There is considerable pressure to define the key requirements of 5G, develop 5G standards, and perform technology trials as quickly as possible. Normally, these activities are best done in series but there is a desire to complete these tasks in parallel so that commercial deployments of 5G can begin by 2020. 5G will not be an incremental improvement over its predecessors; it aims to be a revolutionary leap forward in terms of data rates, latency, massive connectivity, network reliability, and energy efficiency. These capabilities are targeted at realizing high-speed connectivity, the Internet of Things, augmented virtual reality, the tactile internet, and so on. The requirements of 5G are expected to be met by new spectrum in the microwave bands (3.3-4.2 GHz), and utilizing large bandwidths available in mm-wave bands, increasing spatial degrees of freedom via large antenna arrays and 3-D MIMO, network densification, and new waveforms that provide scalability and flexibility to meet the varying demands of 5G services. Unlike the one size fits all 4G core networks, the 5G core network must be flexible and adaptable and is expected to simultaneously provide optimized support for the diverse 5G use case categories. In this paper, we provide an overview of 5G research, standardization trials, and deployment challenges. Due to the enormous scope of 5G systems, it is necessary to provide some direction in a tutorial article, and in this overview, the focus is largely user centric, rather than device centric. In addition to surveying the state of play in the area, we identify leading technologies, evaluating their strengths and weaknesses, and outline the key challenges ahead, with research test beds delivering promising performance but pre-commercial trials lagging behind the desired 5G targets.

5G : A tutorial overview of standards, trials, challenges, deployment, and practice

A Cell-Free Massive MIMO (multiple-input multiple-output) system comprises a very large number of distributed access points (APs), which simultaneously serve a much smaller number of users over the same time/frequency resources based on directly measured channel characteristics. The APs and users have only one antenna each. The APs acquire channel state information through time-division duplex operation and the reception of uplink pilot signals transmitted by the users. The APs perform multiplexing/de-multiplexing through conjugate beamforming on the downlink and matched filtering on the uplink. Closed-form expressions for individual user uplink and downlink throughputs lead to max–min power control algorithms. Max–min power control ensures uniformly good service throughout the area of coverage. A pilot assignment algorithm helps to mitigate the effects of pilot contamination, but power control is far more important in that regard. Cell-Free Massive MIMO has considerably improved performance with respect to a conventional small-cell scheme, whereby each user is served by a dedicated AP, in terms of both 95%-likely per-user throughput and immunity to shadow fading spatial correlation. Under uncorrelated shadow fading conditions, the cell-free scheme provides nearly fivefold improvement in 95%-likely per-user throughput over the small-cell scheme, and tenfold improvement when shadow fading is correlated.

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Cell-Free Massive MIMO Versus Small Cells

A Cell-Free Massive MIMO (multiple-input multiple-output) system comprises a very large number of distributed access points (APs)which simultaneously serve a much smaller number of users over the same time/frequency resources based on directly measured channel characteristics. The APs and users have only one antenna each. The APs acquire channel state information through time-division duplex operation and the reception of uplink pilot signals transmitted by the users. The APs perform multiplexing/de-multiplexing through conjugate beamforming on the downlink and matched filtering on the uplink. Closed-form expressions for individual user uplink and downlink throughputs lead to max-min power control algorithms. Max-min power control ensures uniformly good service throughout the area of coverage. A pilot assignment algorithm helps to mitigate the effects of pilot contamination, but power control is far more important in that regard. 
Cell-Free Massive MIMO has considerably improved performance with respect to a conventional small-cell scheme, whereby each user is served by a dedicated AP, in terms of both 95%-likely per-user throughput and immunity to shadow fading spatial correlation. Under uncorrelated shadow fading conditions, the cell-free scheme provides nearly 5-fold improvement in 95%-likely per-user throughput over the small-cell scheme, and 10-fold improvement when shadow fading is correlated.

Cell-Free Massive MIMO versus Small Cells

The fifth generation (5G) wireless network technology is to be standardized by 2020, where main goals are to improve capacity, reliability, and energy efficiency, while reducing latency and massively increasing connection density. An integral part of 5G is the capability to transmit touch perception type real-time communication empowered by applicable robotics and haptics equipment at the network edge. In this regard, we need drastic changes in network architecture including core and radio access network (RAN) for achieving end-to-end latency on the order of 1 ms. In this paper, we present a detailed survey on the emerging technologies to achieve low latency communications considering three different solution domains: 1) RAN; 2) core network; and 3) caching. We also present a general overview of major 5G cellular network elements such as software defined network, network function virtualization, caching, and mobile edge computing capable of meeting latency and other 5G requirements.

A Survey on Low Latency Towards 5G: RAN, Core Network and Caching Solutions

There has been active research worldwide to develop the next-generation, i.e., fifth-generation (5G), wireless network. The 5G network is expected to support a significantly large amount of mobile data traffic and a huge number of wireless connections and achieve better costand energy-efficiency as well as quality of service (QoS) in terms of communication delay, reliability, and security. To this end, the 5G wireless network should exploit the potential of new developments, including superdense and heterogeneous deployment of cells and massive antenna arrays [i.e., massive multiple-input, multiple-output (MIMO) technologies] and utilization of higher frequencies, particularly millimeter-wave (mmWave) frequencies. This article discusses the potential benefits and challenges of the 5G wireless heterogeneous network (HetNet) that incorporates massive MIMO and mmWave technologies.

Massive MIMO and mmWave for 5G Wireless HetNet: Potential Benefits and Challenges

A cognitive cellular network, which integrates conventional licensed cellular radio and cognitive radio into a holistic system, is a promising paradigm for the fifth generation mobile communication systems. Understanding the trade-off between energy efficiency, EE, and spectral efficiency, SE, in cognitive cellular networks is of fundamental importance for system design and optimization. This article presents recent research progress on the EE-SE trade-off of cognitive cellular networks. We show how EE-SE trade-off studies can be performed systematically with respect to different architectures, levels of analysis, and capacity metrics. Three representative examples are given to illustrate how EE-SE trade-off analysis can lead to important insights and useful design guidelines for future cognitive cellular networks.

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Cognitive radio in 5G: a perspective on energy-spectral efficiency trade-off

Virtual multiple-input multiple-output (V-MIMO) technology promises significant performance enhancements to cellular systems in terms of spectral efficiency (SE) and energy efficiency (EE). How these two conflicting metrics scale up in large cellular V-MIMO networks is unclear. This paper studies the EE-SE trade-off of the uplink of a multi-user cellular V-MIMO system with decode-and-forward type protocols. We first express the trade-off in an implicit function and further derive closed-form formulas of the trade-off in low and high SE regimes. Unlike conventional MIMO systems, the EE-SE trade-off of the V-MIMO system is shown to be susceptible to many factors including protocol design (e.g., resource allocation) and scenario characteristics (e.g., user density). Focusing on the medium and high SE regimes, we propose a heuristic resource allocation algorithm to optimize the EE-SE trade-off. The fundamental performance limits of the optimized V-MIMO system are subsequently investigated and compared with conventional MIMO systems in different scenarios. Numerical results reveal a surprisingly chaotic behavior of V-MIMO systems when the user density scales up. Our analysis indicates that low frequency reuse factor, adaptive resource allocation, and user density control are critical to harness the full benefits of cellular V-MIMO systems.

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Energy-Spectral Efficiency Trade-Off in Virtual MIMO Cellular Systems

For smartphone indoor localization, an INS/WiFi hybrid localization system is proposed in this paper. Acceleration and angular velocity are used to estimate step lengths and headings. The problem with INS is that positioning errors grow with time. Using radio signal strength as a fingerprint is a widely used technology. The main problem with fingerprint matching is mismatching due to noise. Taking into account the different shortcomings and advantages, inertial sensors and WiFi from smartphones are integrated into indoor positioning. For a hybrid localization system, pre-processing techniques are used to enhance the WiFi signal quality. An inertial navigation system limits the range of WiFi matching. A Multi-dimensional Dynamic Time Warping (MDTW) is proposed to calculate the distance between the measured signals and the fingerprint in the database. A MDTW-based weighted least squares (WLS) is proposed for fusing multiple fingerprint localization results to improve positioning accuracy and robustness. Using four modes (calling, dangling, handheld and pocket), we carried out walking experiments in a corridor, a study room and a library stack room. Experimental results show that average localization accuracy for the hybrid system is about 2.03 m.

An INS/WiFi Indoor Localization System Based on the Weighted Least Squares.

Recent literature has suggested the benefits of integrating licensed radio and cognitive radio into a hybrid cooperative communication system. The fundamental properties of such hybrid systems, however, have not been thoroughly investigated. This paper studies the hybrid cognitive Gaussian relay channel (HCGRC), which uses licensed radio resource (RR) and cognitive/unlicensed RR for forward and relay transmissions, respectively. HCGRC fundamentally differs from conventional relay channels in that the licensed and cognitive RRs are not subject to a total resource constraint and that the cognitive RR is opportunistic in nature. With respect to both the upper and lower bounds, we derive the optimal power-bandwidth allocation strategies for the cognitive relay to maximize the capacity, spectrum efficiency (SE), and energy efficiency (EE). The Pareto-optimal EE-SE tradeoff curve is also derived analytically. Our study leads to two key observations. First, the multi-objective power-bandwidth allocation problem is characterized by five regions, each representing a unique performance tradeoff. Second, the reliability of cognitive RR has no impact on the EE-SE tradeoff given unlimited bandwidth and power.

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Optimal Resource Allocation and EE-SE Trade-Off in Hybrid Cognitive Gaussian Relay Channels

On-demand streaming of high-quality video content is a widely anticipated vehicular infotainment service. How to reduce the cost of content streaming is a primary concern of the service providers, but is still an underinvestigated subject in the literature. This paper proposes an integrated mobile streaming and caching scheme that jointly leverages two communication service tiers and on-board caching resource for cost reduction. Algorithms are presented to achieve optimal buffering at the session level and optimal caching at the device level. An analytical framework is established to characterize the average cost as a function of the streaming rate in a large scale network. Numerical results demonstrate how the “cost-streaming rate” function changes with vehicle density, network congestion level, content length, and average packet transmission time. We learn an important insight that there is a minimum cost threshold even when the streaming rate approaches zero. We also show that the proposed protocol can effectively reduce the overall cost when the network is not congested. Our findings can provide useful guidelines for the business planning and operation of vehicular content streaming services.

Jianghong Shi

Papers

Cognitive radio in 5G: a perspective on energy-spectral efficiency trade-off

Energy-Spectral Efficiency Trade-Off in Virtual MIMO Cellular Systems

An INS/WiFi Indoor Localization System Based on the Weighted Least Squares.

Optimal Resource Allocation and EE-SE Trade-Off in Hybrid Cognitive Gaussian Relay Channels

Cost Optimization for On-Demand Content Streaming in IoV Networks With Two Service Tiers