The high-altitude platform station (HAPS), which is anticipated to play a key role in future networks, has drawn increasing attention in both industry and academia. In this paper, a realistic downlink communication scenario is introduced in which an aerodynamic HAPS flies along a circular trajectory and serves handheld users in an urban environment. Stochastic geometry-based analysis is conducted, along with the assimilation of approximations using simulation data to incorporate the circular aperture antenna model of the HAPS. Analytical expressions are derived for the coverage probability and the ergodic capacity, and the validity of these expressions is demonstrated through extensive Monte Carlo simulations. Although there are slight gaps in the performance analysis, the general tendencies between the analysis and simulation results show a close match. The impact of aerodynamic parameters such as flight radius and altitude on the coverage probability is demonstrated to be relatively marginal to that of communication system parameters such as the number of simultaneous service users and the total transmission power. In terms of ergodic capacity, both aerodynamic parameters show improved performance as their values decrease, with the altitude being more dominant. Although smaller flight radii are favorable for improving network performance, such configurations impose additional constraints on aircraft design, highlighting the need to carefully consider the limitations of size, weight, and power (SWaP). A discussion on real-world HAPS design is supplemented in order to offer practical guidelines for balancing communication performances with the SWaP constraints of the HAPS.

This paper addresses the challenges of feeder link switching in non-terrestrial networks (NTNs) by leveraging low Earth orbit (LEO) mega-constellations with multiple shells and multiple gateway (GW) deployments. Due to the high mobility of LEO satellites and frequent feeder link disruptions, it is critical to maintain reliable feeder link connectivity. To tackle these challenges, we propose a novel bilevel optimization framework that combines virtual network embedding (VNE) with advanced feeder link switching strategies. To manage the trade-off between link quality and switching frequency, lower-level optimization maximizes the data transmitted per feeder link connection using a path-finding algorithm with bisection search. The upper-level VNE problem enhances the connectivity between the satellites in the LEO mega-constellation and the ground infrastructure by constructing a dedicated virtual network for NTN GWs. Simulation results based on the Starlink Gen II constellation demonstrate that our proposed method consistently ensures 100% satellite activation, while baseline benchmarks achieve 93% satellite activation rates in the real-world GW deployments. Our optimization framework is well-suited for multiple shell deployments of LEO mega-constellations with multiple GW supports, paving the way for globally connected NTNs.

The high-altitude platform station (HAPS), which is anticipated to play a key role in future networks, has drawn increasing attention in both industry and academia. In this paper, a realistic downlink communication scenario in which an aerodynamic HAPS flies along a circular trajectory and serves handheld users in an urban environment is introduced. A stochastic geometry-based analysis has been conducted, and an analytical expression for the coverage probability (CP) has been derived. The validity of the analyzed expression is demonstrated through extensive Monte Carlo simulations, illustrating a close match between the analytical and simulation results. It is indicated that the parameters related to communications have a substantial impact on CP, while the influence of aerodynamic parameters such as flight radius and altitude is marginal. To ensure non-zero CP by providing users with a higher signal-to-interference-and-noise ratio, the necessity for more effective interference suppression becomes significant. Although smaller flight radii are favorable for improving CP, such configurations impose additional constraints on aircraft design, highlighting the need to carefully consider the limitations of size, weight, and power (SWaP).

By utilizing the flexible operation of unmanned aerial vehicles (UAVs), the integrated terrestrial and aerial network (ITAN) can expand both coverage and network capacity, which are the main key performance indices (KPIs) in 6G wireless networks. For the ITAN, since an additional wireless backhaul link for UAV should be considered, an appropriate access and backhaul strategy that accounts for interference is required. To this end, this paper investigates the in-band reverse time division duplexing (RTDD)-based ITAN. It consists of multiple gNodeBs (gNBs) and a single UAV, where cooperative transmission by the gNBs is employed to support both access and backhaul links. In addition, reconfigurable intelligent surfaces (RISs) are taken into account to enhance interference management and desired signal transmission. Specifically, a sum-rate maximization problem for the ITAN is formulated, and a joint optimization algorithm of transmit beamforming (BF) of gNBs and UAV, the RIS phase shift matrix, and a temporal resource allocation (RA) is developed. Numerical results show that our proposed scheme outperforms other baseline schemes, demonstrating the efficiency of RIS, cooperative transmit BF, and temporal RA in the ITAN.

In this paper, we develop a traffic splitting strategy for low Earth orbit (LEO) mega-constellations using the noncoherent cell-free massive multiple-input multiple-output (CFmMIMO) system. The proposed approach effectively distributes data traffic destined for each user terminal (UT) across multiple satellites, optimizing traffic splitting and power control. We introduce a robust optimization framework that utilizes statistical channel state information (sCSI) and traffic statistics to achieve reliable communications. Numerical results demonstrate that the proposed traffic splitting optimization achieves a performance gain of up to 5 dB compared to non-optimized methods, and provides reliability improved hundreds of times over its coherent counterpart without traffic splitting.

The role of unmanned aerial vehicle (UAV) technologies in the Internet of Things (IoT) data collection is attracting tremendous attention due to its operational flexibility. To guarantee reliability and network efficiency of the IoTecosystem, delay is a crucial performance metric in a IoTdata collection scenario. To reap the benefits of UAVs, this paper investigates delay-oriented IoT data collection scenarios utilizing UAVs. To achieve low-delay data transmission, non-orthogonal multiple access (NOMA), which has high spectral efficiency, is employed. Considering the queue system of IoT, we formulate a problem that jointly optimizes UAV deployment and IoT transmit power to minimize the average packet delay of IoT nodes. As a novel strategy to solve the non-convex problem, a pattern search framework that can obtain near-optimal UAV deployment and IoT transmit power is proposed. Numerical results show that the optimal deployment of UAV depends on the traffic volume of IoT nodes to minimize delay. Furthermore, our proposed pattern search framework achieves significant delay reduction up to 85% compared to other benchmark schemes, highlighting its potential in the UAV-aided IoT data collection.

This paper proposes a globally scalable multicontroller software-defined networking (SDN) framework tailored for low Earth orbit (LEO) mega-constellation. To handle the rapidly changing topology and massive scale of such networks, the novel topology virtualization technique and dynamic mapping strategy are introduced. The framework creates a stable logical network abstraction of the moving satellites that shields the control-plane from constant topological changes. Unlike conventional approaches that require frequent intercontroller cooperation, we formulate a location-based linear assignment problem and derive a closed-form mapping solution to directly assign satellites to SDN controllers without additional cooperation. Extensive evaluations on the Starlink Gen II constellation demonstrate that our framework achieves efficient load balancing among Earth-fixed cells across LEO mega-constellation while significantly reducing computational complexity. Our main contribution is to address the critical challenges of mobility management in dynamic, global-scale LEO mega-constellation, thereby paving the way for enhanced global coverage and efficient use of resources in next-generation communication systems.

Due to the broadcasting nature, satellite signals are vulnerable to potential eavesdropping attacks, which pose a significant security risk for users. Physical layer security (PLS) and cryptography technologies have been independently developed to address this security risk. However, the independent use of each technology in the power-limited satellite systems results in a trade-off problem between onboard power consumption and security performance due to additional encryption costs and dependence of PLS on the performance of eavesdroppers (Eves). In this paper, we integrate the PLS and cryptography considering the complementary properties of the adaptability to wireless channel characteristics and inherent message confidentiality for secure multibeam satellite networks. To this end, we estimate the eavesdropping risk as a function of the given number of Eves for independent and collaborative attacks. Moreover, we design an onboard power model utilized for transmission and computation, and Gaussian beamforming based on the eavesdropping risk. Then, we derive solutions for onboard power allocation, beam scheduling, and security algorithm selection in the non-orthogonal multiple access (NOMA) systems. Finally, we demonstrate that the secure transmission performance improves even under the increment of the eavesdropping risk, and the trade-off performance between cryptography and PLS is provided through analytical and simulation results.

For seamless and ubiquitous connectivity in 6G, three-dimensional (3D) network design using flourishing unmanned aerial vehicles (UAVs) will be an inevitable task. Notably, integrating terahertz (THz) networks with UAVs remains an open question, despite its immense potential to achieve exceptional flexibility and high capacity. Spurred by this, we delve into the promising 3D network: the multi-UAV radio access network (RAN) with THz-empowered backhaul. For reliable last-mile connectivity in the end-to-end (E2E) system, non-orthogonal multiple access (NOMA) is applied with sub-6GHz spectrum. To maximize the E2E throughput, we formulate an optimization problem with diverse design variables of access and backhaul, including 3D UAV deployment, UAV transmit power, and NOMA decoding orders, with hybrid precoding and transmit power of the backhaul terminal. For this intricate problem, we propose an optimization process where the design variables are optimized sequentially. Using numerical results, we verify the feasibility of our optimization process and demonstrate supremacy of the proposed network scheme and optimization algorithm compared to other baselines. Furthermore, we corroborate design guidelines for the proposed network over access and backhaul links with various factors, such as antenna structure, the number of antennas, transmit power level, and interference.

UAVs and millimeter communications are considered as key technologies in the future wireless network that requires ultra-high data rates and seamless communication coverage expansion. In particular, since the terrestrial network is expected to be densified due to a myriad of small cells in B5G/6G, vertical mmWave backhauls using aerial platforms are attracting huge attention. Meanwhile, non-orthogonal multiple access (NOMA) is also being investigated as a next-generation multiple access technique due to its high spectral efficiency. In this paper, we present modeling and analysis of the UAV-aided mmWave backhaul/NOMA network. Taking the elaborate backhauls and access channels into consideration, we formulate an optimization problem in which UAV's position, transmit power and decoding orders are optimized for the maximum end-to-end network throughput. To solve the mixed-integer non-convex problem, we propose an iterative algorithm that can obtain a sub-optimal solution efficiently. Our numerical results show that the optimal deployment of UAV depends on the backhaul link state. In addition, it is shown that the proposed algorithm achieves up to 40% higher end-to-end network throughputs than other benchmark schemes.

Due to introduction of the non-terrestrial network (NTN) and satellite-air-terrestrial integrated network (SATIN), many applications using satellites are being expected and this will result in a severe security issue. In this paper, we propose a satellite communication security method that jointly considers signal transmission and security computation power based on the orthogonal multiple access (OMA). Before transmission, satellite estimates a security threat based on the number of eavesdroppers (Eves) in the large satellite beam coverage as wide as 50 km for LEO satellites at the L-band. With the security threat, the satellite splits onboard power for signals and security, and controls a beam size. Through the sum capacity optimization problem, we derive the optimal onboard power allocation and security algorithm selection with respect to security threats and channel conditions.

For beyond 5G and 6G communications, the satellite terrestrial integrated network (STIN) is expected to provide diverse services with seamless coverage. The first step for implementing the STIN is to make the satellite network capable of supporting advanced functions that the terrestrial counterpart is providing. In this paper, network virtualization with network slices, which is actively studied in the terrestrial network, is analyzed for the satellite network. The main difference between satellite and terrestrial networks is the mobility of satellites. Since the slice services require end-to-end connectivity, the satellite network topology change due to the mobility of satellites can give a huge impact to the slices. The latency is analyzed with time-varying satellite topology with an assumption that the virtual network resource for slice is reserved for low latency. For simulations, the minimum number of handovers is assumed and the end-to-end latency is analyzed for its initial latency, average latency, minimum latency, and maximum latency during the service time of slices in the satellite network.

For 5G and 6G communications, satellites are drawing great attention for global coverage extension and 3-D mobility enhancement. With advancements of satellite hardware, functional satellites are expected to be applied for 6G Internet of Things (IoT) services. In particular, because IoT service has a relatively low computational burden, it is more feasible for satellite edge computing (SatEC) with limited power, making IoT supportable SatEC one of the economically feasible applications for future satellite networks. In this article, an architecture of IoT supportable SatEC is analyzed, and the corresponding network slice scheduling is proposed. First, a multiobjective optimization problem for IoT supportable SatEC is formulated with respect to latency, computational power, and transmission power attenuation. The problem is solved for the satellite offloading rate and altitude by using a heuristic algorithm in low time complexity with time-varying satellite constellation topology and various service requirements for simulations. Next, to analyze the expandability of the SatEC IoT network, a sliced SatEC IoT scheduling problem is formulated in the normalized weighted sum of latency, computational power, and transmission power attenuation. Scheduling rules are proposed to prioritize various applications with the results of the scheduling problem and with the proper SatEC offloading rates predefined in the Pareto optimality of the satellite edge multiobjective Tabu search (SE-MOTS). Finally, efficient satellite constellations are determined by comparing low-Earth orbit (LEO) and very LEO (VLEO) satellite networks, in terms of proper satellite altitudes and offloading strategies for IoT supportable SatEC. Based on simulation results, a scheduling rule for sliced satellite network and a proper offloading strategy of different slices are proposed, and an appropriate altitude of the satellite network for sliced SatEC is discussed.

Various deep learning-based segmentation models have been developed to segment buildings in aerial images. However, the segmentation maps predicted by the conventional convolutional neural network-based methods cannot accurately determine the shapes and boundaries of segmented buildings. In this article, to improve the prediction accuracy for the boundaries and shapes of segmented buildings in aerial images, we propose the boundary-oriented binary building segmentation model (B3SM). To construct the B3SM for boundary-enhanced semantic segmentation, we present two-scheme learning (Schemes I and II), which uses the upsampling interpolation method (USIM) as a new operator and a boundary-oriented loss function (B-Loss). In Scheme I, a raw input image is processed and transformed into a presegmented map. In Scheme II, the presegmented map from Scheme I is transformed into a more fine-grained representation. To connect these two schemes, we use the USIM operator. In addition, the novel B-Loss function is implemented in B3SM to extract the features of the boundaries of buildings effectively. To perform quantitative evaluation of the shapes and boundaries of segmented buildings generated by B3SM, we develop a new metric called the boundary-oriented intersection over union (B-IoU). After evaluating the effectiveness of two-scheme learning, USIM, and B-Loss for building segmentation, we compare the performance of B3SM to those of other state-of-the-art methods using public and custom datasets. The experimental results demonstrate that the B3SM outperforms other state-of-the-art models, resulting in more accurate shapes and boundaries for segmented buildings in aerial images.

Semantic segmentation models based on convolutional neural networks (CNNs) have gained much attention in relation to remote sensing and have achieved remarkable performance for the extraction of buildings from high-resolution aerial images. However, the issue of limited generalization for unseen images remains. When there is a domain gap between the training and test data sets, the CNN-based segmentation models trained by a training data set fail to segment buildings for the test data set. In this article, we propose segmentation networks based on a domain adaptive transfer attack (DATA) scheme for building extraction from aerial images. The proposed system combines the domain transfer and the adversarial attack concepts. Based on the DATA scheme, the distribution of the input images can be shifted to that of the target images while turning images into adversarial examples against a target network. Defending adversarial examples adapted to the target domain can overcome the performance degradation due to the domain gap and increase the robustness of the segmentation model. Cross-data set experiments and ablation study are conducted for three different data sets: the Inria aerial image labeling data set, the Massachusetts building data set, and the WHU East Asia data set. Compared with the performance of the segmentation network without the DATA scheme, the proposed method shows improvements in the overall intersection over union (IoU). Moreover, it is verified that the proposed method outperforms even when compared with feature adaptation (FA) and output space adaptation (OSA).

As the 5G network promises to provide ultra-low latency services, mobile edge computing (MEC) is drawing attentions. MEC in satellite networks will be able to provide lower latency service with the advantage of a global coverage, and make satellite network virtualization feasible. In this paper, an analytic study of the overall satellite server MEC network architecture is presented. A satellite constellation model with the undirected graph representation is proposed for simulations. Propagation delays for constellation topologies and queueing delays with uplink and downlink packet error rate (PER) are considered for performance analysis. The average total latency and jitter are analyzed according to the ground distance between transmitters and receivers with respect to the offloading rate and satellite altitude. In conclusion, a guideline for the efficient satellite MEC architecture is proposed in terms of the MEC satellite parameters.

As the advanced satellite network can serve extremely diverse information data to different types of users, it is of a vital interest to achieve data security using encryption over satellite channels. However, because the avalanche effect makes encryption techniques vulnerable to bit errors in the wireless channel, a more powerful encryption technique is required. In this paper, we propose a novel method of joint encryption and channel coding, called CFB-AES-TURBO, by combining advanced encryption standard (AES) encryption and turbo coding. As we introduce turbo coding in the cipher feedback (CFB) mode with AES, our scheme can protect bits and also correct bit errors. We control system parameters of the encryption block size and the code rate, tailored to the channel state information (CSI). As a result, we can achieve processing time gains, security enhancement, and bit error rate (BER) performance improvement at the same time.

Extracting manufactured features such as buildings, roads, and water from aerial images is critical for urban planning, traffic management, and industrial development. Recently, convolutional neural networks (CNNs) have become a popular strategy to capture contextual features automatically. In order to train CNNs, a large training data are required, but it is not straightforward to use free-accessible data sets due to imperfect labeling. To address this issue, we make a large scale of data sets using RGB aerial images and convert them to digital maps with location information such as roads, buildings, and water from the metropolitan area of Seoul in South Korea. The numbers of training and test data are 72 400 and 9600, respectively. Based on our self-made data sets, we design a multiobject segmentation system and propose an algorithm that utilizes pyramid pooling layers (PPLs) to improve U-Net. Test results indicate that U-Net with PPLs, called UNetPPL, learn fine-grained classification maps and outperforms other algorithms of fully convolutional network and U-Net, achieving the mean intersection of union (mIOU) of 79.52 and the pixel accuracy of 87.61% for four types of objects (i.e., building, road, water, and background).

A fractional frequency reuse (FFR) system is an inter-cell interference coordination scheme used in cellular networks. In FFR systems, the available bandwidth is partitioned into orthogonal subbands such that the users near the cell center adopt subbands of a frequency reuse (FR) factor equal to one (i.e., Full FR), and the users near the cell edge adopt the subbands of an FR factor greater than one (i.e., Partial FR). The proper design of Full FR coverage, which is used to distinguish Full FR regions from Partial FR regions, plays a critical role in FFR system performance. This paper studies the optimal Full FR coverage that maximizes system throughput in the downlink in multiple-input multiple-output (MIMO) cellular networks. For MIMO systems, orthogonal space-time block codes are considered. We analytically compare the outage probabilities of Full FR and Partial FR for a given user's location, where the outage probability is evaluated through small-scale multipath fading. By doing so, subject to the constraint that a given target outage probability (quality-of-service) is satisfied, the optimal Full FR coverage is analyzed as a function of base station (BS) power. We prove that the optimal Full FR coverage is a non-increasing function of BS power when the powers of all BSs in the network are scaled up or down at the same rate. This result offers insight into the design of Full FR coverage in relation to BS power; we gain insight into the complicated relationship between crucial FFR design parameters.

As various wireless devices share the same frequencies in the unlicensed 2.4-GHz industrial scientific medical band, frequency sharing has become a challenging issue in the heterogeneous network. Many Wi-Fi applications increase network traffic and lead to the significant performance loss of other protocol devices including ZigBee for critical missions (e.g., medical devices) in the same band. In this paper, we propose a coexistence solution of the guide busy tone (GBT), providing reliable communications to the ZigBee network under Wi-Fi interference, and present fairness criteria in the tradeoff relation between Wi-Fi and ZigBee with GBT. The proposed GBT design, consisting of a GBT signaler and a busy tone canceller, reserves a channel for ZigBee through the full-duplex technique under heavy Wi-Fi traffic. Our experimental evaluation shows that the packet delivery ratio of the ZigBee network can be improved up to nearly 100% under the saturated Wi-Fi traffic by using GBT, which is scalable for the multinode case as well.

This paper studies the optimal frequency reuse for the transmission of multimedia scalable sources, such as embedded images or scalable video, which need unequal error protection or unequal transmission rates in the bitstream. First, we analyze the crossover of the outage probabilities for full and partial frequency reuse cases in terms of the data rate. We prove that we can find a crossover of the outage probability curves for a data rate lower than a given threshold, which is a function of the parameters such as the partial frequency reuse factor and the user location in the cell. Moreover, the crossover point in the signal-to-noise ratio (SNR) is a strictly increasing function of the data rate. On the other hand, for a data rate higher than or equal to the threshold, there is no crossover; for all SNRs, the outage probability of full-frequency reuse is lower than that of partial-frequency reuse. The results are proven for the arbitrary location of a user in a cell, and for an arbitrary partial frequency reuse factor. Furthermore, the results hold, regardless of the numbers of transmit and receive antennas in the multiple-input multiple-output systems of orthogonal space-time block codes, and of the vertical Bell Labs space-time architecture with a zero-forcing linear receiver. Based on the analysis, we propose unequal-frequency reuse for the optimal transmission of scalable sources. The numerical results show that the peak-SNR performance improves when a sequence of scalable packets is transmitted at the subbands governed by unequal-frequency reuse in cellular networks.

The advanced multibeam satellite equipped with phased array antenna and solid state power amplifiers can generate flexible beams, by managing interbeam interference to serve a very large number of users effectively over its coverage area. Onboard processing (OBP) functionality can enhance the flexibility of a large-scale antenna by speeding up computational processes and saving precious radio link spectrum. In this paper, we derive a cross-layer OBP design of switching/routing, beamforming, and user scheduling as taking advantage of fine spatial resolution capability of phased array antenna satellites. We evaluate tradeoff between OBP computational complexity and throughput performance, showing that the additional complexity of an increased number of switch ports and phased array antenna gain patterning is compensated by high throughput gain achieved by mitigating interference. Our analysis shows that throughput gain for the next generation satellite system can be as high as 40, compared with the conventional multiple beam antenna with travelling wave tube amplifiers, and that beamforming is critical for achieving high spectral efficiency in the crowded service area. We then investigate the impacts of onboard switching and phased array antenna beamforming to practical routing protocols, such as open short path first and routing information protocol.

Security has become an increasingly important issue in wireless communications for the IoT (Internet of Things) environments, to which physical layer approaches can contribute by differentiating desired transceiver and wiretap channels for security of confidential data. In this paper, we propose an optimal power allocation strategy for practical physical-layer security, based on friendly jamming with cancellation for anti-eavesdropping. In particular, secrecy outage probability is evaluated in scenarios involving a pair of transmitter-receiver and a passive eavesdropper near the receiver. We derive the optimal power allocation strategy and the required cancellation capability to enhance secrecy performance. Numerical results verify the tradeoff between jamming power ratio and cancellation capability. Furthermore, our proposed scheme can achieve the improved secrecy rate regardless of availability of eavesdropper channel information.

In satellite communications, narrow spotbeams can provide high power and data rates to the desired location while reducing spatial interference. Advanced transmission antenna technology is critical to generate and switch narrow beams rapidly among a large number of users under quality of service (QoS) constraints such as average delay. In this paper, we jointly optimize resource allocation and congestion control, and compare the performances of two types of satellite transmit antennas: a multiple beam antenna and a phased array antenna. For a multiple beam antenna with traveling wave tube amplifiers (TWTA), throughput is decided by either the most demanding user or the average of all user parameters. For a phased array antenna, joint antenna gain patterning and beam scheduling is given as a function of channel conditions, interference (depending on users' geographical distribution), and average delay requirements. We then develop a low-complexity on-line algorithm of choosing either interference suppression or sequential service for the active users who are closely located within the width of a spotbeam. Due to flexible power allocation, the phased array antenna can provide better performance than the multiple beam antenna when a small number of users are very demanding or many users are densely crowded in a small area.

We propose and study a class of transmit beamforming techniques for systems with multiple transmit and multiple receive antennas with a per-antenna transmit power constraint. The per-antenna transmit power constraint is more realistic than the widely used total (across all transmit antennas) power constraint, since in practice each transmit antenna is driven by a separate power amplifier with a maximum power rating. Under the per-antenna power constraint, from an implementation perspective, it becomes desirable to vary only the phases (as opposed to both power and phase variation) of the signals departing from the transmit antennas. We name this class of techniques generalized co-phasing and formulate an optimization problem to calculate the transmit antenna phases. Furthermore, we propose five heuristic algorithms to solve the optimization problem. All the proposed algorithms except one are optimal for the case of two transmit antennas and an arbitrary number of receive antennas. For an arbitrary number of transmit and receive antennas, simulations indicate that the proposed algorithms perform very close to the optimal solution calculated through an exhaustive search of all possible transmit phases.

This paper examines the detection problem of the preamble sequence index in the WiMAX system. The mobile station receiver knows all the possible preamble sequences and should estimate which preamble sequence has been transmitted from the base station. Since the preamble in the orthogonal frequency division multiplexing (OFDM) transmission is usually the first received symbol, the channel is unknown to the receiver, which makes the problem of preamble sequence estimation complicated. In this paper, this problem is addressed by developing the joint maximum likelihood (ML) estimator of the preamble sequence and the channel. A simple decoupled estimator and a minimum mean square error (MMSE) estimator are also presented as benchmarks for the joint ML estimator. Then it is shown how the joint ML estimator can be used for the segment detection. Since the joint ML estimator can be computationally complex in its general form, low-complexity algorithms are developed depending on the type of pilot subcarrier locations for general OFDM systems including WiMAX. The simulation results show that the joint ML estimator detects the preamble sequence index very well in the absence of the channel knowledge.

This paper examines the detection problem of the preamble sequence index in the orthogonal frequency division multiplexing (OFDM) system. The receiver knows all the possible preamble sequences and should estimate which preamble sequence has been transmitted. Preamble sequence detection is straightforward with the knowledge of channel conditions. However, since the preamble OFDM symbol is usually the first symbol that is received, the channel is unknown to the receiver, which makes this problem complicated. In this paper, the joint maximum likelihood (ML) estimator of the preamble sequence and the channel is derived and compared with a simple decoupled estimator. The estimators are applied to IEEE 802.16e systems, and it is shown by simulation that the joint ML estimator detects the preamble sequence index very well in the absence of the channel knowledge.

For data services over satellite networks, the efficient management of satellite downlink communication resources is crucial for economic competitiveness of the medium. To support a broad spectrum of users with small terminals at high rates, narrow transmit spotbeams on the satellite will be used. Since satellite transmitter resources are expensive and there can be many spotbeam-coverage cells within the satellite service area, it is attractive to use some form of agile scanning beam system and to time-share these precious resources. An optimized design of the multibeam antenna pattern and scheduling can further improve the efficiency of transmission and power management. In this paper, the advantage of parallel multibeams in terms of spectral efficiency and power gain is shown, and the issue of multibeam power allocation based on traffic demands and channel conditions over satellite downlinks with power and delay constraints is addressed. The study indicates that the use of a parallel multibeam scheme with optimum power allocation can achieve a substantial power gain and reasonable proportional fairness. By coupling power allocation with multibeam scheduling when the number of active beams is smaller than the number of cells, the authors show that a modest number of active parallel beams are sufficient to cover many cells efficiently.

A data satellite network would have to support a broad spectrum of bursty unscheduled users with different delay constraints over time-varying atmospheric satellite channels. Since onboard resources are expensive and a broadband satellite will have many spotbeam-coverage cells within its service area, it is desirable to use agile scanning beam systems, adaptive to service requirements and channel conditions, and time-share a small number of active downlink beams. In addition, a congestion control mechanism of incoming traffic into the satellite service queues is required to provide a reasonable queueing delay within delay deadlines. In this paper, a jointly optimized scheme of multibeam allocation and congestion control based on incoming traffic and channel conditions over fading satellite downlinks with beam-sharing and delay constraints is developed. With the assumptions of quasistatic channel conditions and predictable incoming traffic rates (at least in the short term), an analytical solution for joint beam allocation and congestion control is considered. Numerical examples will be given to show that the joint scheme outperforms uniform allocation by serving, with fairness, more accepted traffic (e.g., a factor of 2 in the case of linearly distributed traffic across cells) and/or a smaller queueing delay.

For multimedia services over satellite networks, the efficient management of satellite downlink communications will be crucial due to the high cost of satellite transmission to many users with different quality of service (QoS) requirements and limited amount of onboard resources. An optimized design of the agile antenna multibeam pattern can improve the efficiency of transmission and power management. In this paper, we show the advantage of parallel multibeams in terms of spectral efficiency and power gain, and address the issue of multibeam power allocation based on traffic demands over satellite downlinks with power restriction. The analysis indicates that the use of a parallel multibeam scheme with optimized power splits can achieve a substantial power gain and reasonable proportional fairness. We also give a downlink multibeam scheduling solution when the number of beams is smaller than the number of cells, and show that a modest number of parallel beams are sufficient to obtain large power gains without need for many transmitters.

Effectiveness of prediction and adaptation of fading satellite channels induced by bad weather conditions is presented in terms of power consumption and channel capacity improvement. We suggest adaptive schemes that vary transmission rate, code rate, transmission power or all of these, incorporated with prediction functions for channel states. For prediction, we develop simple linear predictors for received signal attenuation using autoregressive (AR) models. For adaptation, we introduce a continuous power control and discrete rate control strategy. The quantitative analyses of excess average power and channel capacity indicate that the adaptive schemes can achieve substantial performance gains.