Massive multiple-input multiple-output (MIMO) systems combined with beamforming antenna array technologies are expected to play a key role in next-generation wireless communication systems (5G), which will be deployed in 2020 and beyond. The main objective of this review paper is to discuss the state-of-the-art research on the most favourable types of beamforming techniques that can be deployed in massive MIMO systems and to clarify the importance of beamforming techniques in massive MIMO systems for eliminating and resolving the many technical hitches that massive MIMO system implementation faces. Classifications of optimal beamforming techniques that are used in wireless communication systems are reviewed in detail to determine which techniques are more suitable for deployment in massive MIMO systems to improve system throughput and reduce intra- and inter-cell interference. To overcome the limitations in the literature, we have suggested an optimal beamforming technique that can provide the highest performance in massive MIMO systems, satisfying the requirements of next-generation wireless communication systems.

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Beamforming techniques for massive MIMO systems in 5G: overview, classification, and trends for future research

Wireless signal recognition plays an important role in cognitive radio, which promises a broad prospect in spectrum monitoring and management with the coming applications for the 5G and Internet of Things networks. Therefore, a great deal of research and exploration on signal recognition has been done and a series of effective schemes has been developed. In this paper, a brief overview of signal recognition approaches is presented. More specifically, classical methods, emerging machine learning, and deep leaning schemes are extended from modulation recognition to wireless technology recognition with the continuous evolution of wireless communication system. In addition, the opening problems and new challenges in practice are discussed. Finally, a conclusion of existing methods and future trends on signal recognition is given.

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A Survey on Deep Learning Techniques in Wireless Signal Recognition

The rapid development of information and wireless communication technologies together with the large increase in the number of end-users have made the radio spectrum more crowded than ever. Besides, providing a stable and reliable service is challenging, as electromagnetic environments are evolving and becoming more sophisticated. Accordingly, there is an urgent need for more reliable and intelligent communication systems that can improve the spectrum efficiency and the quality of service to provide agile management of network resources, so as to better meet the needs of future wireless users. Specifically, Automatic Modulation Recognition (AMR) plays an essential role in most intelligent communication systems especially with the emergence of Software Defined Radio (SDR). AMR is an indispensable task while performing spectrum sensing in Cognitive Radio (CR). Thanks to the significant advancements in Deep Learning (DL) applications, new and powerful tools have been provided which can tackle problems in this space. Thus, today, integrating DL models into AMR has gained the attention of many researchers. This work aims to provide a comprehensive state-of-the-art review of the most recent Machine Learning (ML) based AMR methods for Single-Input Single-Output (SISO) and Multiple-Input Multiple-Output (MIMO) systems. Furthermore, the architecture of each model will be identified along with a detailed comparison in terms of specifications and performance. Finally, an outline of the open problems, challenges, and potential research directions is provided along with discussion and conclusion.

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Machine Learning Based Automatic Modulation Recognition for Wireless Communications: A Comprehensive Survey

The increasing demand for mobile data traffic brings new challenges on cellular networks in terms of network capacity and increased data throughput. In order to fulfill these demands, Heterogeneous Networks (HetNets) has been considered as a promising solution. On the other hand, Inter-Cell Interference Coordination (ICIC) is a big challenge toward HetNet deployments; in particular users in edge area of low power small cell are mostly vulnerable to stronger interference signals from high power macro cell. To resolve ICIC problem, 3GPP released some technologies based on TDM resource partitioning, FDM resource allocation along with power control, and still continue to find out the more suitable solutions. However, to date, a common platform of all possible solutions, particularly in TDM and FDM based, is not available to readers. In this context, this paper provides a holistic overview of ICIC strategies in this emerging wireless system. Towards this objective, the evolution of inter-cell interference management techniques is thoroughly investigated from TDM based enhanced inter-cell interference coordination (eICIC) techniques to more advanced Coordinated Multi-Point (CoMP) transmission/reception, while comparing and contrasting their common features and differences. Finally, Carrier Aggregation (CA) is explored in detail as an advanced and challenging mechanism in order to have an efficient resource allocation and inter-cell interference mitigation in LTE-Advanced HetNet environments.

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An Overview on Interference Management in 3GPP LTE-Advanced Heterogeneous Networks

For a base station that supports cellular communications in sub-6 GHz LTE and millimeter (mmWave) bands, we propose a supervised machine learning algorithm to improve the success rate in the handover between the two radio frequencies using sub-6 GHz and mmWave prior channel measurements within a temporal window. The main contributions of our paper are: 1) introduce partially blind handovers, 2) employ machine learning to perform handover success predictions from sub-6 GHz to mmWave frequencies, and 3) show that this machine learning based algorithm combined with partially blind handovers can improve the handover success rate in a realistic network setup of colocated cells. Simulation results show improvement in handover success rates for our proposed algorithm compared to standard handover algorithms.

Partially Blind Handovers for mmWave New Radio Aided by Sub-6 GHz LTE Signaling

The next generation wireless network (NGWN) is envisioned as convergence of heterogeneous wireless networks. The vertical handoff plays an important role in supporting the service continuity and guaranteeing the quality of service (QoS). How to select the target network becomes the vital issue of designing the vertical handoff algorithm. In this paper, we propose a novel service-adaptive multi-criteria vertical handoff (SMVHO) algorithm. The algorithm formulates the network selection process as a multiple attributes decision making (MADM) problem. The candidate networks are evaluated by a set of decision metrics including SINR, bandwidth utilization, transmission delay, packet loss rate and monetary cost. The QoS requirements and network conditions determine the comprehensive performance ranking of alternative networks. Under different network conditions, the SMVHO algorithm can keep the mobile terminal connected to the optimal network by changing the weight of each metric. System-level simulations reveal that the SMVHO algorithm is effective in lowering the dropping probability and improving overall system throughput comparing with the RSS based vertical handoff (RVHO) algorithm.

A service-adaptive multi-criteria vertical handoff algorithm in heterogeneous wireless networks

Nowadays, adaptive streaming is quite popular and is expected to develop at a high rate of speed Dynamic Adaptive Streaming over HTTP (DASH) is a new 3GPP and MPEG standard for adaptive steaming over HTTP which is currently being extensively accepted in the industry DASH requests best quality of media under available bandwidth with adaptive algorithm which is implemented in clients To obtain the appropriate quality of media, an accurate estimate of the bandwidth is needed In this paper, we present a novel algorithm to estimate the bandwidth even more accurately This bandwidth estimation algorithm involves bandwidth detective method and bandwidth computing method Outliers within a short term, slight fluctuations and persistent variations can be distinguished through our proposed bandwidth detective method Hereafter, we compute the estimated bandwidth in different methods based on the result of bandwidth detection We build a DASH platform and compare our proposed algorithm to the VLC adaptive algorithm According to the results, it is demonstrated that our algorithm possesses more accurate bandwidth estimation and higher buffer level Our proposed algorithm is sensitive to persistent variations and robust to slight fluctuations

Bandwidth estimation of rate adaption algorithm in DASH

Cognitive radio technology is considered to be an effective solution to improve the spectrum utilization efficiency. The dynamic spectrum allocation algorithm assigns the available spectrum to the secondary users. In this paper, we propose a spectrum allocation algorithm based on the graph-coloring theory. Unlike some already existed allocation algorithm, our algorithm considers the bandwidth matching between secondary users' bandwidth requirements and the available channels. The interference avoidance to the primary users is also taken into account. Our objective is to optimize the total spectrum reward under the premise of fully protecting the primary users. Simulation results prove that this algorithm can improve the total spectrum reward, the satisfaction rate of secondary users, the protection rate of primary users, and the spectrum allocation efficiency.

A spectrum allocation algorithm based on bandwidth matching and interference avoidance in cognitive radio networks

Perfect channel state information (CSI) is essential for precoding and combining in millimeter wave (mmWave) systems. The CSI estimation algorithm based on adaptive compressed sensing (CS) is an efficient method. However, the imperfect multi-resolution hierarchical codebook design possibly leads to wrong detections of angle of arrival (AoA) and angle of departure (AoD), which has a negative effect on the spectral efficiency. In this paper, an enhanced multi-resolution hierarchical codebook design is proposed to realize precise AoD/AoA estimation. First, the necessary number of sampling angles for generating the codebook is given. Then, the algorithm of iterative adjustment (IA) is developed to improve the codebook design. Simulation results show that using the proposed codebook design, the beams can precisely divide the AoA/AoD intervals as the requirements of the adaptive CS based algorithm. Thus, the average angle estimation error is reduced effectively.

Enhanced multi-resolution hierarchical codebook design for adaptive compressed sensing based millimeter wave channel estimation

Cognitive radio is being widely discussed, and resource allocation scheme is drawing a lot of attention. Although the existed resource allocation strategies have successfully enhanced spectrum efficiency, some critical challenges still remain unanswered such as the jointly consideration of the efficiency and fairness, the rational description of the spectrum resources, and the execution mode of the spectrum allocation. This paper presents a system level dynamic frequency spectrum allocation scheme based on a central heterogeneous network architecture, evaluates the matching degree of the spectrum demand and the available spectrum resources with corresponding matrices, jointly considers the efficiency and fairness of different cognitive radio systems. Simulation results and the application scenario are also presented and analyzed.

Lin Sang

Papers

A service-adaptive multi-criteria vertical handoff algorithm in heterogeneous wireless networks

Bandwidth estimation of rate adaption algorithm in DASH

A spectrum allocation algorithm based on bandwidth matching and interference avoidance in cognitive radio networks

Enhanced multi-resolution hierarchical codebook design for adaptive compressed sensing based millimeter wave channel estimation

Dynamic system level frequency spectrum allocation scheme based on cognitive radio technology