Space-air-ground integrated network (SAGIN), as an integration of satellite systems, aerial networks, and terrestrial communications, has been becoming an emerging architecture and attracted intensive research interest during the past years. Besides bringing significant benefits for various practical services and applications, SAGIN is also facing many unprecedented challenges due to its specific characteristics, such as heterogeneity, self-organization, and time-variability. Compared to traditional ground or satellite networks, SAGIN is affected by the limited and unbalanced network resources in all three network segments, so that it is difficult to obtain the best performances for traffic delivery. Therefore, the system integration, protocol optimization, resource management, and allocation in SAGIN is of great significance. To the best of our knowledge, we are the first to present the state-of-the-art of the SAGIN since existing survey papers focused on either only one single network segment in space or air, or the integration of space-ground, neglecting the integration of all the three network segments. In light of this, we present in this paper a comprehensive review of recent research works concerning SAGIN from network design and resource allocation to performance analysis and optimization. After discussing several existing network architectures, we also point out some technology challenges and future directions.

Space-Air-Ground Integrated Network: A Survey

With the development of wireless communication technology, the need for bandwidth is increasing continuously, and the growing need makes wireless spectrum resources more and more scarce. Cognitive radio (CR) has been identified as a promising solution for the spectrum scarcity, and its core idea is the dynamic spectrum access. It can dynamically utilize the idle spectrum without affecting the rights of primary users, so that multiple services or users can share a part of the spectrum, thus achieving the goal of avoiding the high cost of spectrum resetting and improving the utilization of spectrum resources. In order to meet the critical requirements of the fifth generation (5G) mobile network, especially the Wider-Coverage , Massive-Capacity , Massive-Connectivity , and Low-Latency four application scenarios, the spectrum range used in 5G will be further expanded into the full spectrum era, possibly from 1 GHz to 100 GHz. In this paper, we conduct a comprehensive survey of CR technology and focus on the current significant research progress in the full spectrum sharing towards the four scenarios. In addition, the key enabling technologies that may be closely related to the study of 5G in the near future are presented in terms of full-duplex spectrum sensing, spectrum-database based spectrum sensing, auction based spectrum allocation, carrier aggregation based spectrum access. Subsequently, other issues that play a positive role for the development research and practical application of CR, such as common control channel, energy harvesting, non-orthogonal multiple access, and CR based aeronautical communication are discussed. The comprehensive overview provided by this survey is expected to help researchers develop CR technology in the field of 5G further.

Full Spectrum Sharing in Cognitive Radio Networks Toward 5G: A Survey

For conventional wireless networks, the main target of resource allocation (RA) is to efficiently utilize the available resources. Generally, there are no changes in the available spectrum, thus static spectrum allocation policies were adopted. However, these allocation policies lead to spectrum under-utilization. In this regard, cognitive radio networks (CRNs) have received great attention due to their potential to improve the spectrum utilization. In general, efficient spectrum management and resource allocation are essential and very crucial for CRNs. This is due to the fact that unlicensed users should attain the most benefit from accessing the licensed spectrum without causing adverse interference to the licensed ones. The cognitive users or called secondary users have to effectively capture the arising spectrum opportunities in time, frequency, and space to transmit their data. Mainly, two aspects characterize the resource allocation for CRNs: 1) primary (licensed) network protection and 2) secondary (unlicensed) network performance enhancement in terms of quality-of-service, throughput, fairness, energy efficiency, etc. CRNs can operate in one of three known operation modes: 1) interweave; 2) overlay; and 3) underlay. Among which the underlay cognitive radio mode is known to be highly efficient in terms of spectrum utilization. This is because the unlicensed users are allowed to share the same channels with the active licensed users under some conditions. In this paper, we provide a survey for resource allocation in underlay CRNs. In particular, we first define the RA process and its components for underlay CRNs. Second, we provide a taxonomy that categorizes the RA algorithms proposed in literature based on the approaches, criteria, common techniques, and network architecture. Then, the state-of-the-art resource allocation algorithms are reviewed according to the provided taxonomy. Additionally, comparisons among different proposals are provided. Finally, directions for future research are outlined.

Resource Allocation for Underlay Cognitive Radio Networks: A Survey

Nowadays, 5G is in its initial phase of commercialization. The 5G network will revolutionize the existing wireless network with its enhanced capabilities and novel features. 5G New Radio (5G NR), referred to as the global standardization of 5G, is presently under the $3^{\mathrm {rd}}$ Generation Partnership Project (3GPP) and can be operable over the wide range of frequency bands from less than 6GHz to mmWave (100GHz). 3GPP mainly focuses on the three major use cases of 5G NR that are comprised of Ultra-Reliable and Low Latency Communication (uRLLC), Massive Machine Type Communication (mMTC), Enhanced Mobile Broadband (eMBB). For meeting the targets of 5G NR, multiple features like scalable numerology, flexible spectrum, forward compatibility, and ultra-lean design are added as compared to the LTE systems. This paper presents a brief overview of the added features and key performance indicators of 5G NR. The issues related to the adaptation of higher modulation schemes and inter-RAT handover synchronization are well addressed in this paper. With the consideration of these challenges, a next-generation wireless communication architecture is proposed. The architecture acts as the platform for migration towards beyond 5G/6G networks. Along with this, various technologies and applications of 6G networks are also overviewed in this paper. 6G network will incorporate Artificial intelligence (AI) based services, edge computing, quantum computing, optical wireless communication, hybrid access, and tactile services. For enabling these diverse services, a virtualized network slicing based architecture of 6G is proposed. Various ongoing projects on 6G and its technologies are also listed in this paper.

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A Survey on Beyond 5G Network With the Advent of 6G: Architecture and Emerging Technologies

Full-duplex (FD) wireless technology enables a radio to transmit and receive on the same frequency band at the same time, and it is considered to be one of the candidate technologies for the fifth generation (5G) and beyond wireless communication systems due to its advantages, including potential doubling of the capacity and increased spectrum utilization efficiency. However, one of the main challenges of FD technology is the mitigation of strong self-interference (SI). Recent advances in different SI cancellation techniques, such as antenna cancellation, analog cancellation, and digital cancellation methods, have led to the feasibility of using FD technology in different wireless applications. Among potential applications, one important application area is dynamic spectrum sharing (DSS) in wireless systems particularly 5G networks, where FD can provide several benefits and possibilities such as concurrent sensing and transmission (CST), concurrent transmission and reception, improved sensing efficiency and secondary throughput, and the mitigation of the hidden terminal problem. In this direction, first, starting with a detailed overview of FD-enabled DSS, we provide a comprehensive survey of recent advances in this domain. We then highlight several potential techniques for enabling FD operation in DSS wireless systems. Subsequently, we propose a novel communication framework to enable CST in DSS systems by employing a power control-based SI mitigation scheme and carry out the throughput performance analysis of this proposed framework. Finally, we discuss some open research issues and future directions with the objective of stimulating future research efforts in the emerging FD-enabled DSS wireless systems.

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Dynamic Spectrum Sharing in 5G Wireless Networks With Full-Duplex Technology: Recent Advances and Research Challenges

With the deployment of machine-to-machine (M2M) communications, it is expected that the number of devices will enormously increase. When these devices attempt to concurrently access the network, a radio access network overload problem arises. In this case, the conventional random access procedure used in Long Term Evolution-Advanced (LTE-A) networks is rendered inefficient due to the frequent collisions that lead to excessive delay and resource wastage. In this paper, we propose an efficient scalable overload control algorithm for M2M with massive access. The proposed algorithm can allocate the uplink resources within bounded contention time in a distributed manner. Hence, it can achieve full resource utilization that leads to reduced: access delay, energy consumption, and blocking probability. Additionally, we provide a method for estimating the number of backlogged devices in the network. The performance of the proposed algorithm is evaluated analytically and using simulations. To prove its effectiveness, the performance of the proposed algorithm is compared to the dynamic-access class-barring scheme, where the results depict the superiority of the proposed scheme. Finally, a binary integer programming problem is formulated, where we show that the achieved access delay using the proposed algorithm approaches the optimal value.

Machine-to-Machine Communications With Massive Access: Congestion Control

One of the key challenges of cognitive radio networks is the design of efficient resource allocation schemes. Here we propose a distributed resource allocation algorithm for an underlay multiple-input multiple-output (MIMO) cognitive radio network. To reduce the complexity and cost, we introduce two different antenna selection techniques to allow the secondary communication via a single radio frequency (RF) chain. We show that the proposed distributed algorithm approaches the centralized performance while reducing the delay and overhead. Moreover, the simulation results compare the performance of the two proposed techniques showing their merits and demerits. The results show that the proposed algorithm achieves high throughput with low complexity.

On the Distributed Resource Allocation of MIMO Cognitive Radio Networks

Cognitive radio networks have emerged to improve the utilization of the scarce spectrum. In this paper, we propose a distributed resource allocation algorithm that allocates resources opportunistically to the secondary users in a multiple-input multiple-output environment. In order to reduce the complexity and cost, antenna selection schemes are employed to allow the secondary communication using a single radio frequency chain. The proposed algorithm is proved theoretically and using simulations, to give a performance very close to that of a centralized one with lower delay and overhead. Furthermore, we introduce two techniques for the proposed algorithm based on the allowable data rates referred to as limited and maximum rates. We derive closed-form expression for the consumed power and tight upper bounds for the average throughput achieved by each technique. A comparison between the proposed techniques is also provided. Both simulations and analytical results show that the proposed algorithm achieves high throughput with low complexity. Moreover, the results show that the tightness of the bounds improves with the diversity order. Finally, the proposed techniques are compared with two suggested random schemes to investigate their effectiveness. Copyright © 2016 John Wiley & Sons, Ltd.

Distributed opportunistic scheduling for MIMO underlay cognitive radio networks

The random access (RA) procedure used in Long Term Evolution-Advanced (LTE-A) suffers from frequent collisions, and thus it is inefficient for machine-to-machine (M2M) networks with massive access. In such networks, network overload problem arises as a performance bottleneck that leads to high resource wastage and severe access delay. Therefore, developing efficient overload control algorithms plays an essential role in future M2M communication. In this paper, we introduce a distributed scalable algorithm that is able to efficiently allocate the available network resources to massive number of devices with bounded delay and reduced overhead. Additionally, the algorithm overcomes the resource wastage problem resulted from RA collisions, and thus, it achieves full resource utilization. Our simulation results show that the proposed algorithm outperforms the existing dynamic access class barring (DACB) algorithm in terms of service time, access delay, and blocking probability.

Manal El Tanab

Papers

Resource Allocation for Underlay Cognitive Radio Networks: A Survey

Machine-to-Machine Communications With Massive Access: Congestion Control

On the Distributed Resource Allocation of MIMO Cognitive Radio Networks

Distributed opportunistic scheduling for MIMO underlay cognitive radio networks

A scalable overload control algorithm for massive access in machine-to-machine networks