Large Language Models (LLMs) have recently been used as experts to infer causal graphs, often by repeatedly applying a pairwise prompt that asks about the causal relationship of each variable pair. However, such experts, including human domain experts, cannot distinguish between direct and indirect effects given a pairwise prompt. Therefore, instead of the graph, we propose that causal order be used as a more stable output interface for utilizing expert knowledge. When querying a perfect expert with a pairwise prompt, we show that the inferred graph can have significant errors whereas the causal order is always correct. In practice, however, LLMs are imperfect experts and we find that pairwise prompts lead to multiple cycles and do not yield a valid order. Hence, we propose a prompting strategy that introduces an auxiliary variable for every variable pair and instructs the LLM to avoid cycles within this triplet. We show, both theoretically and empirically, that such a triplet prompt leads to fewer cycles than the pairwise prompt. Across multiple real-world graphs, the triplet prompt yields a more accurate order using both LLMs and human annotators as experts. By querying the expert with different auxiliary variables for the same variable pair, it also increases robustness---triplet method with much smaller models such as Phi-3 and Llama-3 8B outperforms a pairwise prompt with GPT-4. For practical usage, we show how the estimated causal order from the triplet method can be used to reduce error in downstream discovery and effect inference tasks.

For text-based AI systems to interact in the real world, causal reasoning is an essential skill. Since interventional data is costly to generate, we study to what extent an agent can learn causal reasoning from passive data. Specifically, we consider an axiomatic training setup where an agent learns from multiple demonstrations of a causal axiom (or rule), rather than incorporating the axiom as an inductive bias or inferring it from data values. A key question is whether the agent would learn to generalize from the axiom demonstrations to new scenarios. For example, if a transformer model is trained on demonstrations of the causal transitivity axiom over small graphs, would it generalize to applying the transitivity axiom over large graphs? Our results, based on a novel axiomatic training scheme, indicate that such generalization is possible. We consider the task of inferring whether a variable causes another variable, given a causal graph structure. We find that a 67 million parameter transformer model, when trained on linear causal chains (along with some noisy variations) can generalize well to new kinds of graphs, including longer causal chains, causal chains with reversed order, and graphs with branching; even when it is not explicitly trained for such settings. Our model performs at par (or even better) than many larger language models such as GPT-4, Gemini Pro, and Phi-3. Overall, our axiomatic training framework provides a new paradigm of learning causal reasoning from passive data that can be used to learn arbitrary axioms, as long as sufficient demonstrations can be generated.

In this paper, we investigate the performance of reconfigurable intelligent surface (RIS)-assisted downlink nonorthogonal multiple access (NOMA) system while accounting for practical limitations, such as imperfect successive interference cancellation (SIC) and phase compensation errors. We derive achievable data rate expressions for NOMA users as functions of these imperfections and establish bounds on power allocation and the SIC imperfection factor to ensure that NOMA outperforms orthogonal multiple access (OMA) systems. Based on these bounds, we propose a hybrid NOMA/OMA scheduling algorithm that performs NOMA pairing and power allocation to maximize the sum rate. Through extensive numerical simulations, we demonstrate that the proposed hybrid scheduling algorithm achieves superior performance compared to existing benchmark algorithms.

In a conventional Domain Adaptation (DA) setting, we only have one source and target domain, whereas, in many real-world applications, data is often collected from several related sources in different conditions. This has led to a more practical and challenging knowledge transfer problem called Multi-source Domain Adaptation (MDA). Several methodologies, such as prototype matching, explicit distance discrepancy, adversarial learning, etc., have been considered to tackle the MDA problem in recent years. Among them, the adversarial-based learning framework is a popular methodology for transferring knowledge from multiple sources to target domains using a minmax optimization strategy. Despite the advances in adversarial-based methods, several limitations exist, such as the need for a classifier-aware discrepancy metric to align the domains and the need to consider target samples' consistency and semantic information while aligning the domains. To mitigate these issues, in this work, we propose a novel adversarial learning MDA algorithm, MDAMA, which aligns the target domain with a mixture distribution that consists of source domains. MDAMA uses margin-based discrepancy and augmented intermediate distributions to align the domains effectively. We also propose consistency of target samples by confidence thresholding and transfer of semantic information from multiple source domains to the augmented target domain to further improve the performance of the target domain. We extensively experiment with the MDAMA algorithm on popular real-world MDA datasets such as OfficeHome, Office31, PACS, Office-Caltech, and DomainNet. We evaluate the MDAMA model on these benchmark datasets and demonstrate top performance in all of them.

In fifth generation (5G) and beyond networks, the millimeter-wave frequency band is expected to cater to the ever increasing user demand for enhanced data rates. However, the denser deployments in mmwave with varying user velocities, will lead to frequent handovers (HOs), resulting in degradation of quality of service (QoS) for the end user in terms of both the number of HOs and lower throughput. Non-orthogonal multiple access (NOMA) is one of the promising radio access techniques for throughput enhancement in 5G and beyond networks. Motivated by this, we analyze the performance of a hybrid OMA-NOMA system considering the imperfections in successive interference cancellation (SIC) for various HO algorithms. We propose two HO algorithms: Algorithm 1 - the HO decision is based on the orthogonal multiple access (OMA) signal to interference-plus noise ratio (SINR) in the control plane, whereas, NOMA is only used for rate enhancements in the data plane; and Algorithm 2 - NOMA-based rates are taken into consideration for the HO decision. We compare the proposed algorithms with benchmark OMA based algorithms. Through extensive simulation we show that the proposed algorithms result in significant enhancement in throughput and reduction in the average number of HOs in the system.

We formulate a scheduling problem to derive the optimal time fraction jointly for a hybrid orthogonal multiple access (OMA)-non-orthogonal multiple access (NOMA) system using the$\alpha$·fair utility function, while accounting for imper-fections in successive interference cancellation (SIC). Our study demonstrates that the optimal solution derived through a solver matches the theoretical results. Furthermore, we assess the performance of these derived time fractions in comparison to an existing adaptive user pairing (AUP) algorithm, designed to mitigate imperfections in SIC. Through extensive simulations, we illustrate that the derived time fraction consistently yields improved mean user link rates when employed with the AUP algorithm, across varying levels of imperfections in SIC, under the log-rate model.

The lifetime of a UAV-assisted wireless network is determined by the amount of energy consumed by the UAVs during flight, data collection, and transmission to the ground station. Routing protocols are commonly used for data transmission in a communication network. However, because of the mobility of UAVs, using a routing protocol with a single communication technology results in higher delay and more energy consumption in a UAV-assisted wireless network. To overcome this, we propose two reinforcement learning (RL) algorithms, Q-learning and deep Q-network (DQN), for energy-efficient data transmission over a hybrid BLE/LTE/Wi-Fi/LoRa UAV-assisted wireless network. We consider BLE, LTE, Wi-Fi, and LoRa for communication over a UAV-GS link. The RL algorithms take any random network as input and learn the best policy to output the network with less energy consumption. The reward/penalty is chosen in such a way that the network with the highest energy consumption is penalized and the one with the lowest is rewarded, thereby minimizing total network energy consumption. Based on learning, it creates a hybrid BLE/LTE/Wi-Fi/LoRa UAV-assisted wireless network by assigning the best communication technology to a UAV-GS link. Further, we compare the performance of proposed RL algorithms with a rule-based algorithm and random hybrid scheme. In addition, we propose a theoretical framework for constructing hybrid network for both free space and free space multipath path loss models. We demonstrate the performance comparison of the proposed work with the conventional shortest path routing algorithm in terms of network energy consumption and average network delay using extensive results. Finally, the effect of the velocity of the UAV and the number of packets on the performance of the proposed framework is analyzed.

Domain Generalization (DG) techniques have emerged as a popular approach to address the challenges of domain shift in Deep Learning (DL), with the goal of generalizing well to the target domain unseen during the training. In recent years, numerous methods have been proposed to address the DG setting, among which one popular approach is the adversarial learning-based methodology. The main idea behind adversarial DG methods is to learn domain-invariant features by minimizing a discrepancy metric. However, most adversarial DG methods use 0-1 loss based $\mathcal{H}\Delta\mathcal{H}$ divergence metric. In contrast, the margin loss-based discrepancy metric has the following advantages: more informative, tighter, practical, and efficiently optimizable. To mitigate this gap, this work proposes a novel adversarial learning DG algorithm, $\textbf{MADG}$, motivated by a margin loss-based discrepancy metric. The proposed $\textbf{MADG}$ model learns domain-invariant features across all source domains and uses adversarial training to generalize well to the unseen target domain. We also provide a theoretical analysis of the proposed $\textbf{MADG}$ model based on the unseen target error bound. Specifically, we construct the link between the source and unseen domains in the real-valued hypothesis space and derive the generalization bound using margin loss and Rademacher complexity. We extensively experiment with the $\textbf{MADG}$ model on popular real-world DG datasets, VLCS, PACS, OfficeHome, DomainNet, and TerraIncognita. We evaluate the proposed algorithm on DomainBed's benchmark and observe consistent performance across all the datasets.

In many classification datasets, the task labels are spuriously correlated with some input attributes. Classifiers trained on such datasets often rely on these attributes for prediction, especially when the spurious correlation is high, and thus fail to generalize whenever there is a shift in the attributes’ correlation at deployment. If we assume that the spurious attributes are known a priori, several methods have been proposed to learn a classifier that is invariant to the specified attributes. However, in real-world data, information about spurious attributes is typically unavailable. Therefore, we propose a method that automatically identifies spurious attributes by estimating their causal effect on the label and then uses a regularization objective to mitigate the classifier’s reliance on them. Although causal effect of an attribute on the label is not always identified, we present two commonly occurring data-generating processes where the effect can be identified. Compared to recent work for identifying spurious attributes, we find that our method, AutoACER, is more accurate in removing the attribute from the learned model, especially when spurious correlation is high. Specifically, across synthetic, semi-synthetic, and real-world datasets, AutoACER shows significant improvement in a metric used to quantify the dependence of a classifier on spurious attributes ($\Delta$Prob), while obtaining better or similar accuracy. Empirically we find that AutoACER mitigates the reliance on spurious attributes even under noisy estimation of causal effects or when the causal effect is not identified. To explain the empirical robustness of our method, we create a simple linear classification task with two sets of attributes: causal and spurious. Under this setting, we prove that AutoACER only requires the ranking of estimated causal effects to be correct across attributes to select the correct classifier.

Non-orthogonal multiple access (NOMA) is consid-ered as one of the predominant multiple access techniques for next-generation cellular networks. We consider a 2-user pair downlink NOMA system with imperfect successive interference cancellation (SIC). We consider bounds on the power allocation factors and then formulate the power allocation as an optimization problem to achieve α-Fairness among the paired users. We show that α-Fairness based power allocation factor coincides with lower bound on power allocation factor in case of perfect SIC and$\alpha > 2$. Further, as long as the proposed criterion is satisfied, it converges to the upper bound with increasing imperfection in SIC. Similarly, we show that for$0 < \alpha < 1$, the optimal power allocation factor coincides with the derived lower bound on power allocation. Based on these observations, we then propose a low complexity sub-optimal algorithm. Through extensive simulations, we analyze the performance of the proposed algorithm and compare it against the state-of-the-art algorithms. We show that the proposed optimal and sub-optimal algorithms ensure that each user achieves a rate at least equivalent to its orthogonal multiple access counterparts. Further, we also show significant improvements in terms of fairness with the proposed algorithm as compared to the state-of-the-art algorithms.

Neural network models trained on text data have been found to encode undesirable linguistic or sensitive concepts in their representation. Removing such concepts is non-trivial because of a complex relationship between the concept, text input, and the learnt representation. Recent work has proposed post-hoc and adversarial methods to remove such unwanted concepts from a model's representation. Through an extensive theoretical and empirical analysis, we show that these methods can be counter-productive: they are unable to remove the concepts entirely, and in the worst case may end up destroying all task-relevant features. The reason is the methods' reliance on a probing classifier as a proxy for the concept. Even under the most favorable conditions for learning a probing classifier when a concept's relevant features in representation space alone can provide 100% accuracy, we prove that a probing classifier is likely to use non-concept features and thus post-hoc or adversarial methods will fail to remove the concept correctly. These theoretical implications are confirmed by experiments on models trained on synthetic, Multi-NLI, and Twitter datasets. For sensitive applications of concept removal such as fairness, we recommend caution against using these methods and propose a spuriousness metric to gauge the quality of the final classifier.

Non-orthogonal multiple access (NOMA) is considered a key technology for improving the spectral efficiency of fifth-generation (5G) and beyond 5G cellular networks. NOMA is beneficial when the channel vectors of the users are in the same direction, which is not always possible in conventional wireless systems. With the help of a reconfigurable intelligent surface (RIS), the base station can control the directions of the channel vectors of the users. Thus, by combining both technologies, the RIS-assisted NOMA systems are expected to achieve greater improvements in the network throughput. However, ideal phase control at the RIS is unrealizable in practice because of the imperfections in the channel estimations and the hardware limitations. This imperfection in phase control can have a significant impact on the system performance. Motivated by this, in this paper, we consider an RIS-assisted uplink NOMA system in the presence of imperfect phase compensation. We formulate the criterion for pairing the users that achieves minimum required data rates. We propose adaptive user pairing algorithms that maximize spectral or energy efficiency. We then derive various bounds on power allocation factors for the paired users. Through extensive simulation results, we show that the proposed algorithms significantly outperform the state-of-the-art algorithms in terms of spectral and energy efficiency.

Non-orthogonal multiple access (NOMA) is a next-generation multiple access technology to improve users’ throughput and spectral efficiency for 5G and beyond cellular networks. Similarly, coordinated multi-point transmission and reception (CoMP) is an existing technology to improve the coverage of cell-edge users. Hence, NOMA with CoMP can potentially enhance the throughput and coverage of the users. However, the order of implementation of CoMP and NOMA can significantly impact the system performance of Ultra-dense networks (UDNs). Motivated by this, we study the performance of the CoMP and NOMA-based UDN by proposing two kinds of user grouping and pairing schemes that differ in the order in which CoMP and NOMA are performed for a group of users. Detailed simulation results are presented, comparing the proposed schemes with the state-of-the-art systems with varying user and base station densities. Through numerical results, we show that the proposed schemes can be used to achieve a suitable coverage-throughout trade-off in UDNs.

Inclusion of massive machine-type-communication (mMTC) devices in 5G cellular Internet of Things (IoT) has significantly raised the issue of network congestion. To address this challenge, a successive interference cancellation-rate splitting random access (SIC-RSRA) mechanism is proposed in this paper. Unlike traditional mechanisms, all selected mMTC devices are allowed to make a finite number of repeated message requests in randomly selected time slots within a radio frame. The gNodeB, on the other hand, applies both intra-slot SIC (utilizing RSRA) and inter-slot SIC to decode messages from a larger number of devices. For the proposed mechanism, the impact of increasing the number of devices as well as the received power difference is investigated. Through extensive simulations, we show that the proposed mechanism outperforms the other mechanisms in terms of number of RACH successes and number of supported devices.

The spectrum cartography finds applications in several areas such as cognitive radios , spectrum aware communications, machine-type communications, Internet of Things , connected vehicles, wireless sensor networks , and radio frequency management systems, etc. This paper presents a survey on state-of-the-art of spectrum cartography techniques for the construction of various radio environment maps (REMs). Following a brief overview on spectrum cartography, various techniques considered to construct the REMs such as channel gain map, power spectral density map, power map, spectrum map, power propagation map, radio frequency map, and interference map are reviewed. In this paper, we compare the performance of the different spectrum cartography methods in terms of mean absolute error , mean square error , normalized mean square error, and root mean square error . The information presented in this paper aims to serve as a practical reference guide for various spectrum cartography methods for constructing different REMs. Finally, some of the open issues and challenges for future research and development are discussed.

The cellular Internet-of-Things has resulted in the deployment of millions of machine type communication (MTC) devices under the coverage of a single gNodeB (gNB). These massive number of devices should connect to the gNodeB (gNB) via the random access channel (RACH) mechanism. Moreover, the existing RACH mechanisms are inefficient when dealing with such large number of devices. To address this issue, we propose the rate-splitting random access (RSRA) mechanism, which uses rate splitting and decoding in rate-splitting multiple access (RSMA), to improve the RACH success rate. The proposed mechanism divides the message into common and private messages and enhances the decoding performance. We demonstrate, using extensive simulations, that the proposed RSRA mechanism significantly improves the success rate of MTC in cellular IoT networks. We also evaluate the performance of the proposed mechanism with increasing number of devices and received power difference.

In this study, the authors propose a hybrid power line communication (PLC)/visible light communication (VLC)/radio frequency (RF) fronthaul with a fibre‐based wired backhaul system to support massive number of smart devices (SDs). Since a signal‐to‐noise ratio‐based access point (AP) association and bandwidth (BW) allocation for each SD do not necessarily improve the system capacity, they propose novel and efficient AP association and BW allocation strategies to maximise the sum rate capacity (SRC) of the hybrid system under consideration. An optimisation problem is formulated for the SRC with the AP association and BW allocation as the optimisation parameters and a hierarchical decomposition method is used to convert the non‐linear optimisation problem into a set of convex optimisation problems. Then, the proposed strategies are used to solve the optimisation problem in an iterative manner until the SRC converges to an optimal value. Further, an analytical approximation for the BW allocated to each SD for a given AP association is derived using the Lagrangian multiplier method. The performance of the proposed system is evaluated through extensive numerical results. Moreover, the effect of the increased number of SDs on the optimal SRC is analysed.

IoT devices penetrate different aspects of our life including critical services, such as health monitoring, public safety, and autonomous driving. Such safety-critical IoT systems often consist of a large number of devices and need to withstand a vast range of known Denial-of-Service (DoS) network attacks to ensure a reliable operation while offering low-latency information dissemination. As the first solution to jointly achieve these goals, we propose LIDOR, a secure and lightweight multihop communication protocol designed to withstand all known variants of packet dropping attacks. Specifically, LIDOR relies on an end-to-end feedback mechanism to detect and react on unreliable links and draws solely on efficient symmetric-key cryptographic mechanisms to protect packets in transit. We show the overhead of LIDOR analytically and provide the proof of convergence for LIDOR which makes LIDOR resilient even to strong and hard-to-detect wormhole-supported grayhole attacks. In addition, we evaluate the performance via testbed experiments. The results indicate that LIDOR improves the reliability under DoS attacks by up to 91% and reduces network overhead by 32% compared to a state-of-the-art benchmark scheme.

Due to the rate adaptation in hypertext transfer protocol adaptive streaming, the video quality delivered to the client keeps varying with time depending on the end-to-end network conditions. Moreover, the varying network conditions could also lead to the video client running out of the playback content resulting in rebuffering events. These factors affect the user satisfaction and cause degradation of the user quality of experience (QoE). Hence, it is important to quantify the perceptual QoE of the streaming video users and to monitor the same in a continuous manner so that the QoE degradation can be minimized. However, the continuous evaluation of QoE is challenging as it is determined by complex dynamic interactions among the QoE influencing factors. Toward this end, we present long short-term memory (LSTM)-QoE, a recurrent neural network-based QoE prediction model using an LSTM network. The LSTM-QoE is a network of cascaded LSTM blocks to capture the nonlinearities and the complex temporal dependencies involved in the time-varying QoE. Based on an evaluation over several publicly available continuous QoE datasets, we demonstrate that the LSTM-QoE has the capability to model the QoE dynamics effectively. We compare the proposed model with the state-of-the-art QoE prediction models and show that it provides an excellent performance across these datasets. Furthermore, we discuss the state space perspective for the LSTM-QoE and show the efficacy of the state space modeling approaches for the QoE prediction.

A rapid increase in the video traffic together with an increasing demand for higher quality videos has put a significant load on content delivery networks in the recent years. Due to the relatively limited delivery infrastructure, the video users in HTTP streaming often encounter dynamically varying quality over time due to rate adaptation, while the delays in video packet arrivals result in rebuffering events. The user quality-of-experience (QoE) degrades and varies with time because of these factors. Thus, it is imperative to monitor the QoE continuously in order to minimize these degradations and deliver an optimized QoE to the users. Towards this end, we propose a nonlinear state space model for efficiently and effectively predicting the user QoE on a continuous time basis. The QoE prediction using the proposed approach relies on a state space that is defined by a set of carefully chosen time varying QoE determining features. An evaluation of the proposed approach conducted on two publicly available continuous QoE databases shows a superior QoE prediction performance over the state-of-the-art QoE modeling approaches. The evaluation results also demonstrate the efficacy of the selected features and the model order employed for predicting the QoE. Finally, we show that the proposed model is completely state controllable and observable, so that the potential of state space modeling approaches can be exploited for further improving QoE prediction.

The long term evolution/system architecture evolution (LTE/SAE) networks have been shown vulnerable to desynchronization attack. In this attack, the adversary compromises a legitimate evolved NodeB and gains access to the encryption keys. The essential feasibility conditions have not been considered in the existing work. Hence, in this paper, we propose the conditions essential for the occurrence of DA. Through numerical results, we evaluate the feasibility of DA for various network scenarios.

In heterogeneous cellular networks (HCNs), dual connectivity (DC) has been introduced to increase throughput of the system. Base station sleeping (BSS) techniques has been proposed to reduce the power consumption of the under-utilized base stations (BSs). In this paper, given a partially shared deployment (PSD) of subchannels between macro BS (MBS) and the small cell base stations (SCBSs), the DC uplink (UL) resource allocation problem is formulated. Given such PSD system, the optimum number of subchannels that can be allocated for SCBSs for a DC user in UL are computed. The computation of MBS and SCBSs thresholds for selection of DC user in downlink is also presented. Further, a framework to select the operating point based on energy, throughput, user density, and SCBSs density is presented.

Narrowband Internet of Things (NB-IoT) is a recent feature introduced by 3rd Generation Partnership Project (3GPP). It is a low power wide area network technology that will enable cellular service to a massive number of low throughput IoT devices. The NB-IoT is a derivative of existing 3GPP Long Term Evolution (LTE) that has to be delay tolerant with low cost devices. In LTE, the decoding of Physical Downlink Control Channel (PDCCH) consumes a significant amount of user equipment's (UE) battery power because of its high computational complexity and complicated search space design. Hence, the existing downlink control channel scheduling schemes for LTE cannot be reused for NB-IoT. The reduction in available bandwidth and introduction of repetitions for achieving wider coverage makes the Narrowband PDCCH (NPDCCH) search space design and its allocation even more challenging. In this paper, we first explain the NPDCCH in detail and present its design rationale. We propose several novel downlink control channel scheduling schemes for 3GPP NB-IoT and compare their performance through extensive Monte Carlo simulations.

A continuous evaluation of the end user's quality-of-experience (QoE) is essential for efficient video streaming. This is crucial for networks with constrained resources that offer time-varying channel quality to its users. In hypertext transfer protocol-based video streaming, the QoE is measured by quantifying the perceptual impact of distortions caused by rate adaptation or interruptions in playback due to rebuffering events. The resulting impact on the QoE due to these distortions has been studied individually in the literature. However, the QoE is determined by an interplay of these distortions, and therefore necessitates a combined study of them. To the best of our knowledge, there is no publicly available database that studies these distortions jointly on a continuous time basis. In this paper, our contributions are twofold. First, we present a database consisting of videos at full high definition and ultrahigh definition resolutions. We consider various levels of rate adaptation and rebuffering distortions together in these videos as experienced in a typical realistic setting. A subjective evaluation of these videos is conducted on a continuous time scale. Second, we present a QoE evaluation framework comprising a learning-based model during playback and an exponential model during rebuffering. Furthermore, we perform an objective evaluation of popular video quality assessment and continuous time QoE metrics over the constructed database. The objective evaluation study demonstrates that the performance of the proposed QoE model is superior to that of the objective metrics. The database is publicly available for download at http://www.iith.ac.in/~lfovia/downloads.html.

Due to proliferation of mobile devices, the demand for video in cellular networks has increased exorbitantly. However, cellular networks have limited resources and the wireless medium is time-varying in nature. This necessitates the video streaming protocols to be re-designed taking into account the overall quality of experience (QoE) of the end users. In this paper, we propose a metric called enhanced-time varying subjective quality (eTVSQ) to measure the QoE of the video users. The eTVSQ accounts for time variation in QoE due to both rate adaption in HTTP streaming and playback interruption caused by rebuffering events. Based on this metric, we propose a rate adaptation strategy for HTTP video streaming in the downlink of cellular networks with α-fair resource allocation. The proposed method results in significant performance gains over the traditional throughput based rate adaptation strategy.

Base station operation consumes a lot of energy, a considerable amount of which can be saved by switching off base stations during low user demand (for example, at night). Base station switching (BSS) can result in loss in coverage if not performed properly. We show that coverage is closely related to scheduling via power management and that the bottleneck is typically the uplink. To save energy, we propose a set of BSS patterns, at a global system-level, that have the potential to provide full coverage if the appropriate schedulers are used. We further show that the existing benchmark uplink scheduling schemes do not provide full coverage when BSS is used in urban as well as rural macro-cell environments (the downlink benchmark scheduling scheme provides full coverage only for some of the BSS patterns). Hence, we propose novel scheduling schemes for both uplink and downlink that realistically model interference, ensure full coverage, and provide good energy-performance trade-offs for the proposed BSS patterns. We also present a low complexity high performance heuristic for the proposed uplink scheduler. Finally, we show the presented models and results can be used to quantify, offline, the energy-performance trade-offs under different operating scenarios.

Consider wireless local area network (WLAN) service providers (SPs) operating in an overlapping service area. The SPs compete with each other to attract users. The price charged is utilised by the SPs as a tool to maximise revenue, resulting in a price competition between the WLAN SPs. The users are assumed to be selfish, trying to maximise their individual utility. They have varied sensitivity towards quality of service experienced and the price charged. In such a scenario, the user demand distribution is the one that achieves Wardrop equilibrium. Approximate analytical expressions are obtained for the best response of SPs to each other's price. Existence of a Nash equilibrium (NE) between the competing SPs is proved and the price vector at which the NE occurs is obtained. It is found that, while in one extreme monopoly leads to very high revenue for WLAN SPs with minimal consumer surplus, in the other extreme unregulated duopoly/oligopoly leads to high consumer surplus at the cost of minimal revenue generation for the competing SPs. Thus, price regulation is proposed in the WLAN market for equitable distribution of the surplus among the SPs and the users.

In the presence of several wireless local area network (WLAN) service providers, the users have to make a choice. The price charged and the congestion experienced by the users play an important role in making this choice. In this paper, we analyze the duopoly price competition between two WLAN service providers in the presence of four types of users. We prove that the distribution of heterogeneous user demand is governed by the Wardrop equilibrium. We also show the existence of the Nash equilibrium between competing WLAN service providers. It is further shown through analysis that the social welfare in Nash equilibrium is close to its maximal value. We find that compared to a strictly regulated monopoly, an unregulated WLAN duopoly market results in significant transfer of the surplus from service providers to users with negligible losses in efficiency.