG-based multiple flow-maximizing routing scheme is proposed to ensure the mission QoS and match the rare network resources, which has low computation complexity. Finally, compared with CGR, simulation results demonstrate the proposed scheme can achieve a higher mission completion rate and resource utilization rate.

This paper aims at shedding light on the impact of unsaturated traffic on the performance of uplink non-orthogonal multiple access (NOMA) transmissions. Nevertheless, the unsaturated traffic gives rise to the discontinuous interference and the inherent interaction of queues, which in turn highly complicates the performance evaluation. By utilizing tools from queuing theory, we first explicitly characterize the stable throughput region, which represents the region of traffic arrival rates on the condition that the queuing delay converges in distribution to a bounded random variable. In light of this, the critical condition under which NOMA can extend the stable throughput region of orthogonal multiple access (OMA) is derived. Then, we propose an algorithmic solution to evaluate the average delay incurred from both queuing and transmission. It is interestingly found that the superiority of NOMA over OMA in terms of average delay heavily hinges on the temporal traffic dynamics of each user. In particular, NOMA enjoys a clear advantage when the traffic arrival rate of the user with stronger channel condition considerably exceeds the traffic arrival rate of the user with weaker channel condition. The derived results can provide helpful guidance to fully leverage the comparative advantages of NOMA under various traffic conditions.

In this paper, the sum rate of opportunistic interference alignment (OIA) is analyzed in multiple-input-multiple-output interfering broadcast channels. The alignment metric upon which users are scheduled is based on the chordal distance between certain interfering subspaces at each receiver, and the closed-form expressions for the rates of the scheduled users are derived. Furthermore, we show that for a system in which each user has$N$receive antennas and the$j$th base station transmits$d_{j}$data streams, where$\sum _{j=1}^{I}d_{j}={N}+1$and${N}\ge 2$, the rate for each user can be approximated by the mean of a Gumbel random variable. Further analysis reveals that if the number of users in cell$i$scales as$\rho ^{\alpha }$, where$\rho $is the normalized transmit power and$\alpha \in [{0,1}]$, then cell$i$can achieve$\alpha d_{i}$degrees of freedom. The simulation results confirm the validity of the theoretical analysis and the accuracy of the approximation. Thus, the sum rate analysis provided herein is an effective performance evaluation method for multi-cell OIA.

Signal processing on antenna arrays has recently received extensive attention in the area of angle-of-arrival (AoA)-based indoor localization. Although sufficient array elements can improve the resolution in the AoA estimation, the array orientation has not been well exploited in research into the localization performance. In this paper, we investigate the effect of array orientations on the performance of AoA-based indoor localization systems. Appropriate array orientation can efficiently reduce the uncertainty in AoA estimation, thereby improving the localization accuracy. Accordingly, we present OpArray, an accurate indoor localization system based on flexible array deployment. First, OpArray designs an array deployment scheme, which establishes the foundation for accurate AoA estimates. The deployment scheme can be easily implemented through array rotations so as to optimize array orientations at receivers. Second, OpArray incorporates two refined phase preprocessing algorithms to mitigate the impact of negative factors, which exist in the practical implementation. In addition, aided by an improved AoA estimation algorithm, OpArray can localize a target on commercial off-the-shelf Wi-Fi platforms. Our experiments in a multipath-rich indoor environment show that OpArray achieves a median localization error of 0.5 m and the 80th percentile error is 1.0 m, which outperforms the state-of-the-art localization systems.

Small satellite networks (SSNs) have attracted intensive research interest recently and have been regarded as an emerging architecture to accommodate the ever-increasing space data transmission demand. However, the limited number of on-board transceivers restricts the number of feasible contacts (i.e., an opportunity to transmit data over a communication link), which can be established concurrently by a satellite for data scheduling. Furthermore, limited battery space, storage space, and stochastic data arrivals can further exacerbate the difficulty of the efficient data scheduling design to well match the limited network resources and random data demands, so as to the long-term payoff. Based on the above motivation and specific characteristics of SSNs, in this paper, we extend the traditional dynamic programming algorithms and propose a finite-embedded-infinite two-level dynamic programming framework for optimal data scheduling under a stochastic data arrival SSN environment with joint consideration of contact selection, battery management, and buffer management while taking into account the impact of current decisions on the infinite future. We further formulate this stochastic data scheduling optimization problem as an infinite-horizon discrete Markov decision process (MDP) and propose a joint forward and backward induction algorithm framework to achieve the optimal solution of the infinite MDP. Simulations have been conducted to demonstrate the significant gains of the proposed algorithms in the amount of downloaded data and to evaluate the impact of various network parameters on the algorithm performance.

In this paper, we analyze the feasibility of interference alignment (IA) schemes with T finite channel extensions for multi-input-multi-output (MIMO) interference broadcast channel (IBC) with common messages, where each base station transmits one common single data stream to all users within its cell. We investigate necessary conditions of feasibility of the IA schemes for this network in three different scenarios, i.e., with no channel extensions (T = 1), with T (T ≥ 2) channel extensions for a positive measure of channel coefficients, and with T (T ≥ 2) dependent channel extensions. These necessary conditions impose constraints on system configurations, e.g., the number of users, the number of antennas, the number of cells, and the number of channel extensions, which must be satisfied when the IA schemes are feasible, and also implicitly provide constraints on the upper bound of degrees of freedom (DoF) with finite channel extensions.

The emerging distributed satellite cluster network (DSCN) holds great promise in various practical fields, including earth observation, disaster rescue, and tracking of forest fires. In the DSCN environment, it is essential to achieve the best data delivery performance by coordinating multi-dimensional heterogeneous and dynamic resources. However, in real-world applications, the distribution of long-term data arrival is not often fully known. Motivated by this fact, we propose a distributionally robust two-stage stochastic optimization framework with considering the dynamic network resources and the partially known distribution information of long-term data arrival. Aiming at maximizing the total network reward, we formulate a two-stage stochastic flow optimization problem based on the extended time expanded graph. Then, we introduce an ambiguity set for the uncertain distribution of the long-term random data arrival inspired by the idea from the distributionally robust optimization. On the basis of the proposed ambiguity set, we further propose a data arrival distribution robust two-stage recourse (DADR-TR) algorithm by converting the original stochastic optimization problem into a deterministic cone optimization problem, which is computationally tractable. The extensive simulations have been conducted to evaluate the impact of various network parameters on the algorithm performance and further validate that the proposed DADR-TR algorithm can achieve high data delivery performance without full distribution information of the long-term data arrival.

In this paper, we propose a harvested energy maximization problem of simultaneous wireless information and power transfer (SWIPT) system with popularity cache scheme in dense small cell networks. Firstly, network model, content request, and popularity cache schemes are provided in the system model. Then, we establish a harvested energy maximization problem of SWIPT system with popularity cache scheme in dense small cell networks, where maximum transmit power of small cell base stations (SBSs), minimum rate requirement, i.e., quality of service (QoS) of user terminals (UTs), and power splitting ratio are considered. Further, an iterative power splitting ratio and power allocation optimization (IPSPA) algorithm is proposed to solve the formulated problem. Finally, the better performance of our proposed method is demonstrated through a number of simulations. These results are of significance for maximizing harvesting energy of UTs and reducing consumption of backhaul resources and energy.

In wireless virtualization communication markets, mobile virtual network operators (MVNOs) lease spectrum resources from mobile network operators (MNOs) and offer certain wireless services to end users. In order to attract more users, one MVNO may provide better service (i.e., more spectrum inventory thus higher QoS) and/or set lower retail price through taking its competitors' decisions into account. In this paper, we study the price strategy and spectrum inventory decision for MVNOs to optimize profits. First, we integrate these price and inventory factors into the utility function of end users and the revenue function of MVNOs, respectively, and analyze the impacts of the price factor on profits given each MVNO's inventory. With non-cooperative game theory, we derive the Cournot Nash equilibrium (C-NE) and further reveal several effective conclusions with guidance on price decision. Significantly, we discover that the increase of one MVNO's inventory would reduce others' revenues even though they adjust their prices to the C-NEs. Based on this observation, we propose to consider the price strategy and inventory decision together. Specifically, for a duopoly MVNOs market, we develop an ordinary differential equation (ODE) based evolutionary model that only contains its own price and inventory, which is very helpful for further analysis. However, since there is no explicit expression for the ODE, we figure out the boundary condition and initial value to facilitate a numerical solution. Overall, the conclusions in this study provide insights into price and inventory decision, and simulation results also validate our analysis.

Task planning plays a vital role in the application of the earth observation satellites (EOS) as it can effectively reduce the task execution delay and the system energy consumption. However, it is a classic NP-hard problem. In this paper, we propose a graph-based scheduling algorithm to match the to-be-observed tasks with the sensor resources for the multi-satellite multi-task scenario. Specially, we construct a collision avoidance clustering graph (CACG) to model the relationship between tasks and resources, where the concept of collision set is creatively constructed to characterize the conflict relationships among tasks. Through analyzing which nodes can be a clique in the graph, we can get the aggregated tasks. As such a CACG-based algorithm is proposed to achieve satellite task scheduling, where three criteria,i.e.,the N-priority criterion, the T-priority criterion and the collision avoidance rule, are proposed to enhance the percentage of the completed task and decrease the delays. Finally, we demonstrate the effectiveness of the proposed schemes through simulations.

In this paper, we investigate the influence of limited backhaul on the performance of caching enabled ultra-dense small cell networks (USCN). In particular, an analytical framework has been presented to evaluate the performance of USCN in terms of area spectral efficiency (ASE). It is shown that the constraint of backhaul capacity would significantly influence the performance of content caching and retrieving in USCN. For instance, it is shown that, if the resulting interference is not properly handled, caching more content would result in a decrease in ASE of USCN in the unlimited backhaul regime. This contradicts with the limited backhaul regime, in which network ASE could be notably improved if more content is pre-fetched. Moreover, it is shown that network ASE would experience a faster diminish as well if more content is retrieved via backhaul. The reason is that USCN turns from interference-limited into backhaul- limited regime with the growing BS and user densities. On this account, we have designed a probabilistic content retrieving strategy to relieve the bottleneck brought by limited backhaul. Targeting at maximizing the ASE of USCN, we further optimize the content retrieving probability (CRP). With the derived sub-optimal CRP, it is shown that the scaling law of network ASE (with varying BS density) could be fundamentally improved. Therefore, the results of this work could provide insight on the application of caching in USCN with limited backhaul constraint.

In this paper, we evaluate the performance of a downlink unmanned aerial vehicle (UAV) assisted cellular network (UACN). In particular, an analytical framework is developed to derive network spatial throughput (ST), an indicator to network capacity, in UACN. Under the framework, we investigate the influence of deployment density and altitude of UAV access points (UAPs) on the performance of UACN. For instance, the closed-form expression of UAP flight altitude upper bound is obtained. Rising the UAP altitude below the upper bound, more line-of-sight (LOS) connections could be provided to improve network ST. Otherwise, cross- layer interference over the LOS paths becomes dominant and accordingly network ST would be significantly degraded. Moreover, to reveal the fundamental limitation of the integration of UAPs, we derive the critical UAP density through a special case study. If the density of deployed UAPs is greater than the critical density, network ST would encounter a rapid decrease. The results could provide insight on the application of UAVs in the next-generation terrestrial wireless networks.

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.

Remote sensing satellite (RSS) plays an increasingly important role in satellite networks. Current studies have paid wide attention to system modeling and performance analysis of RSS traffic acquisition, storing and transmission processes. However, in traffic acquisition process, the transitions between the "on" state and the "off" state of the on-board sensor generally exhibit a Markovian feature. Besides, the continuous stream traffic arrives in the "on" states and no traffic arrives in the "off" states. These features have not been sufficiently investigated. Meanwhile, the idiomatic Poisson traffic models are no more accurate, which inevitably brings great challenges to precise performance analysis. Aiming at above features, we present a Markov Modulated Deterministic Process (MMDP) model to simulate the traffic acquisition process. Afterwards, according to the global coverage of relay satellites, we describe the integrated traffic acquisition, storing and transmission processes as an MMDP/D/1/K queueing model. Further, we derive the closed-form expressions of some important quality of service indices (i.e., the loss probability, the average queue length and the average delay). Finally, we conduct numerous simulations to verify the effectiveness of theoretical results.

Caching content in devices and sharing content via device-to-device (D2D) communications is a promising way to offload the cellular data traffic. In fact, the distribution of caching content has a direct effect on the behavior of D2D communications and thus the results of content sharing. On the other hand, the behavior of D2D communications decides whether the cached content can be shared successfully. In other words, there is an interaction between content caching and D2D communications. Moreover, content caching is usually operated in a slow timescale span (e.g, a half hour), while the communication behavior should be performed in a fast timescale span (e.g, several milliseconds), i.e., channel coherence time, owing to the variable nature of wireless channels. Therefore, it is essential to study the channel-aware content caching and sharing problem under two timescale. In this paper, we formulate the problem as a stochastic mixed- integer nonlinear programming (SMINLP), and then approximate the problem as a mixed-integer nonlinear programming. Subsequently, a heuristic algorithm is proposed to handle the approximated problem. Simulation results show that the proposed scheme is more efficient in traffic offloading than that without considering the interactions under two timescale.

User association has redrawn much attention lately, due to the introduction of content caching in small base stations (SBSs). To reduce traffic burden on backhaul links, users are associated with different SBSs when requesting different contents. However, user-perceived delay increases if the serving SBSs that have the desired contents are overloaded. Moreover, the user association problem becomes complex due to the vast number of contents. In this paper, to reduce user-perceived delay as well as backhaul loads, we propose a cluster-level user association scheme where content clustering is leveraged to simplify user association and reduce its complexity. Particularly, similar contents are clustered together according to the content preferences of users and cached contents in SBSs. Thus, the dimensionality of the user problem becomes smaller. On this basis, we propose a distributed cluster-level user association scheme, where each user selects SBSs based on their traffic loads and cached contents. Simulation results show that our scheme based on clustered contents outperforms the traditional schemes.

Mobile-Edge Computing (MEC) could relieve computing pressure of resource-constrained Smart Mobile Devices (SMDs) by offloading computation-intensive tasks to nearby/MEC server. However, how to achieve energy efficient computation offloading for SMDs under application-dependent latency constraints remains challenging in multiuser MEC systems. Specifically, the optimal system operations are not only inner- coupled for each SMD due to parallel local and cloud execution, but also inter-coupled among SMDs due to competition for limited radio resource. Additionally, the inner- and inter-coupling influence each other, which further complicates multiuser offloading strategy design. In this paper, we address such a challenge by jointly optimizing computational speed of SMDs via Dynamic Voltage Scaling (DVS) technology, subcarrier allocation, transmit power per subcarrier, data size sent per subcarrier, and offloading ratio, to minimize weighted sum of mobile energy consumption, resulting in a mixed-integer optimization problem. To tackle this NP-hard problem, we propose a fast-convergent suboptimal algorithm with the Lagrangian dual decomposition. Additionally, simulation results verify that the algorithm converges fast and significantly outperforms existing schemes in energy consumption reduction. Meanwhile, we discover that given latency mean, total mobile energy consumption remains stable or increases with the variance of latency requirements, which could direct admission control in practice.

MmWave systems with most prior work focused on its narrowband hybrid analog/digital precoding, however, will likely operate on wideband channels with frequency selectivity. Therefore, in this paper we investigate wideband angular beamforming schemes for mmWave massive MIMO-OFDM systems. First, for the RF analog beamforming, the optimal beamforming of an unconstrained antenna array (UAA) is given as the performance benchmark. Then, the optimal angular beamforming (OAB) and a simple dominant angular beamforming (DAB) of a shared antenna array (SAA) are compared in received SNR and implementation cost. Second, for the baseband digital precoding, the space-frequency vector perturbation (SFVP) precoding is proposed to collect both spatial and multi-path diversity. Finally, analytical and simulation results show that: a) DAB is a cost- effective RF beamforming scheme under LOS channel environment; b) the proposed hybrid DAB-SFVP beamforming scheme achieves the array gain equaling the number of transmit antennas Ntand diversity gain equaling the product of the number of RF chains K and the number of temporal resolvable clusters ℒ.

By integrating software defined network and network function virtualization, software defined satellite networks (SDSNs) can enable flexible virtual network function (VNF) deployment to process and forward end-to-end traffic flows. Since one traffic flow has to go through all its required VNFs, the VNF deployment has a significant impact on traffic routing. In this regard, with time-varying network topology and limited network resources taken into account, we aim to match VNF deployment and routing to fulfill traffic flows' requirements in the SDSN in a cost-effective manner. Specifically, we first evolve the traditional time evolving graph as a software defined time evolving graph (SDTEG) to depict the time-varying network topology and meanwhile, provide a shared platform for elastic network resource provisioning. On this basis, we then formulate a cost minimization problem as a multi-slot integer linear programming problem to make a judicious decision on VNF deployment and routing for each traffic flow. To address this challenging problem effectively, we further propose a heuristic algorithm, referred to as time-slot decoupled algorithm (TDA). Finally, the effectiveness of the TDA as well as the superiorities from the joint optimization of VNF deployment and routing are demonstrated through simulation results.

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.

By allocating antenna time blocks to spacecrafts, data relay satellites are of vital importance for the space network to relay data within their visible intervals (i.e., time windows). Existing works concentrate only on the allocation of transmission resources (i.e., antenna time blocks) in time windows and may result in transmission conflicts hard to efficiently resolve, especially when multiple missions are activated simultaneously. To this end, we propose to further integrate computation with transmission resource allocation, to enable data compression so as to alleviate conflicts. Specifically, aiming to maximize the number of completed missions and minimize data loss, we first formulate the joint transmission and computation resource allocation problem as a mixed integer linear programming (MILP) one. Then, for the complexity reduction, we transform the MILP into an integer linear programming (ILP) one by fixing maximal data compression. Meanwhile, by constructing a conflict graph to characterize resource allocation conflicts, a time window scheduling algorithm is proposed to solve the ILP problem efficiently. Next, we further develop a data compression control algorithm to reduce data loss on the prerequisite of invariant mission number. Finally, simulation results show that the space network can benefit from the combination of transmission and computation resources in terms of both mission number and data loss.

Orbital angular momentum (OAM) supplies a new degree of freedom for the electromagnetic (EM) wave, which can be used to increase communication capacity. The amplitude of the OAM wave is null in the center of the beam and the distribution of the electric field intensity is not uniform, so the study of the optimal receiving location of single OAM wave is necessary. Furthermore, in order to efficiently receive OAM waves with different OAM modes simultaneously on single receiving antenna, the common reception sampling region of multi-mode OAM waves should also be studied. However, few experiments study the region of multi-mode OAM waves. Here, concentric circular arrays are proposed to generate OAM waves with different OAM modes. Based on theoretical formulations of its radiation fields in the cylindrical coordinate system, its transmission and reception characteristics would be analyzed. Basically, the common reception sampling region of multi-mode OAM waves can be confirmed by calculating function formulas of the upper and lower boundaries of the region or analyzing amplitudes and phases of the axial ratio of optimal receiving location of multiple OAM waves. Numerical results show that methods of confirming the region are feasible and simple. When other parameters besides frequency are the same and the optimal receiving location of certain OAM mode wave at certain frequency is known, optimal receiving locations of certain OAM mode wave at other frequencies can be confirmed easily, according to the formula related to the optimal receiving location and frequency.

Space information network (SIN) is a promising networking architecture to significantly broaden the observation area and realize continuous information acquisition for earth observation. Over the dynamic and complex SIN environment, it is a key issue to coordinate multi-dimensional heterogeneous network resources (e.g., observation resource and transmission resource) in the presence of multi-resource variations and severe conflicts, such that diverse earth observation service requirements can be satisfied. To this end, this paper studies the multi-resource coordinate scheduling problem in SINs. Specifically, we first characterize the relationship among multi-resource using an event-driven time-expanded graph (EDTEG). Based on the EDTEG, observation resource and transmission resource are jointly considered, and an integer linear programming optimization problem is formulated to maximize the sum priorities of the successfully scheduled tasks. An iterative optimization technique is employed to decompose the problem into separate observation scheduling and transmission scheduling sub-problems, which can be efficiently solved by extended transmission time sharing graph and directed acyclic graph methods, respectively. Simulation results demonstrate the effectiveness of the proposed algorithm and performance impacts of different network parameters.

Mission scheduling algorithms are envisioned as critical to satisfy the increasing mission requirements in broadband data relay satellite networks, which is severely influenced by time-varying inter-satellite contacts (i.e., potential available communication links) and differentiated satellite downlink contacts. Nevertheless, the intertwined effect of such two types of contacts on mission schedules poses daunting challenges for the efficient mission scheduling design. In this paper, to achieve fair performance among user satellites, we maximize the minimum number of successfully scheduled missions over all user satellites by jointly optimizing contact plan design, power allocation (PA) in relay satellites, and mission schedules based on the time-expanded graph. The formulated problem is a mixed-integer nonlinear program optimization problem that is challenging to solve. For tractability purpose, we equivalently decompose the problem into a PA problem and an optimal PA-based mission scheduling (OPA_MS) problem, which is still a mixed-integer linear program. We further devise a new two-stage scheme to efficiently solve the OPA_MS problem. Simulation results validate the significant gains of the proposed algorithm in mission completion number and necessitate the consideration of the time-varying and differentiated inter-satellite and downlink contacts.

The spatial-and-temporal correlation of interference has been well-studied in Poisson networks, where the interfering base stations (BSs) are independent of each other. However, there exists spatial interdependence including attraction and repulsion among the BSs in practical wireless networks, affecting the interference distribution and hence the network performance. In view of this, by modeling the network as a Poisson clustered process, we quantify the effects of spatial interdependence among BSs on the interference correlation and analytically prove that BS clustering increases the level of interference correlation. In particular, it is shown that the level is a monotone-increasing function of the mean number of BSs in each cluster and a monotone-decreasing function of cluster radius, but is independent of the locations of the clusters. Furthermore, we study the effects of spatial interdependence among BSs on network performance with Type-I HARQ retransmission scheme via considering heterogeneous cellular networks in which small-cell BSs exhibit a clustered topology in practice. We derive the numerically integrable expressions and their bounds for the joint success probabilities, defined as the success probability in multiple successive transmissions, for macro-cell users and small-cell users. It is shown that BS clustering improves the performance of macro-cell users. Further, the level is enhanced by the repulsion between the BSs from different tiers.

In this paper, we consider an energy harvesting cognitive radio network where each secondary transmitter (ST) harvests radio frequency energy from ambient primary transmitters (PTs), and communicates with its secondary receiver (SR) which suffers co-channel interference from PTs. A positive correlation is observed between the harvested energy at the ST and the aggregate interference at the SR, which illustrates that an ST will have a higher probability to harvest enough energy if there is strong interference at the SR. To exploit the positive correlation, we propose an interference threshold-based transmission strategy for STs, so as to protect secondary transmissions as well as increase the amount of harvested energy. We model the battery level of each ST as a finite state discrete-time Markov chain and derive the expression of the secondary spatial throughput as a function of the transmission probability and coverage probability. We investigate the impact of interference threshold and density of PTs on the secondary spatial throughput, and provide guidelines in the optimal design of these two factors to maximize the spatial throughput. To further improve the spatial throughput of a secondary network, we consider the successive interference cancellation strategy at SRs, and reveal its superiority in the high density regime of PTs.

In this paper, we investigate the limitation of multi-user multiple-input multiple-output (MIMO) in ultra-dense networks (UDNs) and investigate how to overcome the limitation by designing efficient interference management strategies. Specifically, it is shown that the area spectral efficiency (ASE), an indicator to network capacity, would approach zero with over-deployed base stations (BSs) even when multi-user MIMO is applied. Worse still, it manifests that the multi-user gain of MIMO cannot be harvested in UDN due to the overwhelming interference. In particular, the maximal ASE is shown to be degraded by increasing the number of served users in each cell. To alleviate the bottleneck brought by interference, we have designed and optimized two simple but efficient BS activation policies. It is shown that the application of the optimized BS activation policy could improve network capacity scaling law and boost the potential of multi-user MIMO in UDN. Remarkably, the ASE is shown to increase with BS density λ even when λ and user density are sufficiently large. Moreover, the maximal ASE in the λ → ∞ regime is shown to increase with the number of served users in each cell, which contradicts with the all-BS-on case.

The multi-commodity flow problem plays an important role in network optimization, routing, and service scheduling. With the network partitioning and the intermittent connectivity, the commodity flows in delay tolerant networks (DTNs) are time-dependent, which is very different from that over the static networks. As an NP-hard problem, existing works can only obtain sub-optimal results on maximizing the multi-commodity flow of dynamic networks. To overcome these bottlenecks, in this paper, we propose a graph-based algorithm to solve the maximum two-commodity flow problem over the DTNs. Through analyzing the relationship between the two commodities, we propose a maximum two-commodity flow theorem to simplify the coupling two-commodity flow problem as the two single-commodity flow ones. Then, with the help of the storage time aggregated graph (STAG) (a DTN model with less memory), we construct a pair of flow graphs to describe the reduced two single-commodity flows (addition flow and subtraction flow), and design the corresponding flow calculation methods. Moreover, we design a STAG-based dynamic combined flow algorithm to maximize the two-commodity flow. Finally, the computational complexity of the proposed algorithm is analyzed, and its efficacy has also been demonstrated through an illustrative example and numerical simulations.

We firstly formulate the energy efficiency (EE) maximization problem of joint user association and power allocation considering minimum data rate requirement of small cell users (SUEs) and maximum transmit power constraint of small cell base stations (SBSs), which is NP‐hard. Then, we propose a dynamic coordinated multipoint joint transmission (CoMP‐JT) algorithm to improve EE. In the first phase, SUEs are associated with the SBSs close to them to reduce the loss of power by the proposed user association algorithm, where the associated SBSs of each small cell user (SUE) form a dynamic CoMP‐JT set. In the second phase, through the methods of fractional programming and successive convex approximation, we transform the EE maximization subproblem of power allocation for SBSs into a convex problem that can be solved by proposed power allocation optimization algorithm. Moreover, we show that the proposed solution has a much lower computational complexity than that of the optimal solution obtained by exhaustive search. Simulation results demonstrate that the proposed solution has a better performance.

Capable of significantly reducing cell size and enhancing spatial reuse, network densification is shown to be one of the most dominant approaches to expand network capacity. Due to the scarcity of available spectrum resources, nevertheless, the over-deployment of network infrastructures, e.g., cellular base stations (BSs), would strengthen the inter-cell interference as well, thus in turn deteriorating the system performance. On this account, we investigate the performance of downlink cellular networks in terms of user coverage probability (CP) and network spatial throughput (ST), aiming to shed light on the limitation of network densification. Notably, it is shown that both CP and ST would be degraded and even diminish to be zero when BS density is sufficiently large, provided that practical antenna height difference (AHD) between BSs and users is involved to characterize pathloss. Moreover, the results also reveal that the increase of network ST is at the expense of the degradation of CP. Therefore, to balance the tradeoff between user and network performance, we further study the critical density, under which ST could be maximized under the CP constraint. Through a special case study, it follows that the critical density is inversely proportional to the square of AHD. The results in this work could provide helpful guideline towards the application of network densification in the next-generation wireless networks.

In this paper, we develop a framework using tools from stochastic geometry and queuing theory to evaluate the flow-level performance of downlink cellular networks with spatio-temporal traffic. Under this framework, we first obtain the mean service rate and the non-empty probability of a scheduled user queue by solving a fixed-point equation, which captures the inherent correlation between the interference and the queue status. By leveraging these results, we then derive closed-form expressions for the mean packet throughput and its bounds, defined as the mean number of packets that can be delivered during a given time duration. Simulation results validate the accuracy of the presented analysis, which can provide useful insight on the design of cellular networks while incorporating the spatial and temporal fluctuations of traffic.

In Earth observation satellite networks (EOSNs), imbalance between the observation and transmission opportunities can cause poor network performance, e.g., a reduced number of successfully scheduled targets. To tackle this issue, in this paper, we investigate the multi-dimensional resource scheduling problem in EOSNs to ensure that each EOS can observe an appropriate subset of targets with matched downloading capacity to the destination. Specifically, an optimization problem is formulated and proved to be NP-hard. Then, an iterative optimization technique is employed to decompose the problem into separate observation scheduling and transmission scheduling subproblems, which are further efficiently solved by acyclic directed graph and particle optimization methods, respectively. Extensive simulations have been conducted to demonstrate the efficiency of the proposed scheduling algorithm.

Ultra-dense deployment of small cells can enhance capacity, extend coverage, and improve the spectrum and energy efficiency. However, intra-tier interference among different spectrum-sharing small cells is critical and needs to be well coordinated from a resource utilization perspective, such as the distributed power control. Especially in the ultra-dense small cell networks, the effects of intra-tier interference cannot be ignored, although the perceived interference from one small cell is infinitesimal for a specific small cell when designing a power control policy. It is difficult to make the model, analysis, and design of the distributed power control due to a huge number of small cells. In this paper, we formulate the power control of a generic small cell as a mean field game (MFG) with an interference dominator, where we derived the coupled Hamilton-Jacobi-Bellman (HJB) and the Fokker-Planck-Kolmogorov (FPK) equations. The presented MFG framework of power control can characterize the complex interference interaction and strategically rational decision-making of the generic and dominating players. Simulation results show the dominating effects of the interference dominator on the power control.

As one typical delay tolerant network (DTN), the satellite networks possess the intermittent connections, the large-scale time delays and time- varying topologies. Such features seriously affect the delivery of data with certain constraints about the traffic or the latency. In this paper, we design one QoS support routing strategy for the satellite networks to achieve multiple flow-maximize paths with acceptable delivery delays. In order to satisfy the demands of different missions, the storage time aggregated graph (STAG) is utilized to construct an on-demand mission flow model with some temporal constraints. Moreover, the proposed flow model carefully depicts the relation between the time-varying topologies and the features of different missions. Specially, one flow-maximizing routing scheme with shortest path is proposed for matching the rare contacts, which has low computation complexity. Finally, we demonstrate the efficacy of the proposed schemes through simulations.

The deployment of long term evolution (LTE) in unlicensed spectrum (LTE-U) is a promising solution to overcome the spectrum shortage. However, the interaction between LTE-U and Wi-Fi in unlicensed spectrum has not been well understood. In this paper, we use stochastic geometry to develop a framework for the co-existence between LTE-U and Wi-Fi in unlicensed spectrum. To reduce the intra-and inter-RAT interference, LTE-U employs an ALOHA-like random access scheme and Wi-Fi performs carrier sensing and energy detection before transmission. We derive the retention probability and the coverage probability of Wi-Fi and LTE-U networks. Based on our analysis, we investigate the effect of network parameters on the coverage probability of these networks.

The following topics are dealt with: cellular radio; MIMO communication; wireless channels; optimisation; radiofrequency interference; probability; Long Term Evolution; mobile radio; telecommunication traffic; radio networks.

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.

For the design and performance evaluation of potential massive multiple input and multiple output (MIMO) related algorithms, accurate channel models are indispensable. Spherical wavefront and array non-stationarity due to physically large antenna array are two new characteristics specific to massive MIMO propagation. In this paper, spherical wavefront effects can be well characterized by a predefined two-dimensional multi-confocal ellipse scattering geometry as in reference [1] and a 9- state Markov process is developed to capture the channel behaviors by array non-stationarity. Meanwhile, the associated approach to derive the state transition probabilities of the Markov process based on the outfield measurements is detailed. Finally, simulation results verify the validity of our proposal.

This paper presents an accurate indoor localization system with irregular deployment of antennas. It can be feasibly deployed on commodity Wi-Fi infrastructures, without any hardware or firmware modifications. Aided by elaborate phase processing and an enhanced angle of arrival (AoA) estimation algorithm, our proposed system could provide higher localization accuracy under the coverage of two line-of-sight (LOS) access points (APs) compared to the state-of-the-art localization systems, where at least three APs are utilized to achieve the same accuracy. To be specific, a pertinent phase compensation and sanitization algorithm is designed to eliminate the additional factors that will distort the genuine channel state information (CSI). On this basis, we make the antennas irregularly deployed at each AP such that the linear array symmetry is removed and more AoAs can be obtained. In particular, with two APs equipped with 3 antennas irregularly deployed, up to 4 AoAs could be obtained (more than 2 AoAs with linear array), which provides the ability to localize a target in a 3-D space. Our experiments in a multipath rich indoor environment show that our system achieves a higher localization accuracy than the state-of-the-art localization systems, namely, a median error accuracy of 1.2 m in 2-D localization and 1.45 m in 3-D localization with two APs.

The amplify‐and‐forward (AF) relay system with consideration of the weak direct link is studied. As different data streams are transmitted by the source, there is interference between the different data links. In order to decode the data reliably, the source beamforming for the direct link is designed to make the interference of the source–destination link as low as possible. In the first stage, in order to avoid the interference being amplified by the relay, the source beamforming for the relay link is designed to cancel the inter‐link interference. In the second stage, beamforming of the relay is designed to maximise the spectral efficiency (SE). Two types of source beamformings are proposed by combining the null space vectors of useful signal and by combining the orthogonal subspace vectors of the interference signal, respectively. The obtained beamforming structures are not only used for the SE optimisation problem, but also the energy efficiency (EE) optimisation problem. Simulation results show that the proposed beamforming design algorithms can obtain higher SE or higher EE compared with existing methods.

As the mobile communication technologies evolving, spectrum resource has become extremely scarce. Accordingly, spectrum aggregation (SA) is proposed as an emerging solution to efficiently utilize the dispersive resource. To address such a challenging issue, this paper introduces power domain into the conventional SA which only works in the time and frequency domains, and extends the resource block (RB), which is a time-frequency spectrum management unit in LTE standard, to a time-frequency-power spectrum unit termed resource cube. Based on this, a novel radio resource management (RRM) scheme is proposed to manage the spectrum with multiplexing division in time, frequency, and power domains (3D) simultaneously. Through theoretical derivations, we show that under certain simplifications, the proposed 3D RRM could be formulated as a convex objective function with linear constrains, and can be solved with low computational complexity. When compared with the conventional RB-based RRM, the proposed 3D RRM matches the real-time requirement in SA. Furthermore, it is proved that the proposed scheme could achieve better energy efficiency than the RB-based ones.

Shortage of available licensed spectrum is a major barrier to the development of 5G networks. The deployment of long term evolution (LTE) in unlicensed spectrum (LTE-U) is a promising solution to overcome such a barrier. However, the interaction between LTE-U and Wi-Fi in unlicensed spectrum has not been well understood. In this paper, we use stochastic geometry to develop a framework for a multi-radio access technologies (multi-RAT) heterogeneous network, which consists of an LTE-U tier and a Wi-Fi tier. To reduce the intra- and inter-RAT interference, LTE-U employs an ALOHA-like random access scheme and Wi-Fi performs carrier sensing and energy detection before transmission. We derive the coverage probability and spatial throughput of Wi-Fi and LTE-U networks, and perform the asymptotic analysis when the density of Wi-Fi and LTE-U nodes approach infinity. Based on our analysis, we investigate the effect of network parameters on the coverage probability and spatial throughput of these networks. Furthermore, in order to achieve weighted max-min fairness, we optimize the retention probability of LTE-U nodes to maximize the minimum weighted spatial throughput of Wi-Fi and LTE-U networks.

In this paper, we investigate the interference alignment (IA) for a class of partially connected multi-input multi-output heterogeneous networks, which consist of L fully connected pico cells, J partially connected pico cells, and one macro cell. In particular, we investigate the feasibility of IA conditions with no channel extensions, T-independent channel extensions, and T-dependent channel extensions, respectively, for the single beam case. We present three theorems to characterize some necessary conditions, which L, J, T, and the channel diversity order L̅ must jointly satisfy when IA conditions are feasible. These necessary conditions also implicitly impose the constraints on the upper bounds of achievable degrees of freedom with different channel extensions.

Sparse code multiple access (SCMA) has been recently proposed for the future wireless networks, which allows nonorthogonal spectrum resource sharing and enables system overloading. In this paper, we apply SCMA into device-to-device (D2D) communication and cellular hybrid network, targeted at using the overload feature of SCMA to support massive device connectivity and expand network capacity. Particularly, we develop a stochastic geometry-based framework to model and analyze SCMA, considering underlaid and overlaid modes. Based on the results, we analytically compare SCMA with orthogonal frequency-division multiple access (OFDMA) using area spectral efficiency (ASE) and quantify closed-form ASE gain of SCMA over OFDMA. Notably, it is shown that system ASE can be significantly improved using SCMA and the ASE gain scales linearly with the SCMA codeword dimension. Besides, we endow D2D users with an activated probability to balance cross-tier interference in the underlaid mode and derive the optimal activated probability. Meanwhile, we study resource allocation in the overlaid mode and obtain the optimal codebook allocation rule. It is interestingly found that the optimal SCMA codebook allocation rule is independent of cellular network parameters when cellular users are densely deployed. The results are helpful in the implementation of SCMA in the hybrid system.

Ultra-dense networks can further improve the spectrum efficiency (SE) and the energy efficiency (EE). However, the interference avoidance and the green design are becoming more complex due to the intrinsic densification and scalability. It is known that the much denser small cells are deployed, the more cooperation opportunities exist among them. In this paper, we characterize the cooperative behaviors in the Nash bargaining cooperative game-theoretic framework, where we maximize the EE performance with a certain sacrifice of SE performance. We first analyze the relationship between the EE and the SE, based on which we formulate the Nash-product EE maximization problem. We achieve the closed-form sub-optimal SE equilibria to maximize the EE performance with and without the minimum SE constraints. We finally propose a CE2MG algorithm, and numerical results verify the improved EE and fairness of the presented CE2MG algorithm compared with the non-cooperative scheme.

Small satellite networks (SSNs) are playing an increasing role in nowadays earth observation due to their less development cost and energy consumption. In SSNs, it is pivotal to transmit a huge amount of data for differentiated missions to ground stations. Nevertheless, due to limited transponders and energy budget, not all contacts, i.e., potential available communication links, are feasible in data delivery. Besides, satellite downlink channel conditions are indeed time-varying due to atmospheric precipitation. Therefore, one daunting challenge is searching for feasible contacts termed as contact plan design with consideration of the differentiation for missions. In this paper, we exploit an extended time-evolving graph to characterize network resources. Based on the graph, we formulate the design of mission-aware contact plan, aiming at maximizing network profit in terms of sum weighted data volume as a mixed-integer linear programming. Due to its NP-hardness, we propose a primal decomposition method to efficiently solve the formulated problem by exploiting its special structure. To further reduce the complexity, we propose a link metric considering the issues of residual energy of satellites, time-varying satellite downlink contact capacity, and the differentiation for missions in the conflict graph. Based on the conflict graph, we devise a heuristic algorithm to design contact plan. Simulation results demonstrate the efficiency of the proposed algorithms and necessitate the consideration of the time-varying downlinks and the differentiation of missions for contact plan design.

Hybrid wireless network is composed of both ad hoc transmissions and cellular transmissions. Under the L-maximum-hop routing policy, flow is transmitted in the ad hoc mode if its source and destination are within L hops away; otherwise, it is transmitted in the cellular mode. Existing works study the hybrid wireless network capacity as a function of L so as to find the optimal L to maximize the network capacity. In this paper, we consider two more factors: traffic model and base station access mode. Different from existing works, which only consider the uniform traffic model, we consider a traffic model with social behavior. We study the impact of traffic model on the optimal routing policy. Moreover, we consider two different access modes: one-hop access (each node directly communicates with base station) and multi-hop access (node may access base station through multiple hops due to power constraint). We study the impact of access mode on the optimal routing policy. Our results show that: 1) the optimal L does not only depend on traffic pattern, but also the access mode; 2) one-hop access provides higher network capacity than multi-hop access at the cost of increasing transmitting power; and 3) under the one-hop access mode, network capacity grows linearly with the number of base stations; however, it does not hold with the multi-hop access mode, and the number of base stations has different effects on network capacity for different traffic models.

We investigate distributed flow control and power allocation strategies for delay-aware device-to-device (D2D) communication underlaying large wireless networks, where D2D pairs reuse the resource blocks of interior cellular users (CUEs). We consider a distributed D2D power allocation framework, where the D2D pairs individually attempt to maximize their own time-average throughput utility, while collectively guaranteeing the time-average coverage probability of CUEs in multiple cells. We design a novel method to compute the individual budget of interference from each D2D pair to CUEs based on stochastic geometry tools. Then, accounting for time-varying channel fading and dynamic D2D traffic arrival, we design a distributed interference-and-delay-aware (DIDA) flow control and power allocation strategy based on Lyapunov optimization and several interference estimation methods. We also analytically derive the performance bounds of D2D pairs, and prove that the coverage probability of CUEs can be guaranteed regardless of the interference estimation error at D2D receivers. Finally, simulation results suggest that adaptive interference estimation methods are preferred and demonstrate that the DIDA strategy achieves substantial performance improvement against alternative strategies.

Hybrid automatic repeat request (HARQ) is widely used in heterogeneous cellular networks (HCNs) to improve communication reliability. The temporal interference correlation caused by the common set of interferers makes the performance of HARQ more complex, especially for Type-II HARQ where the unsuccessful packets are combined with the new one to decode the packet. In general, due to the complexity of network performance analysis, the existing research focused on the performance of HARQ in single-tier wireless networks without considering cell association or base station (BS) load or the case that the combined number of transmissions is no larger than 2. In view of this, we study the performance of HARQ in HCNs jointly considering the temporally correlated interference, flexible cell association, and BS load. To this end, we adopt the popular HCN model, where different types of BSs in HCNs are modeled as K independent Poisson point processes with different densities and transmission powers. Leveraging the tool of stochastic geometry, we derive the success probability and delay-limited throughput for HCNs with Type-I HARQ and Type-II HARQ, respectively, for any number of transmissions. We show that the network performance in multiple time slots is decided by the performance in a single time slot and the temporal interference correlation. Finally, we conduct simulations to validate our analysis and show that the analysis without considering temporal interference correlation overestimates the performance of HCNs with HARQ.

Device-to-device (D2D) communications can enhance spectrum and energy efficiency due to direct proximity communication and frequency reuse. However, such performance enhancement is limited by mutual interference and energy availability, especially when the deployment of D2D links is ultra-dense. In this paper, we present a distributed power control method for ultra-dense D2D communications underlying cellular communications. In this power control method, in addition to the remaining battery energy of the D2D transmitter, we consider the effects of both the interference caused by the generic D2D transmitter to others and the interference from all others caused to the generic D2D receiver. We formulate a mean-field game (MFG) theoretic framework with the interference mean-field approximation. We design the cost function combining both the performance of the D2D communication and cost for transmit power at the D2D transmitter. Within the MFG framework, we derive the related Hamilton-Jacobi-Bellman and Fokker-Planck-Kolmogorov equations. Then, a novel energy and interference aware power control policy is proposed, which is based on the Lax-Friedrichs scheme and the Lagrange relaxation. The numerical results are presented to demonstrate the spectrum and energy efficiency performances of our proposed approach.

In this paper, we investigate the energy-saving resource management problem for a new device-to-device (D2D) and cellular coexisting network, where D2D users employ orthogonal frequency division multiple access (OFDMA) and cellular users employ sparse code multiple access (SCMA). This hybrid network can support massive connectivity by exploiting the degrees of freedom in code and space domains, however, the complicated spectrum sharing pattern also leads to serious interference, which further boosts the power consumption of mobile devices (MDs). To tackle this problem, we propose a unified resource management scheme to minimize the total transmit power of all MDs by jointly optimizing mode selection, resource allocation, and power control. First, we analytically get the optimal resource-sharing mode (dedicated mode or reuse mode) for cellular users and D2D users based on the mapping rule between SCMA codebooks and OFDMA resource blocks. For each resource-sharing mode, we reformulate the resource management problems as classical problems in graph theory, and then devise efficient algorithms leveraging the special structure of the constructed graphs. Finally, simulation studies indicate that the network capacity is upgraded with the hybrid multiple access technologies, and the energy efficiency performance is also enhanced through the unified resource management.

For the design, performance evaluation, and optimization of potential 3-D multiple input and multiple output (3-D MIMO) algorithms, an accurate stochastic channel model is indispensable. Simultaneously, it is desirable for the new 3-D MIMO channel model to be a further expansion of already well developed 2-D MIMO channel models to allow easy implementation and facilitate possible $3^{rd}$ generation partnership project standardization. This paper first shows our recent outfield channel measurement campaigns in urban macrocell scenarios with a planar antenna array and a crown-shaped antenna array installed on the base station and the user sides, respectively. A comprehensive methodology from the statistical analysis perspective to characterize the 3-D MIMO channel, particularly, in the elevation domain, is then elaborated upon. The framework of the new approach coincides with mainstream parametric stochastic models. Moreover, it is observed that, by recalculating the statistics of the large scale parameters as well as their cross-correlation matrix to rectify existent non-positive definite problem, adding distance dependent elevation angular spread and introducing a mixture of Von Mises Fisher distributions to describe the interdependence between azimuth and elevation, the accuracy of current standardized 3-D channel models can be improved upon. Finally, numerical results verify the validity of our proposal.

In this paper, we jointly consider mode selection, admission control, partner assignment, and power allocation to investigate the sum rate maximization problem in underlay SCMA device-to-device (D2D) networks. Due to its mixed combinatory, we first decouple the problem to devise efficient algorithms. In particular, we propose a channel gain based mode selection criterion and a greedy-style partner assignment scheme, and obtain closed-form solutions for both admission control and power allocation. To further reduce the computational cost, we also exploit the structure of the formulation to devise a much faster heuristic algorithm. Simulation results exhibit the superiority of the proposed algorithms against other schemes.

The traffic in current wireless networks exhibits large variations in uplink (UL) and downlink (DL), which brings huge challenges to network operators in efficiently allocating radio resources. Dynamic time-division duplex (TDD) is considered as a promising scheme to flexibly adjust resource allocation based on UL and DL traffic demands, known as traffic adaptation. In this work, we study how traffic adaptation decreases cell service time and improves energy efficiency (EE) in small cell networks operating dynamic TDD. According to different UL and DL traffic parameters, we classify small cells into K ≥ 1 types, and accommodate the UL/DL configuration for each type of small cells with the objective to minimize the cell service time and maximize the network EE. In comparison with semi-static TDD scheme, dynamic TDD is shown to achieve larger service time gain as the traffic asymmetry between small cells increases. In summary, the proposed analytical framework allows us to elucidate the benefit of traffic adaptation to service time and EE in future dense networks with dynamic TDD.

The correlation of interference has been well quantified in Poisson networks where the interferers are independent of each other. However, there exists dependence among the base stations (BSs) in wireless networks. In view of this, we study the interference correlation in non-Poisson networks where the interferers are distributed as a Matern cluster process (MCP) and a second-order cluster process (SOCP). We obtain the explicit expressions for the interference correlation coefficients under these two cases and find that they are the same if these two cluster processes have the identical cluster radius and average number of each cluster. We also prove that they are greater than their counterpart for the Poisson networks, which indicates the clustering in interferers increases the interference correlation. It is also shown that the value of the correlation coefficient goes up as the the attraction between the interferers increases. Finally, the numerical results show the relation between the correlation coefficients and system parameters.

In this paper, we integrate device-to-device (D2D) communications with coordinated multi-point (CoMP) in downlink cloud radio access network (C-RAN), targeting at using the proximity nature of D2D communications to improve system spectral efficiency. In particular, using stochastic geometry, we first derive the signal-to-interference ratio (SIR) distribution at a typical downlink user and a typical D2D receiver, considering zero-forcing beamforming (ZFBF) as the CoMP scheme. Based on the results, we analyze the effect of D2D communications on enhancing the area spectral efficiency (ASE) of the CoMP enabled C-RAN system. Numerical results show that increasing the cooperative cluster size would potentially degrade the system ASE when the network is heavily loaded. Furthermore, it is observed that the addition of D2D links, despite introducing excessive cross-tier interference to downlink users, can provide significant gains if properly designed.

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.

A multi-hop multi-AP multi-channel cooperation scheme, referred to as MHACWlan, is proposed for the purpose of improving the efficiency and performance of wireless local area network (WLAN) in dense scenarios, which is the top concern of HEW (High Efficiency WLAN), a IEEE 802.11 study group created in 2013. The proposed MHACWlan provides an effective way to solve the performance anomaly problem, interference problem, and the load imbalance problem in dense WLAN. In MHACWlan, we considers two-hop association to the APs for the wireless STAs with poor single-hop links to improve the throughput, assigning different channels for the two-hop links to reduce the interference and increase the throughput, and associating with more than one AP so as to balance the load. The mathematical analysis and simulation results demonstrate the performance benefits of MHACWlan compared to the traditional WLAN.

In the research of physical layer security, cooperative jamming has recently drawn considerable attention. Jammer plays a friendly role to transmit jamming signal to create interference at the eavesdroppers. However, few literatures consider the scenario in which jammer plays a hostile role to interfere legitimate users. In this paper, we study an ad hoc network where legitimate users transmit with the ALOHA protocol in the presence of hostile users. Each hostile user acts as a jammer or eavesdropper with probability q or 1 - q, respectively. We assess the network performance from both sides of the legitimate user and the hostile user. Particularly, from the perspective of the legitimate user, we evaluate how the connection outage and secrecy outage affect the secrecy transmission capacity, and then derive the optimal ALOHA transmission probability that maximizes the capacity. In view of the hostile user, we obtain the optimal jamming probability that can lead to the largest connection outage probability of legitimate users subject to a given successful eavesdropping probability constraint.

Radio Frequency (RF) energy harvesting has been shown to be a promising way to power wireless devices. In this paper, we consider an energy harvesting cognitive radio network model where each secondary transmitter (ST) harvests RF energy from ambient primary transmitters (PTs). To protect secondary transmissions and improve energy efficiency, we propose an interference threshold-based transmission strategy for STs. We model the battery level of each ST as a finite state discrete-time Markov chain (DTMC) and observe a correlation between the harvested energy at the secondary transmitter and the aggregate interference at the secondary receiver (SR). Based on copula theory, we derive the joint distribution of the harvested energy and the aggregate interference, and then derive the energy outage probability and transmission probability of each ST by combining with the Markov chain model. With the tools from stochastic geometry, we analyze the ST's coverage probability. By analyzing the effect of interference threshold on energy outage probability, transmission probability and coverage probability, we provide guidelines for the optimal design of interference threshold to maximize the spatial throughput of secondary network.

Small satellite networks, with the advantage of remarkably less development cost and energy consumption with respect to geostationary relay platforms, are playing an increased role in nowadays earth observation. However, small satellites have limited transponders and energy budget, which makes it necessary to design efficient contact plans to improve the network throughput. This paper addresses such an issue of joint management of the energy and transponder resource to well match the mission demand and network resources. We adopt an extended time-evolving graph to characterize network resources and then, formulate the contact plan design problem with the goal of maximizing the throughput as a mixed-integer linear programming. Since the computational complexity of this problem coupling multiple time slots is prohibitive, we further propose two heuristic algorithms which operate on a slot-by-slot basis to achieve high throughput. Simulation results present the impact of different factors on the network performance and moreover, demonstrate that both our contact plan approaches can achieve high throughput with low complexity.

In this paper, we investigate the energy-efficient transmission problem by resource allocation in SCMA networks. We formulate it as an optimization problem to maximize the network energy efficiency (EE) subject to quality-of-service (QoS) requirements, codebook assignment, power allocation, and subcarrier reuse constraints. Due to its mixed combinatory, we separate codebook assignment and power allocation to devise suboptimal but cost- efficient algorithms. With power equally distributed, we first propose a novel scheme to assign codebooks. We then develop a derivative- bisection based algorithm to optimally solve the resultant power allocation problem by exploiting its quasiconcave structure. Simulation results exhibit the superiority of the proposed algorithms against the existing classical schemes and of SCMA over OFDMA in terms of the network EE.

To improve the spectrum efficiency (SE), sparse code multiple access (SCMA) has been proposed as an candidate for 5G wireless networks. Although SCMA has good SE performance, it suffers from high detection complexity, which may degrade its energy efficiency (EE) performance. To make up for this deficiency, we in this paper jointly consider codebook design (i.e., mapping matrix and constellation graph design) and codebook assignment to investigate the detection complexity minimization problem for uplink SCMA networks. To tackle this hard problem effectively, we first borrow the idea of dual coordinate search to devise a cost-efficient algorithm to determine the mapping matrix and codebook assignment. Based on the mapping matrix, we exploit the multi-dimensional modulation characteristic of SCMA to carefully design the constellations for each codebook to further reduce the detection complexity. Finally, we present some simulations to illustrate the performance gain of our proposed algorithm as compared with other schemes.

Device-to-Device (D2D) communication underlaid cellular networks has been regarded as a technology with great promise to provide higher transmission rate, lower latency and better energy efficiency in services between user terminals in the future fifth generation (5G) wireless network. In this paper, we consider the resource allocation problem to enhance the system performance. Specifically, we use hypergraph to characterize the interference among cellular uplinks and D2D links when sparse code multiple access (SCMA) is applied as the multiple access strategy. Targeting at maximizing system sum rate, we propose an Interference-Aware Hypergraph based Codebook Allocation (IAHCA) algorithm. Using IAHCA, each orthogonal SCMA resource, i.e., SCMA codebook, is allowed to be shared by one cellular uplink and more than one D2D links. As a consequence, available SCMA resources can be fully exploited, thereby effectively achieving higher system throughput and activating more D2D links. Simulation results confirm that IAHCA outperforms conventional graph based algorithm and other hypergraph based algorithms.

As a promising multiple access technique for 5G wireless networks, sparse code multiple access (SCMA) has been put forward to support massive connectivity and enhance network performance. In this paper, we develop a theoretical framework using stochastic geometry to evaluate the performance of SCMA ad hoc networks. Under this framework, we first derive an explicit matrix form for the successful transmission probability. We then consider two area spectral efficiency (ASE) maximization problems without and with link reliability constraint to investigate the ASE gain of SCMA over OFDMA networks and the tradeoff between the ASE and link reliability, respectively. In particular, we obtain the optimal medium access probability (MAP) to solve both problems. Both numerical and simulation results exhibit that, compared to OFDMA networks, an asymptotically 160% gain in the ASE and a nearly 91% improvement in the transmission opportunity can be achieved by SCMA networks with typical settings.

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.

This paper investigates the problem of revealing the tradeoff between energy efficiency (EE) and delay in device-to-device (D2D) communications underlaying cellular networks. Considering both stochastic traffic arrivals and time-varying channel conditions, we formulate it as a stochastic optimization problem, which optimizes EE subject to the average power, interference-control, and network stability constraints. With the help of fractional programming and the Lyapunov optimization technique, we develop an algorithm, referred to as the TRADEOFF, to solve the problem. To deal with the nonconvex and NP-hard power allocation subproblem in the TRADEOFF, we adopt the prismatic branch and bound algorithm to find its globally optimal solution, where only a linear programming needs to be solved in each iteration. Thus, the TRADEOFF serves as an important benchmark to evaluate performance of other heuristic algorithms and is usually cost-efficient. The theoretical analysis and simulation results show that the TRADEOFF achieves an EE-delay tradeoff of [O(1/V),O(V)] with V being a control parameter and can strike a flexible balance between them by simply tuning V.

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.

The traffic in current wireless networks exhibits large variations in uplink (UL) and downlink (DL), which brings huge challenges to network operators in efficiently allocating radio resources. Dynamic time-division duplex (TDD) is considered a promising scheme that is able to adjust the resource allocation to the instantaneous UL and DL traffic conditions, also known as traffic adaptation. In this paper, we study how traffic adaptation and energy harvesting can improve the energy efficiency (EE) in multi-antenna small cell networks operating dynamic TDD. Given the queue length distribution of small cell access points (SAPs) and mobile users (MUs), we derive the optimal UL/DL configuration to minimize the service time of a typical small cell, and show that the UL/DL configuration that minimizes the service time also results in an optimal network EE, but does not necessarily achieve the optimal EE for SAP or MU individually. To further enhance the network EE, we provide SAPs with energy harvesting capabilities, and model the status of harvested energy at each SAP using a Markov chain. We derive the availability of the rechargeable battery under several battery utilization strategies, and observe that energy harvesting can significantly improve the network EE in the low traffic load regime. In summary, the proposed analytical framework allows us to elucidate the relationship between traffic adaptation and network EE in future dense networks with dynamic TDD. With this work, we quantify the potential benefits of traffic adaptation and energy harvesting in terms of service time and EE.

The incorporation of dynamic voltage scaling technology into computation offloading offers more flexibilities for mobile edge computing. In this paper, we investigate partial computation offloading by jointly optimizing the computational speed of smart mobile device (SMD), transmit power of SMD, and offloading ratio with two system design objectives: energy consumption of SMD minimization (ECM) and latency of application execution minimization (LM). Considering the case that the SMD is served by a single cloud server, we formulate both the ECM problem and the LM problem as nonconvex problems. To tackle the ECM problem, we recast it as a convex one with the variable substitution technique and obtain its optimal solution. To address the nonconvex and nonsmooth LM problem, we propose a locally optimal algorithm with the univariate search technique. Furthermore, we extend the scenario to a multiple cloud servers system, where the SMD could offload its computation to a set of cloud servers. In this scenario, we obtain the optimal computation distribution among cloud servers in closed form for the ECM and LM problems. Finally, extensive simulations demonstrate that our proposed algorithms can significantly reduce the energy consumption and shorten the latency with respect to the existing offloading schemes.

In this paper, we study the inter-linked problems of wireless service provider (WSP) selection of users and bandwidth allocation of WSPs in multi-tier heterogeneous cellular networks employing the approach combining stochastic geometry and game theory. In particular, the expected average user achievable rate is calculated by modeling the distributions of users and base stations (BSs) as independent homogeneous Poisson point processes. Moreover, a hierarchical game framework is presented to model the complicated interactions among users and WSPs. Wherein, the evolutionary game, non-cooperative game, and multi-leader multi-follower Stackelberg game models are, respectively, adopted to formulate the competition among users, competition among WSPs, and cyclic dependence between users and WSPs. According to backward induction, the formulated Stackelberg game would be solved after the formulated evolutionary game and non-cooperative game are sequentially studied. For the evolutionary game, both the closed-form expression and the asymptotically stability of its evolutionary equilibrium (EE) were analyzed. Then, conditioned on the obtained EE, the existence of Nash equilibrium (NE) for the non-cooperative bandwidth allocation game is established; furthermore, a sufficient condition for the uniqueness of the NE is derived. Finally, extensive simulation results verify both the validity of our analysis and the effectiveness of the proposed scheme.

Coordinated multipoint (CoMP) is an efficient technique to increase cell-edge coverage probability and throughput in cloud radio access network (C-RAN). In this paper, we integrate device-to-device (D2D) communications with CoMP by applying a distance based mode selection rule for downlink users in C-RAN, exploiting the proximity of D2D communications to improve system spectral efficiency. Using stochastic geometry, we first derive the signal-to-interference ratio distribution at a typical downlink user and a typical D2D receiver when two types of CoMP schemes, namely, zero-forcing beamforming (ZFBF) and noncoherent joint transmission (NC-JT), are applied in C-RAN. In addition, we analytically compare ZFBF and NC-JT using rate coverage probability. Meanwhile, we analyze the effect of D2D communications on enhancing the area spectral efficiency (ASE) of CoMP enabled C-RAN system. Numerical results show that increasing the co-operative cluster size would potentially degrade the system ASE when the network is heavily loaded. Furthermore, it is observed that enabling D2D mode in C-RAN can effectively offload the traffic of C-RAN and provide significant ASE gains if mode selection threshold is properly designed. Lastly, it is analytically demonstrated that spectrum resources can be better exploited by D2D users if they coexist with ZFBF enabled radio units (RUs) rather than NC-JT enabled RUs.

This paper focuses on a class of important two-hop relay mobile ad hoc networks (MANETs) with limited-buffer constraint and any mobility model that leads to the uniform distribution of the locations of nodes in steady state, and develops a general theoretical framework for the end-to-end (E2E) delay modeling there. We first combine the theories of fixed-point (FP), quasi-birth-and-death process, and embedded Markov chain to model the limiting distribution of the occupancy states of a relay buffer, and then apply the absorbing Markov chain theory to characterize the packet delivery process, such that a complete theoretical framework is developed for the E2E delay analysis. With the help of this framework, we derive a general and exact expression for the E2E delay based on the modeling of both packet queuing delay and delivery delay. To demonstrate the application of our framework, case studies are further provided under two network scenarios with different MAC protocols to show how the E2E delay can be analytically determined for a given network scenario. Finally, we present extensive simulation and numerical results to illustrate the efficiency of our delay analysis as well as the impacts of network parameters on delay performance.

Extensive deployment of small cells in heterogenous cellular networks introduces both challenges and opportunities. Challenges come with the reuse of the limited frequency resource for improving spectral efficiency, which always introduces serious mutual inter- and intracell interference between or among small cells and macrocells. The opportunities refer to more potential chances of inter- and intratier cooperations among small cells and macrocells. Energy efficiency will be a critical performance requirement for future green communications, especially when small cells are densely deployed to enhance the quality of user's experience. We exploit the potential cooperation diversities to combat the interference and energy management challenges. To capture the complicated interference interaction and also the possible coordination behavior among small cells and macrocells, this paper proposes a novel bargaining cooperative game (BCG) framework for energy efficient and interference-aware power coordination in a dense small cell network. In particular, a new adjustable utility function is employed in the BCG framework to jointly address both the spectral efficiency and energy efficiency issues. Using the BCG framework, we then derive the closed-form power coordination solutions and further propose a joint interference-aware power coordination scheme (Joint) with the considerations of both interference mitigation and energy saving. Moreover, a simplified algorithm (Simplified) is presented to combat the heavy signaling overhead, which is one of the significant challenges in the scenario of extensive deployment of small cells. Finally, numerical results are provided to illustrate the effectiveness of the proposed Joint and Simplified schemes.

Abstract Heterogeneous cellular networks (HetNets) have emerged as a new promising paradigm to further enhance capacity, where multiple types of low power smallcells are overlaid in a high power macrocell. They provide more opportunities to explore the potential cognition and cooperation diversities to improve the spectral efficiency. On the other hand, energy efficiency is a critical performance metric, which deserves more attention from academia, industry, and standardization, in particular, in scenarios where smallcells are densely deployed. In this paper, a systematic architecture is presented to efficiently utilize network resources and thus improve the overall energy efficiency. The architecture is referred to as OCRT because it combines a multi‐tier energy efficiency considerations of operators, core networks, radio access networks and terminals (e.g., OCRT). Furthermore, a corresponding triply‐cycle‐based functional structure is proposed for the OCRT to make various interactions between the corresponding functional entities clear. An implementation scheme of OCRT based on cognitive information interaction cycle and an energy efficiency‐aware protocol is presented. Finally, a use case of the presented OCRT green design is provided for energy efficiency optimization in a cognition‐and‐cooperation‐characterized HetNet. Copyright © 2015 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.

We consider the joint channel and power allocation problem for the OFDMA system. The problem is to find a joint channel and power allocation strategy to minimize the total transmission power subject to quality of service constraints and the OFDMA constraint (i.e, at most one user is allowed to access each channel). Since the problem is generally NP-hard, the idea of the existing algorithms is to heuristically allocate the channel and power resources separately. In this paper, we propose a novel iterative reweighted minimization framework based on an effective relaxation, which is beneficial by reformulating the combinatorial OFDMA constraint as an equivalent continuous optimization problem. The proposed framework simultaneously allocates the channel and power resources, and thus is sharply different from the existing ones. Simulation results show the proposed iterative reweighted minimization methods significantly outperform the existing algorithms.

In cognitive radio (CR) technology, the trend of sensing is no longer to only detect the presence of active primary users. A large number of applications demand for primary user behavior correlation in spatial, temporal, and frequency domains. To satisfy such requirements, we study the statistical relationship of primary users by introducing a Bayesian network (BN) based framework. How to learn such a BN structure is a long standing issue, not fully understood even in the statistical learning community. To solve such an issue in CR, this paper proposes a BN structure learning scheme which incurs significantly lower computational complexity compared with previous ones. Thus, with this scheme, cognitive users could efficiently understand the statistical pattern of primary networks.

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.

Hybrid wireless networks are networks that are composed of both ad hoc transmissions and cellular transmissions. Many existing works have analyzed the capacity of hybrid wireless networks. By assuming the uniform traffic model that a source node would select a random node as the destination, the network capacity is a function of number of nodes and number of base stations. Nevertheless, the real network traffic pattern is related to the social behaviors of users. In this work, we study the capacity of hybrid wireless networks with the social traffic model under the L-maximum-hop routing policy. If two nodes are within L hops away, packets will be transmitted in the ad hoc mode; otherwise, packets are transmitted through the base stations. To our best knowledge, we are the first to study this problem and develop the capacity as a function of number of nodes, number of stations, traffic model parameters, and L.

This paper considers the cross-tier interference mitigation for macro-femto networks under the reverse time division duplexing (RTDD) mode. A joint interference alignment (IA) and avoidance scheme is proposed. To mitigate the interference caused by femtocells at the macrocell base station (MBS), the signals of femtocells are aligned into the the null-space of the MBS's signal. The problem of the optimal precoding and decoding matrices is formulated to maximize the femtocell capacity while minimizing the interference leaked to the MBS. In addition, interference avoidance is also proposed to deal with the interference between macrocell users (MUs) and nearby femtocells. Numerical results demonstrate that the proposed scheme can significantly improve the MBS's performance with a slight degradation of the femto-cell's performance.

In this paper, we study the propagation of deadline- constrained content over wireless cellular network, in which the base station transmits a content to a certain set of users. The lifetime of a content is consumed in two aspects: waiting in the queue and transmitting. In our system, the base station first transmits the content to users with a certain data rate, such that some users may not directly receive the content. Afterwards, the users who obtained the content would forward the content to the other users. Due to the deadline constraint of each content, we formulate the scheduling problem by using Markov Decision Processing (MDP) with the objective of maximizing the throughput of the whole system. We propose an algorithm based on value iteration. Extensive simulation results are provided to demonstrate that our scheduling algorithm can efficiently improve system throughput.

This paper studies the interference management of D2D communications in cellular networks. We consider the underlay D2D communication, such that the signal quality of cellular user would be affected by D2D users. We explore the application of network coding to mitigate interference. In our proposed interference management scheme, the helper nodes overhears the signals from cellular users, and then, code the received packets and sends to the base station. We design the transmission policy for the helper node, and also describe how to select the helper nodes. Our simulation results show that the proposed interference management can effectively improve the spectrum efficiency and increase system throughput.

In this paper, we consider a disruption tolerant network (DTN) which enables data transmission with intermittent connectivity and in which instantaneous end-to-end path between a source and destination may not exist. We explore routing problems in such networks in consideration of limited storage for each intermediate node. A graph model called the storage enhanced time-varying graph (STVG) is presented, which fits the dynamic topology characteristics and time- varying parameters of DTN. We transform the nodes with finite buffer in DTN into links with limited bandwidth in STVG. Then, a Shortest Path Computation with Flow Guaranteed (SPFG) algorithm is proposed to select a path with minimum overall cost (delay), meanwhile, to guarantee the transfer of a certain flow. Simulation results show that the proposed algorithm based on the STVG model can obtain better performance in delay and delivery ratio (which can be improved to more than 90%) as compared with existing routing algorithms without considering the buffer constraints.

Heterogeneous networks (HetNets), where multiple low power small cell eNodeBs (SeNBs) are overlaid on the coverage of a high power macrocell eNodeB (MeNB), serve as promising paradigm to enhance spectral efficiency of future cellular wireless networks. To capture the complicated interference interaction and also the coordination behavior among MeNB and SeNBs, this paper proposes a bargaining cooperative game (BCG) framework for interference-aware power coordination in a HetNet. In particular, a new adjustable utility function is employed in the BCG framework to jointly address the spectral and energy efficiencies as well as to achieve the optimal tradeoff between them. We then derive the closed-form power coordination solutions and further propose an interference-aware power coordination scheme with the considerations of both interference mitigation and energy saving. Finally, the numerical results are provided to illustrate the convergence property and efficiency of the proposed power coordination scheme.

This manuscript develops an improved AF-OFDM (Amplify-and-Forward, Orthogonal Frequency Division Multiplexing) scheme where selective relaying policy is fully utilized to promote the spectrum efficiency of cooperative transmission, and presents an achievable rate maximizing resource allocation strategy subject to a sum-power constraint. The high computational complexity for optimization necessitates our investigation of suboptimal approaches. The proposed centralized algorithms can be implemented in a step-by-step manner. Specifically, the binary selective relaying policies are first decided for each subcarrier according to instantaneous or statistical channel state information. Then, relay selection might be conducted by those subcarriers who would like to choose a relay to assist their data transmission from network achievable rate maximization perspective, and finally power loading can be mathematically solved by employing standard Lagrange techniques. Numerical results are shown to illustrate the benefits of the improved AF-OFDM cooperative protocol with optimized resource allocation.

This study investigates energy efficiency (EE) balancing precoding designs for the cellular two way relay network (CTWR). Owing to non‐convexity and NP hard property of the worst case EE problem, the authors use convex approximation and convex relaxation to modify the problem for better mathematical tractability. The Dinkelbach‐type method is adopted to solve the max‐min fractional problem. Then alternating optimisation method is used to solve the inner subproblem as its non‐convexity. After fixing the precoding of one station (base station or relay station), the subproblem is still non‐convex. Then successive convex approximation (SCA) and semi‐definite program relaxation methods are used to obtain the precoding matrices of BS and RS. The proposed algorithm, which can be extended to the two way relay system with direct link model or the one way relay system, has a wide range of application. Simulation results show that the proposed algorithm converges fast and achieves better EE balancing compared with existing method.

In order to effectively utilise the spectrum of wireless network, secondary users (SUs) are often authorised to share spectrum with primary users (PUs) in cognitive networks. To guarantee effective transmission of PUs and SUs, the interference from the SUs to the PUs is limited and only the SUs that have the information rates (rates) than the corresponding thresholds are allowed to transmit their data. In order to select proper SUs to transmit and to maximise their achievable weighted sum rate (WSR) under rate requirement and interference constraint, the authors propose a joint admission control and beamforming design algorithm for partial channel state information (CSI) case. The WSR maximisation problem is first constructed by using approximation and convex relaxation, according to the statistical property of the CSI. Then a penalty factor is introduced to check the feasibility of such problem and instruct the admission of the SUs. Beyond the feasible SUs set, the WSR is maximised by updating the achievable rate of each user with rate profile technique and polyblock outer approximation method. Simulation results show that the proposed beamforming can achieve higher WSR than the beamforming of the existing baseline method.

The exponential growth of services demands further increase of spectral efficiency which drives the next generation wireless access networks towards deploying femtocells with frequency reuse. However, the interference will be severe especially in dense deployment scenarios. The fair resource allocation problem by joint consideration of sub‐channel assignment and interference alignment (IA) is more complicated than the traditional problem. First, not all the users are appropriate for IA since the number of participant users is limited by the feasibility constraint and the interference power levels are different for the path loss. Second, IA can increase the degrees‐of‐freedoms while occupy additional signal dimensions of participant users, hence more sub‐channels are needed by IA compared with the non‐participants when each user transmits the same number of streams. This study models the fair resource allocation problem as an optimisation problem, which is a non‐deterministic polynomial‐time (NP)‐hard. To solve it with low complexity, the authors propose a graph‐based scheme to give the approximate solution, where the selection criteria of IA group are based on the influence of IA on the interference graph. Simulation results show that the authors scheme can approximate the optimal solution in a small network and improve the fairness in dense deployment scenarios.

This study concerns joint channel and power allocation scheme for multi‐user orthogonal frequency division multiple access system. The author's highlight is margin adaptive (MA) resource allocation problem namely minimising the total transmit power of users with rate requirement constraints. MA is generally provable NP‐hard; the typical methods are either to relax and round, or to fix the transmission mode of users (e.g. modulation and coding). Differently, they reorganise MA problem with only power variables left and design a novel relaxation scheme to enable the convexity. The polynomial‐time algorithm‐interior‐point method‐is employed to solve the relaxation problem and the theoretical complexity is further presented. Simulation results demonstrate that the author's scheme can provide high energy efficiency compared with the existing methods, 100% relative error bounds with respect to the optimum in most cases, and low computational complexity.

In wireless communication, the amplitude and phase of the transmitted signal have certain patterns during one symbol duration, which introduces high correlation among the samples obtained by oversampling at the receiver. In this work, we aim to explore such correlation information for cognitive radios to enhance the performance of spectrum sensing. By jointly considering the signal modulation, multipath fading, and oversampling rate, we derive the distribution of the empirical autocorrelation function for the obtained samples, on which we propose two efficient spectrum-sensing algorithms, and then analyze their performance. Our theoretical results reveal that the proposed algorithms with oversampling perform strictly better than the conventional energy detection scheme, while requiring the same level of prior information. Finally, we show through simulations that the derived statistical characteristics approximate the true statistical distribution of the autocorrelation function well, and the proposed sensing algorithms significantly improve the sensing performance compared to several existing sensing schemes.

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.

The optimal weighting matrix design at the relay node for the V-BLAST based amplify-and-forward (AF) MIMO relay transmission scheme has been intensively investigated in literature. However, there are few papers considering the optimal design of the weighting matrix at the relay node for orthogonal space time block codes (OSTBCs) based AF MIMO relay transmission scheme. In this paper, the maximum-SNR weighting matrix at the relay node is designed for the OSTBC based AF MIMO relay scheme, so that for the first time we can quantify the SNR gain by optimizing the weighting matrix at the relay node for the OSTBC based AF MIMO relay scheme. Specifically, the canonical form of the optimal linear weighting matrix is derived by exploiting matrix inequality theory and majorization theory. Furthermore, we propose a simulation based Gamma approximation method for diversity order analysis. The simulation results show that the proposed optimal weighting matrix significantly outperforms the traditional scalar-gain weighting matrix for the OSTBC based AF MIMO relay scheme.

In this paper, we propose interference alignment (IA) schemes for downlink multiple-input-multiple-output heterogeneous networks (HetNets) with partial connectivity, which is induced by the path loss and the low transmission power of small cells. Specifically, we consider two partially connected scenarios of HetNets. In the first scenario, we focus on the partial connectivity among small cells, whereas in the second scenario, we further consider the partial connectivity between the macrocell and small cells. For the first scenario, we first propose a two-stage IA scheme by exploiting the heterogeneity and partial connectivity of HetNets. Then, the influence of the number of served macro users on system degrees of freedom (DoFs) is investigated. In particular, we derive the condition under which serving one macro user achieves more DoFs than serving multiple macro users and design an algorithm to find the optimal number of served macro users to maximize the system DoFs. Afterward, we study the second scenario and extend the two-stage IA to this scenario. The simulation results show that the proposed IA schemes can significantly improve the system sum rate. Moreover, by considering the partial connectivity between the macro cell and small cells, the system performance can be further improved.

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.

A cross-network cross-layer design method is proposed to exploit the trunking, diversity, and best service assignment gains available in a heterogeneous wireless network (HWN), consisting of orthogonal radio access networks (RANs) and interference-limited RANs. Accounting for traffic-level dynamics and channel fading, we jointly design the distribution strategy for elastic and inelastic traffic, and the radio resource management strategy for RANs, in a network-separable control architecture. Optimal and quantified near-optimal radio allocation schemes are proposed for each type of RAN, which are combined into an on-line design framework that over time provides asymptotically optimal performance, maximizing the sum throughput utility for elastic traffic while guaranteeing the throughput requirements of inelastic traffic. Extensive simulation results demonstrate substantial performance improvement against suboptimal alternatives.

Existing works have addressed the tradeoffs between any two of the three performance metrics: throughput, energy efficiency (EE), and delay. In this paper, we unveil the intertwined relations among these three metrics under a unifying framework and particularly investigate the problem of EE-guaranteed throughput-delay tradeoff in interference-free wireless networks. We first propose two admission control schemes, referred to as the first-out and first-in schemes. We then formulate it as two stochastic optimization problems, aiming at throughput maximization (in the first-out scheme) or dropping rate minimization (in the first-in scheme) subject to requirement of EE (RoE), stability, admission control, and transmit power. To solve the problems, the EE-Guaranteed algorithm for throUghput-delAy tRaDeoff (eGuard), respectively called eGuard-I and eGuard-II in the first-out and first-in schemes, is devised. Moreover, with guaranteed RoE, we theoretically show that the eGuard (I and II) can not only push the throughput arbitrarily close to the optimal with tradeoffs in delay but also quantitatively control the throughput-delay performance on demand. Simulation results consolidate the theoretical analysis and particularly show the pros and cons of the two schemes.

We propose a new scheme to mitigate the uplink co-tier interference in dense femtocell networks, assuming that advanced receivers are employed by the femtocell base stations (FBSs). The conventional solution where interfering cells are assigned orthogonal spectrum resources leads to the inefficient spectrum usage. We exploit resource reuse by taking advantage of the successive group decoder (SGD) such that users can opportunistically access the entitled resources of nearby cells. The SGD decodes some interference signals in order to best decode the useful signal. Multi-cell uplink resource allocation with SGDs is formulated as a joint channel, rate and decoding group (CRG) allocation problem to maximize the weighted sum rates of the variable bit rate (VBR) users while meeting the rate requirements of the guaranteed bit rate (GBR) users. Due to the provable NP-hardness of the problem, we propose a greedy algorithm where the idea is to let GBR users opportunistically transmit on the channels of nearby cells and release more interference-free channels for high-rate transmission of VBR users. A semi-analytical framework is developed to provide a rough estimate of the potential throughput gain by the proposed technique. Simulation results show that the throughput gain over the conventional orthogonal resource allocation ranges from 10%–140% with different traffic proportion, number of users and rate requirement.

In this paper, we examine the channel estimation in an amplify-and-forward (AF) one-way relay network (OWRN) under time selective flat fading scenario, where the distributed space-time coding (DSTC) is adopted at relay nodes. Different from most existing works, our target is to estimate and track the individual channels of each relay hop instead of the composite channels. To reduce the number of the channel parameters to be estimated, we apply the polynomial basis-expansion-model (P-BEM) and convert the problem to estimating the channel coefficient-vectors (called in-BEM-CVs) of each relay hop. With the aid of the autoregressive (AR) model, we formulate the dynamic state space for the in-BEM-CV estimation. Specifically, we adopt the unscented Kalman filter (UKF) to track the in-BEM-CV dynamic variations in an forward manner, and utilize the unscented Rauch-Tung-Striebel smoother (URTSS) to smooth the UKF's estimations in an backward manner. To make the study complete, we also derive Bayesian Cramér lower bounds (BCRBs) for the in-BEM-CV estimation. Finally, numerical results are provided to corroborate the proposed studies.

In order to improve spectrum utilization, dynamic spectrum allocation DSA schemes with interference management have been widely investigated, and a special interest is in the current heterogeneous wireless network HWN environment. In the previous work, the approach that guarantees quality of service for the cell-boundary user is extensively used in the DSA scheme. However, such approach gives a pessimistic result. In this paper, we design a DSA scheme for the HWN system, which adopts relatively accurate interference constraints. First, considering that users may randomly locate over the cell, we propose interference constraints across the cell coverage, in which the interference experienced at a point is controlled below the level suffered when using fixed spectrum allocation in only a single network, and the proportion of the cell area where interference is controlled reaches the required area coverage probability. Then under the interference constraints, we formulate the downlink DSA scheme as a combinatorial optimization problem. As the problem is NP-hard, to reduce the computational complexity, we propose two greedy heuristic algorithms for its solution. Simulation results show that the DSA scheme could improve the total spectrum utility for operators and increase the satisfaction rate of spectrum demands for base stations. Copyright © 2013 John Wiley & Sons, Ltd.

A two-phase spatiotemporal adaptive transmission and spectrum sharing mechanism is designed for multi-input multi-output cooperative cognitive radio network (MIMO-CCRN). By employing cognitive user(s) as relay(s), performance of end-to-end primary transmission is improved. On the other hand, secondary user (SU) can access to the spectrum for its own data transmission simultaneously with the primary, as a reward for assisting the primary's data delivery. With the proposed scheme, multiple SU pairs participate in the data relaying, and share the access authority following a time division multiplexing mode. By joint transmit precoding and receive filtering design, co-channel interference can be appropriately suppressed. Moreover, bottleneck effect in the end-to-end primary link is studied, and an adaptive time slot division scheme is employed as a solution to bottleneck elimination. Simulation results show that the primary spectral efficiency is improved with the assistance of SU(s). Time domain adaptation could effectively enhance the end-to-end performance. Meanwhile, spatiotemporal multiplexing of different types of transmission facilitates the spectral efficiency. As a result, a win-win situation for both the primary and secondary is achieved.

Typical interference avoidance methods in HetNets are arranging orthogonal resources between macrocell and small cells such as in frequency domain in inter-cell interference coordination (ICIC) or time domain in enhanced ICIC (eICIC). However, wireless channels experience time-frequency fading appealing for a two dimensional resource allocation scheme. By optimizing the resource pattern based on variable channel conditions, plus the introduction of group lasso term, our scheme exploits channel variations in both frequency and time domains and as well avoid interference. By grouping the resources in our optimization model, our scheme is flexible and robust against heavy bursty traffic. It is also a lightweight (in terms of coordination overhead) and completely distributed approach, making it suitable for practical implementation. Simulation results show the effectiveness of our proposed method compared with sole frequency domain and time domain approaches.

Heterogeneous networks are an attractive means of improving the network capacity. Heterogeneous network are typically composed of multiple radio access points (macro, pico and femto) where the base stations are transmitting with variable power. In this study, we consider the uplink of heterogeneous networks, where each terminal can simultaneously connected to multiple radio access points and requests delay-sensitive traffic (e.g., real-time video). We adopt the framework of Hou, Borkar,and Kumar, and study the delay in heterogeneous networks and analyze the multiaccess transmission delay by considering three extreme scheduling schemes. Moreover, a parallel-aggregation multiple radio access points (PA-MRA) algorithm for packet scheduling is presented to improve the transmission delay in heterogeneous networks. Analysis and numerical results show that the proposed algorithm obtains the optimal and robust transmission delay gain compared with the three extreme scheduling policies.

Wireless world initiative new radio (WINNER) delivers a 3D cross-polarized channel model for B3G/4G system designs. Comprehensive radiation characteristics of polarized antenna are crucial in generating channel coefficients. Being currently supported within WINNER channel model, field patterns are technically obtained by chamber measurement. However, in some channel related performance analysis scenarios, design insight can be crystallized better by starting the derivations with theoretical co-pol and cross-pol components. Specifically, these two components are mathematically linked with field patterns through the proposed polarization projection algorithm. In this paper, we focus on revealing the physical significance of polarization transform between the antenna plane and the propagation plane. In practice, it makes retrieving the field patterns by electromagnetic computation possible. Meanwhile, the impact imposed by distinct antenna orientations is geometrically illustrated and consequently incorporated into the proposed algorithm. The result is analytically verified by the closed-form expression of the dipole field pattern and we find that its 2D degenerative case is aligned with that defined in 3GPP TR 25.996. The benefit is to significantly reduce the cost on generating channel coefficients in WINNER channel simulations.

Interference alignment (IA) is an emerging interference management approach which fully exploits the potential spatial bandwidth in wireless networks, achieving optimal DOF. However, perfect IA is almost unaccessible for 3-user complex constant interference channel without symbol extension. For this, an efficient phase based imperfect interference alignment scheme is proposed in this paper, which uses a deviation compensation factor (DCA) to measure how much the practical channel coefficients deviate from perfect IA feasibility condition. The DCA is allocated to receivers based on a Minimum Interference Leakage (MIL) criterion. Simulation results shows that the MIL based DCA, Max-SINR and TDMA algorithms perform better in different SNR regions, and the proposed scheme can be improved with proper switch between different strategies.

In this work, we study the problem of interference in a two-tiered uplink network which contains a macro-cell and multiple small-cells. We null the interference from small-cells to macro-cell by using Vandermonde-subspace frequency division multiplexing (VFDM). VFDM can exploit frequency selectivity and null space generated by the cyclic prefixes (CP) used by the macro-cell communication. We use an interference alignment (IA) scheme with an extension of a grouping method to align the interference between multiple small-cells into a lower dimensional-subspace, by using this grouping method we reduce the number of receive antennas required to null interference. Finally, we derive the achievable degrees of freedom (DoF).

Spectrum leasing is a novel promising technique to improve the spectrum efficiency (SE) through the spectrum owner leasing its free or under-utilization spectrum to unlicensed users for superfluous spectrum revenue, while the unlicensed users should pay for spectrum renting. Recently, spectrum leasing is used between femtocell service provider (FSP) and the coexisting macrocell service provider (MSP) in heterogeneous networks (HetNets). However, the energy efficiency (EE), a critical performance metric in HetNets, especially when multiple FSPs are densely overlaid on the coverage of MSP, has been largely neglected in available SE-oriented spectrum leasing. In this paper, a cooperative spectrum leasing framework is proposed to both mitigate interference and save energy, where a Stackelberg coordination game is formulated to analyze a joint spectrum leasing, pricing and interference coordination process between FSPs and MSP with the novel energy-aware utility functions for the players of FSPs and MSP. We rigorously derive the optimal closed-form solutions of spectrum leasing, pricing, and power coordination solutions for the FSP and the MSP. We propose a multi-stage distributed algorithm to approach these solutions. Finally, simulation results are provided to clarify effects of multiple parameters in the gaming process and verify the final improved performance.

Mobility load balancing (MLB) is an important use case of Self-Optimizing Networks (SONs). To combat the commonly encountered issues in conventional MLBs, such as the blind offloading without equilibrium and optimality guarantees, we propose an Enhanced MLB (ELB) scheme to conquer these problems with the double threshold design including the common trigger threshold and the fairness-aware ending one. First, we introduce the rationale behind our idea with simple analysis, then the newly presented the fairness-aware ending threshold is given by modeling the fairness metric during the optimal target cell selection process. Based on these analysis, we propose the ELB scheme with a double-threshold design. Simulation results show that the presented ELB scheme can well improve the system performance and the user experience quality.

This manuscript deals with the modeling, analysis, and measurement of a small scale fading (SSF) mobile radio channel. The physics of SSF are first reviewed to reveal the generating mechanisms of channel selectivity. A stochastic channel model is then derived as a function of time, antenna array displacement and frequency, which falls in the category of tapped delay line model. Specifically, the taps can be represented as a combination of a possible line of sight or dominant reflected path and a Gaussian distributed component comprised of unresolvable scattered paths. After that, general analytical solutions are provided for the 3D temporal-spectral-spatial correlations of SSF via the exploitation of channel statistical properties. We show that this function can be expressed as the product of three low order temporal, spectral and spatial correlations individually under appropriate assumptions on the associated wireless propagation environment. This will definitely facilitate the derivations of the closed form expression regarding the correlation function of SSF. From engineering perspective, this analysis can be utilized to develop network correlation maps for example. Finally, out field measurement results verify the validity of our theoretical analysis.

Two new interference alignment algorithms for MIMO interference channel (IC) are proposed. Both the algorithms are based on maximizing useful signal subspace and suppressing interference power (MUSI). The first algorithm is based on the convex optimization. Interference leakage is constrained and the useful signal subspace optimization function is built. Semidefinite programming relaxation and rank 1 decomposition techniques are used for searching the precoder and decoder. The second algorithm is optimized on the Grassmann manifold. Conjugate gradient method is used for searching on the Grassmann manifold. Alternating optimization method between precoder and decoder is used for both the algorithms. Simulation results show that both the proposed algorithms perform well at high SNR.

This study analyses the performance of a roadside two‐way relaying system in which a roadside access point (AP) and a vehicle exchange messages with the aid of amplify‐and‐forward mobile relay (MR) based on partial relay selection. It has been shown that AP‐MR‐vehicle communications may experience severer channel fading than conventional cellular communications. Mixed Nakagami ‐m and ‘double’ Nakagami ‐m fading is adopted to provide a realistic description of the involved AP‐MR‐vehicle channels. In this scenario, a tight closed‐form lower bound and high signal‐to‐noise ratio approximate expression for the system outage probability are derived. By applying these results, the authors obtain the diversity and the coding gains and the average symbol error rate for the considered system. In particular, the optimum number of relays is provided, providing valuable guidelines for practical system design. It is shown that when the number of relays is greater than the optimum number, no performance gain would be further achieved. The simulation results highlight the authors theoretical analysis.

This study studies the joint antenna selection and transmit beamforming optimisation in the downlink of a single‐cell multi‐user distributed antenna system, assuming that all users are delay sensitive and only partial channel state information is available at the transmitter side. Under the constraint of the maximum transmit power of each distributed antenna unit, the authors formulate an optimisation problem to minimise the total power consumption while maintaining the rate outage ratio of each user below a threshold. To mitigate the signalling overhead, they firstly propose an adaptive antenna selection strategy based on the received pilot signal power strength. Then, considering that the remaining transmit beamforming problem may be infeasible and non‐convex, a series of relaxation and convex approximation are applied. Finally, they propose a transmit beamforming algorithm based on successive convex approximation and user admission control. Simulation results show that the authors proposed algorithm can achieve a near optimal system performance in both the satisfied user ratio and total power consumption.

In this study, the authors study the downlink joint user scheduling and power allocation in a single‐cell distributed antennas system. Considering that all users are delay sensitive and without any assumption on the feasibility of the system, they formulate an optimisation problem to minimise the total power consumption while maximising the number of satisfied users. Given a user scheduling result, a deflation‐based joint user removal and power allocation algorithm and a conservative power allocation algorithm are proposed. Owing to the complexity in full enumeration of all user scheduling results, they further propose a joint heuristic user scheduling and power allocation algorithm, in which a priority parameter is defined to determine the user scheduling order and the conservative power allocation algorithm is applied to execute the power allocation. The user with the worst quality of experience is iteratively removed away from the active user set until all the remaining users are guaranteed with their quality of service requirements. Simulation results show that the authors proposed heuristic algorithm obtains a near optimal system performance in both user outage ratio and total power consumption.

Strategic bargaining cooperative games have found extensive applications to resource management in wireless networks. In this survey, basics of a strategic bargaining game and solution concepts are firstly presented. Geometrical interpretations are introduced to better understand real meanings of them. Then, the authors survey the applications of various strategic bargaining games for the emerging wireless networks, where the authors concentrate on several interesting problems based on their previous systematic studies: (i) distributed resource management design for cognitive radio networks based on geometrical interpretation of the cooperative solution; (ii) asymmetric bargaining modelling for green communications; (iii) a unified utility tradeoff design between spectral and energy efficiency in heterogeneous cellular networks; (iv) the cooperative rate splitting game for Long Term Evolution‐coordinated multi‐point system; and (v) a general bargaining formulation with different tradeoffs between efficiency and fairness. In addition, the authors survey the applications of strategic bargaining games to cooperation incentive mechanism, bargaining game on capacity region of interference channel and multiuser and multimedia applications. Finally, challenges and potential research direction are summarised in this work.

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.

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.

Multi-hop heterogeneous cellular networks (MHCNs) consist of conventional macro cellular networks overlaid with an irregular deployment of low-power base stations (BSs), where the communication between BSs and mobile users can be established through a single hop or multiple hops. By modeling different kinds of randomly located BSs as K tiers of independent homogeneous Poisson Point Processes, we first explore the capacity of downlink MHCNs and derive the expression of capacity under Rayleigh fading channels. Particularly, the capacity gain achieved by cell splitting and multi-hop relaying is quantified for the first time. We then study the effects of BS density, transmit power, and signal-to-interference-plus-noise-ratio (SINR) threshold on the capacity of MHCNs. More importantly, we obtain the spectral efficiency enhancement condition under which the increase of BS density and transmit power improve the spectral efficiency, thereby enhancing the capacity. One interesting observation is that at a given SINR threshold, the capacity increases with BS density when all the tiers have the same SINR threshold. Moreover, the capacity of some special networks (i.e., heterogeneous cellular networks, multi-hop cellular networks, and conventional cellular networks) are derived directly by specializing some system parameters in our results. Finally, numerical studies and simulations are conducted to validate our analysis.

Two-path or successive relaying, which aims to establish two relay links transmitting different information symbols in adjacent time slots, has recently emerged as an attractive wireless communication protocol to improve the spectral efficiency in half-duplex cooperative systems. In this paper, we investigate power allocation and relay selection techniques for amplify-and-forward two-path successive relaying networks. Our approach is based on the maximization of the received SNR subject to a total power budget consumed by the source and the relay assisting this specific transmission. Two scenarios including with and without direct link are considered here. We show that the main problem has a closed-form solution and only requires a few amounts of feedback bits to be broadcasted. Numerical results reveal that the proposed approaches are more insensitive to the inter-relay interference and robust to channel estimation errors; meanwhile, they perform better than the existing schemes. Copyright © 2013 John Wiley & Sons, Ltd.

The internal collision managing mechanism in Enhanced Distributed Channel Access (EDCA) makes it more difficult to successfully access the channel for the data with low priority. To address this issue, a novel Mechanism to solve the Internal Collision problem (MIC) is proposed to improve the performance of EDCA. The main concept of MIC is to decrease the backoff time of the Access categories (ACs) with low priority. In this way, MIC can improve the performance of the low priorities without compromising that of the high priorities. A Markov chain model is used to analyze the performance of MIC under saturated conditions. The correctness of the analysis results are verified by simulating MIC with NS-2. Both the analysis results and simulation results confirm that MIC outperforms EDCA in terms of the average access delay.

In this paper, we discuss how to exploit the multiple access points diversity gain in the downlink transmission of a multi-access wireless network. To follow the current user equipment hardware constraint as well as to reduce the network control overhead, we confine that each user equipment can only be serviced by one best suited access point. To achieve this, we formulate such problem as a non-linear integer program, which typically is computational intractable. To derive its solution, we first convert it to a linear integer program, which is then used to obtain a fractional solution, and further we refine the fractional solution to be integer to satisfy integer constraints for the access point selection and the resource unit allocation. By doing so, our algorithm has a low computational complexity. We also show the performance improvement of our algorithm over the distance based AP selection and the random AP selection methods through simulation.

Owing to the capability of accumulating mutual information from the transmission of previous nodes, rateless codes can boost the network performance considerably, and hence have sparked much interest recently. However, how to efficiently schedule multi-hop multi-relay transmissions with the aid of rateless codes remains a challenging work. Particularly, it requires high complexity to find an optimal route due to its inherent combinatorial nature. In this paper, we formulate the optimal transmission scheduling as a dynamic programming (DP) problem by defining a novel state and constructing a tree-structured state transition diagram. It is from a point of view of DP that we further propose two low-complexity algorithms, namely S-DIRAC and Fano-DIRAC, with negligible performance loss based on the idea of sequential decoding of convolutional codes. Simulation results indicate that the low-complexity algorithms almost always find the optimal solution and show the superiority of routing with mutual information accumulation compared to conventional shortest path routing.

Channel allocation has become a significant challenge in multi-channel Ad-Hoc networks due to the fast growing of wireless applications and the limitation of spectrum resource. In this paper, we assume that the demand of each user is different and alterable, which has been known by all the others. We study the problem of demand-aware channel allocation (DaChA) from a non-cooperative strategic game theoretic view. We show that the game would converge to a Nash Equilibrium (NE), but the NE may be sub-optimal. To avoid this possible situation, we propose a novel mechanism which assigns a prior channel to each communication link. If deviating from the prior channel, we design a charging scheme which would induce players to converge to a unique NE. We analyze the Pareto-Optimality (PO) of the game and work out that the unique NE must be Pareto optimal. The simulation results demonstrate that the mechanism is efficient.

With the development of multi-interface terminals, a host can connect to the Internet simultaneously by multiple access technologies. Under multi-access technology, a multi-path transmission can obtain high throughput, increased available bandwidth and enhanced reliability. However, the multi-path transmission with multi-access technology also has the problems that the packet re-ordering is unavoidable, and the fast retransmission is unnecessarily requested. Considering the stochastically varying transmission delay, the problems above may eventually result in a degradation of throughput. As a result, in this paper, we focus on the analysis of buffer overflow probability problem which is influenced by the transmission interval. First, we utilize Reinforcement Learning method to estimate the stochastic delay of end-to-end paths. Then, we discuss problems of re-sequencing buffer occupancy distribution and the overflow probability. In this paper, we model the stochastic delay as a continuous random variable, and then, discuss its mean value and variance. Simulation result shows that the re-sequencing buffer overflow probability is influenced by the transmission intervals and the variance of stochastic delay.

In sharing-spectrum macrocell and femtocell networks, the aim of resource allocation schemes often involves maximizing system capacity or maintaining fairness under the assumption of saturated traffic for users. However, these schemes cannot be easily extended to the more practical case with the finite-queue traffic. In this paper, we design a resource allocation scheme in the finite- queue model to manage interference and keep the network stability. Considering the queue length and the dominant interference, we first construct the weighted interference graph. Then the resource allocation problem is cast into solving the maximum weight independent set problem. Finally, considering the overhead and system performance, we present a hybrid central/local algorithm according to the spatial and temporal varying pattern of traffic distribution. Simulation results show the proposed scheme can guarantee the queue stability and improve throughput in the finite-queue traffic model.

We investigate the interactive behavior and strategic decision-making between multiple secondary users (SUs) and primary users (PUs), both of which are end-to-end performance aware in cognitive networks. A Stackelberg game is utilized to formulate the spectrum utilization maximization problem after the complex interference situation is analyzed. Especially, an interference power cap (IPC) function predefined by PUs as a pricing function is introduced into the utility function design of SUs to guarantee QoS of PUs, as well as to decouple constraints. Further, an asymmetric information situation can be formed by considering PUs as leaders who employ the optimal water-filling algorithm, and the closed-form power strategy of SUs can be derived. And accordingly, SUs as followers can observe the available information to do more foresighted decision by learning. What is more, we prove the optimality and existence of the deceived solutions. Numerical results demonstrate that the proposed distributed algorithm provides more spectrum revenue and better QoS guarantees to PUs with limited iterations.

The emerging Machine-to-machine (M2M) communications are posing serious challenges to cellular network systems. When a high number of M2M devices try to send data almost simultaneously, the air interface congestion would result in significant quality of service degradation in conventional Humanto- Human (H2H) communications. To solve this problem, an uplink intercell coordination admission control and scheduling scheme (ACSS) in a CDMA multicell environment is proposed in this paper, which makes the best of M2M delay tolerant feature. Delay tolerant M2M connections in the home cell and neighbor cells can be interrupted to release resource for H2H services and reestablished later. The final M2M set to be scheduled is determined by the maximum throughput criterion and residual tolerant delay criterion. Simulation results have shown that with ACSS the H2H blocking probability can be guaranteed and M2M transmission failures can be reduced, achieving better system performance and higher resource utilization.

Network coding is considered as a promising technique to increase the bandwidth available in a wireless network. Many studies show that network coding can improve flow throughput only if an appropriate routing algorithm is used to identify paths with coding opportunities. Nevertheless, a good routing mechanism is very difficult to develop. Existing solutions either do not estimate the path bandwidth precisely enough or cannot identify the best path in some situations. In this paper, we describe our coding-aware routing protocol that provides a better path bandwidth estimate and is able to identify high throughput paths. Extensive NS2 simulations show that our protocol outperforms existing mechanisms.

A dynamic optimization algorithm is proposed for the joint allocation of subframes, resource blocks, and power in the Type 1 inband relaying scheme mandatory in the LTE-Advanced standard. Following the general framework of Lyapunov optimization, we decompose the original problem into three sub-problems in the forms of convex programming, linear programming, and mixed-integer programming. We solve the last sub-problem in the Lagrange dual domain, showing that it has zero duality gap, and that a primal optimum can be obtained with probability one. The proposed algorithm dynamically adapts to traffic and channel fluctuations, it accommodates both instantaneous and average power constraints, and it obtains arbitrarily near-optimal sum utility of each user's average throughput. Simulation results demonstrate that the joint optimum can significantly outperform suboptimal alternatives.

ABSTRACT We investigate the problem of precoding optimization in an amplify‐and‐forward multiple‐input‐multiple‐output relay system. Most reported works on this problem focus chiefly on the design of relay precoder without simultaneously optimizing the direct link. In this paper, we propose a method for joint source/relay precoder design, taking both direct and relay links into account. Our design is based on maximizing the mutual information (MI) under limited transmission power constraints at the source and relay, respectively. We first formulate a constrained optimization problem before relaxing the original cost function for tractability and derive a MI lower bound. This elaborate bound can asymptotically approach the exact expression of MI in an iterative fashion. In contrast to previous strategies, we then prove that the optimal structure of the source and relay precoders jointly convert the multiple‐input‐multiple‐output relay channel into a bank of single‐input‐single‐output relay channels without having to assume a beamforming structure to simplify the derivation. Specifically, the linear precoding design problem degenerates into power loading among multiple single‐input‐single‐output relay channels. Applying standard Lagrange technique results in a scalar convex optimization, and it can be readily solved by iterative water filling. Numerical examples demonstrate that the proposed scheme, either exploiting partial or full channel state information, significantly outperforms the existing methods. Copyright © 2012 John Wiley & Sons, Ltd.

In order to manage the whole resource of B3G system efficiently, radio resource mobility management is investigated in this paper. The whole management procedure consists of initial radio access technology selection and load balancing. The corresponding selection rules and ordering rules are designed. In addition, radio resource mobility management framework and the detailed implementations are also presented. The scheme we proposed benefits both user and network with lower dropping probability and higher immediately served probability than other existing and typical schemes. Furthermore, it is easily implemented and its computational complexity is low.

The integration of cellular and wireless local area networks (WLAN) has drawn much attention due to their complementary characteristics. Cellular supports wider coverage and better capability to mobile user but with lower bandwidth whereas WLAN have higher bandwidth and lower cost but is effective only in small areas. Based on the combination of users' mobility and network load, this paper proposes an adaptive network selection scheme, called as DMMT (Dynamic Match of Mobility Threshold) scheme. By considering the distribution of the whole users' movement character and arrival rate, we can get the Mobility Threshold (MT) from the solution of optimization problem, which minimize the global network blocking probability. Then we use MT to differentiate mobile users to access different network. To obtain the parameters in the optimization problem, we use the prediction of arrival and mobility characteristics. The heterogeneous networks adjust the MT to adapt to the variation of environment. Simulation results show that the proposed DMMT scheme can reduce call blocking and handoff probabilities compared to the referenced schemes.

Channel assignment is a significant challenge due to scarcity of number of channels available in multi-channel multi-radio Ad Hoc networks. Meanwhile, traffic allocation is a problem worthy of research especially in the situation that different traffic demands for different users. In this paper, we formulate the problem of joint channel and traffic assignment as a static non-cooperative game with charging scheme, taking into account the different individual demand constraints, the number of channels, and the number of radios. To some extent, the charging scheme alleviates the unfairness problem. We show the existence of Nash Equilibrium (NE) and derive the sufficient and necessary conditions to be NE. Then, the uniqueness of the Nash Equilibrium is demonstrated under reasonable conditions. Finally, we extend the algorithm to multiple collision domains. The simulation results illustrates the algorithm is efficient and has a better performance.

This paper presents a resource allocation strategy for maximizing the lifetime of amplify-and-forward (AF) dualhop cooperative OFDM networks. The high computational complexity for optimization necessitates our study of suboptimal approaches. Our centralized scheme is implemented in two steps. Specifically, during each transmission interval, source-relay selection and subcarrier pairing can be decided according to channel state information of the relay links and residual energy information at each terminal. Next, energy-aware power loading can be solved by employing standard Lagrange techniques. Numerical examples verify the effectiveness of our proposal.

In this contribution, we derive the Discrete-input Continuous-output Memoryless Channel (DCMC) capacity of the non-regenerative Multiple-Input Multiple-Output (MIMO) relay channel, when the source-to-destination link is inferior and hence considered absent. We design near-capacity Forward Error Correction (FEC) codes for approaching this capacity limit. It is shown that our design is capable of approaching the DCMC capacity within 0.4dB, when communicating over uncorrelated Raleigh fading channels, where the source node, relay node and destination node are equipped with two antennas each.

This paper investigates the multiuser interference mitigation for the uplink of an orthogonal frequency division multiple access system in a doubly selective fading channel. The proposed algorithm squeezes the interference of subcarrier k into 2τ+1 neighboring subcarriers by preprocessing the received signal and yields a banded structure interference matrix. Specifically, the value of τ mainly depends on the carrier frequency offsets and determines the squeezing depth which in turn influences the receiver implementation complexity. Simulation results show that the bit error rate performance of our proposed algorithm approaches the existing full length minimum mean square error equalizer with a significant reduction on complexity.

A novel block diagonalisation (BD)-based Multiuser multiple input multiple output (MU-MIMO)-aided relaying scheme is proposed for downlink transmissions, which does not require any channel state information at the base station (BS) and decomposes a MU-MIMO-aided relaying system into several parallel single-user MIMO-assisted relaying schemes. Furthermore, based on the proposed algorithm the optimal linear processing matrix designed for the maximum achievable capacity is derived, which significantly outperforms the so-called naive weighting matrix.

A full-duplex uplink/downlink (UL/DL) system having a UL transmitter/receiver (transceiver) pair and a DL transceiver is considered. Both the UL and DL are improved. The DL is enhanced by a novel vector perturbation (VP)-aided singular value decomposition (SVD)-based precoding scheme, which is capable of exploiting the different-quality SVD eigenbeams and substantially reduces the overall average transmit power requirement of the traditional zero-forcing (ZF) precoder. By contrast, the UL is enhanced by a lattice reduction (LR)-aided receiver scheme designed for SVD-based multiuser multiple-input multiple-output (MU MIMO) UL transmission, when different modulation schemes are employed for different-quality eigenbeams. This new UL scheme avoids the noise-enhancement problem of the classic ZF UL receiver. The authors demonstrate that the proposed VP-aided DL and LR-assisted UL constitute a powerful full-duplex system, which achieves an ∼15 dB signal to noise ratio (SNR) gain for both SVD-based MU-MIMO DL and UL transmissions over the traditional ZF-aided schemes at a bit error rate (BER) of 10−3.

From an energy-saving perspective, the authors investigate the downlink power control issue of the two-tier heterogeneous networks (HetNets) using a cognitive radio train of thought. The authors consider the HetNets scenario of one macro-cell evolved-NodeB (eNB) and multiple femto-cell Home evolved NodeBs (HeNBs) cooperatively coexisting to provide better services. A specific HeNB allows macro-mobile station (macro-MS) previously associated with eNB to access to it for better signal-to-interference plus noise ratio (SINR) guarantee. As a reward, the macro-MS pays a certain of revenue to HeNB as the incentive mechanism for this HeNB's downlink extra power consumption, which is manifested in the design of the price function. The throughput bound of Macro-MSs in HeNB cell is given. Then, the authors select the SINR as the performance measure and formulate the power control of selected HeNBs as multi-constrained optimisation problem. Meanwhile, the authors derive the sub-optimal and closed-form power control of individual HeNB, based on which the authors design the distributed algorithm with the aid of eNB and HeNBs cooperatively to implement the pricing information exchange. Simulation results show the improved performance of the convergence, the energy-efficiency measured by ‘energy-per-bit’ and the throughput of the ‘Proposed-Cognitive-x’ power control algorithm.

Wireless Mesh Network (WMN) has become an important edge network to provide Internet access to remote areas and wireless connections in a metropolitan scale. In this paper, we study the problem of identifying the maximum available bandwidth path, a fundamental issue in supporting quality-of-service in WMNs. Due to interference among links, bandwidth, a well-known bottleneck metric in wired networks, is neither concave nor additive in wireless networks. We propose a new path weight which captures the available path bandwidth information. We formally prove that our hop-by-hop routing protocol based on the new path weight satisfies the consistency and loop-freeness requirements. The consistency property guarantees that each node makes a proper packet forwarding decision, so that a data packet does traverse over the intended path. Our extensive simulation experiments also show that our proposed path weight outperforms existing path metrics in identifying high-throughput paths.

Network coding is known to be a promising technology to increase the bandwidth capacity in wireless networks. To our best knowledge, there is limited work on studying the available bandwidth for a given path with network coding. This paper presents a new method to estimate the available bandwidth of a path that considers network coding and wireless interference simultaneously. We show that our estimated path bandwidth can be easily achieved using a simple scheduling scheme. We also show that path bandwidth estimation is more accurate than other existing method through theoretical analysis and simulation experiments.

Scalability of routing protocols is one of the most important open problems in large scale wireless networks. In this paper, a routing protocol for large scale wireless network, called as SCR (Small-world based Cooperative Routing protocol), was proposed based on the small world phenomenon and cooperative communication. In SCR, each source node selects its short-cut node, through which the path length to the destination node is greatly reduced. The cooperative communication link is formed to decrease the hops between the sender node and its short-cut node to match the small world phenomenon. We show that in a network with nXn nodes, the average path length of SCR is O [n log n)/[Mq)], 1≤q≤log n, where M is the number of cooperative nodes. If the average hops between the sender node and its short-cut node is approximately equal to one hope by using the cooperative communication link, the average path length of SCR is O[(log n)2/q] . Compared with other existing routing protocols, the SCR has much shorter path length and low routing overhead.

Multicarrier transmission over time varying frequency selective fading channels is investigated and a model for such a transmission scheme with emphasis on Multiple Input Multiple Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) systems is developed. The fast fading can destroy the orthogonality of subcarriers and cause serious InterCarrier Interference (ICI), thus leading to significant performance degradation that becomes severe as Doppler shift increases. To tackle this problem, a frequency domain iterative ICI mitigation algorithm is mathematically derived. By fully exploiting the separation property of Channel Transfer Function (CTF) matrix, ICI can be iteratively subtracted from received symbols and then Parallel Interference Cancellation (PIC) detection is performed to suppress multistream interference among different antennas. In particular, the complexity of the proposed strategy can be drastically reduced by restricting the interference computation to limited eighteen adjacent subcarriers, whereas the performance loss is quite minor. Simulation results illustrate that this scheme can effectively alleviate the impairment of ICI and asymptotically approach the ICI free performance at low-to-moderate Signal-to-Noise Ratio (SNR).

Vector perturbation (VP) is a prominent precoding technique attracted a lot of attention in recent years. Until now, however, various extended VP techniques proposed to apply in multiuser precoding are almost restricted to one antenna configuration of each user. The restriction does not meet the development of next generation wireless systems. So, the well-known block diagonal (BD) algorithm and VP is naturally combined and proposed, named BD-VP for short, to solve this problem. However, the BD-VP completely suppressing multi user interference (MUI) at the expense of noise enhancement results in performance degradation. To overcome the shortcoming of BD-VP, we propose generalized channel inversion VP (GCI-VP) algorithms. Analysis and simulation results show that the proposed ZF GCI-VP is equivalent to the BD-VP, while the algorithm MMSE GCI-VP I and MMSE GCI VP II greatly outperform the BD-VP.

In this paper, we propose a new path metric for finding the maximum available bandwidth path in the multi radio multi-channel wireless mesh networks. We formally prove that the path metric is isotonic, which is the necessary and sufficient condition for assuring the proper operation of the routing algorithm. Based on the metric, we develop a routing protocol which jointly considers the path selection and the channel assignment. The time complexity of our routing algorithm is polynomial. We conduct the simulation experiments to compare the proposed metric with the existing metrics for finding the maximum available bandwidth path.

This paper discusses the problem of computing the maximum available bandwidth of a given path in TDMA-based network with network coding, which is a fundamental issue for supporting QoS with bandwidth requirement in wireless networks. We present a new path bandwidth computation mechanism considering physical-layer network coding. To our best knowledge, our work is the first proposal on assigning time slots with consideration of wireless interference and network coding simultaneously. Our simulation experiments show that our approach produces higher throughput than the existing approach.

In this paper, a MIMO routing protocol called as DZMRP (Double Zones MIMO Routing Protocol) for MIMO ad hoc networks was proposed. The DZMRP is a hybrid routing protocol that proactively maintains routes within a local zone of the network, which referred as the local routing zone and is divided into diversity zone and multiplex zone. Different updating frequencies of the changes of link connectivity are associated with the diversity zone and multiplex zone, so that the routing maintenance overhead is decreased dramatically. By leveraging the multiplex and diversity gains of MIMO links for the high data rate and the range extension respectively, the DZMRP improves the efficiency of a reactive route query/reply mechanism. Compared with other existing routing protocols such as ZRP and MIR, the DZMRP has much higher end-to-end throughput and lower packet delivery delay due to the dramatic reduction in protocol overhead.

Emergence of various radio access networks enables users to communicate with each other anytime and anywhere. Handover management strategy directly influences the user's service continuity performance and quality of service. In this paper, we propose a handover trigger time selection strategy based on the prediction of received signal strength. With auto-regression model, the mobile terminal predicts both the earliest and the latest handover trigger time threshold, and then determines the optimal trigger time. Simulation results show that our proposed method can almost eliminate handover failure and has a good adaptability to the varying network overlapping environment.

We investigate the problem of precoding optimization in an amplify-and-forward (AF) multiple-input-multiple-output (MIMO) relay system. Most reported works on this problem focus chiefly on the design of relay precoder without simultaneously optimizing the direct link. In this paper, we propose a method for joint source/relay precoder design, taking both direct and relay links into account. Our design is based on maximizing the mutual information (MI) under limited transmission power constraints at the source and relay, respectively. We first formulate a constrained optimization problem before relaxing the original cost function for tractability and derive a MI lower bound which asymptotically approaches the exact expression of MI in an iterative fashion. In contrast to previous strategies, we then prove that the optimal structure of the source and relay precoders jointly convert the MIMO relay channel into a bank of single-input-single-output (SISO) relay channels without having to assume a beamforming structure to simplify the derivation. Specifically, the linear precoding design problem degenerates into power loading among multiple SISO relay channels. Applying standard Lagrange technique results in a scalar convex optimization which can be readily solved by iterative water filling. Numerical examples demonstrate that the proposed scheme, either exploiting partial or full channel state information (CSI), significantly outperforms the existing methods.

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.

Efficiency and fairness are the two critical and conflicting criterion indicators to measure radio resource management performance including the power and transmit control in the cognitive context. The best tradeoff is achieved using the dynamic game-theoretical approach in this paper, from the individual rationality and the social fairness perspective. Therefore, a two-step resource management scheme is investigated in the cognitive radio networks context, where the initial allocation focus on the personal efficiency and the second adjustment focus on the social fairness and the overall performance. Both the proposed algorithms during the two-steps scheme are distributed, with the help of the iterative water-filling algorithm and the sub-gradient method. Simulation numerical results tell the performance of the proposed algorithm.

This paper deals with the blind source separation (BSS) problem in the overdetermined case, i.e. the number of receive antennas is larger than that of sources. First, a method, which converts the over-determined BSS into complete BSS, is presented. Then a maximum ratio combining (MRC) based BSS algorithm (MRC-FastICA) is proposed. The proposed algorithm includes source separation, estimated signals classification and MRC of multiple estimated signals corresponding to a source signal. Theoretical analysis and simulation results show that the proposed algorithm can obtain diversity gain, and has better performance than traditional methods.

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.

Previous works on network coding capacity for wireless networks have taken the assumption that the network is stationary. In this paper, the mobility of ad hoc networks is considered as a key factor influencing network capacity, and a new and unified analytical expression of the capacity of a mobile ad hoc network applying network coding is derived under the two main mobility models, including random waypoint and random walk. The simulation results show that under the mobility condition, the network capacity of mobile ad hoc networks applying network coding still exhibits a concentration behavior around the mean value of the minimum cut.

According to the demand of the Doppler frequency shift estimation under low signal-noise-ratio (SNR) conditions, the stochastic resonance (SR) technique from the nonlinear science is applied to signal pre-processing and a novel algorithm with the level crossing rate (LCR) estimation is proposed in this paper. Theoretical derivation demonstrates the proposed algorithm can effectively improve SNR and bandwidth ratio of the received signal, thereby reducing estimated error of the LCR algorithm. Moreover, after analysis and discussion of the optimal sampling frequency in stochastic resonance pre-processing (SRP), the best relationship of the sampling frequency with input SNR is obtained. The Monte-Carlo simulation results show that comparing with current algorithms, the proposed algorithm improves estimated performance of 2~4dB without increasing computational complexity under low SNRs, it verifies the consistency with the theoretical conclusions.

We capture both the economical and technical aspects of the dynamic spectrum sharing to improve the underutilization of the scarce but valuable radio spectrum and the service providers' revenue. Especially, we investigate the optimal mapping between the service requester pool (SRP) and the service provider pool (SPP) in the coexistence context of multiple heterogenous cognitive radios. Both the optimal pricing schemes of the SPP for the specific shared spectrum and optimal transmission strategies of the SRP for rational spectrum requirement are designed. The issue of the coexistence spectrum sharing is formulated as the secondary network utility maximization (SNUM) problem. Using Lagrange duality decomposition technique and the Stackelberg game modeling approach, we obtain the distributed algorithms with low implementation complexity and limited iterations. The proposed PBDSS will strategically interact with each other until converge to dominating solution termed as Stackelberg equilibrium solution (SES), and the numerical results reflect the improvement of the overall performance and the spectrum utilization of our proposed spectrum sharing approach.

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.

In this treatise, we propose an iteratively decoded Bell-labs LAyered Space-Time (BLAST) scheme, which serially concatenates an IRregular Convolutional Code (IRCC), a UnityRate Code (URC) and a BLAST transmitter. The proposed scheme is capable of achieving a near capacity performance with the aid of our Extrinsic Information Transfer (EXIT) chart assisted design procedure. Furthermore, a Markov Chain Monte Carlo (MCMC) based BLAST scheme is employed, which is capable of significantly reducing the complexity imposed. For the sake of approaching the maximum achievable rate, iterative decoding is invoked to attain decoding convergence by exchanging extrinsic information among the three serial component decoders. Our simulation results show that the proposed MCMC-based iteratively detected IRCC-URC-BLAST scheme is capable of approaching the system capacity.

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.

This paper deals with the problem of joint timing error, frequency offset and channel estimation in multi-input multi-output (MIMO) frequency selective channels. Two equivalent signal models with frequency offset and timing error are given, and then a joint estimation method is derived. The proposed estimation method consists of two steps. Firstly, a maximum-likelihood (ML) frequency offset (FO) estimator is proposed based on the second signal model. Secondly, based on the FO estimate, we formulate the timing error and channel estimation as a problem of composite hypothesis testing according to the first signal model, and then solve the problem by the composite hypothesis testing approaches. Simulation results are performed to show the effectiveness of the proposed method.

Multiple input multiple output (MIMO) transceivers can potentially increase overall network throughput via spatial reuse of spectrum by allowing multiple simultaneous communications. Efficient use of MIMO technique in wireless ad hoc networks still meets challenges especially for upper layer protocols. In this paper we propose a cross-layer strategy of allowing the data stream and routing control stream transmitting concurrently with different antennas of the MIMO system. Implementing on the ad hoc on-demand distance vector (AODV) protocol, simulation results show that the strategy of queuing different type of packets obviously decreases the duration of route establishment, especially when the network is suffering heavy traffic load.

In point to point MIMO systems, uniform channel decomposition (UCD) has been proven to be optimal in BER performance and strictly capacity lossless when perfect channel state information (CSI) are assumed to be available at both the transmitter and receiver side. However, in practice, CSI is always contaminated by channel estimation error. In this paper, we proposed a novel Robust UCD scheme which is capable of improving the BER and capacity performance in the context of imperfect CSI compared with the conventional UCD scheme. We also derived the capacity lower bound of the MIMO channel using the Robust UCD scheme with channel estimation error.

Wireless body area network (WBAN) solution is an emerging technology to resolve the small area connection issues around human body, especially for the medical applications. Based on the integrated superframe structure of IEEE 802.15.4, this paper proposes a modified medium access control (MAC) protocol for WBAN with focus on the simplicity, dependability and power efficiency. Considering the support to multiple physical layer (PHY) technologies including ultra-wide band (UWB), the slotted ALOHA is employed in the contention access period (CAP) to request the slot allocation. Mini-slot method is designed to enhance the efficiency of the contention. Moreover, sufficient slot allocation in the contention-free period (CFP) makes the proposed protocol adaptive to the different traffic including the medical and non-medical applications. Simulation results show that the protocol effectively decreases the probability of collision in a CAP and extends the CFP slots to support more traffic with quality of service (QoS) guarantee.

Multiple-input multiple-output (MIMO) antennas combined with orthogonal frequency division multiplexing (OFDM) are very attractive for high data rate communications. However, MIMO-OFDM systems are very vulnerable to time selective fading as channel time variation destroys the orthogonality among subcarriers, causing serious intercarrier interference (ICI). In this paper, we employ optimal frequency domain partial response coding (PRC) in MIMO-OFDM systems over frequency selective, fast fading channels to mitigate ICI. We focus on deriving, via an analytical approach, a tractable, closed-form expression of carrier-to-interference ratio (CIR) to quantify the impact of time selective fading and demonstrate the effectiveness of PRC in mitigating ICI in MIMO-OFDM systems. From the numerical results, PRC can effectively increase CIR and the improvement is proportional to the number of subcarriers and the coding length.

In this paper, we address the problem of frequency offset and channel gains estimation for correlated multi-input multi-output (MIMO) channels. The channel is assumed to be frequency-selective and block fading with both spatial and multipath correlations. A maximum-likelihood (ML) frequency offset (FO) estimator is proposed by using the Baysian approach and the mean and variance of the FO estimation are derived. To evaluate the performance of the FO estimator, its Cramer-Rao lower bound (CRLB) is also developed. Based on the FO estimate, we derive the linear minimum mean square error (LMMSE) channel estimator and analytically investigate the effect of frequency offset estimation error on the mean square error (MSE) performance of the channel estimator. Finally, the performances of the FO and channel estimation are evaluated by simulation results.

A parameter adaptively adjustable utility function based on the asymmetric sigmoid function is investigated for the non-cooperative power control game (NPCG) model in cognitive radio networks (CRN). Each secondary user (SU) can adaptively adjust the parameter to track along with the wireless interference environment for the optimal power strategy. From the fairness of view, a pricing function related to the channel gain of each SU is designed, which can improve the Pareto optimality of the Nash equilibrium solution (NES). A parallel utility function choosing approach for each SU is proposed according to channel state information (CSI) and the utility obtained at this time. The simulation results show that the proposed power control scheme achieves a better performance compared with the fixed utility function method, and the pricing function actually improves the optimality of the NES.

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.

This paper addresses the problem of frequency offsets and channel gains estimation for MIMO system in flat-fading channels. Based on the MUSIC (multiple signal classification) and the ML (maximum likelihood) methods, a new joint estimation algorithm of frequency offsets and channel gains is proposed. The new algorithm has three steps. A subset of frequency offsets is first estimated by the MUSIC algorithm. Then all frequency offsets in the subset are identified by the ML method. Finally channel gains are estimated by the ML estimator. The algorithm is a one- dimensional search scheme and therefore greatly decreases the complexity of the joint ML estimation, which is essentially a multi-dimensional search scheme. The performance of this algorithm is evaluated by Monte Carlo simulations.

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.

We discuss the spectrum sensing problem which is crucial in opportunistic spectrum access. An In-band spectrum sensing method based on linear self-signal elimination is proposed. In this method, quiet period is not needed so that transmission performance can be improved without the interruption of on-going transmission. According to the AR model of the wireless fading channel, the adjacent symbols are linear weighted to eliminate the self-signal. Further the optimal detector under Neyman-Pearson criterion is used on the residual signal after self-signal elimination to provide better detection performance. Finally, performance is evaluated with simulations which show that our method can provide better performance in the IEEE802.22 environment.

In cognitive radio system, secondary users should have capability of detecting the signal from primary user to evade interfering harmfully with any primary receiver. The duration used for spectrum sensing influences the performance of secondary user system. In this paper, we show disadvantages of one threshold judgment in cognitive radio spectrum sensing. For improving this defect, we propose a double thresholds scheme and prove this scheme indeed could gain more throughputs under certain condition. Additionally, two instantiations are taken for general illustrating that our scheme performs better than traditional judgment at the conditions shown in these two examples. It is proved this condition is satisfied universally in cognitive radio system. Results indicate the maximal throughput increases while adopting our judgment scheme when parameters meet the condition.

This paper addresses the problem of frequency offsets and channel gains estimation for a multi-input multi-output (MIMO) system in flat-fading channels. The general case where the frequency offsets are possibly different for each transmit antenna is considered. The maximum-likelihood (ML) estimation of the carrier frequency offset for each transmit antenna in a MIMO system is investigated in this paper, assuming that a training sequence is available. The exact solution to this estimation problem turns out to be too complex as it involves a search over a multi-dimensional domain. To solve this complex estimation problem, a novel joint estimation algorithm of frequency offsets and channel gains is proposed. Frequency offsets are first estimated by the particle swarm optimization theory. Then channel gains are estimated by the ML estimator. Simulation results show that the proposed algorithm has better performance as compared with the correlation-based estimation algorithm and asymptotically achieves the Cramer-Rao lower bound (CRLB), which provides a new idea to solve the problem of joint frequency offsets and channel gains estimation for MIMO systems.

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.

Sensing is one of the key technologies for the implementation of cognitive radio. The usual In-band sensing is performed during quiet period to avoid the interference from the network itself. In this paper, we proposed a novel In-band sensing method which can avoid the usage of quiet period to guarantee the performance of the network. In this method, the complementary of adjacent OFDM symbols is utilized to perform power detection. Furthermore, we extend this idea to several applications. Simulation proved that our algorithm has a comparable performance with power detector in quiet period. And the algorithm can achieve an acceptable performance for IEEE802.22 system.

By maximizing a derived lower bound on the ergodic capacity, the optimal design of training length is given for Multiple-Input Multiple-Output (MIMO) systems. The channel is assumed to be flat fading in presence of spatial fading correlation only at the transmitter, which is known a priori by both ends. Different from the case of independent and identically distributed channel, the optimal training length no longer equals the number of transmit antennas but is just the same as the rank of transmit correlation matrix. The stronger the channel correlation is, the less the optimal training length is. This coincides with the fact that the accuracy of channel estimation is being improved when the channel correlation increases under equal training intervals.

Optimal design of training sequences for multiple-input multiple-output (MIMO) channel estimation is considered. The channel is assumed to be frequency-selective and obey block-fading law with both spatial fading correlation and multipath tap gain correlation known at both the transmitter and receiver. To minimize the channel estimation error, optimal training sequences are designed to exploit full information of channel correlation under the criterion of minimum mean square error (MMSE). It is investigated that channel correlation is helpful to decrease the estimation error and the proposed training sequences have good performance via simulations.

The paper deals with the problem of the carrier frequency-offset (CFO) estimation for wireless communications. Since the well-known maximum-likelihood (ML) CFO estimator is rather complex, many complexity-reduced CFO estimators have so far been proposed. However, most of them are based on periodic training sequences and have limited estimation range. We propose a general framework called composite frequency- offset estimator (CFE), which can extend the estimation range of any range-limited correlation-based CFO estimator up to the full transmission spectrum. We first formulate the CFO estimation as a problem of composite hypothesis testing, and then solve the problem by the composite hypothesis testing approaches. We show that the composite frequency-offset estimate is the ML estimate as long as the original range-limited estimator is unbiased and attains the Cramer-Rao Lower Bound. The CFE also allows a flexible tradeoff between the estimation range and computational complexity. Finally, numerical results show the efficiency of the proposed method.

The multiuser power and channel allocation problem in cognitive radio is considered in this paper. Based on game theory, we modeled the problem into a non-cooperative game and proved that this problem is a supermodular game with the purpose to maximize the total system capacity of the network in which secondary users choose their power allocated in each channel according to their payoff function which consider both the capacity gain of themselves and the loss of the others. A distributed multiuser power and channel allocation algorithm is proposed according to our analysis. Simulation results indicate that our algorithm can achieve greater performance improvement compared with selfish power and channel allocation scheme in channel capacity.

Smart antenna is a promising technology to improve the capacity of wireless networks. Implementing smart antenna in WLAN urgently demands new multiple access protocols. In this paper we propose a multiple access protocol for WLAN to effectively support smart antenna. The proposed protocol makes use of the hybrid superframe structure of polling and contention, which is illumed from IEEE 802.11, and distinguishes the stations residing within the AP's broadcast range from those out of the broadcast range. For the stations residing within the AP's broadcast range, the AP uses polling to transmit in broadcasting mode; for those residing out of the AP's broadcast range but in its directional range, the AP uses contention-based method to transmit in beamforming mode. We have evaluated and optimized the protocol performance against its parameters by simulation. To show the efficiency of the proposed protocol, we also compare it with other protocols in average packet delay and throughput. The simulation results show that the proposed protocol performs well; especially, under heavy traffic load, it achieves the smallest average delay and the highest throughput.

Optimal design of training sequences is considered for multiple-input multiple-output (MIMO) systems in the presence of spatial fading correlation. By the method of minimum mean square error (MMSE) channel estimation, we first evaluate the estimation accuracy and the training sequence length under correlated fading, based on which we then design optimal training sequences that have controllable length to make effective use of the correlation properties. It is investigated that the impacts of spatial correlation are helpful not only to improve channel estimation but also to decrease the training length, moreover, that the proposed training sequences have good performance via simulations.

The use of smart antenna to extend the coverage range and capacity of wireless local area networks (WLAN) dictates the employment of novel multiple access protocols, with which the access point (AP) can provide access to remote stations. In this paper, we propose an adaptive multiple access protocol for WLAN with smart antenna. To efficiently support the differentiation of the stations residing within the broadcasting coverage range of the AP from those residing out of the range, the proposed protocol uses the hybrid superframe structure of contention-free period and contention period. Moreover, our protocol can adaptively adjust the antenna mode between beamforming and broadcasting according to the beam width and the number of active stations in the service area. Using simulation we compared the adaptive protocol with other non-adaptive ones. The results show that constructing our adaptive protocol by switching among non-adaptive protocols turns out to be the best strategy which allow us to take advantage of the best protocol providing low delay and high throughput.

The probabilistic data association (PDA) and the minimum-mean-square-error filtering based iterative soft decision interference cancellation (MMSE-ISDIC) are two popular detectors for systems with co-channel interference. In this letter, the relation between the two detectors is analytically derived for the V-BLAST systems. The two detectors perform in a similar manner, whereas differ in the metric calculation. It is proved that for both original signal model and decorrelated signal model, the PDA and the MMSE-ISDIC detectors are equivalent in terms of performance for both uncoded and coded systems.

For the Vertical Bell Laboratories Layered Space-Time systems, the optimuma posterioriprobability detector encounters the problem of calculating Gaussian mixtures with large numbers of components. In this letter, a new detector is proposed, which consists of an minimum-mean-square-error generalized decision feedback equalization-based dominant symbol combinations selection algorithm for mixture components reduction, and a Cholesky factorization-based efficient algorithm for likelihood function calculation. The new detector can obtain near optimum performance with a complexity much lower than that of a previous sequential algorithm with Gaussian approximation.

To improve the performance of the MAC protocol of IEEE 802.11 DCF (distributed coordination function), we develop a new multiple access protocol ATP/spl I.bar/MAP (adaptive token passing multiple access protocol with fairness guarantee) for WLAN. In this protocol, the stations are queued with tokens, and many unnecessary collisions are avoided. Moreover, the token passing parameter p is adjusted adaptively according to the fairness of the network. The effect that the parameters of the protocol and the size of the network have on the performance metric of throughput and fairness is analyzed. The saturation throughput bound of the ATP/spl I.bar/MAP protocol is given. Simulation results have shown that the proposed scheme can improve the performance of MAC protocol by providing higher saturation throughput.

With the development of ad hoc networks, some researchers proposed several geometric routing protocols which depend on the planarization of the network connectivity graph to guarantee the delivery of the packet between any pair of nodes in the network. In this paper, we proposed a new online routing algorithm GLNFR (greedy and local neighbor face routing) for finding paths between the nodes of the ad hoc networks by storing a small amount of local face information at each node. The localized Delaunay triangulation was used to be the backbone of wireless network on which the GLNFR routing algorithm runs. It has the better scalability and adaptability for the change of ad hoc networks. Experiment on NS have been conducted. The results show that the delivery rate of packets and routing protocol message cost under such novel routing protocols performs better than others proposed before.

We address the problem of frequency offset and channel estimation in multi-input multi-output (MIMO) flat-fading channels. By considering MIMO system as equivalent single-input single-output (SISO) sub-systems with multi-antenna interference, a novel iterative parameter estimation method with interference cancellation is proposed. Numerical results show that this iterative method achieves a satisfactory performance.

Large numbers of applications and technical mechanisms in wireless ad hoc networks requiring that the network is connected, so critical nodes, whose removal will disconnect the network into two or more separate components, will play an important role in wireless ad hoc networks. In this paper, a novel critical node detection algorithm-DMCC (detection algorithm based on midpoint coverage circle ) is presented. DMCC is a distributed algorithm which adapts the dynamic topology adaptively, detects the critical node faster and more reliably, and decreases the detection overheads efficiently.

Compared with the small overhead of data payload in sensor network, the overhead of MAC address is significant from the point of view of energy-saving. A distributed algorithm (virtual grid spatial reusing-VGSR) for MAC address assignment is presented in this paper, which is a low energy cost algorithm and reduces the size of the fixed MAC address greatly based on the mapping of geographical position. Moreover, VGSR algorithm scales well with the network size and achieves the optimum performance by adjusting the communication range of sensor nodes. In typical scenarios, the MAC address size is 5 bits and the corresponding average size is only 3.86 bits, which outperforms other existing approaches

A novel MAC protocol called adaptive beamforming time division multiple access (ABF-TDMA) for mobile ad hoc networks with smart antennas is proposed and analyzed ABF-TDMA is based on TDMA and smart antenna and takes the advantage of collision-free reservation. After successful reservation and training, data packet can be transmitted from source node to destination node with adaptive beamform. We analyze the throughput and delay in ABF-TDMA for the case of a fully connected network with variable-length packets and different number of nodes. The simulation results show that ABF-TDMA can achieve high throughput and keep low delay in the traffic-load ranges of interest, especially when the average packet length and the number of nodes are large.

Carrier frequency offsets (CFOs) in orthogonal frequency division multiplexing access (OFDMA) system will introduce intercarrier interference (ICI) and consequently degrade the system performance. A new frequency offsets estimation algorithm based on the combination of ESPRIT (estimation of signal parameters by rotational invariance techniques) and the maximum likelihood (ML) estimation methods for OFDMA system is proposed. The subset of the frequency offsets is first estimated by ESPRIT then the frequency offset of each user is identified in the subset by ML estimation method. The complexity of the combination algorithm is significantly decreased compared with the ML estimator. The algorithm efficiently solves the problem of multi-frequency offsets estimation.

A novel wavelet packet division multiplexing (WPDM) system based on channel equalization is studied for multipath fading channels. The principle of WPDM is presented in this paper. New WPDM structures based on channel equalization and Htilde matrix based on ZF (zero forcing) algorithm are the two novel aspects of this system. The performance of the system under the multipath fading channels is analyzed

A multiple access protocol with busy tone (BT) for mobile ad hoc network (MANET) using directional antennas, named as BT-DMACP, is proposed with consideration of the hidden terminal problems and side lobe interference caused by directional antennas. The RTS/CTS (request-to-send/clear-to-send) handshake, directional busy tones and power control algorithm are integrated in BT-DMACP to accurately control the transmission power and minimize the side lobe interference. An adaptive algorithm is designed to estimate the interference power at the receiver. Simulation results show that BT-DMACP protocol effectively supports the application of directional antennas and provides higher channel utilization and lower energy consumption.

Abstract The IEEE 802.11 wireless local area network (WLAN) media access control (MAC) specification is a hybrid protocol of random access and polling when both distributed coordination function (DCF) and point coordination function (PCF) are used. Data traffic is transmitted with the DCF, while voice transmission is carried out with the PCF. Based on the performance analysis of the MAC protocol for integrated data and voice transmission by simulation, this paper puts forward a self‐adaptive transmission scheme to support multi‐service over the IEEE 802.11 WLAN. The simulation results show that, on the premise of satisfying the maximum allowable delay of packet voice, the self‐adaptive transmission scheme can improve the data traffic performance and increase the WLAN capacity through dynamic and appropriate adjustment of the protocol parameters. Especially, voice traffic is sensitive to delay jitter, and the self‐adaptive scheme can effectively decrease it. Finally, it is worth noting that the adaptive scheme is easy to be realized, whereas no change in the MAC protocol is needed. Copyright © 2006 John Wiley & Sons, Ltd.

To improve the binary exponential backoff algorithm (BEB)'s channel throughput of IEEE 802.11 protocol, we propose a novel collision reduced multiple access (CRMA) protocol based on slow contention window decrease mechanism and runtime optimization method. CRMA protocol can record the current backoff stage of the latest successful transmission under the overload network precisely. As a result, it decreases the collision times and improves the channel utilization.

In this paper, we present a novel algorithm for discovering physical topology in heterogeneous (i.e. multi-vendor) single subnet IP networks. Our algorithm is based on standard SNMP MIB information that is widely supported by modern IP network elements. Algorithm resorts to the port's traffic when AFT information is insufficient. Simulation and analysis results show that this algorithm can discover the physical network topology accurately even if SNMP is not supported by some network elements.

Based on the effective contention access and perfect scheduling transmission, an enhanced multiple access protocol - UPMA++ protocol is proposed, which integrates the advantages of random multiple access and fixed allocation multiple access protocols. It allocates the resource dynamically according to the factual traffic requirement, thus reducing the source waste. This protocol adjusts the length of contention access period and polling period effectively to resolve the conflicts between active stations and transmission stations, which active station expects to access the channel quickly and transmission station wants to transmit packet as soon as possible. By analysis, the optimal contention access period length for the given max polling period length is found, and the performance evaluations show UPMA++ protocol has a high throughput (up to 96%), low average message delay and small average message dropping probability.

The complexity of the soft input soft output (SISO) algorithm is a major concern for iterative detection in serially concatenated continuous phase modulation (SCCPM). In this paper, a reduced state SISO (RS-SISO) algorithm is presented based on the idea of reduced state sequence detection (RSSD). Error events analysis of SCCPM is given. Theoretic analysis and simulation results show that the proposed algorithm provides both significant complexity reduction and little performance degradation compared with the optimal full state SISO algorithm.

The effect of mobility on the critical transmitting range in wireless ad hoc networks is discussed in this paper. A new parameter, pause probability P/sub pause/, which is the function of the minimum velocity V/sub min/, the maximum velocity V/sub max/ and the pause time t/sub pause/ of random waypoint (RWP) mobility model, is defined. Simulation results show that the effect of RWP parameter on the critical transmitting range is determined by P/sub pause/ that is, if P/sub pause/ smaller than 0.5, the critical transmitting range of mobile network is about 10 percent larger than that of stationary one, else it equals that of stationary network approximately. If the values of t/sub pause/, V/sub min/ and V/sub max/ are given, their effect on the critical transmitting range is determined since P/sub pause/ is the function of them.

A novel CEQRP (cost-efficient QoS routing protocol) for mobile ad hoc networks (MANETs) is proposed. CEQRP can provide QoS guarantee and is aimed at extending the battery's lifetime at each host by selecting the hosts with a long battery's remaining lifetime to forward packets. Simulation results show that CEQRP has good network performance. Furthermore, CEQRP shows distinct performance advantages in using a host's electric energy efficiently.

A new QoS (quality of services)-oriented hybrid admission control (QHAC) scheme in IEEE 802.11 WLAN is proposed, in which the influence of the mobile station with slow speed on other stations is decreased by limiting its inter-request time. In addition, end-to-end performance metrics are also measured and applied to make a decision to admission control of new request. Simulation results show that QHAC scheme can efficiently improve the performance of IEEE 802.11 WLAN.

The use of smart antennas in extending coverage range and capacity of wireless LANs dictates the employment of novel media access control (MAC) schemes, with which the access point (AP) provides access to users by learning their spatial signatures. This paper puts forward an exact approach to analyze the performance of the schemes in terms of required time so that the AP becomes aware of user locations and grants access to the system. The numerical results show that the scheme which distinguishes the users residing in or out of broadcasting coverage range of the AP can provide rapider media access. In addition, the simulation results verify the theoretical approach.

This paper presents a modulation identification system based on software radio platform. The signals have been conveyed to baseband by frequency conversion. Only three features have been chosen, which are kurtosis of the envelope, variance of phase histogram differential coefficients and valid area of power spectrum density. A fuzzy classifier is used for identification. It is proved by the results that the system performs well with a high percentage of correct identification.

One of the principal disadvantages of orthogonal frequency division multiplexing (OFDM) is very sensitive to frequency offset. Carrier frequency offset can be divided into two parts: an integer one and a fractional one. The integer frequency offset has no effect on the orthogonality among the subcarriers, however causes a circular shift of the received data symbols, resulting in a BER of 0.5. The maximum likelihood (ML) estimation algorithm of the integer frequency offset is derived under the assumption that the channel impairments only consist of additive noise. Simulation results show that it can perform well even in a time-dispersive channel. Its performance is assessed and compared with the conventional method by computer simulations for the additive white Gaussian noise (AWGN) channel and a multipath fading channel.

This paper studies the performance of the IEEE 802.11 standard MAC protocol for integrated data and voice transmission with the DCF (distributed coordination function) and the PCF (point coordination function). By simulation, we evaluate the network performance for various protocol parameters, especially, the delay jitter for voice traffic. The main factor to influence delay jitter is given. Numerical results show that it is important to choose appropriate parameters and compromise the number of voice stations and the data traffic throughput to get the enhanced performance of IEEE 802.11. The performance of protocol in theory is derived and is verified by the simulation results.

A multiple access control (MAC) protocol - user adaptive scheduling multiple access (UASMA) protocol is proposed in this paper, which can perfectly schedule the packet transmission according to the exact number of active mobile terminals (MTs) determined by self-organizing algorithm, and adjust the number of packets sending by one node in one frame properly. UASMA protocol employs an especial frame structure, which makes it possible to allocate channel resource to uplink and downlink traffic according o their respective service requirements. And the concept of referenced-frame-length is proposed, which can ensure the frame length various in a certain range, and consequently increase the utilization of the channel greatly. Meanwhile, UASMA protocol uses an efficient collision resolution algorithm to guarantee that active MTs can access the channel rapidly. Finally, the performance of UASMA protocol is evaluated by simulation and compared with UPMA protocol, the simulation results show that the former has better performance than the latter.

Nonstationarity and nonlinearity are found by the analysis of various delay series measured from different Internet links. The fact that different types of links have different degrees of self-similarity is also obtained. By constructing appropriate network architecture and neural functions, functional networks can be used to model the Internet end-to-end nonlinear delay time series. Furthermore, by using adaptive parameter studying algorithm, the nonstationarity can also be well modeled. The numerical results show that the provided functional network architecture and adaptive algorithm can precisely characterize the Internet end-to-end delay dynamics.

In this paper, a new maximum likelihood (ML) estimator for the integer frequency offset estimation of OFDM systems is proposed and compared with the conventional SC estimator. Both estimators exploit the differential information between two consecutive blocks of OFDM data symbols in the frequency domain. The reason why the proposed ML estimator has better performance than the conventional SC method is analyzed. The mean and variance of the proposed and conventional estimators are discussed, and the estimation error probability is given. The computer simulation results are in good agreement with the analytical study.

An efficient estimator for integer frequency offset estimation of OFDM systems is derived, which is based on maximum likelihood (ML) technique and exploits the differential information between two consecutive blocks of OFDM data symbols in frequency domain. The reason why the proposed ML estimator has better performance than the conventional method is analyzed. How to select the differential sequence is also studied. By computer simulations, the effects of the various parameters such as length of cyclic prefix, value of integer frequency offset and differential PN sequence on the performances of the two estimators are evaluated, and the performance of the ML estimator is compared with that of the conventional method for the additive white Gaussian noise (AWGN) channel and a multipath fading channel. The simulation results are in good agreement with the analytical study.

The IEEE 802.11 standard MAC is a hybrid protocol of random access and polling when both DCF (distributed coordination function) and PCF (point coordination function) are used. On the base of the performance analysis of the MAC protocol for integrated data and voice transmission by simulation, this paper puts forward a selfadaptive transmission scheme to support multiservice over the IEEE 802.11 WLAN. The simulation results show that, on the premise of satisfying the maximum allowable delay of packet voice, the self-adaptive transmission scheme can improve the data traffic performance and increase the WLAN capacity through dynamic and appropriate adjustment of the protocol parameters. Especially, the scheme is easy to be realized for no change in the MAC protocol is needed.

A noncoherent reduced state differential sequence detection (RSDSD) for continuous phase modulation (CPM) is proposed in this paper. This method makes use of the reduced state trellis based on reduced state sequence detection (RSSD) and performs one-symbol differential Viterbi detection. The minimum Euclidean distance of the reduced state trellis is analyzed for full response CPM and partial response CPM. Bit error rates of RSSD and RSDSD are simulated for octal 2RC signal with h=1/8 in the AWGN channel and comparisons are made between the different RSSD schemes and between coherent RSSD and noncoherent RSDSD with Doppler frequency shifts. Although our proposed scheme is degraded by 2 dB at 10/sup -3/ bit error rate as compared with coherent detection, it is more appropriate and robust when the carrier recovery is difficult.

The IEEE 802.11 standard MAC is a hybrid protocol of random access and polling when both DCF (distributed coordination function) and PCF (point coordination function) are used. This paper evaluates the performance of the MAC protocol for integrated data and voice transmission with the two access mechanisms. By simulation, we evaluate the network performance for various values of the protocol parameters. Especially, voice traffic is sensitive to delay jitter and here we point out the main factors to influence it. Numerical results show that it is important to choose appropriate parameters and we should compromise the number of the voice stations and the data traffic throughput to get the enhanced performance of IEEE 802.11. Finally, the performance of protocol in theory is derived and is verified by the simulation results.

This paper presents a robust MDPSK modulation classifier based on cumulants to frequency offset. The feature proposed in the algorithm is invariant with respect to constellation scale, rotation, the shift and the carrier frequency offset between transmitter and receiver. The invariant property is proved in theory. Through computer simulation the performance is evaluated and the results show that the improved classification algorithm is high in performance and valuable in practice.

The IEEE 802.11 multiple access control protocol was modified for use in multi-channel, multi-hop ad hoc network, through the use of a new channel-status indicator. In particular, we have evaluated the improvement due to the multi-channel use. We report in this paper on the results of the throughput per node and the end-to-end delay for the modified IEEE 802.11 protocol for different network sizes. Using these results, we were able to propose a number of throughput scaling laws. Our simulation results show that the throughputs per node with multiple channels for the line and the grid ad hoc network topologies will increase by 47.89%, and by 1.39-163%, respectively, for networks with 16 to 64 nodes, as compared with that of single channel.

A novel routing protocol for ad hoc networks - DSARP (Delay Sensitive Adaptive Routing Protocol) - is presented in this paper. According to DSARP, the reliable route for delay sensitive traffic can be supplied and the route can be selected based on the constrained condition - "the shortest route and the lowest average delay". Therefore, the "hotspot" on the shortest path can be avoided. Meanwhile, DSARP can provide a QoS guarantee and improve the performance of the network. Simulation results show that DSARP performs better than the DSR routing protocol used in ad hoc wireless networks.

The wireless ad-hoc network is self-organizing. rapidly deployable and without fixed infrastructure. The hosts in ad-hoc networks communicate with each other over a wireless channel without any centralized control. The basic problem is to obtain a distributed routing scheme so that any mobile host can transmit/receive data from any other host in the network. As we know, in terms of the way in which nodes obtain information, routing protocols for ad-hoc networks have been classified as table-driven and on-demand. In table-driven routing protocols, the interactive mode of the control message has a great effect on network performance. In this paper, a novel concept of "different interactive mode for different scale of network" is presented. The network performance is optimized by using appropriate periodical or nonperiodical interactive mode based on the scale of the network. A different interactive mode is given for different scales of the network by theoretical analysis and algorithm simulation. The simulation result is of great practicability.

Employing smart antennas in the central access point (CAP) of wireless Internet access networks is studied. A polling-based adaptive beamforming multiple access protocol (PB-ABFMA) is proposed. In PB-ABFMA, CAP requests the corresponding mobile terminal (MT) to transmit a training sequence just before the data packet transmission, so the temporal reference beamforming (TRB) can be performed by the smart antennas. CAP schedules packet transmissions based on polling mechanism and dynamic TDMA. CAP and MT transmit alternately. A mini-slot is used to keep connectivity when an MT is idle, therefore, the access delay is reduced. A simple and effective access method is proposed to guarantee rapid access of new arrival MT to the channel. Based on the "request-reply" traffic, which is the main traffic in the Internet, the theoretical analysis of channel utilization is presented. The mean turn around delay and performance of the proposed access method are evaluated by simulation. Results show that the proposed PB-ABFMA protocols effectively support the application of smart antennas and provide high channel utilization and low turn around delay.

In this paper, the performance of a distributed mobility management scheme, the Randomized Database Group (RDG), for mobile ad hoc networks is presented. In this scheme, databases are used to store the location of the network nodes and to manage the mobility of nodes. When a mobile's location changes, a number of randomly selected databases are updated. When a mobile's location is needed, such as upon a call arrival, a number of randomly selected databases are queried. A number of different RDG query schemes are studied and their performance are compared. In particular, the optimum update-group size and the query-group size are found. We also present the probability of the first query being successful and the average query delay to find the mobile's location. Finally, we estimate the cost of implementing the RDG scheme as a function of different number of databases.

A flexible and effective adaptive acquisition collision avoidance (AACA) multiple access protocol is proposed. It integrates the concept of multichannel and random reservation with piggyback to effectively solve hidden terminal and exposed terminal problems caused by the multihop architecture. In the protocol, every node adaptively reserves an idle traffic channel by request-to-send and clear-to-send (RTS/CTS) dialogue on the common channel. After successful reservation, the packet transmission of related other nodes do not interrupt other nodes. The protocol can use any number of channels. It performs better than the single channel RTS/CTS protocol under the assumption of the same total bandwidth if the number of the channels is not too large experiments.

A main disadvantage of multicarrier modulation is that it exhibits a high peak-to-average power ratio (PAPR). This paper analyzes the PAPR complementary cumulative distribution function of MC-CDMA signal by using Golay code as spreading code, which is compared with that of Walsh-Hadmard code, and the performances of two systems in multipath channel are analyzed. Simulation results show that the uplink PAPR of MC-CDMA system using Golay code as spreading code is reduced 3 dB and there is no performance lost compared with that using WH code.

This paper presents a channel estimation based equalization algorithm for high speed fast hopping radio. The equalizer coefficients are directly calculated from the impulse response of the channel which is obtained by rapid channel estimation in time domain employing cyclically extended binary sequence. Simulation is done under general two-path channel model and rapidly fading channels. Theoretical analysis and computer simulation results show that compared with traditional adaptive coefficients adjusting equalizer or MLSE receiver, this algorithm is more robust and has less computational overhead, which is desirable under limited time resources.

A QoS (quality-of-service)-supported adaptive slot allocation multiple access protocol (QASAMA) is proposed for the wireless network with smart antennas at basestation. Time-division duplexing (TDD) is used in the QASAMA protocol. Driven by the basestation, each active mobile terminal (MT) transmits a training sequence at the beginning of each frame, so the spatial signature of the active MT can be obtained by the smart antennas. Based on the optimal signal-to-interference-plus-noise ratio (SINR) beamforming, two slot allocation algorithms, point-by-point optimizing (PPO) and global optimizing (GO), are applied. On condition that provides QoS for voice and data traffics. the basestation dynamically schedules uplink and downlink traffic transmission so that multiple packets can be transmitted simultaneously in one traffic slot. Therefore, space-division-multiple-access (SDMA) is realized. Simulation results show that the proposed protocol can support the application of smart antennas effectively and provide high channel utilization and QoS for voice and data traffics.

This paper presents a new multiple access control (MAC) protocol called adaptive acquisition multiple access with common-transmission channel assignment (AAMA-CT) and its improved one (AAMA-ICT) for wireless multihop mobile ad hoc networks. In the protocol, every node adaptively chooses a usable channel as its transmission channel in advance in multihop mobile environment and takes advantage of request-to-send (RTS) packet transmitted on a common channel to evoke its recipient to complete their communication process on their transmission channels without packet collisions. With this protocol, resource reservation is flexibly and effectively made on multiple channels by half-duplex radios without any help of powerful central controllers and wired backbone networks in an asynchronous fashion. The protocol can solve hidden and exposed terminal problems perfectly as is validated through analysis and simulation results.

A new method to correct the block timing offset in MC-CDMA system with frequency offset is proposed. Consecutive training sequences an used to acquire the initial time synchronization. The effect of the frequency offset on this estimator is analyzed. We compared this new method with Schmidl and Cox's (1997) method (for short, SC method) through simulation in terms of estimator mean and variance, both in AWGN channel and time-varying frequency channel. Results show that the new method is better than SC's method.

A new universal algorithm of modulation and demodulation is presented to process most signals of digital phase-related modulation schemes such as M-DPSK, MSK and even some M-FSK with low modulation level. It can easily deal with modulation and demodulation of the signals with different modulation schemes and data rates by only setting a few input arguments of the versatile software modules. The computational complexity of the algorithm is far less than that of conventional methods. The average processing capacity of the algorithm is about 15 instructions per symbol when processing DQPSK signals. It can be applied to software radios.

A new universal algorithm based on the criterion of MMSE is presented to estimate timing error and frequency offset for most signals of digital phase-related modulation schemes such as M-DPSK, MSK and FSK. It can easily deal with signals with different modulation schemes and data rates by only setting a few input arguments of the versatile software modules. The algorithm consumes far less operating resources than the traditional methods and the average processing capacity of the algorithm is about 40 instructions per symbol. It is applied to process signals with high data rate in software radios.

This paper proposes a novel multiple access control (MAC) protocol, User-dependent Perfect-scheduling Multiple Access (UPMA) protocol, which supports joint transmission of voice and data packets. With the self-organizing algorithm, the exact number of mobile terminals (MT) in active state can be determined. Thus the transmission of MT can be perfectly scheduled by means of polling. Meanwhile, the unique frame structure of the UPMA protocol guarantees that the voice traffic is always transmitted prior to the data traffic. Furthermore, an effective collision resolution algorithm is proposed to guarantee rapid channel access for activated MT. Finally, performance of the UPMA protocol is evaluated by simulation and compared with the MPRMA protocol. Simulation results show that the UPMA protocol has better performance.

This paper presents a new multiple access control (MAC) protocol called adaptive acquisition collision avoidance multiple access with common-transmission code assignment (AACA-CT) for multihop ad hoc wireless networks. Multiple channels are employed in this scheme. One channel is used as the common channel for mobile users (or nodes) contending to reserve traffic channels by means of request-to-send and clear-to-send (RTS/CTS) dialogue. Other channels are used as traffic channels to transmit traffic packets without collision. With this protocol, resource reservation is flexibly and effectively made on multiple frequency bands, frequency-hopping (FH) codes or direct-sequence spread-spectrum (DSSS) codes by half-duplex radios without any help of powerful base stations (BS) or central controllers and wired backbone networks in an asynchronous fashion. The protocol can solve hidden and exposed terminal problems perfectly as is validated through analysis and numerical results. A comparison among the proposed protocol, IEEE 802.11 MAC protocols and the slotted ALOHA protocol shows that the proposed reservation-based approach has advantages over other protocols.