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5G Networks: Planning, Design and Optimization

By: , Posted on: September 14, 2018

We sit down with the author of 5G Networks: Planning, Design and Optimization, Christofer Larsson, to find out more about him and his recent publication.

  1. What got you interested in network planning and optimization?

I have been working with network design and optimization for over two decades and I have encountered many different problems in live networks. The largest contributing problem factors have traditionally been related to either insufficient capacity or poor resilience. Applying rules-of-thumb often fails to address the actual root causes and a thorough understanding of network response with changing user behavior is therefore very important.

Many cases require both empirical analysis and research by means of modeling or simulation. It is particularly significant to investigate traffic aggregation points, as fractal effects often result from aggregation. I found troubleshooting on network level fascinating and that led me to do independent consulting and the subsequent publication of my books.

  1. How much can be gained by proper planning and optimization?

The achievable gain depends on many factors, such as the resource type being optimized and the reference value used for comparison. It also depends on the modelling approach and optimization algorithms used. It is almost impossible give a representative range of the gain, but from my experience and as a rule of thumb, improvements are usually 10 – 30% for the subsystem being optimized.

It is important to notice that an originally optimal configuration no longer is optimal after expansion or change of the assumptions on which the optimization was based. In this sense, optimization is a “dynamic”, ever changing characteristic.

  1. What is the difference between 4G and 5G planning?

The differences are in fact quite considerable – at least in theory. The applications and performance-critical use cases 5G networks are intended for require quantification and design with respect to a broader range of parameters than traditionally has been used. These parameters include availability, resilience, recoverability and security.

At the same time, Software Defined Networks (SDN) and Network Function Virtualization (NFV) change the principles of resource allocation from static to dynamic. This leads to profound changes in both design principles and network management, which needs to take time- or load-dependent resource allocation into account.

  1. Which are the three most critical design targets in 5G?

Arguably, the three most important targets for network improvement are resilience, quality of service and cost.

Planning for resilience has somewhat had lower priority than planning for capacity. However, with increasing bandwidth demand and the dominance of data applications, networks and users are more vulnerable to outages. In optical networks a failing connection can lead to losses of enormous volumes of data. In 5G networks, resilience and availability are therefore crucial.

Quality of service is likely described by a larger set of parameters than for traditional call services. Combined with an SDN/NFV-based architecture, service quality can be optimized to comply with service level agreements (SLA) and this compliance ultimately reflects successful network operation.

Investment and operational cost are a very complex optimization targets but always have high priority. Many operators have mature networks and large subscriber bases, and planning for high resource utilization while meeting demand and SLA is crucial for cost efficiency. Increasing network size and complexity makes this very challenging. Optimization models should therefore incorporate both technical and economic factors.

  1. What network aspects can typically be optimized?

Since networks are extremely complex, we need to look at different subsystems separately. Looking hard enough, in principle anything can be optimized. This does not only pertain to topology such as node placement and assigning links between them, but also control policies including routing, resource allocation, scheduling and failure recovery. Energy efficient solutions are also gaining attention and energy is increasingly becoming a design target. It is worth noting that in most cases energy rationalization follows as a result from optimization with respect to other criteria.

  1. How is network management affected by these architectural changes?

Control and restoration in high-speed networks must be carried out quickly, since any failure may cause the loss of huge amounts of data. Switch-over decisions must be fast and actions accurate. This puts requirements on the measurement and network state estimation process.

Network management is greatly affected by introduction of SDN/NFV. This new architecture offers great flexibility in managing network resources, but the control logic also needs constant measurements from the entire network to determine the best actions for every network state. In broadband optical networks, packet data needs to be collected at very high frequency and processed fast to obtain a reliable base for resource control. To collect and process such huge data volumes require specially designed Big Data algorithms. A number of typical estimation problems in high-speed networks are described in “5G Networks”.

  1. What measures can be taken to lower energy consumption in 5G?

Optimization of resources means reduction of waste and as a consequence energy consumption is reduced too. In particular centralized (or cloud) radio access network (C-RAN) architecture saves lots of energy due to pooling of idle baseband equipment.

Optical fiber eliminates energy losses in electrical analog signals over coaxial cables. In general, hardware evolution leads to increasingly energy efficient components.

More interestingly, perhaps, is how resources like processing power actually are used. In this case we rely on another aspect of design – algorithms. As discussed in “5G Networks”, most design tasks are NP-hard and in principle, the resource demand (including energy) increases exponentially with the size of the task. The development and use of efficient algorithms is therefore imperative. The three inherent phenomena which challenging algorithm efficiency are NP-hardness, self-similarity and big data.

  1. Which models and methods are used in 5G design and optimization?

Networks can be represented by graphs, a very general and versatile class of models in network design. Graphs can provide insight into most aspects of a network topology – in particular resilience and flow aspects. They have successfully been used to study very large networks, such as the Internet and social networks.

In optimization, we are usually trying to find “good” solutions with high probability. Metaheuristic optimization have shown very good results on a variety of problems. This class of methods includes algorithms mimicking the behavior of biological systems. Search is performed in parallel by a number of “individuals” that are allowed to pass information between themselves. These methods are likely to produce reasonable results with a relatively modest modeling effort.

  1. What aspects are important in IoT design?

IoT can be set up in many different ways, on a deterministic or random topology, with stationary or mobile sensors, and having various bandwidth demand.

We are often interested in energy efficiency, that is network lifetime. As many IoT can be deployed in inaccessible or hazardous environments where repairs are difficult or expensive, we are interested how to achieve good sensor coverage and connectivity properties, which then clearly are dependent on network age. Design aspects of wireless sensor networks (WSN) are is discussed in “5G Networks”.

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About the book:

5G Networks: Planning, Design and Optimization presents practical methods and algorithms for the design of 5G Networks, covering issues ranging from network resilience to how Big Data analytics can used in network design optimization. The book addresses 5G optimization issues that are data driven, high dimensional and clustered.

Key Features:

  • 5G concepts, how they are linked and their effect on the architecture of a 5G network
  • Models of 5G at a network level, including economic aspects of operating a network
  • The economic implications of scale and service diversity, and the incentive for optimal design and operational strategies
  • Network topologies from a transport to a cloud perspective
  • Theoretic foundations for network design and network optimization
  • Algorithms for practical design and optimization of 5G subsystems based on live network projects
  • Efficient Bayesian methods for network analytics
  • The trade-off and multi-objective character of QoS management and cost saving
  • Practical traffic and resilience measurement and QoS supervision

Frameworks for performance analytics and network control

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