Multi-sensory Extended Reality (mXR) applications represent a cornerstone technology for immersive communications in the 6G and IMT-2030 frameworks, enabling synchronized stimulation across multiple human sensory modalities to create authentic immersive experiences. mXR applications demand simultaneous satisfaction of stringent data rate, delay, and reliability requirements across all sensory streams, necessitating an unprecedented convergence of enhanced Mobile Broadband (eMBB) and Ultra-Reliable Low-Latency Communications (URLLC) service paradigms. Existing 5G scheduling schemes, designed to optimize either eMBB or URLLC services independently and typically handling decoupled data streams, do not address the cross-modal synchronization and joint optimization challenges of mXR applications. This paper proposesDREAM-X (Delay-Reliability Aware Scheduling for Multi-sensory XR Applications), a unified scheduling framework specifically designed for next generation (NextG) networks to support mXR services. This framework includes a novel Multi-modal Delay Tracking Queue architecture that organizes multi-sensory data units based on transmission deadlines, enabling precise cross-modal delay coordination. We formulate the scheduling problem as a constrained optimization and develop a Model Predictive Control (MPC) solution with rolling horizon optimization to ensure computational tractability for real-time deployment. The simulation results show that DREAM-X provides multi-fold gains in the number of satisfied mXR users compared to conventional and state-of-the-art XR scheduling approaches, while maintaining strict delay and reliability constraints.

The development of high data rate communication technologies have resulted in cell densification, which in turn has led to the development of centralized radio access networks (C-RANs) followed by open radio access networks (O-RANs). The O-RAN segregates the base station into three logical entitities; the central unit (CU), the distributed unit (DU) and the radio unit (RU). The CU, DU and RU require low latency, low jitter and high data rate connections for seamless operation, which is known as X-haul. A passive optical network (PON) is a potential solution for X-haul design. Unfortunately, a PON along with its original uplink protocol is not readily suitable for the purpose. The packetization procedure of PON introduces jitter to the Xhaul bit stream. Further, the delay requirements of the X-haul limit the number of sources that can be connected to the Xhaul. Advanced features like coordinated multipoint requires synchronisation among the different X-haul bit streams as well. Therefore, in this paper, we develop an optimal uplink system that allows PON to be used as an X-haul connection technology. The proposal maximizes the throughput of the PON while conforming to the delay and synchronisation requirements. Moreover, the proposal nullifies the jitter introduced by the PON scheduler. We have performed extensive simulations for verifying our results.

Supporting Extended-Reality (XR) services in 5G New Radio (NR) and beyond has been an active area of study and research in the 3rd generation partnership project (3GPP) and other standardization bodies. Existing studies have looked into improving the state-of-art Connected mode Discontinuous Reception (C-DRX) method for power savings in 5G NR, specifically for XR traffic. C-DRX achieves power savings by periodically (typically integer - in multiples of 1ms) switching-off components of UE. Depending on the components that are switched off, different ‘sleep modes’ namely micro sleep, light sleep, and deep sleep provide increased energy savings. For XR, the traffic pattern has been observed to be of Quasi-periodic nature having non-integer periods along with jitter around its mean arrival instant. This jitter introduces unpredictability in the period of operation of the C-DRX method. Existing proposals place dense Wake-up Signals (WUS) which leaves micro sleep to be the only feasible sleep mode due to the stringent latency requirements for XR. In this paper, we propose a method of skipping WUS, that allows XR devices to employ other available sleep modes leading to improved energy savings. Our results show that, compared to the existing proposals, our proposal achieves a significant gain in energy-saving figures (up to 25% for the frame rate of 30 fps).

In this paper, we focus on a cross-layer resource allocation problem for an opportunistic cognitive radio network, where secondary users (SUs) share a primary network’s licensed spectrum only when the primary network is sensed to be idle. We consider cooperative spectrum sensing, where SUs participate in sensing only when a benefit from resource allocation is guaranteed. Number of SUs for cooperative spectrum sensing is an important parameter as it defines both sensing and resource allocation performance. The fusion centre captures interaction between physical layer spectrum sensing, channel condition, and higher layer data buffer while selecting SUs for cooperative sensing. We consider heterogeneous data types at SUs’ data buffers during SU selection to make our system model more general. We form a mixed integer non-linear optimization problem, from which we select SUs’ set for sensing and resource allocation, appropriate sensing thresholds, and corresponding resource allocation parameters. Due to it’s combinatorial nature, the optimization problem is non-trivial. We devise an optimal algorithm to solve the optimization problem. Moreover, we also propose a computationally efficient and near-optimal greedy algorithm with suitable performance bounds. We also show advantage of our proposed optimal algorithms compared to other traditional methods through extensive simulations.

This paper focusses on Service Level Agreement (SLA) based end-to-end Quality of Service (QoS) maintenance across a wireless optical integrated network. We use long term evolution/5G based spectrum access system (SAS) in the wireless network and the optical network is comprised of an Ethernet Passive Optical Network (EPON). The proposal targets a learning-based intelligent SAS where opportunistic allocation of any available bandwidth is done after meeting the SLA requirements. Such an opportunistic allocation is particularly beneficial for nomadic users with varying QoS requirements. The opportunistic allocation is carried out with the help of Vickrey-Clarke-Groves (VCG) auction. The proposal allows the users of the integrated network to decide the payment they want to make in order to opportunistically avail bandwidth. Learning automata is used for the users to intelligently converge to the optimal payment value based on the network load. The payment made by the users is later used by the optical network units of the EPON to prepare the bids for the auction. The proposal has been verified through extensive simulations.

We analyze a Cognitive Radio-based Internet-of-Things (CR-IoT) network employing Microwave Power Transfer (MPT) method for energy-harvesting, targeted for smart city applications. In our model, multiple IoT sensors, operated by an IoT operator, coexist with a Primary Network Provider (PNP), where the IoT sensors opportunistically exploit the spectrum holes in the PNP’s licensed band. We develop an Imbedded Markov Chain based approach to analyze this system. During PNP’s inactivity periods, the Accesss Points (APs) under the IoT operator have to judiciously use PNP’s licensed band for data collection from the IoT sensors and charging them via the MPT method. Our analysis finds the balance between these activities while maintaining the Quality-of-Service and preventing energy deficit in the IoT sensors. We characterize PNP’s activity using any distribution, rather than exponential distribution used in literature, making our framework more accurate. We also consider the possibility of interruptions in the IoT operator’s activity due to PNP’s transmission, which has been overlooked in the literature. Extensive simulations demonstrate the accuracy of our analysis. Our analysis can identify the values of system parameters that makes the system sustainable. We have also shown how to extend our model to accommodate multiple PNPs in the same CR-IoT network.

An upper bound on the delay in the control channels is necessary as control state inconsistency significantly degrades the performance of a logically-centralized and physically-distributed control plane. Statistically multiplexing control traffic with the data traffic (in-fiber in-band) or reserving a low-rate control channel does not ensure QoS guarantees in terms of delay specially if the network is congested. Therefore, control traffic must be viewed and treated differently from data traffic. This article presents ac MILP and a heuristic to design distributed SDN-based in-fiber out-of-band control plane for elastic optical network (EON). The objective is to minimize the number of controllers while reserving resources for the control traffic to ensure that delay in the control channel is bounded. The designed inter-controller virtual tree topology avoids control traffic replication. For better resource utilization it requires OEO conversion at every controller node for grooming control traffic and this incurs queuing delay at each controller node. The design takes into account queuing delay along with transmission and propagation delay in control plane links and maintains the total delay within a specified bound. To the best of our knowledge, this is the first work to analyze queuing delay along with transmission and propagation delay in inter-controller links of the control plane. We further present a MILP to make this virtual control plane tree topology survivable against single link failure by utilizing the concept of fundamental cycles. The numerical results show that our proposed control plane design performs better than existing control plane designs where control traffic is statistically multiplexed with data traffic in terms of delay in the control plane and blocking probability in the data plane.

In this paper, we consider an opportunistic cognitive radio (CR) network comprising of a primary base station (PBS) and a secondary base station (SBS), both associated with multiple users. SBS obtains the right to use the licensed spectrum of PBS by paying a remuneration. Moreover, both PBS and SBS provide service to their corresponding users in exchange of an economic price. PBS, being a profit maximizer, optimally balances between the revenue directly coming from its own users and the monetary reward provided by SBS. On the other hand, SBS must maintain a positive gain to support its infrastructure. Thus, a two-person non-cooperative bargaining game is formulated where both PBS and SBS concur at the values of two parameters, i.e., the activity factor of PBS and the payment made by SBS for opportunistically accessing the licensed spectrum. PBS has knowledge of detection and false-alarm probabilities of the spectrum sensing by SBS and hence can infer how the interference created by missed detection can degrade its achievable capacity. In this framework, we utilize Nash solution to determine a mutually beneficial outcome of the bargaining process. The solution leads to a mathematical relation dictating the viability of the network in the long-run. This result holds for arbitrary continuous user demand functions. For linear user demand, we obtain closed-form expression of the bargaining solution that enables us to analyze the influence of different key system parameters on the economic outcomes.

Encouraging new service providers to participate in the competition is one of the key ways to enhance users' utility in cellular networks. However, entry-deterrence strategy employed by the incumbents turns out to be a major obstacle in achieving this goal. Fortunately, we have recently demonstrated that the government, in its capacity as the sole provider of bandwidth, can resist such predatory behaviour by choosing suitable quadratic pricings for spectrum leasing. However, this result was premised on the presumption that both entrant and incumbent deploy base stations with same density. Moreover, their operational costs were also taken to be same. Evidently, such assumptions do not align with the reality. In this article, we investigate predation blocking strategies in real cellular networks where the above presumptions might not hold true. This generalization reveals several insightful results. For example, it shows that the government cannot block predation if the entrant's base station density is below a certain threshold. Alongside, we have also examined how these predation blocking strategies influence the spectrum revenue earned by the government. Numerical results suggest that blocking of predation is likely to degrade the government's revenue for a large number of network scenario. Finally, we study how the network economics evolves with time if the predation blocking strategies are applied repeatedly. Interestingly, we observe that after a sufficiently large number of repetitions, the incumbent eventually loses an incentive to exhibit the predatory behavior.

Cloud-based computing technology is one of the most significant technical advents of the last decade and extension of this facility towards access networks by aggregation of cloudlets is a step further. To fulfill the ravenous demand for computational resources entangled with the stringent latency requirements of computationally-heavy applications related to augmented reality, cognitive assistance and context-aware computation, installation of cloudlets near the access segment is a very promising solution because of its support for wide geographical network distribution, low latency, mobility and heterogeneity. In this paper, we propose a novel framework, Cloudlet Cost OptiMization over PASSIve Optical Network (CCOMPASSION), and formulate a nonlinear mixed-integer program to identify optimal cloudlet placement locations such that installation cost is minimized whilst meeting the capacity and latency constraints. Considering urban, suburban and rural scenarios as commonly-used network deployment models, we investigate the feasibility of the proposed model over them and provide guidance on the overall cloudlet facility installation over optical access network. We also study the percentage of incremental energy budget in the presence of cloudlets of the existing network. The final results from our proposed model can be considered as fundamental cornerstones for network planning with hybrid cloudlet network architectures.

Directional antennas in millimeter wave (mmWave) communication networks enable spatial reuse by reducing interference during concurrent transmission. However, higher spatial multiplexing gain is realized at the cost of increased power consumption in antenna arrays. In this paper, we develop a multi-slot scheduling scheme based on mixed integer linear programming (MILP) for energy efficient scheduling in mmWave concurrent transmission. Energy efficiency (EE) is defined as the ratio of data rate achieved to the sum of circuit power consumption and transmit power. EE is achieved by the joint optimization of data rate and power consumption; while the optimum data rate request is calculated using a reinforced learning strategy, power consumption is minimized through antenna beamwidth control. We also prove that the energy efficient scheduling problem is NP-complete, and propose three novel low complexity energy efficient scheduling algorithms for mmWave networks - energy efficient directional medium access control (EEDMAC)-Naive, EEDMAC-Greedy, and EEDMAC-Double Greedy (DGreedy). The proposed multi-slot scheduling scheme is seen to outperform the existing protocol in terms of EE, and delay fairness. We further analyze the degradation in system performance due to antenna scanning range. The simulation results provide fresh insights into resource allocation and scheduling for mmWave directional communications.

In the post-4G era, "low latency" has become one of the most important network requirements along with support for ultra-high capacity and ultra-high reliability. This has led to the evolution of "cloudlets" to support similar services provided by legacy cloud technology viz., storage capacity and computational capability. In this paper, we propose a novel framework for cloudlet-empowered-cloud network design and planning, based on optical access infrastructures. Our focus is on network planning to optimize network infrastructure cost by formulating a nonlinear programming model to identify placement locations of cloudlet servers subjected to capacity and latency constraints. We demonstrate the feasibility of the proposed model against urban, suburban and rural scenarios, providing guidance on the installation and maintenance costs. Furthermore, we assess the percentage of incremental energy arising from the presence of cloudlets in the optical access network. The proposed framework is a first in yielding insights that will serve as a foundation for further cloudlet network planning strategies.

Millimeter wave (mmWave) communications utilizing directional antennas are capable to support multi-Gbps data rate spanning indoor short range to outdoor cellular scenarios. However, unique channel characteristics of mmWave channels including high pathloss, penetration loss and sparse multi-path characteristics requires new channel models for mmWave network design. In this paper, a novel geometry based model is introduced for outdoor directional non-line-of-sight (NLOS) mmWave channels. By approximating signal propagation with first order reflections from buildings, a more tractable analytical expression for average NLOS received signal power is obtained. Further, the accuracy of the model is tested using exhaustive simulations for different input parameters such as building density, antenna beamwidth etc.

Due to the large swath of available bandwidth, millimeter wave (mmWave) spectrum is potentially suitable to meet enhanced network capacity requirements of future generation communication systems. However, the high Analog to Digital Converter (ADC) sampling rate required to process wide band signals in the mmWave regime results in significant power consumption in the receiver hardware. In this work, an Orthogonal Frequency Division Multiplexing (OFDM) based subsampling architecture is proposed to achieve power efficient receiver design. Subbanding in association with orthogonal spreading codes using subsampling technique reduces the high sampling rate requirement of the receiver. Power consumption in the receiver is further reduced by utilizing low resolution ADC with minimal degradation in Bit Error Rate (BER) performance. In addition to this, OFDM transmission with reduced number of Fast Fourier transform (FFT) points alleviates the multi-path effects significantly, and improves the spectral efficiency. The combined effect of ADC bit resolution and subsampling on the system error performance and power consumption is studied.

Millimetre wave bands are envisaged to be utilised for next generation ultra‐high data rate communications due to the availability of large bandwidths. However, high sampling rate requirement at these frequencies leads to significant power consumption in the devices. In this study, a subband‐based subsampled radio architecture is proposed for indoor millimetre wave communications with the objective of improving power efficiency along with scalable data rates and multi‐user support under varying Quality of Service (QoS) requirements. An optimum bandwidth partitioning scheme is presented to support the proposed architecture. Subbanding in association with orthogonal spreading codes enables the reduction of sampling rate requirement at the receiver, resulting in significant power savings for medium data rate communication systems. Furthermore, the performance evaluation of the scheme is performed by deriving closed‐form analytical expressions for probability of bit error under line‐of‐sight (LOS) and non‐LOS conditions considering the effect of antenna half‐power beamwidth. The analytical expressions and simulation results for different requirements including maximum data rate, variable number of users, and variable QoS are evaluated and compared.

Long term evolution uplink-scheduling is one of the major research area nowadays; since third generation partnership project standard has not specified any particular algorithm for uplink scheduling. In general, the existing algorithms ensure to maximize the achievable physical layer throughput by considering only the channel gains of the user equipments (UE). However, they do not guarantee the actual media access control (MAC) layer throughput or the fairness of the achievable throughput among users as they do not take the traffic of individual UEs into account. Therefore, in this paper, we propose a novel scheduling algorithm using both the traffic and the channel gains of the individual UEs. Our algorithm uses weighted bipartite matching and achieves an optimal MAC throughput with minimal computational complexity. We further enhance the algorithm to increase the fairness with a little sacrifice in the MAC throughput.

Millimeter wave communications is a promising technology for future multi-Gbps wireless networks. However, the wide bandwidth and high sampling rate requirement involved leads to significant power consumption in the communication systems. In this paper we propose a subband based subsampled system architecture for indoor millimeter wave communications with a primary objective of improving energy efficiency in the transceiver with scalable data rates and multi-user support for varying Quality of Service (QoS) requirements. An optimum bandwidth partitioning scheme in support of proposed system architecture is also presented. Subbanding in association with orthogonal spreading codes enables the reduction of sampling rate requirement at the receiver, resulting in significant power savings for medium data rate communication systems. The BER simulation results for variable data rate, number of users, and QoS support are presented for the proposed scheme.