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1900 results in Distributed, Networked and Mobile Computing

Page 17 of 95

  • 9 - Advanced Metering Infrastructure

  • from Part II - Cloud Computing Applications for Smart Grid
  • Sudip Misra, Samaresh Bera
  • Book:
    Smart Grid Technology
    Published online:
    20 October 2018
    Print publication:
    28 March 2018, pp 118-146

  • Summary

    Advanced metering infrastructure (AMI) is an important component of a smart grid that can help in fulfilling the objectives of the latter. It is a combination of smart meters and bi-directional communication networks. With the help of AMI, customers, service providers, and other entities exchange information among themselves in order to have a well-balanced smart grid environment. Consequently, with the wide deployment of smart meters supported by bi-directional communication networks, it is easier to monitor real-time energy supply–demand information, automatic billing, and many others. Moreover, it is one of the primary requirements for establishing a smart grid environment.

    Requirements

    Typically, an AMI system should meet the following requirements [1]:

  • • Data storage: Energy consumption data is required to be stored in the smart meter. Therefore, the AMI should help smart meters to store real-time and past energy consumption data. To fulfill this requirement, adequate memory should be installed in the smart meters. Moreover, it is also required to ensure that the stored data is accessible by only authenticated users.

  • • Communication: Different communication protocols are also required to communicate with different entities; examples include the IEEE 802.15.4, IEEE 802.11, and wide area network protocols. For example, home appliances may communicate with the home energy management unit (HEMU) using the IEEE 802.15.4 protocol. On the other hand, a HEMU may communicate with the local aggregator unit using the IEEE 802.11 protocol. Finally, the aggregator units may communicate with the service provider through the protocols used in wide area networks. Therefore, suitable communication protocols are required to be deployed, taking into consideration both their advantages and disadvantages.

  • • Management: Different management functions include remote management, billing management, and load control. All these tasks should be performed in an adaptive manner without the involvement of human beings. In case of remote management, fault in the smart meter must be rectified, and the corresponding action should be taken in order to avoid service disruption. In case of billing management, customers can be automatically billed according to their energy consumption. Finally, in case of load control, energy demand from some of the smart meters can be discarded to establish a balance between the energy supply and demand.

  • • User interface: It can be used as an alternative to users for accessing AMI information; the interface includes LCD displays, graphics constructions, keyboard, and a website with authorization facility.

  • 13 - Smart Buildings

  • from Part III - Smart Grid Data Management and Applications
  • Sudip Misra, Samaresh Bera
  • Book:
    Smart Grid Technology
    Published online:
    20 October 2018
    Print publication:
    28 March 2018, pp 207-224

  • Summary

    Buildings are one of the most prominent energy consumers in modern-day power grids. In particular, commercial buildings consume large amounts of energy for operating lighting systems, heating–ventilation and air-conditioning (HVAC), and IT equipment including servers. High energy consumption at the buildings increases energy consumption cost and environment pollution. To deal with these issues, buildings can be converted to ‘smart buildings’. Through the concept of smart buildings, electric loads can be controlled in an adaptive manner. Unused equipment can be switched off so that the total energy consumption in the building can be reduced. The appliances that are installed inside a building will generate huge data in order to have real-time information inside the building. To fulfill these objectives, different methodologies exist. In this chapter, we focus on commercial buildings as smart buildings for efficient energy management.

    Concept of Smart Building

    Most commercial buildings are equipped with HVAC systems, IT equipment, servers, and plug-in equipment. These equipments can be integrated with different sensing and actuation technologies, so that they can be controlled dynamically depending on their requirements. Different useful techniques that are deployed in different buildings to make them smart buildings, are discussed here.

  • • Sensing: It is the key technology required for setting up a smart building. Different sensors are installed in the building (inside and outside), so that different parameters (such as temperature, smoke, humidity, and motion) can be sensed in real-time. The sensed data are forwarded to the data center network for processing, computing, and decision making. For data collection and forwarding, wireless sensor network (WSN) is an emerging technology that can be used due to its unique features such as the capability of operating in low power. Typically, IEEE 802.15.4-based technologies (such as Zig-Bee and 6LowPAN) are used in WSN to forward real-time data. User context, environment, and energy consumption are three major factors to be monitored to establish a smart building. The sensors that are installed to monitor these components generate huge amount of data. This also requires suitable data management techniques for real-time energy management inside the building, as discussed in Chapter 11.

  • • Actuation: After sensing the users’ contexts, environment, and energy consumption, actuation is another important task to be employed. HVACs should be controlled in an efficient manner considering the sensed information received from individual sensors.

  • 5 - Demand Response

  • from Part II - Cloud Computing Applications for Smart Grid
  • Sudip Misra, Samaresh Bera
  • Book:
    Smart Grid Technology
    Published online:
    20 October 2018
    Print publication:
    28 March 2018, pp 61-80

  • Summary

    Typically, in traditional power delivery systems, energy is supplied to the customers using non-renewable energy sources. Therefore, the energy generators are based on fossil fuels. Consequently, carbon emission in the environment is very high, and there is potentially a great impact on the environment. In contrast, in the smart grid system, energy is supplied to the end-users using renewable and non-renewable energy sources. Additionally, the implementation of distributed energy sources plays an important role for real-time energy management in the smart grid. Consequently, in the presence of distributed energy sources at the customers’ end, electricity consumers can schedule their shiftable appliances to the off-peak hours to relieve the extra load on the grid during on-peak hours. This mechanism is widely known as demand response (DR). The objective of any demand response mechanism is to reduce energy consumption cost to the customers and reduce imbalance between the energy supply and demand, while modifying the energy consumption pattern according to real-time price signals.

    Fundamentals of Demand Response and Challenges

    Different technologies are introduced in a smart grid system in order to establish the demand response mechanism – demand scheduling, online energy monitoring, and deployment of distributed energy sources. As mentioned earlier, customers can schedule their shiftable appliances to off-peak hours so as to minimize their energy consumption cost. Additionally, the real-time energy demand to the grid can also be relieved during peak hours. In the traditional power delivery system, such strategies cannot be deployed by the service providers in order to schedule the customers’ energy demand. Therefore, we have on-peak and off-peak hours in which energy demand is more and less, respectively, than the energy supply to the grid. On the other hand, using the demand response mechanism, the energy demand from the customers can be modified according to the energy supply to the grid in order to minimize the imbalance between the energy supply and the demand to the grid. However, there are several challenges that should be considered in order to install demand response mechanisms in real-life environments.

  • 1. Peak load reduction: In the smart grid, there are three different time-slots depending on the energy load on the grid – on-peak, mid-peak, and off-peak. During on-peak hours, energy demand from the customers is high. On the other hand, during mid-peak and off-peak hours, demands from the customers are moderate and low, respectively.

  • 10 - Cloud-Based Security and Privacy

  • from Part II - Cloud Computing Applications for Smart Grid
  • Sudip Misra, Samaresh Bera
  • Book:
    Smart Grid Technology
    Published online:
    20 October 2018
    Print publication:
    28 March 2018, pp 147-166

  • Summary

    Smart grid is essentially a cyber-physical system (CPS) integrated with electric distribution supported with bi-directional communication networks. To support the communication and computing requirements, third party service providers are also expected to participate. Consequently, the third party service providers also access customers’ information that is sent through smart meters and backbone networks. As a result, the smart grid system should have appropriate security and privacy policies to secure the smart meters data and other components from unauthorized access. It is a challenging issue to secure the smart grid from cyber attacks in the presence of online connectivity of all components with the Internet [1]. Among different threats that are present in the smart grid in terms of security, energy theft is one of the most important issues to consider. The intruder can access the control point of the smart meters, and thereby, modify real-time information to change energy consumption information reported to utility providers. Therefore, adequate security policies are needed to deal with such issues. In this chapter, we will discuss general security requirements in a communication network. Then specific security issues present in the smart grid and their remedies are discussed.

    Security in Data Communication

    In data communication, at least two parties (the sender and the receiver) are involved in message exchange. The sender sends a message for an intended receiver. On receiving the message, the receiver takes adequate actions or decisions. In such a scenario, if the sender sends the message directly to the receiver, it may happen that the message is modified or accessed by an attacker. However, the receiver assumes that the received message is the original one sent by the sender. Additionally, it may also happen that an attacker uses the sender's identity to send malicious data to the receiver. Finally, after sending a message, the sender should not be able to repudiate the sent message. Therefore, it is required to implement security mechanisms to prevent such incidents. For example, in banking transactions, appropriate security mechanisms should be in place so that no one other than the actual user can withdraw money. In this chapter, we focus on the cryptography-based security aspects in data communication instead of focusing on other aspects such as intrusion detection and denial-of-service (DoS) attacks. The basic terms used in security are as follows.

  • 3 - Introduction to Big Data Analytics

  • from Part I - Introduction
  • Sudip Misra, Samaresh Bera
  • Book:
    Smart Grid Technology
    Published online:
    20 October 2018
    Print publication:
    28 March 2018, pp 38-48

  • Summary

    Big data is conceptualized as a large data set with high complexity that cannot be managed with the traditional data processing schemes in an adequate manner. Big data technology is described as a holistic information management of new types of data alongside traditional data [1]. For example, traditionally, the backbone network carries different homogeneous information generated by end-users. In addition to this, due to the deployment of more number of sensors, actuators, and other different data sources, the network needs to carry heterogeneous information. It is also expected that the same network channel would be used to carry this heterogeneous information. The challenges in big data analytics include analyzing, capturing, processing, searching, storing, and visualizing the large data set. The term ‘big data’ refers to predictive analysis, i.e., predicting the future condition based on the results obtained from processing existing data. Thus, we can predict the system status in advance to take adequate measures to deal with situations in real-time.

    According to the existing literature, there is no hard and fast definition of big data. As mentioned earlier, big data is a conceptualized term that is used to represent a large data set, which is complex and heterogeneous in nature. Moreover, it is hard to process such large data set using the traditional applications. Due to the growing need to digitize data, it is required to store and process large amounts of data generated from heterogeneous devices. To get an idea about the amount of data generated over several past years, Figure 3.1 presents certain statistics regarding growth of information storage capacity with digitization of data (adopted from [2]). As can be seen in Figure 3.1, there is a large amount of data being stored in digital storage devices. However, we need efficient processing applications to process such large amount of data before storing it for future use. Different attributes of big data are discussed in the subsequent sections.

    Attributes of Big Data

    As the name suggests, one of the main attributes of ‘big data’ is volume, i.e., the size of the data. However, it is not only about the volume, but also other factors such as velocity and variety of the data being analyzed. Figure 3.2 presents the attributes diagrammatically. Let us discuss the attributes in detail.

  • 11 - Smart Meter Data Management

  • from Part III - Smart Grid Data Management and Applications
  • Sudip Misra, Samaresh Bera
  • Book:
    Smart Grid Technology
    Published online:
    20 October 2018
    Print publication:
    28 March 2018, pp 169-188

  • Summary

    It is evident that smart meters will play a key role in managing real-time energy supply–demand and billing customers, as discussed in Chapter 9. Typically, smart meters are expected to be deployed at the distribution side to monitor real-time power usage by the customers. The smart meters’ information (such as energy demand) is sent to the service providers to take adequate decisions (such as real-time price of energy and switching on more generators). Consequently, it is required to have adequate mechanisms in place to store, manage, and process the smart meter data. In this chapter, we will learn about different methodologies, which are useful to manage smart meter data and take appropriate decisions in order to establish an improved smart grid environment.

    Smart Metering Architecture

    Figure 11.1 presents a schematic view of the smart metering architecture, while focusing on different layers in smart meter information collection. Smart meters are deployed at the customers’ end to monitor in-house or in-building energy consumption. The data from the smart meters are reported to the utility provider both in real-time and on a day-ahead basis. The information sent by the smart meters are aggregated by multiple aggregators deployed on the distribution side. The collected information is aggregated depending on the content of the information. Further, the aggregated information is stored in the local servers, which are maintained by specific service providers. For simplicity, we can assume that such local servers are maintained by the owners of the micro-grids, in order to have energy consumption information within that micro-grid area. Finally, the information collected from all local servers are stored at the central data center. The data center helps all authenticated users to have real-time energy supply–demand information of all generators and consumers present in the smart grid architecture.

    Challenges and Opportunities

    In this section, we will discuss some of the challenges and opportunities present in managing smart meter data in the smart grid.

    Requirement of scalable computing facility

    In the smart grid, there are at least three different energy periods – on-peak, off-peak, and mid-peak, as discussed in Chapter 5. Therefore, the number of customers in different time periods will vary depending on their energy requirements. Consequently, the number of smart meters reporting the real-time energy consumption information will also vary.

  • 2 - Introduction to Cloud Computing

  • from Part I - Introduction
  • Sudip Misra, Samaresh Bera
  • Book:
    Smart Grid Technology
    Published online:
    20 October 2018
    Print publication:
    28 March 2018, pp 18-37

  • Summary

    The term ‘cloud computing’ was initially coined to refer to on-demand computing services that were offered by commercial service providers such as Amazon, Google, and Microsoft. Cloud computing is an emerging computation model that shares resources over the Internet and provides on-demand facilities to its customers. In other words, it can be seen as a computation platform combined with shared computer processing resources over the Internet. Computing resources can be shared from anywhere using Internet connectivity. Thus, cloud computing enables ubiquitous and on-demand access to a shared pool of resources to meet users’ requirements [1]. Figure 2.1 depicts an overview of the cloud computing technology.

    As depicted in Figure 2.1, cloud computing consists of several shared computer resources, such as computing devices, software, and applications, which are accessed in an on-demand basis. Additionally, different consumers, who access the cloud platform through mobile phones, laptops, desktops, and other peripheral devices, are also present in the cloud computing technological framework. All the resources and consumers are linked through Internet connectivity. The entire framework is part of a cloud platform. Therefore, a cloud platform helps its users to store and process their data in data centers. The important feature of cloud computing is that a central management entity controls all operations associated with different users situated in different geographical regions. Thus, multiple agents can participate in a single platform to share/procure resources in an

    on-demand basis. Figure 2.2 shows an abstracted view of the cloud computing technology. The cloud manager maps the incoming requests with the available resources, which are shared with the cloud manager. So, it is evident that the cloud manager only manages the available resources and incoming requests in an optimum manner, so that the users of the cloud-services can get seamless and ubiquitous services. However, the cloud-service subscribers do not know from which hardware/software resources, the requested services are provided. Similarly, the owners of the shared resources also do not know to whom their resources are mapped. Therefore, although a common platform is used for multiple parties, an abstraction level is always used for security and privacy concerns. As evident from Figure 2.2, there are multiple parties that participate in the resource sharing/procuring/managing process – publisher, subscriber, and manager. The publishers publish their shared resources to the cloud-service providers.

  • 10 - WPNC in Complex Stochastic Networks

  • from Part III - Design of Source, Relay, and Destination Strategies
  • Jan Sykora, Alister Burr, University of York
  • Book:
    Wireless Physical Layer Network Coding
    Published online:
    01 February 2018
    Print publication:
    15 February 2018, pp 270-283

  • Summary

    Principles of Wireless Cloud Coding

    The termWireless Cloud Coding (WCC) will denote the coding concept suited for large and complex networks with a stochastic structure and connectivity. A simple low-scale WPNC network with a known deterministic structure can be designed in a “handcrafted” style.We usually also easily identify the critical points of performance or design aspects that need to be optimized. On the other hand, a complex large stochastic network adds some specific problems.

    Stochastic Network Structure

    The network structure and connectivity may be stochastic and unknown. A standard NCM design needs to know the participating signals forming the H-constellation in the H-MAC stage, and also the full end-to-end hierarchical encapsulation of all involved HNC maps to guarantee the end-to-end solvability. In a large randomly changing network, this creates a significant problem for (a) identifying and estimating the system/ channel state, (b) making a decision on the processing form, and (c) signaling this decision to all involved nodes. Instead of using this traditional solution, we can try to devise the NCM that is blind to the network structure that will not require a separate NCM/WPNC control protocol but will treat the unknown network structure as an additional degree of freedom fully integrated with the actual payload data (see Section 10.2).

    Clustered, Nested, and Modular Cloud Framework

    The number of the design degrees of freedom may be prohibiting in a large network. A nested modular encapsulation of some of its fragments/clusters can help. This has two aspects. (a) It simplifies the overall design by clustering the nodes and nesting the clusters. (b) Each cluster performs some processing as a “black-box” self-contained virtual super-node. It does not reveal its internal structure to the outside world and simply provides some functionality as a super-node. Its global level of clustering can even comprise the whole cloud. It can serve its outside terminals as if it was some super-relay performing some complex internal hidden processing (see Section 10.3).

    Wireless Cloud-Coding-Based Design of NCM

    Random Channel Class H-MAC and Joint HNC Map

    Joint HNC map vs. Classical Signaling

    A classical signaling solution (Figure 10.1a) is based on the channel sensing and then switching the NCM map at the node depending on the channel class (connection/ disconnection).

  • 8 - NCM Design for Partial HSI and Asymmetric H-MAC

  • from Part III - Design of Source, Relay, and Destination Strategies
  • Jan Sykora, Alister Burr, University of York
  • Book:
    Wireless Physical Layer Network Coding
    Published online:
    01 February 2018
    Print publication:
    15 February 2018, pp 226-250

  • Summary

    Introduction

    As shown in Chapter 5 in general, and in Chapter 6 in more concrete forms, the design of NCM is relatively straightforward and its performance provides an advantage only under specific conditions. It particularly applies to the form of HNC maps and some requirements on the SNR balance of component channels in H-MAC. For example, the isomorphic layered NCM for HDF strategy gives an H-rate advantage if the HNC map is a GF-based linear minimal cardinality map and the component channels have roughly the same SNRs. When applied for example to a butterfly network, it implies symmetric distances on the source-to-relay channels and a perfect HSI with the same rate as the H-rate at the relay. All relays that use a minimal cardinality linear GF HNC map must be on the same GF vector space as the source messages. The resulting equations and therefore also the HI processed by the relay have the same codebook size as the component messages. Similarly, CF strategy requires a common nested lattice code on sources and the HNC map also implies that all flows of hierarchical information from relays to the destination will have the same codebook size.

    Clearly, many practical WPNC networks will need to operate with asymmetric SNR conditions or asymmetric parametrization on the H-MAC, and also the destination might have available only a limited HI or HSI (w.r.t. source codebook sizes) to compute the desired data. The “brute force” solution is to use minimal common source rates, which are bottlenecked by the weakest channel, either on the H-MAC or on the HSI side. This chapter, however, offers a solution allowing us to design NCM for partial HSI and asymmetric H-MAC. It is based on splitting the source data streams into several lower-rate independent components. The split sources can be viewed as virtual component nodes. The split allows us to use different strategies for selected virtual components. We can also group a selected subset of virtual nodes and apply a given strategy that matches to the selected group. For example, we can use layered NCM for the group of virtual nodes having the same rate and having a similar SNR. We can form multiple groups in this way and combine them by another coding strategy. For example, if the groups have very asymmetric rates or SNRs, we can use a classical multi-user strategy.

  • 1 - Introduction

  • from Part I - Motivation and Gentle Introduction
  • Jan Sykora, Alister Burr, University of York
  • Book:
    Wireless Physical Layer Network Coding
    Published online:
    01 February 2018
    Print publication:
    15 February 2018, pp 3-14

  • Summary

    Introduction

    Wireless networks are becoming more and more ubiquitous in the modern world, and more and more essential to today's society. In 30 years they have progressed from the province of a tiny minority of the world's population in only the most developed nations, to the point where there are very nearly as many wireless subscriptions as people in the world [24]. The services offered have extended from very limited speech services at the introduction of first-generation mobile systems in 1985, to broadband Internet access and full motion video today. Moreover, we are at the point where wireless networks will extend beyond connecting people (of whom there are a limited number), to connecting their devices – an effectively unlimited number. Some believe that there are already more devices than people connected to the Internet, and predictions that 50 billion or more devices will be connected by 2020 are circulating widely [60]. Of course, that is only the start.

    All this implies that the density of wireless networks will inevitably increase. To provide telecommunication services to the human populations of our cities, at continually increasing data rates, will require increasing numbers of access points, for which backhaul will become an increasing problem, and require more widespread use of wireless backhaul. The devices will also form a network many times as dense as any current wireless networks, also likely to require connection to the core network. In both cases it is likely that the current point-to-multipoint architecture of wireless networks, exemplified by both cellular and WiFi systems, will be replaced by a multi-hop mesh network architecture.

    The concept of the mobile ad-hoc network (MANET), one of the best-established concepts in wireless mesh networking, has been in existence for many years [9], yet has not really fulfilled its predicted potential. There are very few wireless networks in use today that implement a truly multi-hop networking approach. There seems to be a barrier to the practical implementation of multi-hop wireless networking that will surely have to be overcome in order to implement the ultra-dense wireless networks that are likely to be required in the near future.

  • 7 - NCM Design and Processing for Parametric Channels

  • from Part III - Design of Source, Relay, and Destination Strategies
  • Jan Sykora, Alister Burr, University of York
  • Book:
    Wireless Physical Layer Network Coding
    Published online:
    01 February 2018
    Print publication:
    15 February 2018, pp 207-225

  • Summary

    Introduction

    This chapter is dedicated to the NCM design and receiver processing that we need to use to properly address the effects of the channel parametrization. This generally leads to various synchronization and equalization techniques or NCM design resistant (at least to some extent) against the parametrization. However, all these must properly reflect two fundamental WPNC characteristics that dominate to any H-MAC channel. First, the received signals from component nodes interact in a common signal space. Second, the target of our effort at the receiving node is to decode the H-codeword and the receiver node is neither interested nor potentially having access to the individual component codebooks.

    Section 7.2 deals with a straightforward task of estimating the channel state in the H-MAC channel. We concentrate on a pilot-based technique. The goal is, if possible, to avoid the orthogonal pilot signals. The usage of orthogonal pilots would require additional resources dedicated purely to pilots that also might require an excessive resource management in large networks. It also somewhat goes against the WPNC paradigm of sharing the signal space among all H-MAC component signals. We will see that a complete sharing of pilot signal space among component nodes creates fundamental problems, which can be somewhat reduced by a partial sharing or under some specific channel state conditions.

    Section 7.3 extends the scope for frequency selective channels and also, as a special case, for frequency flat channels with uneven delays among H-MAC components. We show that OFDM type of NCM waveform can easily turn this case, similarly to classical single-user systems, into the set of multiplexed flat fading sub-carrier channels. This appears as an intuitive step. However, rather more important, and less obvious, is the fact that it solves the case of uneven delays. This is specific for WPNC and cannot be ignored. We will show that NCM with OFDM waveform easily accepts arbitrary timing misalignment among the signals received from H-MAC components approximately up to the length of a cyclic prefix.

    Finally, in Section 7.4, we analyze the conditions under which the NCM could be made resistant to various forms of H-MAC parametrization. We will define various levels of resistance, ranging from complete invariance w.r.t. some channel parameters, to the uniformly most powerful performance.

  • 9 - Joint Hierarchical Interference Processing

  • from Part III - Design of Source, Relay, and Destination Strategies
  • Jan Sykora, Alister Burr, University of York
  • Book:
    Wireless Physical Layer Network Coding
    Published online:
    01 February 2018
    Print publication:
    15 February 2018, pp 251-269

  • Summary

    Introduction

    This chapter deals with a very WPNC specific problem. It can be explained with reference to the classical interference mitigation technique, where we utilize the knowledge of the codebook and the signal space structure of the interfering components in order to minimize their impact on the desired data. But in the WPNC solution context provided in this chapter (cf. Chapter 8), it has two fundamental distinguishing aspects. First, the structure of the interfering and/or the desired signals/codebooks is not known uniquely. Both of them are allowed to be hierarchical codebooks which have multiple signal space representations of H-data. Second, the interfering and desired signals are not independent. They share some common, potentially hierarchical-only information, and we expect that they can support each other – it creates a friendly hierarchical interference. These aspects, the hierarchical self-dispersion and supporting aspects of the interaction, require us to adopt a fundamentally different approach including a revisiting of some coding theorems. We will show that friendly hierarchical interference can help in the desired data decoding. We will show the solutions for both Compute and Forward and Layered NCMs.

    Joint Hierarchical Interference Processing

    A relay in a WPNC network can receive signals from many component nodes. Some system scenarios allow us to define and utilize (particularly in the HDF class of scenarios) multiple HNC maps at one receiving relay. For example, the CF-based NCM fulfills the isomorphic property for multiple HNC map coefficient sets. Another example is a specific case corresponding to the structured scenarios of Chapter 8, where several subsets of real or virtual component nodes form multiple NCMs. However, the situation is specific in the sense that the component messages for these multiple HNC maps are independent and we observe signals internally coupled by HNC maps as a superposition. The system model treated in this chapter assumes that the multiple maps are generally defined over a common set of component messages. In all these cases, we can utilize the fact that the signals have defined multiple internal mutual structures (formed by NCMs) and can be used to help the relay processing. In order to clearly distinguish this case, we will call it Joint Hierarchical Interference Processing.

Page 17 of 95