Distributed edge learning (DL) is considered a cornerstone of intelligence enablers, since it allows for collaborative training without the necessity for local clients to share raw data with other parties, thereby preserving privacy and security. Integrating DL into the 6G networks requires a coexistence design with existing services such as high-bandwidth (HB) traffic like eMBB. Current designs in the literature mainly focus on communication round-wise designs that assume a rigid resource allocation throughout each communication round (CR). However, rigid resource allocation within a CR is a highly inefficient and inaccurate representation of the system’s realistic behavior, especially when CR duration far exceeds the channel coherence time due to large model size or limited resources. This is due to the heterogeneous nature of the system, as clients inherently may need to access the network at different time instants. This work zooms into one arbitrary CR, and demonstrates the importance of considering a time-dependent design for sharing the resource pool with HB traffic. We first formulate a time-slot-wise optimization problem to minimize the consumed time by DL within the CR while constrained by a DL energy budget. Due to its intractability, a session-based optimization problem is formulated assuming a CR lasts less than a large-scale coherence time. Some scheduling properties of such multi-server joint communication scheduling and resource allocation framework have been established. An iterative algorithm has been designed to solve such non-convex and non-block-separable-constrained problems. Simulation results confirm the importance of the efficient and accurate integration design proposed in this work.

Integrated sensing and communication (ISAC) will be a key new feature in the next-generation wireless networks, with IEEE 802.11bf extending Wi-Fi capabilities to support applications like object localization and activity recognition. However, coexistence with legacy Wi-Fi in densely populated networks poses challenges, as contention for channels can impair both sensing and communication quality. This paper develops an analytical framework and a system-level simulation in ns-3 to evaluate the coexistence of IEEE 802.11bf and legacy 802.11ax in terms of sensing delay and communication throughput. We provide the first coexistence analysis between IEEE 802.11bf and IEEE 802.11ax, supported by link-level simulation in ns-3 to assess impacts on sensing delay and network performance. Our findings reveal key trade-offs between sensing intervals and throughput and the need for balanced sensing parameters to ensure effective coexistence in Wi-Fi networks.

In this paper, we address the problem of scheduling sensing and communication functionality in an integrated sensing and communication (ISAC) enabled base station (BS) operating in an indoor factory (InF) environment. The BS is performing the task of detecting an AGV while managing downlink transmission of ultra-reliable low-latency communication (URLLC) data in a time-sharing manner. Scheduling fixed time slots for both sensing and communication is inefficient for the InF environment, as the instantaneous environmental changes necessitate a higher frequency of sensing operations to accurately detect the AGV. To address this issue, we propose an environment-aware scheduling scheme, in which we first formulate an optimization problem to maximize the probability of detection of AGV while considering the survival time constraint of URLLC data. Subsequently, utilizing the Nash bargaining theory, we propose an adaptive time-sharing scheme that assigns sensing duration in accordance with the environmental clutter density and distributes time to URLLC depending on the incoming traffic rate. Using our own Python-based discrete-event link-level simulator, we demonstrate the effectiveness of our proposed scheme over the baseline scheme in terms of probability of detection and downlink latency.

Distributed learning (DL) is considered a cornerstone of intelligence enabler, since it allows for collaborative training without the necessity for local clients to share raw data with other parties, thereby preserving privacy and security. Integrating DL into the 6G networks requires coexistence design with existing services such as high-bandwidth (HB) traffic like eMBB. Current designs in the literature mainly focus on communication round (CR)-wise designs that assume a fixed resource allocation during each CR. However, fixed resource allocation within a CR is a highly inefficient and inaccurate representation of the system's realistic behavior. This is due to the heterogeneous nature of the system, where clients inherently need to access the network at different times. This work zooms into one arbitrary communication round and demonstrates the importance of considering a time-dependent resource-sharing design with HB traffic. We propose a time-dependent optimization problem for minimizing the consumed time and energy by DL within the CR. Due to its intractability, a session-based optimization problem has been proposed assuming a large-scale coherence time. An iterative algorithm has been designed to solve such problems and simulation results confirm the importance of such efficient and accurate integration design.

Cooperative multi-monostatic sensing enables accurate positioning of passive targets by combining the sensed environment of multiple base stations (BS). In this work, we propose a novel fusion algorithm that optimally finds the weight to combine the time-of-arrival (ToA) and angle-of-arrival (AoA) likelihood probability density function (PDF) of multiple BSs. In particular, we employ a log-linear pooling function that fuses all BSs’ PDFs using a weighted geometric average. We formulated an optimization problem that minimizes the Reverse Kullback–Leibler Divergence (RKLD) and proposed an iterative algorithm based on the Monte Carlo importance sampling (MCIS) approach to obtain the optimal fusion weights. Numerical results verify that our proposed fusion scheme with optimal weights outperforms the existing benchmark in terms of positioning accuracy in both unbiased (line-of-sight only) and biased (multipath-rich environment) scenarios.

Enabling passive sensing of the environment using cellular base stations (BSs) will be one of the disruptive features of the sixth-generation (6G) networks. However, accurate local-ization and positioning of objects are challenging to achieve as multipath significantly degrades the reflected echoes. Existing lo-calization techniques perform well under the assumption of large bandwidth available but perform poorly in bandwidth-limited scenarios. To alleviate this problem, in this work, we introduce a 5G New Radio (NR)-based cooperative multi-monostatic sensing framework for passive target localization that operates in the Frequency Range 1 (FRl) band. We propose a novel fusion-based estimation process that can mitigate the effect of multipath by assigning appropriate weight to the range estimation of each BS. Extensive simulation results using ray-tracing demonstrate the efficacy of the proposed multi-sensing framework in bandwidth-limited scenarios.

Joint communication and sensing (JCAS) networks are envisioned as a key enabler for a variety of applications which demand reliable wireless connectivity along with accurate and robust sensing capability. When sensing and communication share the same spectrum, the communication links in the JCAS networks experience interference from both sensing and communication signals. Therefore, it is crucial to analyze the interference caused by the uncoordinated transmission of either sensing or communication signals, so that effective interference mitigation techniques could be put in place. We consider a JCAS network consisting of dual-functional nodes operating in radar and communication modes. To gain access to the shared communication channel, each node follows carrier sense multiple access (CSMA)-based protocol. For this setting, we study the radar and communication performances defined in terms of maximum unambiguous range and aggregated network throughput, respectively. Leveraging on the stochastic geometry approach, we model the interference of the network and derive a closed-form expression for both radar and communication performance metrics. Finally, we verify our analytical results through extensive simulation.

The recent advances in Internet of Things (IoT)-based healthcare systems pave the path for the development of body-to-body network (BBN), wherein a group of wireless body area network (WBAN) users collaborates and shares their individual resources. Since these WBAN users have individual decision-making capabilities and are self-centric in nature, they always aim to maximize their own performance while expecting benefits through resource sharing. In this article, we analyze the interaction among participating WBANs in BBN and develop joint data uploading and relaying strategy. In BBN, each WBAN not only utilizes its resources (uplink capacity and battery energy) to upload physiological data but also trades resources with other participating WBANs. Specifically, WBAN users with unused resources trade with other users deprived of Internet connection and low battery for mutual gain. Therefore, we model this interaction as an$N $-person bargaining game and design an efficient incentive mechanism to facilitate user cooperation. The proposed mechanism ensures efficient resource sharing and fair division of mutual benefit among the participating WBAN users. Also, we propose a distributed algorithm for the practical implementation of the proposed mechanism in decentralized BBN. The simulation results demonstrate that the proposed mechanism always improves WBAN user’s individual performance together with the overall BBN performance. Furthermore, the overall performance increases with an increase in participating WBAN users’ resource heterogeneity.

The body-to-body network (BBN), which enables a group of wireless body area network (WBAN) users to collaborate and share their individual network resources, has emerged as a promised technology for the Internet of Things (IoT)-based healthcare system. In BBN, WBAN users with good Internet connectivity act as gateway users and help their nearby WBAN users with poor Internet connectivity to upload their physiological data in exchange for incentives. The WBAN users are heterogeneous in terms of their data priority, which depends on the criticality of medical data and require varying uplink transmission rates for uploading. Designing an incentive mechanism for such a scenario is very challenging because the data priority is a private information to the WBAN user. In this work, we propose an incentive scheme based on contract theory, to model the economic interaction between the gateway and requesting WBAN users and ensure priority-aware data uploading in BBN. First, the requesting WBAN users are categorized into different types based on their data priority. Thereafter, we formulate a contract design problem to maximize the payoff of gateway WBAN user while satisfying the requirements of requesting users. The gateway WBAN user offers a contract to requesting users and each requesting user selects it based on its type. Finally, the simulation results demonstrate that the proposed mechanism improves the payoffs of both the gateway and requesting WBAN users.

In this paper, we study a body-to-body network (BBN) framework, which enables wireless body area network (WBAN) users located in close proximity to cooperate and share their network resources to improve the overall network performance. Our main aim is to design a distributed resource allocation mechanism that encourages each participating WBAN user to participate and upload each other’s data collaboratively. We propose an auction-based mechanism that optimizes data uploading for all participating users and the corresponding reimbursement. In the proposed auction mechanism, each user acts as both auctioneer and bidder. Fist, the auctioneer initiates the auction by announcing the amount of resource it wants to share and its price and each bidder submits their bid to each auctioneer based on its demand. We further propose a distributed algorithm for the auction mechanism that jointly solves both the auctioneers’ and the bidders’ optimization problems and determines the optimal amount of resource users should reserve for their own and the portion they should share. Our theoretical analysis demonstrates that the proposed distributed algorithm converges to the solution that maximizes the aggregated benefit of the users. Finally, the simulation results exhibit that the proposed algorithm always improves WBAN user’s individual performance together with overall BBN performance.

To meet the demands of new healthcare applications with high computation complexity, multi-access edge computing (MEC) is emerging as a key component of modern healthcare systems which provides rich computing services to the users. With the increase in the number of wireless body area network (WBAN) users requesting computing services, the computational load on the MEC servers increases. A major issue related to the operation of these MEC servers is their sustainability in terms of energy consumption and heavy carbon emission. Therefore, in this work, we propose a resource management scheme, which minimizes the energy consumption of the MEC server without compromising on the quality-of-experience (QoE) of the WBAN users. For that, we propose a cooperative framework between the MEC server and WBAN users, where the MEC server motivates WBAN users to opt for partial offloading instead of full offloading of the computing services. More specifically, the MEC server bargains with each WBAN user for the amount of the task it compute locally and the corresponding reimbursement. We model this economic interaction between the MEC server and all the participating WBAN users using the Nash bargaining theory. Thereafter, we derive the closed-form Nash bargaining solutions (NBS) for two different bargaining protocols. Finally, numerical results show the proposed bargaining scheme is capable of improving the MEC server payoff by$ 44.3\%$,$ 51.4\%$, and$ 56.1\%$, respectively, compared to the state-of-the art schemes.

In this article, we present an efficient criticality-aware decision-making system, named Devote, for fog-based Internet of Things (IoT) environment. Devote introduces an intelligent algorithm for the service of data based on the criticality, while considering the current availability of the resources at the fog node (FN). To cope with the dynamic IoT environment, we adopt a reinforcement-learning-based algorithm for the processing of the IoT data based on time-varying conditions. Additionally, we propose an efficient online secretary-based algorithm for choosing the best suitable candidate FN for offloading the data. To show the effectiveness of Devote, we obtained the numerical results for assessing its performance, while collating it with the benchmark schemes. We analyze different performance metrics, such as service delay, economy, and user satisfaction, which show that Devote incurs less service delay, as compared to other systems, while achieving user satisfaction of 88.4%.

Wi-Fi enabled hand-held devices have quickly occupied the consumer market as a result of the remarkable customer acceptance of IEEE 802.11 standard. In this regard, the demand of high throughput introduces high throughput standards such as IEEE 802.11ac. It supports Dynamic Bandwidth Channel Access (DBCA), where a wireless station selects channel bandwidth dynamically based on the availability of the secondary channels. But the widely-used contention based medium access mechanism provides an opportunistic access of secondary channels and affects the performance of DBCA. Consequently, unfairness in channel access is increased in DBCA, which further reduces average throughput of stations. In this paper, we develop a hybrid adaptive resource reservation mechanism, Hybrid Adaptive DBCA (HA-DBCA), for supporting fair channel access in DBCA. In HA-DBCA, a polling based online learning mechanism is designed to avoid starvation of primary channel users. Through IEEE 802.11ac testbed implementation, we show that HA-DBCA improves throughput fairness in DBCA significantly along with other performance parameters.