Digital twins (DTs) enable smarter, self-optimizing mobile networks, but they rely on a steady supply of real world data. Collecting and transferring complete traces in real time is a significant challenge. We present a compact traffic generator that combines hidden Markov model, capturing the broad rhythms of buffering, streaming and idle periods, with a small feed forward mixture density network that generates realistic payload sizes and inter-arrival times to be fed to the DT. This traffic generator trains in seconds on a server GPU, runs in real time and can be fine tuned inside the DT whenever the statistics of the generated data do not match the actual traffic. This enables operators to keep their DT up to date without causing overhead to the operational network. The results show that the traffic generator presented is able to derive realistic packet traces of payload length and inter-arrival time across various metrics that assess distributional fidelity, diversity, and temporal correlation of the synthetic trace.

Machine learning (ML) grows to be an inseparable part of future networks and an enabler for internet of things use-cases. Input components of machine learning units (MLU) have different levels of relevancy for determining accurate output. Assuming that attributes of each MLU are transmitted from multiple terminals, we revisit the resource allocation problem for a ML-based centralized control system, considering relevancy in order to provide best effort MLU outcome as key performance indicator. To achieve this goal, we propose a heuristic greedy resource allocation algorithm in combination with lookup tables of various payload requirements. These tables are generated by a Kullback-Leibler divergence (KLD)-based approach for each MLU. The KLD assigns number of quantization bits for input attributes of each terminal considering their relevancy.The proposed resource allocation is applied to a network of inverted pendulums on carts with MLUs as controllers. Simulation results demonstrate significant gains in MLU performance and efficiency in resource utilization when employing the proposed technique comparing with two benchmarks: the greedy maximum sum rate (GMSR) algorithm allocating resources once with equal payload requirements, and once KLD lookup table.

Industry 4.0 brings up new types of use cases, whereby mobile use cases play a significant role. These use cases have stringent requirements on both automation and communication systems that cannot be achieved with recent shop floor technologies. Therefore, novel technologies such as IEEE time-sensitive networking (TSN) and Open Platform Communications Unified Architecture (OPC UA) are being introduced. In addition, for the realization of mobile use cases, wireline technologies cannot be used and have to be replaced by wireless connections, which have to meet the high demands of the industrial landscape. Here, 5th generation wireless communication system (5G) is seen as a promising candidate.Especially encouraging and similarly challenging is the cooperative work of mobile robots, where particularly high demands on time synchronization arise. Therefore, this paper introduces a concept for the integration of TSN time synchronization (IEEE 802.1AS) conform with 5G to fulfill the requirements of these use cases. Furthermore, the paper describes a testbed for discrete manufacturing, consisting pre-dominantly of industrial equipment, in order to evaluate the presented approach.

The increasing demand for highly customized products, as well as flexible production lines, can be seen as trigger for the “fourth industrial revolution”, referred to as “Industrie 4.0”. Current systems usually rely on wire-line technologies to connect sensors and actuators. To enable a higher flexibility such as moving robots or drones, these connections need to be replaced by wireless technologies in the future. Furthermore, this facilitates the renewal of brownfield deployments to address Industrie 4.0 requirements. This paper proposes representative use cases, which have been examined in the German Tactile Internet 4.0 (TACNET 4.0) research project. In order to analyze these use cases, this paper identifies the main challenges and requirements of communication networks in Industrie 4.0 and discusses the applicability of 5th generation wireless communication systems (5G).

In a virtualized radio access network (RAN), baseband processing is performed by software running in cloud-computing platforms. However, current protocol stacks were not designed to run in this kind of environment; the high variability on the computational resources consumed by RAN functions may lead to eventual computational outages (where frames are not decoded on time), severely degrading the resulting performance. In this paper, we address this issue by re-designing two key functions of the protocol stack: 1) scheduling, to select the transmission of those frames that do not result in computational outages, and 2) modulation and coding scheme (MCS) selection, to downgrade the selected MCS in case no sufficient computational resources are available. We formulate the resulting problem as a joint optimization and compute the (asymptotically) optimal solution to this problem. We further show that this solution involves solving an NP-hard problem, and propose an algorithm to obtain an approximate solution that is computationally efficient while providing bounded performance over the optimal. We thoroughly evaluate the proposed approach via simulation, showing that it can provide savings as high as 80% of the computational resources while paying a small price in performance.

A wireless system is considered, where, computationally complex algorithms are offloaded from user devices to an edge cloud server, for the purpose of efficient battery usage. The main focus of this paper is to characterize and analyze, the trade-off between the energy consumed for processing the data locally, and for offloading. An analytical framework is presented, that minimizes the in-device energy consumption, by providing an optimal offloading decision for multiple user devices. A closed form solution is obtained for the offloading decision. The solution also provides the amount of computational data that should be offloaded, for the given computational and communication resources. Consequently, reduction in the energy consumption is observed.

We argue for flexible network design as an architecture prototype for next generation networks. Such flexible design is developed by capitalizing on the concept of network function decomposition in conjunction with with its relation to network slicing. A detailed view of the proposed functional architecture is put forward, where the role of network function blocks for forming network slices with given requirements is underlined. We further highlight the impact of common architecture over multiple tenants and elaborate on the emerging multi-tenancy business models along with the resulting implications on security.

In a centralized or cloud radio access network, certain portions of the digital baseband processing of a group of several radio access points are processed at a central data center. Centralization improves the flexibility, scalability, and utilization of computational assets. However, the performance depends critically on how the limited data processing resources are allocated to serve the needs of the different wireless devices. As the processing load imposed by each device depends on its allocated transmission rate and channel quality, the rate- allocation aspect of the scheduling should take into account the available computing resources. In this paper, two computationally aware schedulers are proposed that have the objective of maximizing the system sum-rate while satisfying a constraint on the offered computational load. The first scheduler optimally allocates resources and is implemented according to a water-filling algorithm. The second scheduler is suboptimal, but uses a simpler and intuitive complexity-cut-off approach. The performance of both schedulers is evaluated using an LTE- compliant system level simulator. It is found that both schedulers avoid outages that are caused by an overflow of computational load (i.e., computational outages) at the cost of a slight loss of sum-rate.

This paper focuses on the practical implementation of a Cloud-RAN architecture in the context of future 5G systems, with particular emphasis on different aspects of the functional split between the cloud platform and the radio access points. First, we provide a comprehensive overview of implementation aspects and how different hardware options impact the implementation of RAN functionality. We further discuss a virtualized infrastructure which may have a significant impact on how algorithms are implemented, how they interact with each other, and how they can be scaled within the RAN. We also analyze implementation constraints to be considered to provide backwards compatibility with 3GPP LTE; such constraints on the computing platforms result from the RAN requirements in terms of latency and throughput. Finally, the level of flexibility achievable by the proposed architecture is described from a practical point of view.

Mobile networks of the future are predicted to be much denser than today's networks to cater to increasing user demands. In this context, cloud-based radio access networks have garnered significant interest as a cost-effective solution to the problem of coping with denser networks and providing higher data rates. However, to the best of the authors' knowledge, a quantitative analysis of the cost of such networks is yet to be undertaken. This paper develops a theoretic framework that enables computation of the deployment cost of a network (modeled using various spatial point processes) to answer the question posed by the paper's title. Then, the framework obtained is used along with a complexity model, which enables computing the information processing costs of a network, to compare the deployment cost of a cloud-based network against that of a traditional LTE network, and to analyze why they are more economical. Using this framework and an exemplary budget, this paper shows that cloud-based radio access networks require approximately 10% to 15% less capital expenditure per square kilometer than traditional LTE networks. It also demonstrates that the cost savings depend largely on the costs of base stations and the mix of backhaul technologies used to connect base stations with data centers.

In wireless fading channels, multi-user scheduling has the potential to boost the spectral efficiency by exploiting diversity gains. In this regard, proportional fair (PF) scheduling provides a solution for increasing the users' quality of experience by finding a balance between system throughput maximization and user fairness. For this purpose, precise instantaneous channel state information (CSI) needs to be available at the transmitter side to perform rate adaptation and scheduling. However, in practical setups, CSI is impaired by, e.g., channel estimation errors, quantization and feedback delays. Especially in centralized cloud based communication systems, where main parts of the lower layer processing is shifted to a central entity, high backhaul latency can cause substantial CSI imperfections, resulting in significant performance degradations. In this work, robust rate adaptation as well as robust PF scheduling are presented, which account for CSI impairments. The proposed rate adaptation solution guarantees a fixed target outage probability, which is of interest for delay critical and data intensive applications, such as, video conference systems. In addition to CSI imperfections, the proposed scheduler is able to account for delayed decoding acknowledgements from the receiver.

In a centralized radio access network (RAN), the signals from multiple radio access points (RAPs) are centrally processed in a data center. A centralized RAN enables advanced interference coordination strategies while leveraging the elastic provisioning of data processing resources. It is particularly well suited for dense deployments, such as within a large building where the RAPs are connected via fiber and where many cells are underutilized. This paper considers the computational requirements of a centralized RAN with the goal of illuminating the benefits of pooling computational resources. A new analytical framework is proposed for quantifying the computational load associated with the centralized processing of uplink signals in the presence of block Rayleigh fading, a distance-dependent path loss, and fractional power control. Several new performance metrics are defined, including the computational outage probability, the outage complexity, the computational gain, the computational diversity, and the complexity-rate tradeoff. The validity of the analytical framework is confirmed by numerically comparing it with a simulator compliant with the 3GPP LTE standard. Using the developed metrics, it is shown that centralizing computing resources provides a higher net throughput per computational resource as compared with local processing.

Centralized radio access network architectures consolidate the baseband operation towards a cloud-based platform, thereby allowing for efficient utilization of computing assets, effective inter-cell coordination, and exploitation of global channel state information. This paper considers the interplay between computational efficiency and data throughput that is fundamental to centralized RAN. It introduces the concept of computational outage in mobile networks, and applies it to the analysis of complexity constrained dense centralized RAN networks. The framework is applied to single-cell and multi-cell scenarios using parameters drawn from the LTE standard. It is found that in computationally limited networks, the effective throughput can be improved by using a computationally aware policy for selecting the modulation and coding scheme, which sacrifices spectral efficiency in order to reduce the computational outage probability. When signals of multiple base stations are processed centrally, a computational diversity benefit emerges, and the benefit grows with increasing user density.

Proportional fair (PF) scheduling is known to provide a balance between overall throughput maximization and fairness, by ensuring that users are served at one point in time, even if they experience poor channel conditions. In this regard, the PF scheduler accounts for the current channel state as well as for the throughput a user previously obtained. However, in slow fading scenarios with feedback delays the channel state information (CSI) known to the transmitter is outdated. Consequently, the transmission rates supported by the actual channel are only known imperfectly, which affects the scheduling as well as the rate adaptation, resulting in potential outages. While current schedulers typically ignore the CSI impairment, this work proposes a scheme which accounts for the variance of the CSI impairment and targets to achieve a fixed outage probability, referring to average delay constraints. Simulation results validate the advantages of the robust PF scheduling scheme when it is compared to non-robust solutions.

This paper introduces the concept of virtual cells in Time-Division Long Term Evolution (TD-LTE) systems, which enables users residing in overlapping cells' coverage regions to utilize resources from multiple base stations. The benefits of virtual cells are realized by the efficient resource utilization, via adapting the network resource availability with the traffic demand taking also into account the impact of cross-slot interference. Besides the increased resource flexibility, virtual cells also resolve pseudo congestion, and introduce a customized, user specific, resource utilization. Such a feature enables mobile users to utilize sub-frames from different base stations creating on demand virtual frame. A simulation study elaborates the benefits of our approach, and demonstrates the performance enhancements in comparison with the conventional TD-LTE configurations.

Future cellular networks need to harvest existing spectral resources more efficiently. Hence, networks will be deployed at a higher density in order to increase the spatial reuse. This will require advanced interference mitigation techniques that allow to cope with the increased interference level. In this paper, the two-way interference channel is analyzed as a model for a typical inter-cell interference scenario. Based on this model, a new inter-cell interference mitigation approach is derived. This new approach reshapes interference by asymmetrically assigning uplink and downlink to communication pairs, i.e., one communication pair operates in uplink while an adjacent communication pair is in downlink. In addition, backhaul resources are taken into account, which are used to exchange support information between radio access points and to support the interference mitigation process. The introduced approach is compared to cooperative multi-point techniques which employ joint transmission and reception algorithms. The evaluation is done under consideration of limited backhaul resources and imperfect channel state information. It shows that assigning uplink and downlink asymmetrically is able to outperform cooperative multi-point techniques for terminals close to the cell border with gains of up to about 20 % compared to noncooperative transmission and 10 % compared to CoMP.

This paper derives an analytical framework to evaluate achievable rates in a multi-user OFDMA system. Opportunistic schedulers and OFDMA allow for exploiting multi-user diversity using a flexible and simplified resource assignment. However, the granularity of resource assignments determines the gains in spectral efficiency and requires to consider signaling overhead and finite channel coherence. Additionally to the signaling and pilot overhead, practical systems suffer from partial CSI, i.e., neither scheduler nor receiver have exact knowledge of the channel state. This paper investigates the trade-off between achievable net- rates and channel characteristics as well as system parameterization. An analytical framework for the expected net-rates in a multi-user system with partial CSI at scheduler and receiver is derived.

Mobile operators are facing increasing energy prices to operate their networks. At the same time, the demand for broadband wireless access is steadily increasing, driven by the success of smart phones and new services like machine-to-machine communication. In this paper we propose and evaluate a novel asymmetric user assignment scheme, which enables operators to save energy, while satisfying the capacity demands. The main innovation is to separate the association of users to base stations in uplink and downlink, such that in macro/micro overlay scenarios parts of the radio access network can be switched off. Based on system-level simulations we show that our scheme enables energy savings of up to 15% and 60% over typical macro and micro cell deployments, respectively. We further discuss its technical feasibility and implications on the system design. All numerical results are obtained in accordance with the guidelines and requirements of IMT-Advanced systems.

In this paper, relaying and multi-cell MIMO transmission are investigated as approaches for improving resource reuse and more flexible organization of cellular networks. The analysis focuses on approaches for future cellular systems, which jointly exploit relaying and multi-cell MIMO transmission. Possible candidate approaches are identified, simplified for practical applications and evaluated using a system-level model from the European research project WINNER. Their achievable throughput is analyzed under practical constraints using three different normalization approaches: cost-normalization, energy-normalization, and joint cost-energy-normalization. It can be shown that the combined approach of relaying and multi-cell MIMO provides significant gains for the uplink communication. The selected approach exploits cooperative multi-cell MIMO processing between base stations and relay nodes, and uses a resource coordination technique on the links between relay nodes and user terminals. If a relay-based deployment is subject to a cost- and energy-normalization, multi-cell MIMO outperforms relaying with respect to achievable downlink throughput.

Full spatial reuse of spectral resources (frequency reuse of 1) is an inevitable consequence of the demanding requirements on mobile communication networks. A prerequisite to obtain full reuse is to either cancel or mitigate inter-cell interference as it otherwise limits the achievable performance. In order to derive new strategies for cellular interference-mitigation, this paper introduces the two-way interference channel and analyzes the achievable uplink-downlink data rates for different inter-cell interference scenarios. Of particular interest is an asymmetric protocol that exploits cross-uplink-downlink interference, i. e. two adjacent cells do not operate simultaneously in uplink or downlink but only one of both is active in uplink and one is active in downlink. Using analytical results, it is shown that under specific conditions this approach provides performance gains over conventional approaches and close to multi-cell MIMO. Although this approach is not able to improve the performance under all channel conditions, it provides a new degree of freedom which might be exploited if inter-cell interference significantly impairs the performance.

Most mobile communication systems deployed use 3-fold sectorization with directed antennas to avoid inter-sector interference and to spatially reuse available radio resources. However, innovations in next generation networks such as multiple antenna systems and coordinated multi-point techniques allow mitigation or even exploitation of interference. In this paper, we discuss how sectorization and antenna directivity can be adjusted to intentionally avoid or create interference in order to maximize the throughput of non-cooperative and cooperative transmission concepts. A key result is: the introduction of cooperative transmission renders higher sectorization using a larger number of inexpensive antennas with low directivity per site attractive.

Coordinated Multi-Point and relaying are two likely candidates for the upcoming LTE-Advanced standard, as both are able to satisfy the ever increasing demands for ubiquitous services with higher data rates. In this paper, we present field trial results for both techniques and discuss the most challenging problems during the implementation process in the EASY-C testbed in downtown Dresden.

In this paper the interference channel is extended by additional relay nodes in order to investigate the influence of interference on the design and performance of relaying protocols. We introduce a framework in which the relay interference channel is decomposed into a cascade of individual interference channels with finite conference links at the transmitters. Each of these stages is able to implement interference cancellation and mitigation schemes such as dirty-paper and Han-Kobayashi coding. We discuss the dependencies between individual stages and propose specific approaches with reasonable complexity. Finally, we compare the performance of these protocols using a simplified model for the channel and network geometry of a mobile communications system. The results show that a reasonable choice is to coordinate the base-station to relay links using distributed dirty-paper coding and to mitigate interference on the relay to user link using Han-Kobayashi coding.

Infrastructure-based wireless communications systems as well as ad hoc networks experience a growing importance in present-day telecommunications. An increased density and popularity of mobile terminals poses the question how to exploit wireless networks more efficiently. One possibility is to userelaynodessupporting the end-to-end communication of two nodes. In their landmark paper, Cover and El Gamal proposed different coding strategies for the single-relay channel. These strategies are thedecode-and-forwardandcompress-and-forwardapproach, as well as a general lower bound on the capacity of a single-relay network which relies on the combined application of the previous two strategies. So far, only parts of their work-the decode-and-forward and the compress-and-forward strategy-have been applied to networks with multiple relays. In this paper a generalizing framework for multiple-relay networks is derived using a combined approach of partial decode-and-forward and the ideas of successive refinement with different side information. After describing the protocol structure, the achievable rates for the discrete memoryless relay channel as well as the Gaussian multiple-relay channel are presented and analyzed. Using these results the derived framework is compared with protocols of lower complexity, e.g., multilevel decode-and-forward and distributed compress-and-forward.

In this paper we analyze several protocols for Gaussian half-duplex multiple relay networks where each node either transmits or receives on a particular resource. We present achievable rates for a multilevel partial decode-and-forward approach which generalizes previous results presented by Kramer and Khojastepour et al. Furthermore, we present a compress- and-forward protocol and a mixed strategy which use a regular encoding scheme. Finally, we discuss results for a four-terminal line network and compare the performance for fixed and random transmission strategies as well as for coherent and non-coherent transmission.

In their landmark paper Cover and El Gamal proposed different coding strategies for the relay channel with a single relay supporting a communication pair. These strategies are the decode-and-forward and compress-and-forward approach. They also derived a general lower bound on the capacity of a relay network which relies on the mixed application of both strategies. So far, only parts of their work - the decode-and-forward and the compress-and-forward strategy - have been applied to networks with multiple relays This paper formulates a mixed strategy for Gaussian multiple relay networks using a combined approach of partial decode-and- forward with N + 1 levels and the ideas of successive refinement with different side information at the receivers. After describing the protocol structure and presenting the achievable rates, we compare it with some special cases, e.g., multilevel decode- and-forward, distributed compress-and-forward and a mixed approach where one relay node operates in decode-and-forward and the other in compress-and-forward mode.

Future mobile communications systems demand for higher data rates and service quality compared to state-of-the-art systems. One way to achieve this ambitious goal is to use relaying which improves channel conditions by adding one or more intermediate nodes to support communication pairs. As relays do not rely on a wired backhaul, they can be flexibly deployed while keeping infrastructure costs lower than for additional base stations. Furthermore, relaying offers a variety of possible protocols - one promising concept is the cooperative transmission of different access points. Besides, the concurrent usage of different strategies is likely to be realized in next generation mobile communications systems, such that depending on the actual channel conditions the most beneficial strategy can be chosen. In this paper we present an adaptive and simple approach of partitioning the radio resources among different single-path and multi-path protocols. Our numerical analysis uses system level simulations of a 4G mobile communications system with relay enhanced cells and multiple antenna transmission.

The demand for even higher data rates in future mobile communications systems calls for new techniques capable of improving the performance of cellular systems. One possibility are relay enhanced cells where additional radio access points without wired backhaul are mounted and served by a base station using in-band resources. Although relaying can significantly improve channel conditions in terms of pathloss and fading, it suffers from a limited link between base station and relay node as well as from the half-duplex operation in which relay nodes retransmit messages. In this work we discuss and evaluate relaying strategies for multiple antenna based systems in terms of average user throughput. The performance of a conventional cellular system is compared with single-path relaying and cooperative relaying where base stations and relay nodes cooperatively serve user terminals.

Cooperative relaying is a promising alternative for conventional mobile communications systems as it is able to increase coverage and throughput of these systems. Due to practical constraints most cooperative relaying protocols employ half-duplex relays which implies the need for an increased data rate on the individual links and causes a significant performance loss at higher rates. This paper presents a novel cooperative relaying approach exploiting existing large scale space diversity as well as an additional coding gain by employing a spatial duplex approach. The scheme utilizes a network of alternatingly sending and receiving relay terminals where only one relay node is transmitting at a particular time. This spatial duplexing approach is used to implement a distributed Space-Time Trellis Code (STTC) for virtual antenna arrays. In this work, the protocol is applied to a convolutionally coded system. Due to the serial concatenation of an inner STTC and an outer convolutional code, the destination is able to use well-known turbo-decoding algorithms to reduce the frame error rate of the system. The assessment of the approach is done by comparing it with conventional relaying, a time/frequency duplex cooperative relaying protocol and direct transmission.

This work presents and analyzes a cooperative relaying approach which offers large scale spatial diversity and an additional coding advantage in a virtual antenna array. It utilizes a network of alternately sending and receiving relays whereas only one relay is sending at a particular time. The relay network is used to inherently implement a serial concatenation of a distributed Space-Time Trellis Code and an outer convolutional code. This paper discusses the influence of the source power allocation, network size and inter-relay interference. It further shows the performance compared to conventional relaying and a cooperative relaying protocol using orthogonal subchannels by means of the Eb/N0gain over direct transmission.

Future infrastructure based wireless systems are likely to use relaying due to energy savings, simpler roll-out of cellular networks, simpler increase of coverage and so forth. To increase the performance of relaying based systems cooperative relaying has emerged as an additional option to exploit spatial diversity. In order to allow for a fair comparison between single- hop and multi-hop schemes, an N-fold more spectrally efficient use of each link needs to be assumed for the multi-hop case. We propose a novel protocol relying on two relaying nodes which does not require the need for an increase in spectral efficiency in comparison to direct transmission. However, we achieve a better performance in the low SNR/high rate regime at the expense of a worse performance in the high SNR/low rate regime. The proposal is compared to direct transmission, conventional relaying, transmit diversity and a distinct cooperative relaying scheme considering their outage probability.