5G and Beyond 5G (B5G) networks are regarded as effective solutions for serving numerous emerging applications through 5G Vehicle-to-Everything (V2X) communications, that is, 5G V2X. Vehicles experience several messages such as safety messages like Decentralized Environmental Notification Messages (DENMs) and non-safety messages like entertainment data packets having varied requirements. In this work, we consider the co-existence of DENMs along with background data traffic and explore the impact of DENMs on such low-priority background data traffic. We explore the multicast and broadcast techniques for robust 5G V2X communications. We consider the delivery of DENMs using the multicast of gNodeBs (gNBs) as Single-Cell Point-to-Multipoint (SC-PTM) over Vehicle-to-Infrastructure (V2I) links as well as the broadcast of the vehicles over Vehicle-to-Vehicle (V2V) links. User-specific data traffic is unicasted over a high-capacity V2I link. Next, we propose a joint V2I resource allocation algorithm for the two traffic classes. We mathematically model average DENM delivery delay, average DENM packet reception ratio, and average throughput of background data traffic. Our mathematical analysis is further validated by extensive simulations. Through these simulations, it is shown that our proposed scheme outperforms the existing schemes in terms of average DENM delivery delay.

Next generation 6G wireless envisions a much higher data rate and a lower latency compared to 5G wireless networks. Directional antennas with narrow beams across high mmWave frequencies hold the key to achieving such high data rates. However, Beam Management (BM), which is the process of finding appropriate transmit and receive beams, offers significant challenges. Dynamic channel variation, user mobility, and narrow beams in high frequency mmWave channels further complicate these challenges. Efficient Machine Learning (ML) strategies can be used to alleviate this overhead. In spite of their underlying differences, sub-6 GHz and high frequency mmWave channels share some similarities in array geometry, number of paths, and surrounding environment. As sub-6 GHz channel characteristics are relatively easier to acquire and learn, we introduce a new machine learning framework, using transformer and LSTM to learn sub-6 GHz channel information over time for efficient beam prediction across high frequency mmWave channels. System Level simulation results point out that when using time-series data-based learning of beam patterns with transformers or LSTMs in sub-6 GHz channels, our proposed scheme achieves up to 99.5% top-5 beam prediction accuracy while reducing the BM overhead by over 50% compared to existing work.

The growing demand for new applications, like digital twins, Extended reality(XR), autonomous vehicles, smart environments, and other Internet of Things(IoT) has set the stage for wireless evolution towards 6G. In 6G wireless sensing, communication and computation are expected to be imbued in a ubiquitous manner. However, optimizing resource allocation in such an integrated sensing, communication, and computation (ISCC) raises critical challenges. In this paper, we focus on the efficient and optimal distribution of orthogonal, homogeneous, subcarriers among these computation, communication, and sensing domains. We propose a novel bi-level multi-objective optimization approach to maximize communication throughput, sensing coverage, and data offloaded from user equipment (UE) to the base station (BS) for computation, resulting in a Pareto efficient front for channel allocations. A 2-stage future Pareto front estimation framework is introduced where the exogeneous parameters are forecasted using using a) Long Short Term Memory (LSTM) or b) Convolutional Neural Network (CNN) model. Then, Non-dominated sorting genetic algorithm II (NSGA-II) is used to estimate the Pareto front for each time step within a prediction window. The framework's performance is evaluated by using a comparison of the generational distance (GD) between the estimated Pareto front from the predicted parameters to the Pareto front generated by the true parameters. Simulation results point out that compared to baseline, LSTM and CNN-based frameworks respectively reduce the GD by 47% and 84% in objective space and 56% and 65% in decision space.

The proliferation in the demand of high data rate and improved performance has urged wireless vendors to think of extending 5G New Radio (NR) in unlicensed bands. The unlicensed band is mainly used by Wi-Fi and Wi-Gig resulting in the reduced probability of channel availability. The User Equipment (UE) has to remain active and wait for the availability of unlicensed channel. This process escalates the energy expense of battery-constrained mobile devices. Discontinuous Reception (DRX) introduced in 3GPP can also be used in NR-Unlicensed (NR-U) to reduce UEs energy consumption. This article introduces a DRX mechanism over NR-U networks for both Standalone (SA) and Non-Standalone (NSA) deployment modes. We have used two different flavors, Beam-Search and Beam-Aware, to model DRX mechanism. Hence, the proposed DRX mechanisms are: Standalone DRX with Beam-Search (SBS-DRX), Standalone DRX with Beam-Aware (SBA-DRX), Non-Standalone DRX with Beam-Search (NBS-DRX), and Non-Standalone DRX with Beam-Aware (NBA-DRX). Semi-Markov based modeling is used to show the estimation of power-saving, average delay, and resource utilization. The power-saving achieved with SBA-DRX is 21.13% and 24.84% higher than 5G NR DRX for both Trace 1 and Trace 2, respectively. Moreover, delay achieved with NBA-DRX is 19.16% less than NBS-DRX with 18% less resource utilization.

Emerging mmWave enabled 5G wireless communications, offer tremendous increase in data demands and better quality expectations. However, high mmWave frequencies call for directional beamforming to overcome propagation limitations and enhance spatial capabilities. The establishment of new directional paradigm challenges the current Discontinuous Reception (DRX) mechanism for power saving in the User Equipment (UE). In this article, we introduce new Directional-Discontinuous Reception (DDRX) for directional air interface expected in mmWave enabled 5G communications. DDRX mechanism emphasizes the importance of beam searching for alignment of directional beams between UE and 5G base station (gNB), after every sleep cycle. Beam searching, though inevitable, reduces the effective sleep time. We propose three new DDRX mechanisms: Integrated DDRX (I-DDRX), Standalone DDRX (S-DDRX), and Cooperative DDRX (C-DDRX) to limit the impediments on power saving. Probabilistic estimation of UE's power saving and delay is performed for all the three different DDRX mechanisms using semi-Markov process. The power saving achieved in IDDRX is 12.1 percent higher than S-DDRX. The power saving of C-DDRX is 6.1 percent higher than S-DDRX. Analytical results of DDRX proposals are validated by simulation studies, performed over real wireless trace.

Ever-increasing demand of data rate and subscriber penetration are resulting in continuous growth of mobile data traffic. In order to meet this exponential data traffic growth 3GPP Release 13, we have decided to enable the operation of Long Term Evolution (LTE) in the unlicensed band, termed as Licensed-Assisted Access (LAA). As the unlicensed spectrum is mainly used by WiFi, LTE evolved NodeB (eNB) has to perform Listen Before Talk (LBT) procedure to access the unlicensed channel. If the unlicensed spectrum is occupied by other services, User Equipment (UE) should remain active and wait for unlicensed channel to become idle. UE consumes a high power by remaining active, when unlicensed channel is not available. The UE's energy consumption could be saved by configuring Discontinuous Reception (DRX) in the unlicensed band, similar to DRX in LTE. In this article, we introduce and analyze a new Licensed-Assisted Access DRX mechanism (LAA-DRX) over LTE networks. Using our novel four state semi-Markov model, we show the probabilistic estimation of power saving and wake up latency associated with our proposed LAA-DRX process. Mathematical analysis and simulation results, using real wireless trace, point out that comparing to existing LTE DRX process, our LAA-DRX can achieve almost 4 percent higher power savings and up to 58 percent reduction in resource usage.

In the past few decades, many Transmission Control Protocol (TCP) congestion control algorithms have been investigated to meet the growing network demands and to enhance the performance of TCP in lossy or high-bandwidth-delay-product (high-BDP) networks. However, it is still challenging to implement a dynamic congestion control algorithm for wide range of diverse mobile users, network conditions and applications. This paper explores avenues for enhancement of TCP congestion control algorithm for next generation mobile networks by dynamically learning the available bandwidth and deriving the Congestion Control Factor N. N is used to Adaptive Increase/Adaptive Decrease (AIAD) the Congestion Window (CWND) dynamically instead of using the traditional approach that is Additive Increase/ Multiplicative Decrease (AIMD) paradigm. Once there is congestion, our proposed algorithm will not allow the CWND to decrease multiplicatively or steeply. After dropping to a certain level (lower than legacy), we try to take the CWND to previous state adaptively with the help of calculated bandwidth (based on learning). This in turn helps to efficiently control the CWND for better network utilization especially in case of lossy and high-BDP conditions. As soon as it reaches the original state, it remains stable for longer time as compared to legacy until packet loss or time out. We demonstrate the effectiveness of our algorithm with the help of live air experiments (performed in Samsung R&D India, Bangalore) and NS3 based simulation experiments. Through our experiments, we show that our algorithm outperforms the legacy congestion control algorithms (like CUBIC, RENO, TCPW) and the existing CLTCP algorithm in terms of goodput, intra algorithm fairness and inter algorithm fairness maintaining the scalability and friendliness.

Emerging 5G wireless networks are expected to herald significant transformation in industrial applications, with improved coverage, high data rates, and massive device capacity. However, the introduction of 5G wireless makes the network configuration, management, and planning extremely challenging. For efficient network configuration, every cell needs to be allocated a particular Physical Cell Identifier (PCID), which is unique in its vicinity. Wireless standards (e.g., 3GPP) typically specify a limited number of PCIDs. However, the number of cells in 5G Ultradense Networks (UDN) is expected to significantly outnumber these limited PCIDs. Hence, these PCIDs need to be efficiently allocated among the myriad of cells, such that two cells which are neighbors or neighbor’s neighbor are assigned with different PCIDs. This complicated network configuration problem becomes even more complex by dynamic introduction and removal of 5G small cells (e.g., micro, femto, and pico). In this paper, we introduce BiSON, a new Bioinspired Self‐Organizing Solution for automated and efficient PCID configuration in 5G UDN. Using two different extensions, namely, “always near‐optimal” and “heuristic,” we explain near‐optimal and dynamic auto‐configuration in computationally feasible time, with negligible overhead. Our extensive network simulation experiments, based on actual 5G wireless trials, demonstrate that the proposed algorithm achieves better optimality (minimum PCIDs in use) than earlier works in a reasonable computational complexity.

The exponential increase in wireless sensor network (WSN) applications and devices crave for the communication resources. Regardless of the access mechanism, the unlicensed industrial, scientific and medical spectrum band is insufficient for massive simultaneous connections. On the other hand, the 4G and the emerging 5G cellular networks provide high data rates of 75–300 Mbps and 7.5 Gbps, respectively. Nevertheless, WSN's extempore inclusion in the cellular network can heavily congest its Random Access Channel (RACH). In this study, the authors investigate a unique concept of maximum frequency reuse in small evolved NodeBs, and subsequently, recommend their deployment in WSN congested areas. They also propose a cognitive radio (CR) algorithm to offload SeNB load over Macrocell. Authors formulate proposed SeNB‐based cellular network in an analytical RACH model, where dynamics of SeNB's and Macrocell users are scrutinised. Observations show that the proposed scheme allocates more users with low collision probability. Simulation results show that the proposed scheme imparts a significant increase of 1–3 Mbps in downlink throughput and culminate a lift of 2–9 Mbps in uplink throughput. The average uplink signal‐to‐interference‐plus‐noise‐ratio (SINR) is also proliferated by approximately 10–15 dB. Moreover, CR‐based SeNB's smart offloading outperforms the conventional offloading in terms of downlink and uplink SINR by about 1–3dB.

Smart Grids (SGs) are emerging cyber-physical systems, equipped with sophisticated communication and information technologies, for efficient and large-scale power supply and management. SGs demand advanced communication technologies between energy providers and consumers, for enabling a control-feedback loop, by using time-dependent pricing. In this paper, we propose an efficient planning of 5G small cells, with evolved multimedia broadcast and multicast communication, between aggregators (small cells) and SG consumers, for efficient demand-response (DR) programs. After pointing out that optimal multicast scheduling and radio resource management problem is NP-complete, we propose two different solutions, based on dynamic programming (DP) and greedy heuristics, for minimizing the energy cost for DR customers. We also analyze the performance of SG user capacity for 5G single cell multicast and multicast broadcast single frequency network. Extensive OPNET simulation results, over actual energy data and real wireless trace, demonstrate that our proposed 5G small cell planning and multicast solutions are capable of reducing energy production cost by 30%, with up to 35% shift in peak energy load, low latency and packet drop.

Next generation 5G wireless networks envision innovative radio technologies for ultra dense deployment with improved coverage and higher data rates. However, the deployment of ultra dense 5G networks, with relatively smaller cells, raises significant challenges in network energy consumption. Emerging green cloud radio access networks (C-RANs) are providing assurance of energy efficient cellular operations for reduction of both greenhouse emissions and operators' energy bill. Cellular traffic dynamics play a significant role in efficient network energy management. In this paper, we first identify the complexity of the optimal traffic awareness in 5G C-RAN and design a framework for traffic-aware energy optimization. The virtual base station cluster (VBSC) of C-RAN exploits an information theoretic approach to model and analyze the uncertainty of cellular traffic, captured by remote radio heads (RRH). Subsequently, using an online, stochastic game theoretic algorithm, the VBS instances optimize and learn the cellular traffic patterns. Efficient learning makes the C-RAN aware of the near-future traffic. Traffic awareness helps in selective switching of a subset of RRHs, thus reducing the overall energy consumption. Our VBS prototype implementation, testbed experiments, and simulation results, performed with actual cellular traffic traces, demonstrate that our framework results in almost 25% daily energy savings and 35% increased energy efficiency with a negligible overhead.

The vision of smart grids lie in large scale, distributed electrical power systems, equipped with communication and information technologies for automated and sustainable electricity production and distribution. Smart grids require communication with individual energy consumers for enabling energy providers to create a control-feedback loop using time-dependent pricing. Emerging broadband wireless networks hold the key in providing this efficient communication between energy consumers and smart power grids. In this paper we first propose the use of LTE multicast between the aggregator and energy consumers for efficient demand-response management in smart grids. We formulate the multicast scheduling and resource allocation problem for optimal SG-DR programs and provide a greedy solution. Performance analysis and OMNET++ simulation results, using data from Korean power corporation, demonstrate that our proposed LTE multicast and MBSFN solutions are capable of providing low latency (≤ 40ms) and packet drop (≤ 6%), thus resulting in almost 25% shift in peak energy load and up to 30% reduction in energy production cost.

The recently emerging location-based services in long-term evolution (LTE) systems require the accurate and efficient tracking of mobile users. An optimal information-theoretic framework is developed for tracking area update (TAU) for next-generation LTE cellular systems. Shannon's entropy is used to characterise the location uncertainty of the mobile users. Based on this entropy-based tracking framework, two practical location management schemes, Bayesian-based TAU and entropy-coding based TAU, are proposed. The proposed schemes capture the users' mobility patterns online and perform profile-based paging to optimise the TAU cost. Of the two proposed schemes, the Bayesian-based TAU operates as an independently identically distributed process and improves the paging cost with less storage and computational overhead, whereas the entropy-coding-based TAU using the Lempel–Ziv strategy asymptotically minimises both the update and paging costs with higher storage and computational overheads than the Bayesian-based one. There is some trade-off between the update/paging costs and storage/computational overheads in the two proposed schemes. The simulation results demonstrate that both proposed schemes outperform the existing comparable schemes for LTE systems in all of the performance metrics.

Supporting multimedia applications over 802.11 wireless LANs requires low latency and seamless handover between multiple access points. However, the existing handover process in 802.11 products suffers from very high delay and frequent service disruption, which are not acceptable for streaming multimedia applications. In order to reduce this high delay and service disruption, the authors have designed and implemented a new proactive handover strategy over 802.11. The strategy intelligently issues proactive scan and handover triggers to reduce the effective channel scanning delay. Subsequently, it reserves resources in advance, to reduce the handoff reconnection delay and provide necessary QoS guarantee. Using actual implementation and simulation study, the authors demonstrate that their proposed strategy is capable of achieving magnitudes of latency, jitter and throughput improvements during the 802.11 handover operations, thereby providing seamless multimedia transmission.

In recent years, high-bandwidth and low-cost wireless technologies have emerged as a competitive element, enabling the smart home environment. On the other hand, with the increasing demands for various services, the current access point allocation schemes give rise to significant challenges for a stable connection service, bandwidth and load distribution. In this paper, we present an optimal access point allocation scheme based on genetic algorithm that attempts to optimize multiple parameters, such as bandwidth, and load-balancing requirements. The proposed allocation scheme provides a set of approximately efficient solutions, which allows a wireless user to choose an access point based on its capacity and load balancing, not only regarding its signaling strength or service set identifier. The simulation results are provided to demonstrate the impact of the proposed optimization procedure on overall system performance in terms of connection distribution, load balancing and call dropping probability.

An information-theoretic, optimal framework is developed for tracking the residents in a context-aware heterogeneous smart environment. The framework envisions that each individual sensor system operates fairly independently and does not require public knowledge of individual topologies. The resident-tracking problem is formulated in terms of a new concept of weighted entropy. The framework is truly universal and provides an optimal, online learning and prediction of inhabitant's movement (location) profiles from the symbolic domain. The overall optimal tracking in heterogeneous smart homes is proved to be an NP-complete problem, and a greedy heuristic for near-optimal tracking is proposed. The concept of asymptotic equipartition property is also explored to predict the inhabitant's most likely path segments (comprising coverage areas of different sensor systems) with very good accuracy. Successful prediction helps in on-demand operations of automated indoor devices along the inhabitant's future paths and locations, thus providing the necessary comfort at a near-optimal cost. Simulation results on a typical smart home corroborate a high prediction success of ∼91%, thereby providing sufficient resident-comfort (≥7 in the scale of 10) while reducing the daily energy consumption and manual operations to less than one-third of its original values.

Abstract Rapid penetration of smart wireless devices and enormous growth of wireless communication technologies has already set the stage for deployment of wireless sensor networks (WSNs). While these small sensor nodes are often considered as the future of wireless communications, they also suffer from energy constraints. On the other hand, with increasing demand for real‐time services in next generation wireless networks, quality‐of‐service (QoS)‐based routing has emerged as an interesting research topic. Naturally offering some QoS‐guarantee in sensor networks raises significant challenges. The network needs to cope up with battery‐constraints, while providing precise QoS (end‐to‐end delay and bandwidth requirement) guarantee. More precisely, designing such QoS‐protocols, optimizing multiple objectives, is computationally intractable. Based on the multi‐objective genetic algorithm (MOGA), in this paper we propose a QoS‐based energy‐efficient sensor routing (QuESt) protocol that determines application‐specific, near‐optimal sensory‐routes demand, by optimizing multiple QoS parameters, (end‐to‐end delay and bandwidth requirements) and energy consumption. Simulation results demonstrate that the proposed protocol is capable of discovering a set of QoS‐based, near‐optimal routes, even with imprecise network information. Copyright © 2007 John Wiley & Sons, Ltd.

Emerging applications like video surveillance, telemedicine and traffic monitoring requires multimedia transmission over wireless sensor networks. Offering some precise QoS-guarantee (e.g.end-to-end delay and bandwidth requirement) for wireless multimedia over already battery-constrained sensor networks raises significant challenges. This demands for a suitable sensory MAC protocol capable of achieving application- specific QoS. In this paper we have proposed a new QoS-based sensory MAC protocol, which not only adapts to application-oriented QoS, but also attempts to conserve energy without violating QoS-constraints. Performance modelling, analysis and simulation results demonstrate that the proposed protocol is capable of providing lower delay and better throughput, at the cost of reasonable energy consumption, in comparison to other existing sensory MAC protocols.

In this paper we have developed a new service classification strategy in hybrid scheduling scheme to support differentiated quality of service (QoS) among the different set of clients. The scheme dynamically computes the data access probabilities and amalgamates the push and pull scheduling schemes to develop the hybrid scheduling framework. While a flat scheduling is used for push system, the major novelty of the work lies in differentiating the clients based on their priority-classes and incorporating the effect of priority in selecting an item from the pull-system. Modeling and analysis of the system is performed to get an average behavior of the QoS parameters like delay in our hybrid scheduling framework. Simulation results points out that the average waiting time for the highest priority clients can be kept very low, while simultaneously minimizing the number of requests dropped by assigning appropriate fraction of available bandwidth. It also demonstrates that by intelligent selection of the cut-off point, used to segregate push and pull systems, the overall cost associated with the system can be minimized.

Broadcast is an efficient and scalable technique for disseminating data over wireless channels to an arbitrary large number of clients. The prime objective of broadcast schedules, both on-line and off-line, is to minimize the average amount of time a client needs to wait before receiving its desired item. In off-line schedules, it is assumed that both the entire set of data items and the demand probability for each data is known in advance. Such schedules are guaranteed to obtain the minimum average waiting time only when a skewed partition of the data among multiple-channels and flat schedules per channel are assumed. On the contrary, the on-line broadcast schedules decide which item to transmit without any a-priori knowledge of either the entire set of data or the data demand probabilities. Although no optimal solutions are known for the latter schedules, the volatility of the set of data items to be transmitted makes on-line schedules much more desirable than its off-line counterparts. In this paper, a new on-line broadcast schedule for broadcast over multiple channels is presented. The strategy partitions the data among the broadcast channels in a balanced way, and adopts a hybrid push-pull broadcast schedule per channel. Simulation experiments point out that the results of our new algorithm outperforms the minimum average waiting time achieved by the optimal off-line schedule (skewed partition of data among channels and flat schedule for channel), and the on-line broadcast schedule based on the square root rule.

The ever-increasing popularity of Web services, growing demand for wireless multimedia and introduction of new, feature-enhanced, hand-held devices has already given birth to a new set of data-centric applications. Providing such applications with enhanced data processing capability calls for an efficient scheduling and transmission technique. The goal of most scheduling strategy lies in reducing the average waiting time. However, in most practical systems the variation of waiting time often results in client's impatience, thus provoking the clients to send repeated requests for the particular data item(s). In this paper we have developed a new hybrid scheduling framework for heterogeneous, asymmetric environments, by exploring the advantages of broadcasting very popular (push) data and dissemination of less popular (pull) data. The data access probabilities and the cut-off point used to segregate push and pull sets are dynamically computed. Packet fair scheduling (PFS) and stretch-optimal scheduling principle is deployed to obtain the push and pull schedule respectively. The framework explicitly takes care of the repeated requests originating from the impatient clients and minimizes the overall expected access time by obtaining an optimal cut-off point. Extensive performance analysis and simulation experiments are performed to show the efficiency of the system in reducing the overall expected access time (delay).

Summary form only given. A hybrid scheduling is introduced that effectively combines broadcasting of more popular data (push data) and dissemination upon-request for less popular data (pull data), for asymmetric environments where the clients outnumber the servers having higher downlink communication (server to client) bandwidth than the uplink communication (client to server) bandwidth. We do not assume prior knowledge of the data access probability; in fact data access probabilities and thereby the cut-off-point to separate the push and the pull set are computed dynamically. In our scheme all the data items are assumed to be of variable length arranged in descending order of their access probabilities. The clients send their requests to the server, which queues them up for the pull items. At any instant of time, the item to be broadcast is designated applying a pure-push scheduling; whereas the item to be pulled is the one stored in the pull-queue, which has accumulated, so far, the highest number of pending requests with minimum service time requirement. The expected waiting time spent by a client in the hybrid schedule is evaluated analytically, and the cut-off point between push and pull items is chosen to minimize waiting time.