Wireless Operators Research Articles

External Interferences Detection by Examples in LTE Networks for 4G and 5G Networks Efficient Deployments

The relentless growth in wireless traffic forces mobile operators to aggressively increase network capacity to meet subscriber demand. However, spectrum availability is not growing at the same rate as traffic; in fact spectrum resources are in markedly short supply, and, as a result, their cost remain high. The reuse of existing spectrum is a key point for network expansion through capacity densification. But this spectrum reused for network capacity expansion faces the problem of interference in general and external interference in particular which are most often very difficult to detect, identify and eliminate. In this article, we focus on the case of external interferences and on the basis of measurements and experiences obtained in the field, we propose methods and procedures to help identify external interferences and remove them. At the end of the paper a set of recommendations are proposed in other to reduce and cancel external interferences.

Efficient Video QoE Prediction in Intelligent O-RAN

Open Radio Access Network (O-RAN) is a platform developed by a collaboration between wireless operators, infrastructure vendors, and service providers for deploying mobile fronthaul and midhaul networks, built entirely on cloud-native principles. The vision of O-RAN lies in the virtualization of traditional wireless infrastructure components, like Central Units (CU), Radio Units (RU), and Distributed Units (DU). O-RAN decouples the above-mentioned wireless infrastructure components into open-source elements, operating consistently with other elements of different vendors in the network. Quality of Experience (QoE) deals with a user's subjective measure of satisfaction. RAN Intelligent Controller (RIC) in O-RAN provides flexibility to intelligently program and control RAN functions using AI/ML-based models. We argue that various QoE parameters can be measured and operated by the RIC in O-RAN. We propose to improve the efficiency of O-RAN's radio resources by creating a RIC xApp that estimates the QoE measured using Video Mean Opinion of Score (MOS), and accurately optimizes the usage of radio resources across multiple network slices. We use predictive AI/ML-based models to accurately predict the QoE parameters in the network after which we can optimize the usage of network compo-nents leading to an enhanced user experience. Simulation results on 3 simulated data sets show that our proposed approach can achieve up to 95% QoE prediction accuracy.

Energy-Efficient Massive MIMO Network

Massive Multiple-Input Multiple-Output (Massive MIMO) is widely regarded as a highly promising technology for the forthcoming generation of wireless systems. The massive MIMO implementation involves the integration of a substantial number of antenna elements into base stations (BSs) to enhance spectral efficiency (SE) and energy efficiency (EE). The energy efficiency (EE) of base stations (BSs) has become an increasingly important issue for telecommunications network operators due to the need to take care of profitability while simultaneously minimizing their detrimental effects on the environment and addressing economic challenges faced by wireless communication operators. In this paper, the EE of massive MIMO networks and the relationship between EE, SE, and other parameters like bandwidth (B), number of antennas (M), circuit power, and number of users’ equipment (K) are discussed and investigated. For a fixed circuit power (PFIX), simulation results showed that the EE could be increased by about 1.12 as the number of antennas was doubled. The findings in this work also indicated an almost linear relationship between maximum EE and optimal SE, with a massive increase in the number of antennas when the power consumed by each antenna (PBS) was included in circuit power. In addition, when considering the power consumed per user’s equipment (PUE) impact, the SE increased with the ratio (M/K), in which SE showed a cubic relationship against M/K. On the other hand, the EE increased with M/K ratio until M/K reached a specific value. The maximum EE (and hence optimum SE) was achieved by massive MIMO, where the number of antennas was three times the number of users. However, EE started degrading after this value, as the number of antennas was considered larger than the users’ and consumed more energy, resulting in EE degradation.

Implementation of Spectrum Sensing Algorithm Based on Television White Space Scenarios for Spectrum Aggregation Applications

With the proliferation of mobile devices and diverse mobile applications, wireless operators are experiencing phenomenal growth in the demand for mobile services around the world. These demands have led to the technological responses such as the advancement in radio access, coding and modulation schemes as well as spectrum (carrier) aggregation and cognitive radio capabilities. The aspect of spectrum or carrier aggregation uses cognitive radio technology that implements the dynamic spectrum access for spectrum utilization by sensing and using the underutilized spectrum on co-primary basis without interference to the primary users. In this work, an energy-based, non-parametric TVWS spectrum sensing algorithm was implemented in MATLAB environment. The developed sensing algorithm was aimed at finding free TVWS frequency channel(s) that would be used during spectrum aggregation of channels from TVWS and LTE-A spectrums. The chosen free channel would be configured as the component carrier two (CC2) at the configuration management platform of the TVWS eNode B. The result showed the instantaneous TV channels statuses; free, busy and interfered by computing the PSD expressed in dB while the estimated received power level in dBm for free channels was computed for the UHF range from channel 21 through channel 69. The detectable TV signals’ threshold value was set at -114 dBm. In the ten simulation five cycles carried out, the developed algorithm instantaneously selected channels: 68,34,48,60 and 28 corresponding to the centre (carrier) frequencies of 850MHz, 578MHz, 690MHz, 786MHz and 530MHz, would be dynamically configured as the component carrier two (CC2) in the TVWS eNode Bs during spectrum aggregation process.

From 5G to beyond 5G: A Comprehensive Survey of Wireless Network Evolution, Challenges, and Promising Technologies

The histrionic growth of mobile subscribers, disruptive ecosystems such as IoT-based applications, and astounding channel capacity requirements to connect trillions of devices are massive challenges of the earlier mobile generations, 5G turned up the key solution. The prime objective of the 5G network is not only to maintain a 1000-fold capacity gain and 10 Giga Bits per second delivered to a single user, but it also assured quality-of-service, higher spectral efficiency, the ultra-reliable and improved battery lifetime of devices and massive machine-type communication (mMTC). The huge traffic load and high amount of resource consumption in 5G applications, augmented reality and virtual reality for magnificent virtual experience, and wireless body area networks will seriously affect the channel capacity of cellular cells and interrupt the admission and service of other users which makes compulsory new means of channel capacity and spectral efficiency enhancement techniques. In this research, we review several key emerging wireless technologies to increase channel capacity and spectral efficiency that will not only lead to improve network performance but also meets the ever-increasing user demands. We investigate various benefits and current research challenges of using these technologies. We analyze massive multi-input multi-output technology (mMIMO) an efficient technique and promising solution for the 5G and Beyond 5G (B5G) networks with several benefits and features. Moreover, this paper will be of vast help to the researchers who will involve advance investigation and also to the wireless network operator industry that is in the search for smooth development of state-of-the-art 5G and B5G networks.

UKRAINIAN NATIONAL ISSUE IN THE EMERGING U.S. STRATEGY TOWARDS THE SOVIET UNION (1946 – 1953)

The purpose of the article is to reveal the consideration of the Ukrainian national issue in designing and implementing the U.S. postwar strategy towards the Soviet Union at the outbreak of the Cold War, under the presidency of Harry Truman. Methodology. This is qualitative empirical research, based on a critical analysis of primary and secondary sources. The research topic is studied against the background of developments of the international situation after World War II, which influenced the shaping of the U.S. post-war strategy towards the USSR, as well as the development of anti-Soviet armed resistance in the western part of Ukraine, and Ukrainian Americans’ efforts to use the new international situation in Ukrainian national interests. The scientific novelty of the research consists in the coverage of a topic that has not been sufficiently studied, and in the introduction of new primary sources for this purpose. Conclusions. The postwar U.S. foreign policy strategy was driven by Soviet expansionism, and a threat of a new world war. The American Government began to consider the Ukrainian national issue in designing the post-war strategy towards the USSR because of three major factors: the heroic struggle of the Ukrainian Insurgent Army (UPA) in the western part of Ukraine; the willingness of the representatives of Ukrainian political émigré with close ties to the UPA to cooperate with the U.S. intelligence, and the activities of the Ukrainian Congress Committee of America, aimed at obtaining American Government’s assistance and support for Ukrainians in Europe. In 1946–1951, the representatives of some Ukrainian émigré groups were involved in gathering intelligence about Soviet military capabilities and intentions, and in operations to root out Soviet agents among Displaced Persons in Germany. In 1949–1951, the CIA parachuted the Ukrainian couriers, trained as wireless operators, into the western part of Ukraine to collect intelligence and to organize a network of active resistance behind the Soviet lines in case of the new world war. U.S. intelligence cooperated mostly with the most moderate group of Ukrainian political émigré – the Foreign Representation of the Ukrainian Supreme Liberation Council. After this strategy had failed, the U.S. Government focused on the development of a psychological warfare strategy. One of the components of its implementation was the foundation of Prolog Research and Publishing Association (1952) with CIA assistance. However, the understanding of the importance of the Ukrainian national factor in the U.S. «Soviet» strategy was only emerging.

Statistical Approach to Estimating Audience from MAC-Randomized WiFi Probe Requests.

In the past few years, the ability of wireless network operators to monitor audience using control frames emitted by client devices has been compromised, both by legislation treating client MAC addresses as private information and by the difficulty of distinguishing genuine client frames from those arising from the Internet of Things or from certain enhanced services. Here, a deterministic model, based on characteristics of human activity and on seasonal trends, is used to reveal underlying client statistics in raw MAC-randomized WiFi Probe Request data. The method proposes a candidate conversion factor, X, between probe request counts and the client population, which offers plausible predictions on real-world datasets.

Bidding strategy for wireless charging roads with energy storage in real-time electricity markets

The combination of wireless charging roads and energy storage systems is a promising option for electric vehicle charging because of their capabilities in mitigating range anxiety of electric vehicle drivers. Wireless charging road operators can purchase electric energy by submitting price-sensitive demand bids in real-time electricity markets. Efficient bidding strategies are crucial to minimizing the energy costs for providing wireless charging services. In this study, we first propose a composite statistical model based on graph signal processing and linear regression to forecast the future locational marginal prices (LMPs) in a power network. Then an estimate of future electric load on each wireless charging road is derived by simulating its traffic flow using a point queue-based traffic flow model. An efficient price-sensitive bidding strategy for each individual wireless charging road is developed based on its LMP forecast, wireless charging load estimate, and a model predictive control framework. Our numerical example shows that the proposed price-sensitive demand bidding strategy reduces the electric energy cost for operating a wireless charging road with an energy storage system by 6% compared to a baseline bidding strategy.

Minimization of nth Order Rate Matching in Satellite Networks with One to Many Pairings

This paper studies the minimization of nth (positive integer) order rate matching in high-throughput multi-beam satellite systems, based on one-to-many capacity allocation pairings, for the first time in the literature. The offered and requested capacities of gateways and users’ beams are exploited, respectively. Due to the high complexity of the binary optimization problem, its solution is approached with a two-step heuristic scheme. Firstly, the corresponding continuous, in [0, 1], pairing problem is solved applying the difference of convex optimization theory, and then, a transformation from continuous to binary feasible allocation is provided to extract the pairings among gateways and users’ beams. Comparing with the exponential-time optimal exhaustive mechanism that investigates all possible pairs to extract the best matching for minimizing the rate matching, extended simulations show that the presented approximation for the solution of the non-convex optimization problem has fast convergence and achieves a generally low relative error for lower value of n. Finally, the simulation results show the importance of n in the examined problem. Specifically, pairings originated by the minimization of rate matching with larger n result in more fair rate matching among users’ beams, which is a valuable result for satellite and generally wireless systems operators.

Deep Learning in Network-Level Performance Prediction Using Cross-Layer Information

Wireless communication networks are conventionally designed in model-based approaches through utilizing performance metrics such as spectral efficiency and bit error rate. However, from the perspectives of wireless service operators, network-level performance metrics such as the 5%-tile user data rate and network capacity are far more important. Unfortunately, it is difficult to mathematically compute such network-level performance metrics in a model-based approach. To cope with this challenge, this work proposes a data-driven machine learning approach to predict these network-level performance metrics by utilizing customized deep neural networks (DNN). More specifically, the proposed approach capitalizes on cross-layer information from both the physical (PHY) layer and the medium access control (MAC) layer to train customized DNNs, which was considered impossible for the conventional model-based approach. Furthermore, a robust training algorithm called weighted co-teaching (WCT) is devised to overcome the noise existing in the network data due to the stochastic nature of the wireless networks. Extensive simulation results show that the proposed approach can accurately predict two network-level performance metrics, namely user average throughput (UAT) and acknowledgment (ACK)/negative acknowledgment (NACK) feedback with great accuracy.

Radio Wave Propagation in a Forested Channel

The appearance of vegetation along radio communication channel can significantly reduce quality of propagating signal. Hence, the need for wireless signal operators to understand the characteristic of channel components for effective radio planning and deployment.
 This paper has investigated the influence of vegetative channel on wireless signals and examined the dependence of signal loss on path geometry. Measurements were conducted in a sort-depth forest (at SHF frequencies) following two different paths (paths 1 & 2) within the woodland. The experimentation was conducted in autumn and summer so as to determine likely seasonal effect on losses along different path geometries.
 Results show decay in signal level as the depth of penetration increases which also varies from path to path. These (results) were later compared with standard empirical models MED, FITUR and COST 235 and it shows a statistical adherence with only one of the models, the MED.
 It is therefore imperative that for effective signal deployment devoid of significant impairment, wireless network operators must take cognisance of the channel characteristics and path geometries in order to guarantee proper planning, modeling and deployment.

Blockchain-Enabled Resource Trading and Deep Reinforcement Learning-Based Autonomous RAN Slicing in 5G

The advent of radio access network (RAN) slicing is envisioned as a new paradigm for accommodating different virtualized networks on a single infrastructure in 5G and beyond. Consequently, infrastructure providers (InPs) desire virtualized networks to share their subleased resources for effective resource management. Nonetheless, security and privacy challenges in the wireless network deter operators from collaborating with one another for resource trading. Lately, blockchain technology has received overwhelming attention for secure resource trading thanks to its security features. This paper proposes a novel hierarchical framework for blockchain-based resource trading among peer-to-peer (P2P) mobile virtual network operators (MVNOs), for autonomous resource slicing in 5G RAN. Specifically, a consortium blockchain network that supports hyperledger smart contract (SC) is deployed to set up secure resource trading among seller and buyer MVNOs. With the aim of designing a fair incentive mechanism, we model the pricing and demand problem of the seller and buyers as a two-stage Stackelberg game, where the seller MVNO is the leader and buyer MVNOs are followers. To achieve a Stackelberg equilibrium (SE) for the formulated game, a dueling deep Q-network (Dueling DQN) scheme is designed to achieve optimal pricing and demand policies for autonomous resource allocation at negotiation interval. Comprehensive simulation results analysis prove that the proposed scheme reduces double spending attacks by 12% in resource trading settings, and maximizes the utilities of players. The proposed scheme also outperforms deep Q-Network (DQN), Q-learning (QL) and greedy algorithm (GA), in terms of slice and system level satisfaction and resource utilization.

6G - a generation too far? [News - analysis

Now that 5G has arrived with an advertising onslaught from operators like EE, what else could there be for the telecom industry to work on but 6G? Due out around the end of this decade, 6G would keep wireless communications on much the same timescale set since 3G first appeared. But questions are now being asked as to whether these generational shifts are working out the way the industry intended. One of the early aims for 5G was to bring cellular wireless to industrial applications that could not use its predecessors. Although 4G made some attempts to cut the round-trip latency for delivering packets over the air, 5G was designed to cut that to a millisecond or so. In principle, this makes it possible to coordinate robots without having to cable them up, assuming you do not try to have the control systems for those robots running in a data centre overseas. In practice, the wireless operators are, as with 3G and 4G, pushing new phones to consumers and, by all accounts, are not doing that badly at it. Both 3G and 4G suffered from false starts and so took a while to get out of the gate. 5G was somewhat luckier and may have had a boost from the pandemic and the shift to remote working, and living. At the recent 6G World Symposium, Andreas Müller, general chair of the 5G Alliance for Connected Industries and Automation and head of corporate-sector communication and network technology at Bosch's corporate research centre, said there is still some work to do on the standard to get it ready for factories. “The problem for industrials is that a lot for them only comes with Releases 16 and 17,” he said. The 3GPP group's Release 16 or 5G Phase 2 was finalised last summer, two years after the initial Release 15; Release 17 is due to become a finalised standard next year.

On the Optimal Duration of Spectrum Leases in Exclusive License Markets With Stochastic Demand

This paper addresses the following question which is of interest in designing efficient exclusive-use spectrum licenses sold through spectrum auctions. Given a system model in which customer demand, revenue, and bids of wireless operators are characterized by stochastic processes and an operator is interested in joining the market only if its expected revenue is above a threshold and the lease duration is below a threshold, what is the optimal lease duration which maximizes the net customer demand served by the wireless operators? Increasing or decreasing lease duration has many competing effects; while shorter lease duration may increase the efficiency of spectrum allocation, longer lease duration may increase market competition by incentivizing more operators to enter the market. We formulate this problem as a two-stage Stackelberg game consisting of the regulator and the wireless operators and design efficient algorithms to find the Stackelberg equilibrium of the entire game. These algorithms can also be used to find the Stackelberg equilibrium under some generalizations of our model. Using these algorithms, we obtain important numerical results and insights that characterize how the optimal lease duration varies with respect to market parameters in order to maximize the spectrum utilization. A few of our numerical results are non-intuitive as they suggest that increasing market competition may not necessarily improve spectrum utilization. To the best of our knowledge, this paper presents the first mathematical approach to optimize the lease duration of spectrum licenses.

A Self-Optimizing Technique Based on Vertical Handover for Load Balancing in Heterogeneous Wireless Networks Using Big Data Analytics

With the heterogeneity and collaboration of many wireless operators (2G/3G/4G/5G/Wi-Fi), the priority is to effectively manage shared radio resources and ensure transparent user movement, which includes mechanisms such as mobility support, handover, quality of service (QoS), security and pricing. This requires considering the transition from the current mobile network architecture to a new paradigm based on collecting and storing information in big data for further analysis and decision making. For this reason, the management of big data analytics-driven networks in a cloud environment is an urgent issue, as the growth of its volume is becoming a challenge for today’s mobile infrastructure. Thus, we have formalized the problem of access network selection to improve the quality of mobile services through the efficient use of heterogeneous wireless network resources and optimal horizontal–vertical handover procedures. We proposed a method for adaptive selection of a wireless access node in a heterogeneous environment. A structural diagram of the optimization stages for wireless heterogeneous networks was developed, making it possible to improve the efficiency of their functioning. A model for studying the processes of functioning of a heterogeneous network environment is proposed. This model uses the methodology of big data evaluation to perform data transmission monitoring, analysis of tasks generated by network users, and statistical output of vertical handover initiation in (2G/3G/4G/5G/Wi-Fi) mobile communication infrastructure. The model allows studying the issues of optimization of operators’ networks by implementing the algorithm of redistribution of its network resources and providing flexible load balancing with QoS users in mind. The effectiveness of the proposed solutions is evaluated, and the performance of the heterogeneous network is increased by 16% when using the method of static reservation of network resources, compared to homogeneous networks, and another 13% when using a uniform distribution of resources and a dynamic process of their reservation, as well as compared to the previous method. An appropriate self-optimizing technique based on vertical handover for load balancing in heterogeneous wireless networks, using big data analytics, improves the QoS for users.

VoLTE problem location method based on big data

This paper mainly introduces the key information extracting method from User Equipment (UE) test data, the synchronous algorithm of various interfaces X Detailed Record (XDR) data, the key information filling technique of multi interfaces X Detailed Record (XDR), the analytical procedure based on big data is descript in details. An example is given to explain that more accurate reason for Voice over Long-Term Evolution (VoLTE) call not connected can be found with big data from core network, compared with UE data only. With this method VoLTE problem can be quickly solved, it is very useful for wireless operator to improve the user voice service awareness rapidly.

Economics of Strategic Network Infrastructure Sharing: A Backup Reservation Approach

In transitioning to 5G, the high infrastructure cost, the need for fast rollout of new services, and the frequent technology/system upgrades triggered wireless operators to consider adopting the cost-effective network infrastructure sharing (NIS), even among competitors, to gain technology and market access. NIS is a bargaining mechanism whose terms and conditions must be carefully determined based on mutual benefits in a market with uncertainties. In this work, we propose a strategic NIS framework for contractual backup reservation between a small/local network operator with limited resources and uncertain demands, and a more resourceful operator with excessive capacity. The backup reservation agreement requires the local operator (say, operator A) to reserve a certain amount of resources (e.g., spectrum) for future sharing from the resource-owning operator (say, operator B). In return, operator B guarantees availability of its reserved resources to meet the need of operator A. We characterize the bargaining between the operators in terms of the optimal reservation prices and quantities with and without consideration of their competitions in market share, respectively. The conditions under which competing operators have incentive to cooperate are explored. The impact of competition intensity and redundant capacity on performance under backup reservation are also investigated. Our study shows that NIS through backup reservation improves both resource utilization and profits of operators, with the potential to support higher target service levels for end users. We also find that, under certain conditions, operator B may still have the incentive to share its resources even at the risk of impinging on its own users.

Simple OFDM Error Floor Prediction with Sub-optimal Time Sampling

The in-service measurable block-error-rate (BLER) is the only transmission performance indicator aimed for wireless network operators to test the physical-layer of the long-term evolution and the emerging 5G new radio systems, both utilizing the orthogonal frequency-division multiplexing (OFDM). However, the small so obtained BLER values may not be accurate, which compromises estimation of the residual channel that the physical layer delivers to higher-layer protocols, finally affecting the application-layer performance and efficiency. Moreover, this is not the only reason why the out-of-service testing of the bit-error-rate (BER)—still the ultimate physical-layer performance indicator representing the probability of a bit error, remains inevitable in research, development and equipment manufacturing. Specifically, it is often needed to estimate the peak OFDM performance usually referred to as error floor or residual BER (which can be used for BLER estimation), straight from common and physically interpretable parameters. So, in this paper, after identifying the optimal sample instant in a power delay profile, the sub-optimal sampling just upon the first pulse arrival, is found to add minor residual BER degradation, and therefore can be adopted to provide a simple OFDM error floor prediction that is linear with the rms delay spread. The proposed model is verified by Monte Carlo simulations.

Equilibrium Problems With Equilibrium Constraints Analysis for Power Control and User Scheduling in NOMA Networks

With limited wireless resources but increasing demands for wireless applications, non-orthogonal multiple access (NOMA) has been considered as a promising technology for future wireless networks. Nevertheless, it remains challenging on motivating the mobile users (MU) to join NOMA networks, scheduling MUs on the same sub-bands, reducing interference among the MUs and setting optimal transmit power in the NOMA networks. In this paper, first we consider the quantization error due to the limitation of receivers’ equipments and the decoding error during the successive interference cancellation (SIC) procedure. Second, we model the resource allocation problems as the four-level equilibrium problems with equilibrium constraints (EPEC), where the wireless operator, on behalf of each MU, first predicts the utilities and assign the MU as a NOMA user (NMU) or a traditional MU (TMU). For the NMUs, the interference penalty pricing strategy of other NMUs are proposed. Based on the announced prices of all the NMUs, the sub-channel allocation is managed in either distributed or collaborative ways. According to the announced interference prices of other NMUs and the coalition results, the optimal transmit power to gain the maximum utility is considered. Through extensive simulation analysis, the superior performance obtained by using the proposed scheme is demonstrated.

Prophet model and Gaussian process regression based user traffic prediction in wireless networks

User traffic prediction is an important topic for wireless network operators. A user traffic prediction method based on Prophet and Gaussian process regression is proposed in this paper. The proposed method first employs discrete wavelet transform to decompose the user traffic time series to high-frequency component and low-frequency component. The low-frequency component bears the long-range dependence of user network traffic, while the high-frequency component reveals the gusty and irregular fluctuations of user network traffic. Then Prophet model and Gaussian process regression are applied to predict the two components respectively based on the characteristics of the two components. Experimental results demonstrate that the proposed model outperforms the existing time series prediction method.