Large language model (LLM)-based agents are envisioned as cornerstones for autonomous, zero-touch 6G AI-RAN operations. Numerous frameworks adopt LLM-based agents as decision-makers to optimize network configurations, orchestrate resources, and interact with users and connected use cases. However, intrinsic limitations (hallucinations, misaligned human values) and extrinsic adversarial threats (jail-breaks, prompt injections) pose critical risks to network safety, reliability, and privacy—challenges largely overlooked in existing literature. This paper addresses this gap by reviewing state-of-the-art guardrail techniques for 6G AI-RAN. We categorize guardrails across model-level and agent-level layers and map them to common agent application patterns in 6G networks, providing practical foundations for designing trustworthy agentic decision-making frameworks in future 6G AI-RAN systems.

6G's AI native vision of embedding advance intelligence in the network while bringing it closer to the user requires a systematic evaluation of Generative AI (GenAI) models on edge devices. Rapidly emerging solutions based on Open RAN (ORAN) and Network-in-aBox strongly advocate the use of low-cost, off-the-shelf components for simpler and efficient deployment, for example, in provisioning rural connectivity. In this context, conceptual architecture, hardware testbeds, and precise performance quantification of Large Language Models (LLMs) on off-theshelf edge devices remain largely unexplored. This research investigates computationally demanding LLM inference on a single commodity Raspberry Pi serving as an edge testbed for ORAN. We investigate various LLMs, including small, medium, and large models, on a Raspberry Pi 5 Cluster using a lightweight Kubernetes distribution (K3s) with modular prompting implementation. We study its feasibility and limitations by analyzing throughput, latency, accuracy, and efficiency. Our findings indicate that CPU-only deployment of lightweight models, such as Yi, Phi, and Llama3, can effectively support edge applications, achieving a generation throughput of 5 to 12 tokens per second with less than 50% CPU and RAM usage. We conclude that GenAI on the edge offers localized inference in remote or bandwidthconstrained environments in 6 G networks without reliance on cloud infrastructure.

Generative AI (GenAI) is expected to play a pivotal role in enabling autonomous optimization in future wireless networks. Within the ORAN architecture, Large Language Models (LLMs) can be specialized to generate xApps and rApps by leveraging specifications and API definitions from the RAN Intelligent Controller (RIC) platform. However, fine-tuning base LLMs for telecom-specific tasks remains expensive and resource-intensive. Retrieval-Augmented Generation (RAG) offers a practical alternative through in-context learning, enabling domain adaptation without full retraining. While traditional RAG systems rely on vector-based retrieval, emerging variants such as GraphRAG and Hybrid GraphRAG incorporate knowledge graphs or dual retrieval strategies to support multi-hop reasoning and improve factual grounding. Despite their promise, these methods lack systematic, metric-driven evaluations, particularly in high-stakes domains such as ORAN. In this study, we conduct a comparative evaluation of Vector RAG, GraphRAG, and Hybrid GraphRAG using ORAN specifications. We assess performance across varying question complexities using established generation metrics: faithfulness, answer relevance, context relevance, and factual correctness. Results show that both GraphRAG and Hybrid GraphRAG outperform traditional RAG. Hybrid GraphRAG improves factual correctness by 8%, while GraphRAG improves context relevance by 11%.

The deployment of AI agents within legacy Radio Access Network (RAN) infrastructure poses significant safety and reliability challenges for future 6G networks. This paper presents a novel Edge AI framework for autonomous network optimisation in Open RAN environments, addressing these challenges through three core innovations: (1) a persona-based multi-tools architecture enabling distributed, context-aware decision-making; (2) proactive anomaly detection agent powered by traffic predictive tool; and (3) a safety, aligned reward mechanism that balances performance with operational stability.Integrated into the RAN Intelligent Controller (RIC), our framework leverages multimodal data fusion, including network KPIs, a traffic prediction model, and external information sources, to anticipate and respond to dynamic network conditions. Extensive evaluation using realistic 5G scenarios demonstrates that the edge framework achieves zero network outages under high-stress conditions, compared to 8.4% for traditional fixed-power networks and 3.3% for large language model (LLM) agent-based approaches, while maintaining near real-time responsiveness and consistent QoS. These results establish that, when equipped with the right tools and contextual awareness, AI agents can be safely and effectively deployed in critical network infrastructure, laying the framework for intelligent and autonomous 5G and beyond network operations.

Effective resource management and network slicing are essential to meet the diverse service demands of vehicular networks, including Enhanced Mobile Broadband (eMBB) and Ultra-Reliable and Low-Latency Communications (URLLC). This paper introduces an Explainable Deep Reinforcement Learning (XRL) framework for dynamic network slicing and resource allocation in vehicular networks, built upon a near-real-time RAN intelligent controller. By integrating a feature-based approach that leverages Shapley values and an attention mechanism, we interpret and refine the decisions of our reinforcement learning agents, addressing key reliability challenges in vehicular communication systems. Simulation results demonstrate that our approach provides clear, real-time insights into the resource allocation process and achieves higher interpretability precision than a pure attention mechanism. Furthermore, the Quality of Service (QoS) satisfaction for URLLC services increased from 78.0% to 80.13%, while that for eMBB services improved from 71.44% to 73.21%.

Federated learning (FL) has recently emerged as a tool for efficient distributed learning. In edge-assisted internet of things (IoT) paradigm, FL can achieve a promising speed of convergence in the learning process while preserving privacy. To overcome the inherent wireless network connectivity challenge in FL based IoT networks, unmanned aerial vehicles (UAVs) can play a vital role in offloading the learned models to a central learning server or play as a flying learning server. In this paper, we investigate the physical layer aspects of harnessing UAVs as flying FL server for the distributed model averaging. In particular, we study the effect of the UAV-to-ground communication model in the convergence of the model accuracy and loss curves using well-known benchmark datasets to illustrate the effects. We illustrate the effect of altering the deployment geometry of IoT clients simultaneously with the trajectory design of the UAVs.

Federated Learning (FL) enables distributed model training on edge devices while preserving data privacy. However, FL deployments in wireless networks face significant challenges, including communication overhead, unreliable connectivity, and high energy consumption, particularly in dynamic environments. This paper proposes EcoFL, an integrated FL framework that leverages the Open Radio Access Network (ORAN) architecture with multiple Radio Access Technologies (RATs) to enhance communication efficiency and ensure robust FL operations. EcoFL implements a two-stage optimisation approach: an RLbased rApp for dynamic RAT selection that balances energy efficiency with network performance, and a CNN-based xApp for near real-time resource allocation with adaptive policies. This coordinated approach significantly enhances communication resilience under fluctuating network conditions. Experimental results demonstrate competitive FL model performance with 19 % lower power consumption compared to baseline approaches, highlighting substantial potential for scalable, energy-efficient collaborative learning applications.

This work focuses on the analysis of link quality in the context of monitoring buried pipelines using wireless underground sensor networks. The study examines three communication channels: pipe to ground, ground to pipe and in-pipe. The investigation considers factors such as burial depth and the material of the pipe. Various metrics, including received signal strength indicator, signal-to-noise ratio and packet delivery ratio, are observed and analyzed for all the communication channels. The experimental investigation conducted at a frequency of 868 MHz using LoRa technology reveals strong signal strength and reliable communication across the three communication channels.

Electromagnetic nano-networks operating in the THz band offer a promising solution for enabling communication among many nanoscale devices. However, the inherent limitations of nano-nodes, such as restricted energy and processing resources and short communication range, pose significant challenges for efficient data transmission. While prior research has explored Reinforcement Learning (RL) for optimising traffic routing in electromagnetic nano-networks, this paper proposes a novel approach that jointly optimises routing and sub-channel bandwidth allocation to minimise network energy consumption using RL. We leverage the Q-learning algorithm to develop a dynamic single-hop or multi-hop routing scheme that considers each node's location, energy storage capability, and the available sub-channel bandwidth. Our model formulates a reward function that balances these multiple objectives and enables the selection of optimal transmission policies for each nano-node. Our findings suggest carefully choosing the number of hops and increasing bandwidth in sub-channels can lead to substantial energy savings in nano-networks.

Using Unmanned Aerial Vehicles (UAVs) in Search and rescue operations (SAR) to navigate challenging terrain while maintaining reliable communication with the cellular network is a promising approach. This paper suggests a novel technique employing a reinforcement learning multi Q-learning algorithm to optimize UAV connectivity in such scenarios. We introduce a Strategic Planning Agent for efficient path planning and collision awareness and a Real-time Adaptive Agent to maintain optimal connection with the cellular base station. The agents trained in a simulated environment using multi Q-learning, encouraging them to learn from experience and adjust their decision-making to diverse terrain complexities and communication scenarios. Evaluation results reveal the significance of the approach, highlighting successful navigation in environments with varying obstacle densities and the ability to perform optimal connectivity using different frequency bands. This work paves the way for enhanced UAV autonomy and enhanced communication reliability in search and rescue operations.

This study presents a novel power allocation optimisation strategy that does not rely on traditional power constraints. Unlike previous works in the literature, we introduce a ratio between the allocated and requested data rates and incorporate this ratio into the reward function of our deep reinforcement learning algorithm. The highest reward of 1 is achieved when the allocated and requested data rates are equal. Additionally, we jointly optimise power and numerology allocation, considering the users' delay and data rate requirements. Any numerology can be allocated to users as long as their requirements are satisfied. This approach enables users to be allocated optimum numerology and transmit power. By addressing the challenge posed by greedy users, our approach enhances the flexibility and performance of the power and numerology allocation process.

The rapid proliferation of consumer UAVs, or drones, is reshaping the wireless communication landscape. These agile, autonomous devices find new life as UE in cellular networks. This paper explores their integration, emphasizing the myriad applications, standardization efforts, challenges, and research community solutions. Key areas of investigation include the complexities of 3D deployment, channel modelling, and energy efficiency. Moreover, we highlight the open questions and research opportunities these flying UEs present. The evolving landscape of UAV integration into cellular networks promises transformative enhancements for next-generation communications, addressing challenges while fostering innovation across industries. The paper encapsulates the essential aspects of UAV integration within the cellular ecosystem, offering a concise yet comprehensive overview of this dynamic field, where UAVs as UEs redefine wireless communication with promise and complexity.

Federated Learning (FL) has emerged as a promising approach for privacy preservation, allowing sharing of the model parameters between users and the cloud server rather than the raw local data. FL approaches have been adopted as a cornerstone of distributed machine learning (ML) to solve several complex use cases. FL presents an interesting interplay between communication and ML performance when implemented over distributed wireless nodes. Both the dynamics of networking and learning play an important role. In this article, we investigate the performance of FL on an application that might be used to improve a remote healthcare system over ad hoc networks which employ CSMA/CA to schedule its transmissions. Our FL over CSMA/CA (FLCC) model is designed to eliminate untrusted devices and harness frequency reuse and spatial clustering techniques to improve the throughput required for coordinating a distributed implementation of FL in the wireless network.In our proposed model, frequency allocation is performed on the basis of spatial clustering performed using virtual cells. Each cell assigns an FL server and dedicated carrier frequencies to exchange the updated model’s parameters within the cell. We present two metrics to evaluate the network performance: 1) probability of successful transmission while minimizing the interference, and 2) performance of distributed FL model in terms of accuracy and loss while considering the networking dynamics.We benchmark the proposed approach using a well-known MNIST dataset for performance evaluation. We demonstrate that the proposed approach outperforms the baseline FL algorithms in terms of explicitly defining the chosen users’ criteria and achieving high accuracy in a robust network.

Abstract Reconfigurable intelligent surfaces (RISs) are envisioned to transform the propagation space into a smart radio environment (SRE) to realize the diverse applications of sixth‐generation (6G) wireless communication. By smartly tuning the massive number of elements via controller, an RIS can passively phase‐shift the electromagnetic (EM) waves to enhance the system performance. The absence of radio‐frequency (RF) chains makes RIS an energy‐efficient and cost‐effective solution for future wireless networks. In this paper, the state‐of‐the‐art research on different aspects of RIS‐assisted communication is explored. Specifically, the fundamentals of RIS are first introduced, including the RIS's structure, operating principle, and deployment strategies. The emerging applications of RISs are then comprehensively discussed for 6G wireless networks. In addition, the crucial challenges for RIS‐assisted networks are elaborated, namely, RIS channel state information (CSI) acquisition and passive beamforming optimization. Furthermore, the recent research contributions leveraging the artificial intelligence (AI) based techniques for channel estimation, phase‐shift optimization, and resource allocation in RIS‐assisted networks are presented. Finally, to provide effective guidance for future research, important research directions for realizing RIS‐assisted network are highlighted.

Convolutional neural networks (CNNs) have been widely utilized to identify hand gestures from surface electromyography (sEMG) signals. However, due to the nonstationary characteristics of sEMG, the classification accuracy usually degrades significantly in the daily living environment involving complex hand movements. To further improve the reliability of a classifier, unconfident classifications are expected to be identified and rejected. In this study, we propose a novel approach to estimate the probability of correctness for each classification. Specifically, a confidence estimation model is established to generate confidence scores (ConfScore) based on posterior probabilities of CNN, and an objective function is designed to train the parameters of this model. In addition, a comprehensive metric that combines the true acceptance rate (TAR) and the true rejection rate (TRR) is proposed to evaluate the rejection performance of ConfScore, so that the tradeoff between system security and control lag could be fully considered. The effectiveness of ConfScore is verified using data from public databases and our online platform. The experimental results illustrate that ConfScore can better reflect the correctness of CNN classifications than traditional confidence features, i.e., maximum posterior probability and entropy of the probability vector. Moreover, the rejection performance is observed to be less sensitive to variations in rejection thresholds.

Age of information (AoI) refers to the freshness of data generated by a status-update system. It is a crucial metric in networks such as Internet of things (IoT), specially when the underlying application demands fresh update. In environmental monitoring and smart agriculture, apart from the importance of AoI, energy efficiency (EE) becomes inevitable owing to network longevity. This paper studies an IoT network where the end devices transfer their information to a central gateway residing on a moving platform such as a tractor, which collects information from a large number of sensors in an agri-field. An optimal trajectory of the mobile reader is proposed using a modified nearest neighbor algorithm to gather the information from randomly distributed sensors. A clustering algorithm is also used to cluster the data in such a way that the overall EE of the network is maximized keeping a desired AoI and outage probability.

This special issue (SI) aims to present recent advances in the design and analysis of communication interfaces for Industry 4.0. The Industry 4.0 paradigm aims to integrate advanced manufacturing techniques with Industrial Internet-of-Things (IIoT) to create an agile digital manufacturing ecosystem. The main goal is to instrument production processes by embedding sensors, actuators and other control devices which autonomously communicate with each other throughout the value-chain[1].

In the past decades, classical machine learning (ML) methods have been widely investigated in wrist kinematics estimation for the control of prosthetic hands. Currently deeper structures have shown great potential to further improve prediction accuracy. In this paper we present a single stream convolutional neural network (CNN) for mapping surface electromyography (sEMG) to wrist angles within three degrees-of-freedom (DOFs). Two types of two dimensional (2D) sEMG images are constructed in time domain and spectrum as CNN inputs, respectively. Six typical linear and nonlinear ML models are implemented for comparison, where four efficient time-spatial hand-crafted features are extracted to represent feature engineering. Experiment results with four able-bodied participants illustrate that CNN with 2D spectrum sEMG images can achieve highest accuracy in most testing sessions. In other sessions, it is still competitive to the most promising ML techniques. The core strength of deep learning (DL), i.e. feature learning via deep structures and efficient algorithms, is verified to be more powerful than classical feature engineering, particularly in smaller datasets.

In this article, we develop a comprehensive framework to characterize the performance of drone assisted Backscatter communication based Internet of things (IoT) sensor network. We consider a scenario such where drone transmits RF carrier which is modulated by IoT sensor node (SN) to transmit its data. The SN implements load modulation which results in amplitude shift keying (ASK) type modulation for the impinging RF carrier. In order to quantify the performance of the considered network, we characterize the coverage probability for the ground based SN node. The statistical framework developed to quantify the coverage probability explicitly accommodates dyadic backscatter channel which experiences deeper fades than that of the one-way Rayleigh channel. Our model also incorporates Line of Sight (LoS) and Non-LoS (NLoS) propagation states for accurately modeling large-scale path-loss between drone and SN. We consider spatially distributed SNs which can be modelled using spatial Binomial Point process (BPP). We practically implement the proposed system using Software Defined Radio (SDR) and a custom designed SN tag. The measurements of parameters such as noise figure, tag reflection coefficient etc., are used to parametrize the developed framework. Lastly, we demonstrate that there exists an optimal set of parameters which maximizes the coverage probability for the SN.

In this paper, we tackle the design issue of optimal deployment of low power wide area network (LPWAN) Internet of Things (IoT) gateways (GWs). We classify GW deployment problem into two different categories, i.e., network-aware and network-agnostic. In network-aware GW deployment, precise location of IoT end devices (EDs) is known and thus the design questions are: 1) where to place GWs, i.e., to maximize received signal strength and 2) given received signal strength which GW should the ED be associated with to balance the network load. For, Network-agnostic GW deployment, same questions are answered in the absence of precise knowledge for the locations of EDs. For the network-aware deployment we borrow tools from machine-learning such as K-means clustering for determination of optimal GW location. Subsequently, the link assignment problem is presented as an integer linear programming optimization. We prove that the network-agnostic GW deployment principle of placement of GWs at highest altitudes, if applied automatically, may lead to very deteriorated network performance increasing the network operational costs. Consequently, we introduce the concept of network-agnostic GW placement algorithm whereby the location of GWs can be estimated without prior knowledge of specific locations of EDs and we use it as a guiding principle to design spatial algorithm for finding GW locations. We show that spatial algorithm can, in principle, provide effective GW placement suggestions compared to a network-aware method such as K-means clustering. We show that using a computational method for GW placement like K-means or spatial algorithm, has a potential of creating competitive network performance using just the same number of GWs, thus cutting down the financial costs of the network and increasing its sustainability.

In this paper, we present a detailed analysis of the coverage and spectral efficiency of a downlink cellular network. Rather than relying on the first order statistics of received signal-to- interference-ratio (SIR) such as coverage probability, we focus on characterizing its meta- distribution. Our analysis is based on the alpha- beta-gamma (ABG) path-loss model which provides us with the flexibility to analyze urban macro (UMa) and urban micro (UMi) deployments. With the help of an analytical framework, we demonstrate that selection of underlying degrees-of-freedom such as BS height for optimization of first order statistics such as coverage probability is not optimal in the network-wide sense. Consequently, the SIR meta-distribution must be employed to select appropriate operational points which will ensure consistent user experiences across the network. Our design framework reveals that the traditional results which advocate lowering of BS heights or even optimal selection of BS height do not yield consistent service experience across users. By employing the developed framework we also demonstrate how available spectral resources in terms of time slots/channel partitions can be optimized by considering the meta-distribution of the SIR.

Ambitious targets for aggregate throughput, energy efficiency and ubiquitous user experience are propelling the advent of ultra- dense networks. Intercell interference and high energy consumption in an ultra-dense network are the prime hindering factors in pursuit of these goals. To address the aforementioned challenges, in this paper, we propose a novel user-centric network orchestration solution for Cloud RAN based ultra-dense deployments. In this solution, a cluster (virtual disc) is created around users depending on their service priority. Within the cluster radius, only the best remote radio head (RRH) is activated to serve the user, thereby decreasing interference and saving energy. We follow a stochastic geometry based approach to quantify the area spectral efficiency (ASE) and RRH power consumption models to quantity energy(EE) efficiency of the proposed user-centric Cloud RAN (UCRAN). Through extensive analysis, we observe that the cluster sizes that yield optimal ASE and EE are quite different. Subsequently, we propose a game theoretic self-organizing network (GT-SON) framework that can orchestrate the network between ASE and EE focused operational modes in real-time in response to changes in network conditions and the operator's revenue model, to achieve a Pareto optimal solution. A bargaining game is modeled to investigate the ASE-EE tradeoff through adjustment in the exponential efficiency weightage in the Nash bargaining solution (NBS). Results show that compared to current non user-centric network design, the proposed solution offers the flexibility to operate the network at multiple folds higher ASE or EE along with significant improvement in user experience.

Direct device-to-device (D2D) communication presents as an effective technique to reduce the load at the base station (BS) while ensuring reliable localized communication. In this paper, we propose a large-scale M2M data Aggregation and Trunking (MAT) scheme, whereby the user equipments (UEs) aggregate M2M data from the nearby MTDs and trunk this data along with their own data to the BS in the cellular uplink. We develop a comprehensive stochastic geometry framework by considering a Poisson hard sphere model for UE coverage. The main motivation of this model is to capture the fact that a UE can gather data from short range, low-power MTDs located only in its close proximity while ensuring that an MTD is associated to at most one UE. We explore the inherent trade-off between the time reserved for aggregation and successful trunking of data to the BS and compare our results with the baseline case where no aggregation mechanism is used. We show that while the baseline case of connecting a bulk of MTDs directly with the BS is prohibitive, MAT scheme can efficiently gather data from selected MTDs in a distributed manner.

IEEE 802.11ah has recently emerged as a promising standard for enabling massive machine-to-machine (M2M) communication. In order to support uplink data transmission from dense machine type clients (such as smart meters, IoT end nodes etc.), 802.11ah relies upon the restricted access window (RAW) based Medium Access Control (MAC) protocol. The underlying motivation behind this protocol is to reduce the contention for spectrum access among a large number of devices. The nodes contend with each other in their assigned RAW slot using Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA). In each RAW slot, the throughput depends upon the number of nodes. Current studies have suggested that the duration of each RAW slot should be the same in the entire RAW frame. However in this paper, we argue that the duration of each RAW slot should be chosen according to the size of the group. We present a model where a RAW frame is divided into two sub-frames and the duration of RAW slots in each sub-frame is chosen according to the size of the group. With the help of an analytical framework, we demonstrate that the throughput under our proposed scheme can be significantly enhanced when compared to a conventional implementation.

In this article, we investigate how the performance of Massive MIMO cellular systems can be enhanced by introducing D2D communication. We consider a scenario where the base station (BS) is equipped with large, but finite number of antennas and the total number of UEs is kept fixed. The key design question is that what fraction of users should be offloaded to D2D mode in order to maximize the aggregate cell level throughput. We demonstrate that there exists an optimal user offload fraction, which maximizes the overall capacity. This fraction is strongly coupled with the network parameters such as the number of antennas at the BS, D2D link distance and the transmit SNR at both the UE and the BS and careful tuning of the offload fraction can provide up to 5× capacity gains.1

While stochastic geometric models based on Poisson point processes (PPPs) provide a tractable approach for the analysis of uniform two-tier network deployments, the performance evaluation of a non-uniform deployment remains an open issue, which we address in this paper. This is due to the fact that smaller cells can be more efficiently deployed in areas where the QoS of traditional macro base stations is poor. Therefore, in this paper, we introduce Stienen's model, which allows us to analyse such non-uniform deployment. In contrast to traditional PPP-based analysis, performance characterization under the Stienen model is more challenging due to location and density dependencies. However, we demonstrate that the performance can be approximated in a tractable manner. The developed statistical framework is employed to characterize the gains in terms of energy efficiency (EE) for non-uniform deployments. Results show an achievable 19% to 124% improvement in the macrocell coverage as compared to a uniform deployment, while the femtocell coverage and system EE are of the same order of magnitude for both deployments. These results are complemented with the fact that OPEX and CAPEX are reduced due to a lesser number of FAPs deployed.

In this paper, we develop a comprehensive analytical framework for cellular networks that are enhanced with coordinated device-to-device (D2D) communication, where the D2D devices are equipped with content caching capabilities. The base station (BS) coordinates the D2D communication by establishing a D2D link between the requesting user and the nearest D2D helper within the same cell if the latter contains the requested content, otherwise, the BS serves the user itself. The motivation behind restricting D2D pairs within a macro cell is to make coordinated D2D communication realizable as the BS can keep track of the content of the devices without the increased overhead of inter-BS coordination. This approach is similar to LTE direct, where D2D pairing is managed by the BS. We model the locations of BS and D2D helpers using a homogeneous Poisson point process (HPPP). The distribution of the distance between the tagged user and its neighboring D2D helper within the cell is derived using disk approximation for the Voronoi cell, which is shown to be reasonably accurate. We fully characterize the cellular and D2D coverage and the link spectral efficiency of such a network. Our results reveal that cache enabled D2D communication becomes more effective as the requesting user moves away from the BS and high performance gains can be achieved compared to conventional cellular networks, especially when the popularity distribution is skewed and most popular files are requested.

In this paper we introduce Stienen's model for analysing the performance of a non-uniform two-tier networks. The topology of the network consists of a set of macro base stations (MBSs) uniformly deployed, and a set of femtocell access points (FAPs) deployed only outside exclusion areas (discs) surrounding the MBSs. The MBSs serve users within the innermost areas of each macrocell, while the femtocells are restricted to serve users located in the outermost areas towards the edge of the macrocells. Results show that the edge user performance in terms of coverage is highly increased by the addition of femtocells. Moreover, the coverage in the macrocell tier can be also increased in comparison with a macrocell-only network if the number of femtocells deployed is judiciously selected. Furthermore, a well balanced network can be achieved, where the same performance is expected throughout the entire area.

In this paper, the downlink performance of two-tier heterogenous network is investigated. We consider a scenario where the macro-tier is empowered by massive antenna-array thus allowing for Massive multiple-input multiple-output (MIMO) transmission scheduling. The small cellular network complements the macro-tier capacity. We propose a novel channel allocation mechanism which optimally splits the spectral resources to maximize network level throughput and energy efficiency. Our proposed channel allocation mechanism is robust to the topological and channel variations. More specifically, the proposed scheme is designed by capturing the random locations of the users in both tiers by a Poisson Point Process (PPP). The channel uncertainty is captured by considering Rayleigh fading complemented by large scale power law path-loss. Our analysis shows that there exists an optimal split which maximizes the network wide throughput and energy efficiency. We also demonstrate that there exists an optimal transmit power which maximizes the energy efficiency for the network. Under different scenarios, massive MIMO plays a vital role in improving sum rate capacity as compared to single antenna femtocells. Finally, using implementation parameters, we obtain the optimal configurations that improve system capacity and energy efficiency.

In this article we employ the third generation partnership project (3GPP) recommended path loss models for the analysis of cellular networks overlaid with D2D communication and channel inversion power control in the uplink. We characterize the coverage and average network throughput with the help of stochastic geometry. More specifically, we develop tractable expressions for the coverage in cellular and D2D modes. Our theoretical results differ significantly from previous work, which uses simple power law path loss models. The traditional methodology does not account for the presence of line-of-sight (LoS), non-line-of-sight (NLoS) and free space (FS) links. We demonstrate that such classification of links significantly impacts the inference which can be derived from the analysis for the design of overlaid D2D networks. In particular, we show that, contrary to the previous findings, the average throughput of the network does not saturate with the increase in the density of base stations (BS), but there exists an optimal mode selection threshold and BS density which maximizes the average throughput.

Software Defined Networking (SDN) has recently emerged to become one of the promising solutions for the future Internet. With the logical centralization of controllers and a global network overview, SDN brings us a chance to strengthen our network security. However, SDN also brings us a dangerous increase in potential threats. In this paper, we apply a deep learning approach for flow-based anomaly detection in an SDN environment. We build a Deep Neural Network (DNN) model for an intrusion detection system and train the model with the NSL-KDD Dataset. In this work, we just use six basic features (that can be easily obtained in an SDN environment) taken from the forty-one features of NSL-KDD Dataset. Through experiments, we confirm that the deep learning approach shows strong potential to be used for flow-based anomaly detection in SDN environments.

The increasing demand for any-time any-where wireless connectivity has posed a formidable ‘1000 × data challenge’ for service providers. With the envisioned 1000 × explosion in mobile data traffic by the end of year 2020, wireless network architecture needs to rapidly evolve. In particular, the evolution trajectory should be charted such that exponential gains can be realised in network wide resource efficiency. This requires a clean slate design for future 5G wireless networks while provisioning interoperability with the legacy deployment. Both operators and technology providers realise that 5G will not merely be a newer version of 4G simply provisioning faster data transfers. These 5G networks are expected to be more dynamic due to heterogeneity in terms of devices, technologies, spectral bands and deployment models. Heterogeneity is indeed the intrinsic and central feature of the evolving networking paradigm. Now several potential solutions have recently been proposed to meet the aforementioned challenges and all address both network architecture and technologies. On the architectural front, concepts such as (i) cloudification & softwarisation of radio access networks; (ii) split-plane deployment; (iii) licensed shared access; (iv) decoupled uplink and downlink transmissions; and (v) information/content centric networking are all being considered as the enabling candidates. In terms of new technologies: (i) mmWave communications; (ii) massive MIMO; (iii) D2D communications; (iv) small cell deployment; and (v) low power IoT communication technologies (such as Bluetooth Low Energy, 802.11.ah WiFi, LoRA, SIGFOX) are all vital design tools for future 5G HetNets. In addition, the so-called concept of ‘tactile internet’, which has a wide spectrum of requirements ranging from ultra-low latency to ultra-high throughput via deployment of HetNets, cannot be realised without significant advances in signal processing algorithms. Thus, the main objective of this Special Section is to provide a platform for the dissemination of important results in those signal processing techniques necessary for enabling large scale, heterogeneous, 5G wireless networks. The first part of this Special Section presents four contributions. In the first paper, Mumtaz et al. present an energy efficient algorithm for D2D users in the presence of other cellular users (CUs). The authors employ Lagrangian duality theory for optimising both the power and rate of the D2D users while guaranteeing an acceptable quality-of-service (QoS) for the CUs. Finally, the solution of the proposed algorithm is then employed to achieve proportional fairness between the D2D and the CU users. The second paper (Butt et al.) reflects a growing interest in the area of green communication. It has recently been accepted that opportunistic exploitation of ambient energy sources is going to be the cornerstone of future wireless networks. The authors discuss relay selection schemes with the objective of minimising outage probability for a network consisting of a single source, multiple relays and a single destination. The relays are powered by radio frequency (RF) signals from the source and the authors present an optimal relay selection strategy to minimise outage probability for the system. Finally, a numerical solution is developed to determine the optimal number of relays. In the third paper, Gurjar et al. examine the significance of wireless channel estimation error on the performance of an analogue network coding (ANC)-based MIMO two-way relay system employing zero-forcing (ZF) transceivers in a Rayleigh fading environment. An analytical framework has been developed to study the overall outage analysis and some interesting (exact) expressions have been derived for special cases such as when the relay is equipped with less than two antennas. Some of the important contributions of this work are: a) exact expressions for the overall outage probability and the ergodic sum-rate have been derived within the context of channel estimation error; b) the authors have shown that system diversity may reduce to zero in the presence of channel estimation error due to imperfect self-interference cancellation; c) they conclude that a low complexity solution can be further derived by exploiting channel estimation error with ZF transmission/reception for an ANC based MIMO two-way relay system. In the final paper, Li et al. propose a hierarchical precoding approach for multi-cell, multi-user systems with any number of base stations and users, which is suitable for any number of data streams. The key feature of this approach is to align the inter-user interferences within the same cell to the room spanned by the inter-cell interferences, by which both the inter-cell and inter-user interferences are cancelled simultaneously. The effectiveness of this proposed method is demonstrated with an extensive set of simulations. In summary, this Special Section presents some important recent advances in D2D and relay assisted communication networks with a special focus on energy efficiency. Moreover, some of the state-of-the-art methods in multiuser MIMO systems have also been studied. For those interested in future 5G wireless networks, these articles will serve as a good springboard to appreciate further developments in this important topic. Finally, we would like to thank (i) all the submitting authors for considering this Special Section as a potential journal in which to publicise their research work; (ii) the reviewers for their high quality evaluations; and (iii) the Editorial team of the IET Signal Processing journal for their professional support. Syed Ali Raza Zaidi is currently University Academic Fellow (Assistant Professor) at the University of Leeds, UK. Prior to this, he was a Research Fellow in SPCOM Research Group at Leeds. He received his B. Eng. degree in information and communication system engineering from the School of Electronics and Electrical Engineering, NUST, Pakistan in 2008. He was awarded the NUST's most prestigious Rector's gold medal for his final year project. From September 2007 till August 2008, he served as a Research Assistant in Wireless Sensor Network Lab on a collaborative research project between NUST, Pakistan and Ajou University, South Korea. In 2008, he was awarded overseas research student scholarship along with Tetley Lupton and Excellence Scholarships to pursue his PhD at the School of Electronics and Electrical Engineering, the University of Leeds, U.K. He was also awarded with COST IC0902, DAAD and Royal Academy of Engineering grants to promote his research. In 2013, he was conferred with the prestigious F.W. Carter Prize for outstanding Doctoral thesis by the University of Leeds. Dr. Ali was a visiting Research Scientist at Qatar Innovations and Mobility Centre from October to December 2013. He has served as an invited reviewer for IEEE flagship journals and conferences. Dr. Ali is also UK Liaison for the European Association for Signal Processing (EURASIP). He is currently serving as an editor for IEEE Communication Letters and Lead Guest Editor for IET Signal Processing Special Section on 5G Wireless Networks. He is also the general secretary for IEEE Technical Committee on 5G Networks. He has published more than 60 papers in leading IEEE journals and conferences and has chaired several IEEE workshops/conferences. His current research interests are in the area of design and implementation of large scale networks for machine-to-machine communication (including robotics and autonomous systems). Des McLernon received his B.Sc in electronic and electrical engineering and his MSc in electronics, both from the Queen's University of Belfast, N. Ireland. He then worked in industry on radar systems research and development with Ferranti Ltd in Edinburgh, Scotland and later joined Imperial College, University of London, where he took his PhD in signal processing. After first lecturing at South Bank University, London, UK, he moved to the School of Electronic and Electrical Engineering, at the University of Leeds, UK, where he is a Reader in Signal Processing. His research interests are broadly within the domain of signal processing for wireless communications (in which area he has published over 285 journal and conference papers). He has supervised over 35 PhD students, given many invited talks in the UK and abroad and is Associate Editor of the IET Signal Processing journal. He has been a member of various international conference TPC's and conference organisation committees - recent conference organisation includes IEEE SPAWC 2010, European Signal Processing Conference (EUSIPCO) 2013, IET Conference on Intelligent Signal Processing (London, 2013/2015) and IEEE Globecom 2014/2015 (2nd /3rd Workshops on Trusted Communications with Physical Layer Security). His current research projects include distributed sensing, PHY layer security, caching and energy efficiency in heterogeneous networks, energy harvesting, robotic and drone communications, intrusion detection in software defined networks, compressive sensing and time-frequency analysis. Muhammad Ali Imran received his M.Sc. (Distinction) and Ph.D. degrees from Imperial College London, UK, in 2002 and 2007, respectively. He is currently a Reader in Communications in the Institute for Communication Systems (ICS - formerly known as CCSR) at the University of Surrey, UK and an adjunct Associate Professor at the University of Oklahoma, USA. He has lead a number of multimillion-funded international research projects encompassing the areas of energy efficiency, fundamental performance limits, sensor networks and self-organising cellular networks. He is also leading the new physical layer work area for 5G innovation centre at Surrey. He has a global collaborative research network spanning both academia and key industrial players in the field of wireless communications. He has supervised 21 successful PhD graduates and published over 200 peer-reviewed research papers including more than 20 IEEE Transaction papers. He has been giving a series of expert tutorials on emerging Green 5G technologies and networks at IEEE flagship conferences such as WCNC, PIMRC and ICC. Recently, he has been appointed as an area Chair for IEEE ComSoc Technical Committee on Backhaul/Fronthaul Networking and Communications (TCBNC). He secured first rank in his B.Sc. and a distinction in his M.Sc. degree along with an award of excellence in recognition of his academic achievements conferred by the President of Pakistan. He has been awarded IEEE ComSoc's Fred Ellersick award 2014 and FEPS Learning and Teaching award 2014 and twice nominated for Tony Jean's Inspirational Teaching award. He is a shortlisted finalist for The Wharton-QS Stars Awards 2014 for innovative teaching and VC's learning and teaching award in University of Surrey. He is a senior member of IEEE and a Senior Fellow of Higher Education Academy (SFHEA), UK. Muhammad Zeeshan Shakir is a Senior Research Fellow at Carleton University, Canada. In recent years, he has been involved in several joint R&D initiatives with Telus, DragonWave, University of Surrey, KAUST, and TAMUQ. His research interests include design and deployment of diverse wireless communication systems, including hyper-dense heterogeneous networks and related 5G technologies. He has published more than 75 technical journal and conference papers and has contributed to seven books, all in reputable venues. He is an author of three research monographs including one authored book. He earned his PhD degree in electronic and electrical engineering from University of Strathclyde, Glasgow, UK in 2010. He is an Associate Technical Editor of IEEE Communications Magazine and has served as a Lead Guest Editor for IEEE Communications and IEEE Wireless Communications Magazines. He has been serving as Chair/Co-chair of several workshops/symposia in IEEE flagship conferences, such as ICC and GlobalSIP. He has been giving a series of expert tutorials on emerging Green 5G technologies and networks at IEEE flagship conferences such as Globecom, ICUWB, PIMRC and ICC. Recently, he has been appointed as a Chair to IEEE ComSoc Technical Committee on Backhaul/Fronthaul Networking and Communications (TCBNC). He is an active member of IEEE, IEEE ComSoc and IEEE Standard Association. Mounir Ghogho received his MSc degree in 1993 and PhD degree in 1997 from the National Polytechnic Institute of Toulouse, France. He was an EPSRC Research Fellow with the University of Strathclyde, Glasgow (Scotland), from September 1997 to November 2001. Since December 2001, he has been a faculty member with the school of Electronic and Electrical Engineering at the University of Leeds, UK, where he currently holds a Chair in Signal Processing and Communications. Since 2010, he has also been a Research Director at the International University of Rabat (Morocco). He was awarded the UK Royal Academy of Engineering Research Fellowship in September 2000. He is one of the recipients of the 2013 IBM Faculty award. He is currently an Associate Editor of the IEEE Signal Processing magazine. He served as an Associate Editor of the IEEE Transactions on Signal Processing from 2005 to 2008, the IEEE Signal Processing Letters from 2001 to 2004, and the Elsevier's Digital Signal Processing journal from 2011 to 2012. He is currently a member of the IEEE Signal Processing Society SAM Technical Committee. He served as a member of the IEEE Signal Processing Society SPCOM Technical Committee from 2005 to 2010 and a member of IEEE Signal Processing Society SPTM Technical Committee from 2006 to 2011. He was the General Chair of the 11th IEEE workshop on Signal Processing for Advanced Wireless Communications (SPAWC2010) and the 21st edition of the European Signal Processing Conference (EUSIPCO 2013), and the Technical co-Chair of the MIMO symposium of IWCMC 2007 and IWCMC 2008. His research interests are in signal processing and communication networks. He has published over 260 journal and conferences papers. He was awarded the UK Royal Academy of Engineering Research Fellowship in September 2000. He is also one of the recipients of the 2013 IBM Faculty award and is the EURASIP Liaison in Morocco.

We investigate beamforming and artificial noise generation at the secondary transmitters to establish secure transmission in large scale spectrum sharing networks, where multiple noncolluding eavesdroppers attempt to intercept the secondary transmission. We develop a comprehensive analytical framework to accurately assess the secrecy performance under the primary users' quality of service constraint. Our aim is to characterize the impact of beamforming and artificial noise generation (BF&AN) on this complex large scale network. We first derive exact expressions for the average secrecy rate and the secrecy outage probability. We then derive an easy-to-evaluate asymptotic average secrecy rate and asymptotic secrecy outage probability when the number of antennas at the secondary transmitter goes to infinity. Our results show that the equal power allocation between the useful signal and artificial noise is not always the best strategy to achieve maximum average secrecy rate in large scale spectrum sharing networks. Another interesting observation is that the advantage of BF&AN over BF on the average secrecy rate is lost when the aggregate interference from the primary and secondary transmitters is strong, such that it overtakes the effect of the generated AN.

In this paper, we investigate secure device-to-device (D2D) communication in energy harvesting large-scale cognitive cellular networks. The energy constrained D2D transmitter harvests energy from multiantenna equipped power beacons (PBs), and communicates with the corresponding receiver using the spectrum of the primary base stations (BSs). We introduce a power transfer model and an information signal model to enable wireless energy harvesting and secure information transmission. In the power transfer model, three wireless power transfer (WPT) policies are proposed: 1) co-operative power beacons (CPB) power transfer, 2) best power beacon (BPB) power transfer, and 3) nearest power beacon (NPB) power transfer. To characterize the power transfer reliability of the proposed three policies, we derive new expressions for the exact power outage probability. Moreover, the analysis of the power outage probability is extended to the case when PBs are equipped with large antenna arrays. In the information signal model, we present a new comparative framework with two receiver selection schemes: 1) best receiver selection (BRS), where the receiver with the strongest channel is selected; and 2) nearest receiver selection (NRS), where the nearest receiver is selected. To assess the secrecy performance, we derive new analytical expressions for the secrecy outage probability and the secrecy throughput considering the two receiver selection schemes using the proposed WPT policies. We presented Monte carlo simulation results to corroborate our analysis and show: 1) secrecy performance improves with increasing densities of PBs and D2D receivers due to larger multiuser diversity gain; 2) CPB achieves better secrecy performance than BPB and NPB but consumes more power; and 3) BRS achieves better secrecy performance than NRS but demands more instantaneous feedback and overhead. A pivotal conclusion is reached that with increasing number of antennas at PBs, NPB offers a comparable secrecy performance to that of BPB but with a lower complexity.

Mobile robots that wish to communicate wirelessly often suffer from fading channels. They need to devise an energy-efficient strategy to search for a high-channel-gain position in a near vicinity from which to begin communications. Such a strategy has recently been introduced through the mobility diversity with multithreshold algorithm (MDMTA). In this paper, we establish the theoretical framework for a generalized version of the MDMTA. This allows improved wireless communications in fading channels for mobile robots via intelligent robotic motion with low mechanical energy expenditure.

In this paper, we develop a game theoretic formulation for empowering cloud enabled HetNets with adaptive Self Organizing Network (SON) capabilities. SON capabilities for intelligent and efficient radio resource management is a fundamental design pillar for the emerging 5G cellular networks. The C-RAN system model investigated in this paper consists of ultra-dense remote radio heads (RRHs) overlaid by central baseband units that can be collocated with much less densely deployed overlaying macro base-stations (BSs). It has been recently demonstrated that under a user centric scheduling mechanism, C-RAN inherently manifests the trade-off between Energy Efficiency (EE) and Spectral Efficiency (SE) in terms of RRH density. The key objective of the game theoretic framework developed in this paper is to dynamically optimize the trade-off between the EE and the SE of the C- RAN. More specifically, for an ultra-dense C- RAN based HetNet, the density of active RRHs should be carefully dimensioned to maximize the SE. However, the density of RRHs which maximizes the SE may not necessarily be optimal in terms of the EE. In order to strike a balance between these two performance determinants, we develop a game theoretic formulation by employing a Nash bargaining framework. The two metrics of interest, SE and EE, are modeled as virtual players in a bargaining problem and the Nash bargaining solution for RRH density is determined. In the light of the optimization outcome we evaluate corresponding key performance indicators through numerical results. These results offer insights for a C-RAN designer on how to optimally design a SON mechanism to achieve a desired trade-off level between the SE and the EE in a dynamic fashion.

We investigate beamforming and artificial noise generation at the secondary transmitters to establish secure transmission in large scale spectrum sharing networks, where multiple non-colluding eavesdroppers attempt to intercept the secondary transmission. We develop a comprehensive analytical framework to accurately assess the secrecy performance under the primary user's quality of service constraint. Our aim is to characterize the impact of beamforming and artificial noise generation on this complex large scale network. We first derive the exact expressions for the average secrecy rate and the secrecy outage probability. Our results show that there exists an average secrecy rate wall beyond which the primary user's quality of service is violated. Interestingly, we find that different from the conventional network with fixed nodes where equal power allocation achieves near optimal average secrecy rate, the equal power allocation may not be a good option for large scale spectrum sharing networks.

In this paper, we investigate secure device-to-device (D2D) communication in energy harvesting large-scale cognitive cellular networks. The energy constrained D2D transmitter harvests energy from multi-antenna equipped power beacons (PBs), and communicates with the corresponding receiver using the spectrum of the cellular base stations (BSs). We introduce a power transfer model and an information signal model to enable wireless energy harvesting and secure information transmission. In the power transfer model, we propose a new power transfer policy, namely, best power beacon (BPB) power transfer. To characterize the power transfer reliability of the proposed policy, we derive new closed-form expressions for the exact power outage probability and the asymptotic power outage probability with large antenna arrays at PBs. In the information signal model, we present a new comparative framework with two receiver selection schemes: 1) best receiver selection (BRS), and 2) nearest receiver selection (NRS). To assess the secrecy performance, we derive new expressions for the secrecy throughput considering the two receiver selection schemes using the BPB power transfer policies. We show that secrecy performance improves with increasing densities of PBs and D2D receivers because of a larger multiuser diversity gain. A pivotal conclusion is reached that BRS achieves better secrecy performance than NRS but demands more instantaneous feedback and overhead.

We address the optimal transmit power allocation problem (from the sensor nodes (SNs) to the fusion center (FC)) for the decentralized detection of an unknown deterministic spatially uncorrelated signal which is being observed by a distributed wireless sensor network. We propose a novel fully distributed algorithm, in order to calculate the optimal transmit power allocation for each sensor node (SN) and the optimal number of quantization bits for the test statistic in order to match the channel capacity. The SNs send their quantized information over orthogonal uncorrelated channels to the FC which linearly combines them and makes a final decision. What makes this scheme attractive is that the SNs share with their neighbours just their individual transmit powers at the current states. As a result, the SN processing complexity is further reduced.

In this paper, the energy efficiency (EE) of different MIMO diversity schemes is analyzed for the downlink of a two-tier network consisting of both macro- and femto-cells. The locations of the base stations (BSs) in both tiers are modeled by spatial Poisson point processes (PPPs). The EE of the system in b/J/Hz is obtained for different antenna configurations under various diversity schemes. Adaptive modulation is employed to maximize both the throughput and the EE across both tiers. Borrowing well established tools from stochastic geometry, we obtain closed-form expressions for the coverage, throughput, and power consumption for a two tier rate adaptive cellular network. Building on the developed analytical framework, we formulate the resource allocation problem for each diversity scheme with the aim of maximizing the network-wide EE while satisfying a minimum QoS in each tier. We consider that both the number of antennas and the spectrum allocated to each tier constitute the network resource which must be efficiently selected for both tiers to maximize network-wide performance. The best performance in terms of the EE is provided by the schemes which strike a good balance between the achievable maximum throughput and the consumed power (both increasing with the number of RF chains used). In addition, the potential savings in EE by using femto-cells with sleeping mode capabilities are analyzed. It is observed that, when the density of active co-channel femto-cells exceeds a certain threshold, the EE of the system can be significantly improved by sleep scheduling.

In this work, we address the antenna tilt optimization problem for a two-tier cellular network consisting of macrocells and femtocells, where both tiers share the same spectrum and their positions are modeled via two independent Poisson point processes (PPPs). First, we derive the coverage probability for a traditional cellular network consisting only of macrocells and obtain the optimum tilt angle that maximizes the overall energy efficiency (EE). Gains of up to 400% in EE were found for a scenario (approximately) equivalent to a hexagonal cell deployment with cell radius of 200 m when the optimum tilt was selected. We then proceed to model the heterogeneous network (HetNet) scenario where femtocells are also deployed in the network's area. We observe that the macrousers performance is highly sensitive to the interference emanating from the femtocell tier. In order to circumvent this issue, interference coordination employing a guard zone for the macrocell user is proposed. Subsequently, we formulate a joint optimization problem where we derive both, the radius of a guard zone protecting the macrouser and the tilt angle that maximize the EE of the network.

In this article, we develop a comprehensive analytical framework to characterize the area spectral efficiency of a large scale Poisson cognitive underlay network. The developed framework explicitly accommodates channel, topological and medium access uncertainties. The main objective of this study is to launch a preliminary investigation into the design considerations of underlay cognitive networks. To this end, we highlight two available degrees of freedom, i.e., shaping medium access or transmit power. While from the primary user's perspective tuning either to control the interference is equivalent, the picture is different for the secondary network. We show the existence of an area spectral efficiency wall under both adaptation schemes. We also demonstrate that the adaptation of just one of these degrees of freedom does not lead to the optimal performance. But significant performance gains can be harnessed by jointly tuning both the medium access probability and the transmission power of the secondary networks. We explore several design parameters for both adaptation schemes. Finally, we extend our quest to more complex point-to-point and broadcast networks to demonstrate the superior performance of joint tuning policies.

Sensing-throughput tradeoff under outage detection constraint has been studied before by assuming no uncertainty in estimation of the noise power, but this might not be the case in practice for an energy detector (ED). In this paper we examine analytically the effect of spectrum sensing on both the secondary user (SU) and the primary user (PU) throughputs under outage detection probability constraint in the presence of noise uncertainty and flat fading channels. First, we apply Jensen's Inequality to derive a new tight closed-form bound for the energy threshold that satisfies a certain outage detection probability. In addition, we derive both the secondary and the primary throughputs over flat fading channels in terms of the new threshold. The simulation results show that there exists an optimum sensing time that maximizes the secondary throughput. Finally, we show that the secondary throughput is more sensitive to noise uncertainty compared to the primary throughput.

In this article, we quantify the achievable spatial throughput of a multi-antenna Poisson cognitive radio network (CRN) collocated with a Poisson multi-antenna primary network. CR users employ Slotted-ALOHA medium access control. The success probability (SP) of a primary link is quantified in the presence of the secondary and primary interferers. It is demonstrated that two fold gains are experienced by employing multiple antennas at primary, i.e., (i) the fixed high desired SP threshold is met; (ii) CRs can also be accommodated without QoS deterioration. Further in this paper, the maximum permissible medium access probability (MAP) for CRN is derived from the link SP and primary users QoS constraint. The impact of the number of antennas and modulation employed at the primary on the permissible MAP of the CRN is also explored. Assuming that CR users employ multi-hop communication, QoS aware relaying with a radian sector forwarding area is studied. The average forward progress (AFP) and isolation probability for a CR user with QoS based connectivity is characterized under the permissible MAP. The spatial throughput for the CRN is quantified by the analysis of the AFP and the permissible MAP. It is shown that there exists an optimal MAP which maximizes the spatial throughput of the CRN. This optimal MAP is coupled with the permissible MAP, density of users, number of antennas and modulation schemes employed in both primary and secondary networks. Lastly, a few important design questions are investigated for multi-hop MIMO underlay CRNs.

This paper investigates the effects of power saving strategies on the performance of wireless local area networks (WLANs). More specifically, a power management model is formulated as an integer linear program that the network planner can use in order to achieve power savings while maintaining an acceptable quality of service (QoS), measured by the signal to interference ratio (SIR) for interference limited WLAN. Furthermore, through a network simulation implemented in NS-2, it is shown that the adaptive power saving scheme can guarantee the same average throughput as the non-adaptive counterpart, while significantly reducing the total transmitted power. Considering a realistic scenario, we show that, using the proposed power management model, one can save about 55% of the transmitted power while the SIR is increased by 6 dB thus improving the QoS. Also, using a simple experiment with two access points it is shown that, in the case of users within the overlap of the two coverage areas, the throughput remains constant when the transmit power is changed from a low value to a high value although a minor degradation of the average delay is noticed. As a conclusion, the commonly assumed fact that increasing the transmit power results in better network performance is not necessarily true and can result as shown in this paper in energy waste.

One of the fundamental problems in the wireless sensor networks is the coverage problem. The coverage problem fundamentally address the quality of service (surveillance or monitoring) provided in the desired area. In past, several studies have proposed different formulation and solutions to this problem. Nevertheless none of them has addressed minimum cost solution to the coverage problem. In this paper we consider minimum cost wireless sensor network which provides full coverage to any arbitrary geometric profile under deterministic/random deployment. We then formulate probabilistic coverage model which provides k - coverage probability for minimum cost wireless sensor networks.1