In the era of pervasive Internet use, managing large volumes of image data becomes crucial. To mitigate storage and bandwidth costs, image compression plays a pivotal role. Traditional image compression techniques like JPEG and PNG, while widely used, may suffer from limited compression rates. In this work, we propose a novel approach using shallow Convolutional Neural Network (CNN) autoencoders for on-the-fly image compression. Our model aims to achieve high compression rates with improved image quality compared to classical methods. Additionally, it supports decompression on the CPU in realtime, making no assumptions about client-side computational resources. We present a comprehensive methodology, including architecture design, experiments, and performance metrics. Our results demonstrate the effectiveness of the proposed approach, providing a competitive alternative for online content compression and decompression that outperforms JPEG in image quality assessment metrics at 33% compression rate. We also outperform other Learned Image Compression (LIC) techniques in both the decompression time and number of trainable parameters with an improvement in the order of 10-times.

Cardiovascular diseases (CVDs) are a major cause of mortality worldwide, requiring early and accurate diagnosis. Electrocardiogram (ECG) signals are essential for detecting CVDs, but manual analysis is time-consuming, error-prone, and often inaccessible in remote areas. This paper presents an open-source deep learning model for extracting interpretable morphological and temporal ECG features with high accuracy to aid physicians in diagnosis. Our approach preprocesses raw ECG signals and uses a convolutional neural network (CNN) for feature extraction. Using the PTB-XL+ dataset, our model shows high correlation with ground truth features, effectively capturing global and leadspecific ECG characteristics. We demonstrate that a single lead (Lead II) performs comparably to all $\mathbf{1 2}$ leads, reducing computational complexity. Additionally, a lower sampling frequency (100 Hz) maintains performance, offering an efficient alternative to 500 Hz. This work provides a practical and interpretable ECG feature extraction tool for regions with limited access to commercial ECG analysis software.

Cardiovascular diseases (CVD) have emerged as the leading cause of death globally. High cost Electrocardiogram (ECG) equipment challenges CVD diagnosis in developing countries, especially in rural, underserved areas. This paper presents a novel approach for mapping patient-friendly Ballistocardiogram (BCG) waveforms to ECG waveforms needed for CVD diagnosis, using machine learning. This is inspired by the inherent corre-lation between the two waveforms originating from the same person's heart. More specifically, we propose a Deep Learning (DL) model composed of Convolutional Neural Networks (CNNs) and Long-Short Term Memory (LSTM) for BCG-to-ECG signal mapping. To train and test the proposed model, a bed-based ballistocardiography public dataset is employed. For performance evaluation, we employ the Pearson Correlation Coefficient (PCC) as a widely-accepted metric for quantitatively assessing signals' correlation. Our proposed approach is proven effective as evidenced by achieving a PCC of 0.97, on average, when correlating our deep learning-generated ECG waveform to the real, measured ECG waveform (groundtruth). BCG-to-ECG signal mapping opens an ample room for the integration of ECG's rich diagnostic knowledge and the BCG's continuous monitoring for early diagnosis of cardiovascular diseases, in a cost-effective manner.

Federated learning is a machine learning approach where multiple devices collaboratively learn with the help of a parameter server by sharing only their local updates. While gradient-based optimization techniques are widely adopted in this domain, the curvature information that second-order methods exhibit is crucial to guide and speed up the convergence. This paper introduces a scalable second-order method, allowing the adoption of curvature information in federated large models. Our method, coined Fed-Sophia, combines a weighted moving average of the gradient with a clipping operation to find the descent direction. In addition to that, a lightweight estimation of the Hessian's diagonal is used to incorporate the curvature information. Numerical evaluation shows the superiority, robustness, and scalability of the proposed Fed-Sophia scheme compared to first and second-order baselines.

This paper studies the Multi-Input-Multi-Output (MIMO) interference networks with arbitrary number of transmitters and receivers, where both the transmitters and receivers are equipped with caches. The main goal is to design content placement and delivery schemes that minimize the worst case normalized delivery time (NDT). First, we propose a delivery scheme for the cache-aided Single-Input-Multiple-Output (SIMO) interference networks. Then, we obtain the achievable NDT of the cache-aided MIMO interference networks using the decomposition property of splitting each multi-antenna transmitter into multiple single antenna transmitters. Furthermore, we derive an information-theoretic bound on the optimal NDT of the cache-aided MIMO interference network. Analytical results show that the proposed scheme is within a multiplicative gap of 2 from the derived lower bound independent of all system parameters for any uncoded cache placement scheme. We also derive a novel delivery scheme for the cache-aided Multi-Input-Single-Output (MISO) interference network outperforming our proposed scheme for the cache-aided MIMO interference network. The numerical results show the superiority of our proposed scheme over the state-of-the-art schemes in the literature. Our results show that the coded caching gain has a more significant contribution in reducing the transmission latency than the spatial multiplexing gain. Our results indicate that the receive-antennas become more effective in reducing the NDT than the transmit-antennas in the presence of caches at the receiver-side. In addition, we show that increasing the number of transmit-antennas has a higher gain in reducing the NDT than adding more transmitters in the cache-aided MISO interference network.

Proactive content caching has been proposed as a promising solution to cope with the challenges caused by the rapid surge in content access using wireless and mobile devices and to prevent significant revenue loss for content providers. In this paper, we propose an end-to-end Deep Learning framework for proactive content caching that models the dynamic interaction between users and content items, particularly their features. The proposed model performs the caching task by building a probability distribution across different content items, per user, via a Deep Neural Network model and supports, both, centralized and distributed caching schemes. In addition, the paper addresses the key question: Do we need an explicit user-item pairs-based recommendation system in content caching? i.e., do we need to develop a recommendation system while tackling the content caching problem? To this end, an end-to-end Deep Learning framework is introduced. Finally, we validate our approach through extensive experiments on a real-world, public data set, coined MovieLens. Our experiments show consistent performance gains against its counterparts, where our proposed Deep Learning Caching module, dubbed as DLC, significantly outperforms state-of-the-art content caching schemes, serving as a baseline. Our code is available here: https://github.com/heshameraqi/Proactive-Content-Caching-with-Deep-Learning.

In this paper, we propose a reinforcement learning (RL) approach to design an access scheme for secondary users (SUs) in a cognitive radio (CR) network. In the proposed scheme, we introduce a deep Q-network to enable SUs to access the primary user (PU) channel based on their past experience and the history of the PU network's automatic repeat request (ARQ) feedback. In essence, SUs cooperate to avoid collisions with other SUs and, more importantly, with the PU network. Since SUs cannot observe the state of the PUs queues, they partially observe the system's state by listening to the PUs' ARQ packets. To model this system, a Partially Observable Markov Decision Process (POMDP) is adopted, and an RL deep Q-network is employed for the SUs to learn the best actions. A comparative study between the proposed scheme with baseline schemes from the literature is presented. We also compare the proposed scheme with the perfect sensing system (which constitutes an upper bound on the performance) and the system exploiting only the last ARQ feedback. Our results show that the proposed RL based access scheme yields comparable performance to the baseline ARQ-based access schemes, yet, with minimal knowledge about the environment compared to the baseline which assumes perfect knowledge of key system parameters, e.g., PUs arrival rates. On the contrary, our proposed scheme autonomously learns these parameters and, hence, dynamically adapts to their variation.

In this paper, we propose a Reinforcement Learning-based MAC layer protocol for cognitive radio networks, based on exploiting the feedback of the Primary User (PU). Our proposed model relies on two pillars, namely an infinite-state Partially Observable Markov Decision Process (POMDP) to model the system dynamics besides a queuing-theoretic model for the PU queue, where the states represent whether a packet is delivered or not from the PU's queue and the PU channel state. Based on the stability constraint for the primary user queue, the quality of service (QoS) for the PU is guaranteed. Towards the paper's objectives, three Reinforcement Learning approaches are studied, namely Q-Learning, Deep Q-Network (DQN), and Deep Deterministic Policy Gradient (DDPG). Our ultimate objective is to enhance the channel access techniques in the MAC protocols by solving the POMDP without any prior knowledge of the environment.

In this paper, we study a wireless powered cellular network (WPCN) supported with network coding capability. In particular, we consider a network consisting of k cellular users (CUs) served by a hybrid access point (HAP) that takes over energy transfer to the users on top of information transmission over both the uplink (UL) and downlink (DL). Each CU has k+1 states representing its communication behavior, and collectively are referred to as the user demand profile. Opportunistically, when the CUs have information to be exchanged through the HAP, it broadcasts this information in coded format to the exchanging pairs, resulting in saving time slots over the DL. These saved slots are then utilized by the HAP to prolong the network lifetime and enhance the network throughput. We quantify, analytically, the performance gain of our network-coded WPCN over the conventional one, that does not employ network coding, in terms of network lifetime and throughput. We consider the two extreme cases of using all the saved slots either for energy boosting or throughput enhancement. In addition, a lifetime/throughput optimization is carried out by the HAP for balancing the saved slots assignment in an optimized fashion, where the problem is formulated as a mixed-integer linear programming optimization problem. Numerical results exhibit the network performance gains from the lifetime and throughput perspectives, for a uniform user demand profile across all CUs. Moreover, the effect of biasing the user demand profile of some CUs in the network reveals considerable improvement in the network performance gains.

Energy efficiency (EE) is one of the main parameters to be considered in recent networks targeting green technology. Our system is based on cloud radio access networks and it consists of a macro base-station and many small remote radio heads (RRHs). We solve an optimization problem to improve the system's EE through resource allocation and power control. We also reduce the power consumption through switching the RRHs ON/OFF based on the current users' distribution. We formulate the problem as EE maximization with constraints to provide full frequency reuse between RRHs. Our solution divides the problem into multiple stages to relax the complexity. We first select the RRHs to be switched ON/OFF, and then based on the operating RRHs; we optimize the users' assignment and RRHs' transmission power. Finally, we assign any starving users to the macro base-station. The performance evaluation shows that our solution significantly improves the system EE and power consumption compared to other solutions where we achieve EE improvement by more than 57%.

This paper studies the Multi-Input-Multi-Output (MIMO) interference networks with arbitrary number of transmitters and receivers, where both the transmitters and receivers are equipped with caches. Our objective is to propose content placement and delivery schemes that minimize the worst case normalized delivery time (NDT). First, we design a delivery scheme for the cache-aided Single-Input-Multiple-Output (SIMO) interference networks. Then, we obtain the achievable NDT of the cache-aided MIMO interference networks by using the decomposition property. The numerical results show the superiority of our proposed scheme over the state-of-the-art schemes in the literature. Furthermore, we show that increasing the receiver-cache sizes achieves a higher gain than increasing the number of receive-antennas. In other words, the coded caching gain has a more significant contribution in reducing the transmission latency than the spatial multiplexing gain.

In this paper, we exploit tools from stochastic geometry to characterize the average probability of successful content delivery in a cache-enabled device-to-device (D2D) network under Nakagami- m fading. Specifically, we focus on the impact of temporal interference correlation due to erroneous packets retransmissions. The aggregate network interference is characterized under a slotted Aloha scheme in a homogeneous Poisson field of static interferers. In addition, the effect of different system parameters, such as the content popularity, intensity of devices, and D2D communication range, on the optimal activity pattern of devices is investigated. Theoretical findings of this work have been validated via Monte-Carlo simulations. Finally, we compare the system performance under temporal correlation to the approximate independent interference assumption. Furthermore, this work draws conclusions about several network design insights.

In this paper, we introduce a novel geographic routing with cooperation scheme for 5G Device-to-Device ad hoc networks. The prime focus of our scheme (coined Cooperative Routing for Reliable Paths (CRRP)) is to enhance the routing performance in cooperative communication networks in terms of the path length and reliability. In particular, it introduces novel algorithms for cooperative relay and forwarder selections, which strike the best balance between path length and achieved symbol error rate, compared to prior work in the literature. Moreover, it exhibits the flexibility of deciding cooperative vs. direct communications, on a hop-by-hop basis, depending on the scenario. Our extensive simulation study reveals valuable insights. First, CRRP reduces the path length compared with conventional Greedy Forwarding (GF) routing scheme and the recently introduced Relay Aware Cooperative Routing (RACR) scheme. GF scheme is a direct communication scheme which always selects the closest forwarder to the destination. RACR is a cooperative routing scheme that selects the closest relay to the optimum relay position and similarly selects the closest forwarder to the optimum forwarder position. The superiority of CRRP in path length results in energy saving, crucial for battery operated Device to Device communications. Second, CRRP considerably outperforms GF and RACR with respect to the path failure probability (on the average 56.5% and 50% better than GF and RACR, respectively), critical for low density networks, while maintaining the symbol error rate within acceptable limits.

We consider a fog radio access network (F-RAN) with multiple transmitters and receivers, where each transmitter is connected to the cloud via a fronthaul link. Each network node has a finite cache, where it fills its cache with portions of the library files in the off-peak hours. In the delivery phase, receivers request each library files according to an arbitrary popularity distribution. The cloud and the transmitters are responsible for satisfying the requests. This paper aims to design content placement and coded delivery schemes for minimizing both the expected normalized delivery time (NDT) and the peak NDT which measures the transmission latency. We propose achievable transmission policies, and derive an information-theoretic bound on the expected NDT under uniform popularity distribution. The analytical results show that the proposed scheme is within a gap of 2.58 from the derived bound for both the expected NDT under uniform popularity distribution and the peak NDT. Next, we investigate the expected NDT under an arbitrary popularity distribution for an F-RAN with transmitter-side caches only. The achievable and information-theoretic bounds on the expected NDT are derived, where we analytically prove that our proposed scheme is optimal within a gap of two independent of the popularity distribution.

We investigate dynamic content caching in relay networks where an intermediate relay station (RS) can adaptively cache data content based on their varying popularity. With the objective of minimizing the time average cost of content delivery, we formulate and study the problem of optimal RS cache allocation when the popularities of data content are unknown apriori to the network. While optimal dynamic cache control suffers the curse of dimensionality, we develop a fundamental lower bound on the achievable cost by any caching policy. Inspired by the structure of such lower bound, we develop a reduced-complexity policy that is shown numerically to perform close to the lower bound.

In this paper, we study a two-tier cellular network with cache-enabled small base stations (SBSs). In our model, a SBS has the ability to coordinate with neighboring SBSs and fetch data from their caches. We focus on the effect of SBSs' density on the network's energy efficiency. To this end, stochastic geometry theory is used to model the SBSs and users distributions, which enables us to find closed-form expressions for the network's energy efficiency as a function of the SBSs density and the cache size at each SBS. The optimal SBSs density that maximizes the energy efficiency is characterized, and the relation between this optimal density and the cache size at the SBS is highlighted. Furthermore, the effect of the users' density in the network on the energy efficiency and the optimal SBS density is investigated1This work was supported in part by the Egyptian National Telecommunications Regulatory Authority (NTRA).

In this paper, we consider a class of wireless powered communication devices using hybrid automatic repeat request (HARQ) protocol to ensure reliable communications. In particular, we analyze the trade-off between accumulating mutual information and harvesting RF energy at the receiver of a point-to-point link over a time-varying independent and identically distributed (i.i.d.) channel. The transmitter is assumed to have a constant energy source while the receiver relies, solely, on the RF energy harvested from the received signal. At each time slot, the incoming RF signal is split between information accumulation and energy accumulation with the objective of minimizing the expected number of re-transmissions. A major finding of this work is that the optimal policy minimizing the expected number of re-transmissions utilizes the incoming RF signal to either exclusively harvest energy or to accumulate mutual information. This finding enables achieving an optimal solution in feasible time by converting a two dimensional uncountable state Markov decision process (MDP) with continuous action space into a countable state MDP with binary decision space.

We investigate the performance of caching in relay networks where an intermediate relay station (RS) caches content for future demand by end users. With uncertain user demand over multiple data items and dynamically changing wireless links, we characterize the optimal transmission time for serving data items, cached data portion allocation of relay station and optimal service portion, which represents a part from the cached portion, to minimize the total average transmission energy. We argue that under several settings fully caching the higher popular items is the optimal caching policy which minimizes the total expected transmission energy.

We characterize time and power allocations to optimize the sum-throughput of a Wireless Powered Communication Network (WPCN) with Non-Orthogonal Multiple Access (NOMA). In our setup, an Energy Rich (ER) source broadcasts wireless energy to several devices, which use it to simultaneously transmit data to an Access Point (AP) on the uplink. Differently from most prior works, in this paper we consider a generic scenario, in which the ER and AP do not coincide, i.e., are two separate entities. We study two NOMA decoding schemes, namely Low Complexity Decoding (LCD) and Successive Interference Cancellation Decoding (SICD). For each scheme, we formulate a sum-throughput optimization problem over a finite horizon. Despite the complexity of the LCD optimization problem, due to its non-convexity, we recast it into a series of geometric programs. On the other hand, we establish the convexity of the SICD optimization problem and propose an algorithm to find its optimal solution. Our numerical results demonstrate the importance of using successive interference cancellation in WPCNs with NOMA, and show how the energy should be distributed as a function of the system parameters.

This paper studies the decentralized coded caching for a Fog Radio Access Network (F-RAN), whereby two edge-nodes (ENs) connected to a cloud server via fronthaul links with limited capacity are serving the requests of K r users. We consider all ENs and users are equipped with caches. A decentralized content placement is proposed to independently store contents at each network node during the off-peak hours. After that, we design a coded delivery scheme in order to deliver the user demands during the peak-hours under the objective of minimizing the normalized delivery time (NDT), which refers to the worst case delivery latency. An information-theoretic lower bound on the minimum NDT is derived for arbitrary number of ENs and users. We evaluate numerically the performance of the decentralized scheme. Additionally, we prove the approximate optimality of the decentralized scheme for a special case when the caches are only available at the ENs.

In this paper, we characterize optimal resource allocation for the uplink and downlink of wireless powered cellular networks (WPCNs). In particular, we investigate a time-slotted WPCN, where a hybrid access point (HAP) is in charge of energy replenishing of M cellular users (CUs), along with transmission/reception of information to/from them. Unlike prior works, which give attention to information transmission in only one direction (either uplink or downlink), our work incorporates information transmission in both directions, along with energy transfer over the downlink. Besides harvesting energy explicitly from the HAP, each user opportunistically harvests the RF energy borne by the information sent by the HAP over the downlink to the remaining M−1 users, in order to boost its uplink throughput. Towards this objective, we formulate the total (uplink and downlink) sum throughput maximization problem to characterize the optimal time allocation for energy, downlink information, and uplink information slots, subject to time allocation constraints. Furthermore, we establish the convexity of the problem, and an efficient algorithm based on the gradient projection method is proposed. The obtained numerical results reveal valuable insights about the fairness-throughput trade-offs. Moreover, and among others, the network dynamics are introduced, which clarify how the optimal time allocation pattern is changed in accordance with the change of the users' placement, and the HAP's transmission power, in both energy and information slots.

This paper characterizes the performance of a generic -tier cache-aided heterogeneous network (CHN), in which the base stations (BSs) across tiers differ in terms of their spatial densities, transmission powers, pathloss exponents, activity probabilities conditioned on the serving link and placement caching strategies. We consider that each user connects to the BS which maximizes its average received power and at the same time caches its file of interest. Modeling the locations of the BSs across different tiers as independent homogeneous Poisson Point processes (HPPPs), we derive closed-form expressions for the coverage probability and local delay experienced by a typical user in receiving each requested file. We show that our results for coverage probability and delay are consistent with those previously obtained in the literature for a single tier system.

We investigate the performance of dynamic proactive caching in relay networks where an intermediate relay station caches content for potential future use by end users. A central base station proactively controls the cache allocation such that cached content remains fresh for consumption for a limited number of time slots called proactive service window. With uncertain user demand over multiple data items and dynamically changing wireless links, we consider the optimal allocation of relay stations cache to minimize the time average expected service cost. We characterize a fundamental lower bound on the cost achieved by any proactive caching policy. Then we develop an asymptotically optimal caching policy that attains the lower bound as the proactive caching window size grows. Our analytical findings are supported with numerical simulations to demonstrate the efficiency of the proposed relay-caching.

In this paper we study social networks as an enabling technology for new applications and services leveraging, largely unutilized, opportunistic mobile encounters. More specifically, we quantify mobile user similarity and introduce a novel mathematical framework, grounded in information theory, to characterize fundamental limits and quantify the performance of sample knowledge sharing strategies. First, we introduce generalized, non-temporal and temporal profile structures, beyond geographic location, as a probability mass function. Second, we examine classic and information-theoretic similarity metrics using data in the public domain. A noticeable finding is that temporal metrics give lower similarity indices on the average (i.e., conservative) compared to non-temporal metrics, due to leveraging the wealth of information in the temporal dimension. Third, we introduce a novel mathematical framework that establishes fundamental limits for knowledge sharing among similar opportunistic users. Finally, we show numerical results quantifying the cumulative knowledge gain over time and its upper bound, the knowledge gain limit, using public smartphone data for the user behavior and mobility traces, in the case of fixed as well as mobile scenarios. The presented results provide valuable insights highlighting the key role of the introduced information-theoretic framework in motivating future research along this ripe research direction, studying diverse scenarios as well as novel knowledge sharing strategies.

We consider the problem of optimizing the performance of a cooperative cognitive radio user subject to constraints on the quality-of-service (QoS) of the primary user (PU). In particular, we design the probabilistic admission control parameter of the PU packets in the secondary user (SU) relaying queue and the randomized service parameter at the SU under non-work-conserving (non-WC) and WC cooperation policies. In the non-WC policy, two constrained optimization problems are formulated; the first problem is maximizing the SU throughput while the second problem is minimizing the SU average delay. In both problems, a constraint is imposed on the maximum allowable average delay of the PU. We show the equivalence of the two problems and develop a low-complexity line search algorithm to find the optimal parameters. Subsequently, the idea of optimizing the SU average delay is developed for the more complex WC policy, for its superior resource utilization and performance. Due to the sheer complexity of this optimization problem, we formulate another problem whose solution yields a suboptimal upper bound on the optimal SU delay. Afterwards, a practical WC-policy-based algorithm is designed in order to closely approach the optimal value of the SU delay. We show, through numerical results, that the proposed cooperation policies represent the best compromise between enhancing the SU QoS and satisfying the PU QoS requirements. Furthermore, the superior performance of the suboptimal WC policy over the non-WC policy is illustrated. Finally, the merits of the WC-policy-based algorithm are demonstrated through extensive simulations.

In this paper, we characterize the total degrees of freedom (DoFs) of the full-duplex asymmetric multiple-input multiple- output (MIMO) three-way channel. Each node has a separate-antenna full-duplex MIMO transceiver with a different number of antennas, where each antenna can be configured for either signal transmission or reception. We study this system under two message configurations; the first configuration is when each node has two unicast messages to be delivered to the two other nodes, while the second configuration is when each node has two unicast messages as well as one broadcast message to be delivered to the two other nodes. For each configuration, we first derive upper bounds on the total DoF of the system. Cut-set bounds in conjunction with genie-aided bounds are derived to characterize the achievable total DoF. Afterward, we analytically derive the optimal number of transmit and receive antennas at each node to maximize the total DoF of the system, subject to the total number of antennas at each node. Finally, the achievable schemes for each configuration are constructed. The proposed schemes are mainly based on zero-forcing and null-space transmit beamforming. We show that the derived outer and inner bounds on the total DoF are tight for each message configuration.

In this paper, we characterize the asymmetric total degrees of freedom (DoF) of a multiple-input multiple-output (MIMO) 3-way channel. Each node has a separate-antenna full-duplex MIMO transceiver with a different number of antennas, where each antenna can be configured for either signal transmission or reception. Each node has two unicast messages to be delivered to the two other nodes. We first derive upper bounds on the total DoF of the system. Cut-set bounds in conjunction with genie-aided bounds are derived to characterize the achievable total DoF. Afterwards, we analytically derive the optimal number of transmit and receive antennas at each node to maximize the total DoF of the system, subject to the total number of antennas at each node. Finally, the achievable schemes are constructed. The proposed schemes are mainly based on zero-forcing and null-space transmit beamforming.

Compressive Sensing has been utilized in Cognitive Radio Networks (CRNs) to exploit the sparse nature of the occupation of the primary users. Also, distributed spectrum sensing has been proposed to tackle the wireless channel problems, like node or link failures, rather than the common “centralized approach” for spectrum sensing. In this paper, we propose a distributed spectrum sensing framework based on consensus algorithms where SU nodes exchange their binary decisions to take global decisions without a fusion center to coordinate the sensing process. Each SU will share its decision with its neighbors, and at every new iteration each SU will take a new decision based on its current decision and the decisions it receives from its neighbors; in the next iteration, each SU will share its new decision with its neighbors. We show via simulations that the detection performance can tend to the performance of majority-rule Fusion Center based CRNs.

We study the problem of optimizing the performance of cognitive radio users with opportunistic real-time applications subject to primary users quality-of-service (QoS) constraints. Two constrained optimization problems are formulated; the first problem is maximizing the secondary user throughput while the second problem is minimizing the secondary user average delay, subject to a common constraint on the primary user average delay. In spite of the complexity of the optimization problems, due to their non-convexity, we transform the first problem into a set of linear programs and the second problem into a set of quasiconvex optimization problems. We prove that both problems are equivalent with identical feasible sets and optimal solutions. We show, through numerical results, that the proposed cooperation policy represents the best compromise between enhancing the secondary users QoS and satisfying the primary users QoS requirements.

Cell Outage Compensation (COC) is a self-healing functionality in the overall Self-Organizing Networks vision, which is defined by 3GPP. The Operation Administration and Maintenance system triggers the COC for mitigating the outage problem, e.g. out-of-service cell, by providing an adequate level of service to the affected users in the outage cell. We propose an outage compensation algorithm based on reconfiguring the surrounding cells by the proposed control parameters (the base station total transmission power and the mobility parameters) to provide an adequate capacity for the affected users wih minimal deterioration in network performance. The algorithm works in both frequency reuse one (FRone), and soft frequency reuse (SFR) networks. The SFR configuration has another degree of freedom that can be used to increase the affected users' capacity. Simulation results show the effectiveness of the algorithm to rescue the affected users in a homogeneous LTE networks under a full traffic load scenario.

The optimal resource allocation scheme in a full-duplex Wireless Powered Communication Network (WPCN) composed of one Access Point (AP) and two wireless devices is analyzed and derived. AP operates in a full-duplex mode and is able to broadcast wireless energy signals in downlink and receive information data in uplink simultaneously. On the other hand, each wireless device is assumed to be equipped with Radio-Frequency (RF) energy harvesting circuitry which gathers the energy sent by AP and stores it in a finite capacity battery. The harvested energy is then used for performing uplink data transmission tasks. In the literature, the main focus so far has been on slot-oriented optimization. In this context, all the harvested RF energy in a given slot is also consumed in the same slot. However, this approach leads to sub-optimal solutions because it does not take into account the Channel State Information (CSI) variations over future slots. Differently from most of the prior works, in this paper we focus on the long-term weighted throughput maximization problem. This approach significantly increases the complexity of the optimization problem since it requires to consider both CSI variations over future slots and the evolution of the batteries when deciding the optimal resource allocation. We formulate the problem using the Markov Decision Process (MDP) theory and show how to solve it. Our numerical results emphasize the superiority of our proposed full-duplex WPCN compared to the half-duplex WPCN and reveal interesting insights about the effects of perfect as well as imperfect self-interference cancellation techniques on the network performance.

Energy harvesting (EH) is a promising technology for realizing energy-efficient wireless networks. In this paper, we utilize the ambient RF energy, particularly interference from neighboring transmissions, to replenish the batteries of the EH enabled nodes. However, RF energy harvesting imposes new challenges into the analysis of wireless networks. Our objective in this paper is to investigate the performance of a slotted Aloha random access wireless network consisting of two types of nodes, namely Type I, which has unlimited energy supply and Type II, which is solely powered by an RF energy harvesting circuit. The transmissions of a Type I node are recycled by a Type II node to replenish its battery. We characterize an inner bound on the stable throughput region under half-duplex and full-duplex energy harvesting paradigms as well as for the finite capacity battery case. Additionally, we analyze the case where RF energy harvesting serves as a backup for an unlimited energy source. We present numerical results that validate our analytical results, and demonstrate their utility for the analysis of the exact system.

In this paper, we study and analyze cooperative cognitive radio networks with arbitrary number of secondary users (SUs). Each SU is considered a prospective relay for the primary user (PU) besides having its own data transmission demand. We consider a multi-packet transmission framework that allows multiple SUs to transmit simultaneously because of dirty-paper coding. We propose power allocation and scheduling policies that optimize the throughput for both PU and SU with minimum energy expenditure. The performance of the system is evaluated in terms of throughput and delay under different opportunistic relay selection policies. Toward this objective, we present a mathematical framework for deriving stability conditions for all queues in the system. Consequently, the throughput of both primary and secondary links is quantified. Furthermore, a moment generating function approach is employed to derive a closed-form expression for the average delay encountered by the PU packets. Results reveal that we achieve better performance in terms of throughput and delay at lower energy cost as compared with equal power allocation schemes proposed earlier in the literature. Extensive simulations are conducted to validate our theoretical findings.

This paper studies optimal resource allocation in a wireless powered communication network with two groups of users; one is assumed to have radio frequency (RF) energy harvesting capability and no other energy sources, while the other group has legacy nodes that are assumed not to have RF energy harvesting capability and are equipped with dedicated energy supplies. First, the base-station (BS) with a constant power supply broadcasts an energizing signal over the downlink. Afterwards, all users transmit their data independently on the uplink using time division multiple access (TDMA). We propose two transmission schemes, namely OPIC and OPAC, subject to different energy constraints on the system. Within each scheme, we formulate two optimization problems with different objective functions, namely maximizing the sum throughput and maximizing the minimum throughput, for enhanced fairness. We establish the convexity of all formulated problems which opens room for efficient solution using standard techniques. Our numerical results show the superiority of our realistic system accommodating legacy nodes, along with RF harvesting nodes, compared to the baseline WPCN system with RF energy harvesting nodes only. Moreover, the results reveal new insights and throughput-fairness trade-offs unique to our new problem setting.

In this paper we study the problem of secondary user channel access in cognitive radio networks. In particular, we address the problem of deciding the secondary user sensing vs. transmission at any point of time, assuming the availability of the primary user idle time statistics. Towards this objective, we make the following contributions. First, unlike prior work, we assume unconstrained general idle time distribution for the primary user under secondary user imperfect sensing and imperfect collision detection. Second, we propose a novel approach grounded in reliability theory to analyze the time based activity of the primary user. Finally, motivated by the sheer complexity of the problem, we propose three heuristic schemes for deciding the secondary user sensing/transmission actions at any point of time. We conduct computer simulations to evaluate the performance of the proposed schemes and compare them to the traditional per-packet sensing/transmission scheme and to each other. Our numerical results reveal that, for an experimentally verified idle time distribution in heterogeneous network activity, at least one of our proposed schemes can achieve 27% throughput increase and down to 3.85%, under different QoS requirements for the PU. Also we show through simulations that our heuristic schemes are very close to optimal, when the optimal scheme can be applied.

We study the impact of a full-duplex secondary node on a cognitive cooperative network with Multipacket Reception (MPR) capabilities at the receivers. Motivated by recent schemes that make full-duplex communication feasible, we study a model with one primary and one secondary transmitter-receiver pair, where the secondary transmitter is able to relay primary unsuccessful packets. Cooperation between primary and secondary users has been previously shown to be beneficial for the primary and the secondary users in terms of stable throughput. Our model assumes an imperfect full-duplex secondary node that can transmit and receive simultaneously, cancelling self-interference to a certain extent. Furthermore, we assume that the secondary transmitter chooses between cooperating with the primary user and transmitting secondary packets probabilistically according to some optimized probabilities that depend on both the channels in the network and the state of the primary user. We determine these probabilities by formulating a constrained optimization problem with the secondary throughput as the objective function and the stability of the primary queues as constraints. Using the dominant system approach, we show that the optimization problem has a quasi-concave structure, to which the optimal solution can be easily found. Using Numerical results, we characterize the cases where the full-duplex capability is beneficial to the system, namely, we show that the full-duplex secondary node greatly increases both the secondary throughput and the primary maximum stable throughput in channels with receivers that have strong MPR capability.

This paper studies fundamental throughput and delay tradeoffs in cognitive radio systems with cooperative secondary users. We focus on randomized cooperative policies, whereby the secondary user (SU) serves either its own queue or the primary users (PU) relayed packets queue with certain service probability. The proposed policy opens room for trading the PU delay for enhanced SU delay, and vice versa, depending on the application QoS requirements. Towards this objective, the system's stable throughput region is characterized. Furthermore, the moment generating function approach is employed and generalized for our system to derive closed-form expressions for the average packet delay for both users. The accuracy of these expressions is validated through simulations. Analytical and simulation results reveal that the service probability can steer the system into prioritizing PU's traffic at the expense of SU's QoS, or vice versa, independently from the admission probability. Alternatively, the ability of the admission probability to control the throughput and delay at the PU or the SU depends on the selected value for the service probability as well as the channel conditions. Finally, it is shown how the service and admission probabilities could be used to achieve the desired QoS level to both PU and SU.

Abstract In this paper, we address the problem of distributed interference management of femtocells that share the same frequency band with macrocells using distributed multi‐agent Q‐learning. We formulate and solve two problems representing two different Q‐learning algorithms, namely, femto‐based distributed and sub‐carrier‐based distributed power controls using Q‐learning (FBDPC‐Q and SBDPC‐Q). FBDPC‐Q is a multi‐agent algorithm that works on a global basis, for example, deals with the aggregate macrocell and femtocell capacities. Its complexity increases exponentially with the number of sub‐carriers in the system. Also, it does not take into consideration the sub‐carrier macrocell capacity as a constraint. To overcome these problems, SBDPC‐Q is proposed, which is a multi‐agent algorithm that works on a sub‐carrier basis, for example, sub‐carrier macrocell and femtocell capacities. Each of FBDPC‐Q and SBDPC‐Q works in three different learning paradigms: independent (IL), cooperative (CL), and weighted cooperative (WCL). IL is considered the simplest form for applying Q‐learning in multi‐agent scenarios, where all the femtocells learn independently. CL and WCL are the proposed schemes in which femtocells share partial information during the learning process in order to strike a balance between practical relevance and performance. We prove the convergence of the CL paradigm when used in the FBDPC‐Q algorithm. We show via simulations that the CL paradigm outperforms the IL paradigm in terms of the aggregate femtocell capacity, especially in networks with large number of femtocells and large number of power levels. In addition, we propose WCL to address the CL limitations. Finally, we evaluate the robustness and scalability of both FBDPC‐Q and SBDPC‐Q, against several typical dynamics of plausible wireless scenarios (fading, path loss, random activity of femtocells, etc.). We show that the CL paradigm is the most scalable to large number of femtocells and robust to the network dynamics compared with the IL and WCL paradigms. Copyright © 2014 John Wiley & Sons, Ltd.

The global adoption of smart phones has raised major concerns about a potential surge in the wireless traffic due to the excessive demand on multimedia services. This ever increasing demand is projected to cause significant congestions and degrade the quality of service for network users. In this paper, we develop a proactive caching framework that utilizes the predictability of the mobile user behavior to offload predictable traffic through the WiFi networks ahead of time. First, we formulate the proactive scheduling problem with the objective of maximizing the user-content hit ratio subject to constrains stemming from the user behavioral models. Second, we propose a quadratic-complexity (in the number of slots per day) greedy, yet, high performance heuristic algorithm that pinpoints the best download slot for each content item to attain maximal hit ratio. We confirm the merits of the proposed scheme based on the traces of a real dataset leveraging a large number of smart phone users who consistently utilized our framework for two months.

In this paper, we study the problem of cooperative communications in cognitive radio systems where the secondary user has limited relaying room for the overheard primary packets. More specifically, we characterize the stable throughput region of a cognitive radio network with a finite relaying buffer at the secondary user. Towards this objective, we formulate a constrained optimization problem for maximizing the secondary user throughput while guaranteeing the stability of the primary user queue. We consider a general cooperation policy where the packet admission and queue selection probabilities, at the secondary user, are both dependent on the state (length) of the finite relaying buffer. Despite the sheer complexity of the optimization problem, attributed to its non-convexity, we transform it to a linear program. Our numerical results reveal a number of valuable insights, e.g., it is always mutually beneficial to cooperate in delivering the primary packets in terms of expanding the stable throughput region. In addition, the stable throughput region of the system, compared to the case of infinite relaying queue capacity, marginally shrinks for limited relaying queue capacity.

This paper presents an energy efficient relay deployment algorithm that determines the optimal location and number of relays for future wireless networks, including Long Term Evolution (LTE)-Advanced heterogeneous networks. We formulate an energy minimization problem for macro-relay heterogeneous networks as a Mixed Integer Linear Programming (MILP) problem. The proposed algorithm not only optimally connects users to either relays or eNodeBs (eNBs), but also allows eNBs to switch into inactive mode. This is possible by enabling relay-to-relay communication which forms the basis for relays to act as donors for neighboring relays instead of eNBs. Moreover, it relaxes traffic load of some eNBs in order to allow them to enter the inactive mode. We characterize the optimal as well as provide an approximate solution, which, however, performs very closely to the optimum. Our performance evaluation shows that an optimal relay deployment with relays acting as donors can significantly improve system energy efficiency.

Energy efficient operation of cellular systems becomes a core design goal for economic and environment-friendly network operation. Several studies have shown that the energy consumed in base stations represents 60-80% of the energy consumption in cellular networks. In this paper, we develop an optimization framework that exploits several energy efficient techniques including switching power modes of base stations, Adaptive Modulation (AM), and the use of relays. Our main objective is to reduce both, transmitted and circuit power, subject to satisfying the quality of service constraints. To accommodate the complexity of the target problem, we further propose two sub-optimal algorithms, minimum power heuristic (MPH) and minimum relays heuristic (MRH). The simulation results show that energy saving merits of our proposed schemes can be up to 80%.

In this paper, we examine a cognitive spectrum access scheme in which a secondary user exploits the primary feedback information. We consider an overlay model in which the secondary user accesses the channel by certain access probabilities that are function of the spectrum sensing metric. In setting our problem, we assume that the secondary user can receive the primary link's feedback automatic repeat request (ARQ), but through an erasure channel. This means that the primary feedback may either be received correctly or is erased with a certain erasure probability. We study the cognitive radio network from a queuing theory point of view. Access probabilities are determined by solving a secondary throughput maximization problem subject to a constraint on the primary queues' stability. Fortunately, our problem is convex and can be solved using standard optimization techniques. Our scheme yields improved results in the secondary throughput than the non-feedback based access scheme attributed to the efficient utilization of the primary user's unerased feedback messages.

This paper studies distributed interference management for femtocells that share the same frequency band with macrocells. We propose a multi-agent learning technique based on distributed Q-learning, called subcarrier-based distributed resource allocation using Q-learning (SBDRA-Q). SBDRA-Q operates under three different learning paradigms: Independent (IL), Cooperative (CL) and Weighted Cooperative (WCL). In the IL paradigm, all femtocells learn independently from each other. In both, CL and WCL, femtocells share partial information during the learning process in order to enhance their performance. The results show that WCL outperforms both CL and IL in terms of aggregate femtocell capacity, while slightly affecting fairness. Also, the results show that CL and WCL are more robust, when compared to IL, to new femtocells being deployed during the learning process. Finally, we show SBDRA-Q achieves higher aggregate femtocell capacity under the three learning paradigms when compared to a power allocation scheme (SBDPC-Q) that was proposed in the literature.

In this paper, we study and analyze fundamental throughput-delay tradeoffs in cooperative multiple access for cognitive radio systems. We focus on the class of randomized cooperative policies, whereby the secondary user (SU) serves either the queue of its own data or the queue of the primary user (PU) relayed data with certain service probabilities. The proposed policy opens room for trading the PU delay for enhanced SU delay. Towards this objective, stability conditions for the queues involved in the system are derived. Furthermore, a moment generating function approach is employed to derive closed-form expressions for the average delay encountered by the packets of both users. Results reveal that cooperation expands the stable throughput region of the system and significantly reduces the delay at both users. Moreover, we quantify the gain obtained in terms of the SU delay under the proposed policy, over conventional relaying that gives strict priority to the relay queue.

Cognitive radio technology would enable a set of secondary users (SU) to opportunistically use the spectrum licensed to a primary user (PU). On the appearance of this PU on a specific frequency band, any SU occupying this band should free it for PUs. Typically, SUs may collaborate to reduce the impact of cognitive users on the primary network and to improve the performance of the SUs. In this paper, we propose and analyze the performance of virtual reservation in collaborative cognitive networks. Virtual reservation is a novel link maintenance strategy that aims to maximize the throughput of the cognitive network through full spectrum utilization. Our performance evaluation shows significant improvements not only in the SUs blocking and forced termination probabilities but also in the throughput of cognitive users.

In this paper, we aim at protecting the privacy of the communicating parties while ensuring the authenticity of source nodes. In particular, we exploit intra-flow network coding to preserve the anonymity of communicating parties. Towards this objective, we propose an anonymity preservation scheme, namely closed group anonymity (CGA) that preserves the anonymity of the communicating parties via mixing their flows. Afterwards, we explore an instance of the Authentication-Privacy Trade-off in the context of Network Coding. We analyze the trade-off with the aid of the proposed anonymity scheme and a previously proposed Source Authentication Scheme using Network Coding (SANC). We present simulation results showing that the proposed algorithm successfully leverages network coding to preserve anonymity against traffic analysis attacks. Finally, we not only confirm the fundamental authentication-privacy trade-off in the context of intra-flow network coding but also parameterize it via introducing a tunable parameter to dynamically control, and potentially balance, this trade-off depending on the security provisions dictated by the operational scenario and application of interest.

No abstract available.

No abstract available.

The recent witnessed evolution of cellular networks from a carefully planned deployment to more irregular, heterogeneous deployments of Macro, Pico and Femto-BSs motivates new analysis and design approaches. In this paper, we analyze the coverage probability in cellular networks assuming repulsive point processes for the base station deployment. In particular, we characterize, analytically using stochastic geometry, the downlink probability of coverage under a Matern hardcore point process to ensure minimum distance between the randomly located base stations. Assuming a mobile user connects to the nearest base station and Rayleigh fading, we derive two lower bounds expressions on the downlink probability of coverage that is within 4% from the simulated scenario. To validate our model, we compare the probability of coverage of the Matern hardcore topology against an actual base station deployment obtained from a public database. The comparison shows that the actual base station deployment can be fitted by setting the appropriate Matern point process density.

In this paper, we address the problem of distributed interference management of cognitive femtocells that share the same frequency range with macrocells using distributed multi-agent Q-learning. We formulate and solve three problems representing three different Q-learning algorithms: namely, centralized, femto-based distributed and subcarrier-based distributed power control using Q-learning (CPC-Q, FBDPC-Q and SBDPC-Q). CPC-Q, although not of practical interest, characterizes the global optimum. Each of FBDPC-Q and SBDPC-Q works in two different learning paradigms: Independent (IL) and Cooperative (CL). The former is considered the simplest form for applying Q-learning in multi-agent scenarios, where all the femtocells learn independently. The latter is the proposed scheme in which femtocells share partial information during the learning process in order to strike a balance between practical relevance and performance. In terms of performance, the simulation results showed that the CL paradigm outperforms the IL paradigm and achieves an aggregate femtocells capacity that is very close to the optimal one. For the practical relevance issue, we evaluate the robustness and scalability of SBDPC-Q, in real time, by deploying new femtocells in the system during the learning process, where we showed that SBDPC-Q in the CL paradigm is scalable to large number of femtocells and more robust to the network dynamics compared to the IL paradigm.

In this paper, we examine a cognitive spectrum access scheme in which secondary users exploit the primary feedback information. We consider an overlay secondary network employing a random access scheme in which secondary users access the channel by certain access probabilities that are functions of the spectrum sensing metric. In setting our problem, we assume that secondary users can eavesdrop on the primary link's feedback. We study the cognitive radio network from a queuing theory point of view. Access probabilities are determined by solving a secondary throughput maximization problem subject to a constraint on the primary queues' stability. First, we formulate our problem which is found to be non-convex. Yet, we solve it efficiently by exploiting the structure of the secondary throughput equation. Our scheme yields improved results in, both, the secondary user throughput and the primary user packet delay as compared to the scheme where no feedback information is exploited. In addition, it comes very close to the optimal genie-aided scheme in which secondary users act upon the presumed perfect knowledge of the primary users' activity.

We study a cellular setting in which an introduced multiple-antenna relay station (RS) can possibly assist the bidirectional communication between a multiple-antenna base station (BS) and a set of single-antenna mobile stations (MSs). Through a proposed six-phase communication protocol with arbitrary number of antennas and MSs, we characterize the maximum number of degrees of freedom (DoF) that can be attained when the BS-MSs direct link is active or down. When the direct link is available, we show that the introduction of a multiple-antenna RS cannot increase the maximum DoF regardless of the number of antennas it is equipped with. In the absence of a BS-MSs direct link, the maximum DoF can be limited by the number of RS antennas since all ongoing communication takes place through the RS. It is also shown that the characterized maximum DoF is achieved via recently proposed network-coding based two-way relaying techniques. Finally, we conclude that a widely used two-phase multiple access/broadcast (MABC) two-way relaying protocol can be DoF-limiting in some cases due to its inherent inability to exploit the possibly available BS-MSs direct-link.

No abstract available.

Space frequency block coding with orthogonal frequency division multiplexing (SFBC-OFDM) suffers from the effect of inter-carrier interference (ICI) in doubly-selective communication channels. In this paper, a scheme is proposed in which windowing is applied to the received signal to reduce the effect of ICI to a limited number of neighboring sub-carriers. The sub-carriers holding the SFBC components of each codeword are separated by a number of sub-carriers larger than the ICI range, and hence, they do not interfere with each other. In order to preserve the structure of the SFBC, the separation between the codeword components is also selected within the coherence bandwidth of the channel. As a result, the diversity gain of the SFBC is preserved. A decision feedback equalizer is proposed to estimate the transmitted data symbols with low complexity. Simulation results are presented showing the ability of the proposed scheme to significantly improve the performance of SFBC-OFDM and preserve its diversity gain.

In this paper, we propose a distributed reinforcement learning (RL) technique called distributed power control using Q-learning (DPC-Q) to manage the interference caused by the femtocells on macro-users in the downlink. The DPC-Q leverages Q-Learning to identify the sub-optimal pattern of power allocation, which strives to maximize femtocell capacity, while guaranteeing macrocell capacity level in an underlay cognitive setting. We propose two different approaches for the DPC-Q algorithm: namely, independent, and cooperative. In the former, femtocells learn independently from each other, while in the latter, femtocells share some information during learning in order to enhance their performance. Simulation results show that the independent approach is capable of mitigating the interference generated by the femtocells on macro- users. Moreover, the results show that cooperation enhances the performance of the femtocells in terms fairness and aggregate femtocell capacity.

In this paper, we consider a cognitive relaying network in which the secondary user accesses the channel with a certain access probability that depends on the feedback information sent by the primary destination. In addition, the secondary user is granted relaying capabilities by which it can relay primary traffic that was unsuccessfully transmitted by the primary user. We show that this proposed scheme enhances the performance of the secondary user as well as the primary user, while the QoS requirements of the primary user is unviolated. The secondary user can avoid sure collisions with the primary transmissions exploiting the feedback information from the primary user. Also, due to the fact that relaying the unsuccessfully transmitted primary traffic increases the availability of the channel for its own packets, the secondary throughput is increased and the primary delay is decreased.

In this paper, a multiuser cellular two-way relaying scenario is considered where multiple single-antenna mobile stations (MSs) and one multiple-antenna base station (BS) communicate, bidirectionally, via one half-duplex multiple-antenna relay station (RS). Furthermore, the case when the number of antennas at the RS is not sufficient to decode the individual messages is addressed. For this case, a two-phase two-way relaying scenario is considered. In the first phase, the multiple access, a minimum Mean Square Error (MSE) optimization problem is formulated which is found to be non-convex. Thus, an iterative scheme is proposed to compute the MS transmit powers, the BS beamforming vectors, and the corresponding RS linear receivers to minimize the maximum MSE for multiple pairs subject to power constraints on the transmitting terminals. In the second phase, the broadcast phase, the beamforming vectors at the RS are designed to minimize the maximum MSE at the MSs subject to relay power constraints, and the receivers at the BS are designed accordingly. In a two-pair scenario, simulation results are provided showing the superior performance of the proposed methods compared to earlier approaches in terms of the bit-error rate. Also, it is shown that as the system scales up in terms of signal space dimensions and number of accommodated pairs, the performance gap between the proposed scheme and the earlier approaches increases.

In this paper we consider a single cognitive radio seeking a transmission opportunity by sequentially sensing a number of statistically independent primary channels. We study the joint optimization of the time spent to sense a channel, the decision threshold to determine whether the channel is free or busy, together with the order with which the channels are sensed. The sensing time and decision threshold are assumed to be the same for all channels. The design objective is to maximize the expected secondary throughput taking sensing errors into account and penalizing for collisions that may disrupt the primary transmission. Motivated by the computational complexity of the problem, we propose suboptimal solutions that significantly reduce the complexity without sacrificing accuracy. Our results reveal a fundamental trade-off between minimizing the probability of collision with the primary user via reducing the sensing errors, which favors a longer sensing time, and increasing the secondary user's throughput, which favors shorter sensing time. The suboptimal approach, for plausible simulation scenarios, is found to reduce the computational complexity by more than 89%, while maintaining a near-optimal throughput within 0.28% of the optimal performance.

Abstract In this paper we explore the inherent coupling between MAC and routing in wireless multi‐hop networks attributed to interference. This is primarily motivated by the observation that shortest path routing could potentially lead to degrading the single‐hop MAC throughput which constitutes an upper bound on the end‐to‐end multi‐hop throughput. First, we formulate an optimization problem that maximizes the MAC throughput subject to path length, scheduling, SINR, and power constraints and establish bounds on the optimal performance. Second, we propose a novel cross‐layer routing framework (set‐based routing) that reduces problem complexityviaresolving intra‐ and inter‐set interference among sets of spatially close transmitters. Third, we propose joint routing, scheduling and power control (RSP) to solve the problem within each set. Finally, we show, through simulations, that set‐based routing achieves not only 60% of the optimal performance for plausible scenarios but also up to 50% improvement over a generic reference system that represents a broad class of state‐of‐the‐art protocol stacks and uses minimum hop (MH) routing and collision‐free scheduling with no interaction. Copyright © 2010 John Wiley & Sons, Ltd.

In this paper we study the problem of efficient data dissemination over one- and two-dimensional multi-hop wireless sensor grids with spatially correlated sample measurements. In particular, we investigate the trade-offs of exploiting correlations via cooperatively compressing the sensor data as it hops around the network. We focus on two performance metrics, namely transport traffic and scheduling latency. More specifically, we investigate using basic information theory the feasibility of sublinear scaling laws , with the number of nodes, under a variety of cooperation strategies ranging from naive non-cooperative forwarding to sophisticated hierarchical cooperation. First, we show that a simple cooperation scheme, namely forward/reverse cooperation, achieves a logarithmic growth rate for the transport traffic and a linear growth rate for the schedule length with the number of nodes. Thus, we shift our focus to multi-phase cooperation to show that: i) O(radicN) schedule length is achievable using two-phase cooperation which is a combination of noncooperative and forward/reverse cooperation schemes and ii) Logarithmic schedule length and transport traffic are both achievable using hierarchical cooperation, yet at the expense of more complexity in coordinating nodes' cooperation. This also opens room for optimizing these performance measures for a given network size. Finally, we analyze the impact of fixed bit rate and derive upper bounds on the scheduling latency.

In this paper we study the problem of reliable transmission in interference-limited MIMO networks. This is motivated by a fundamental tradeoff between scheduling full diversity non-interfering links vs. scheduling interfering links using lower diversity gain in conjunction with nulling. First, we formulate a distributed cross-layer optimization problem that jointly decides the scheduling, diversity gain and nulling in order to minimize the probability of error of individual links subject to signal-to-interference-and-noise-ratio (SINR) constraints. Second, we characterize the optimal solution for 2 links. It reveals simple decision rules that constitute the basis for solving the problem for arbitrary number of links using Scheduling Space-time coded Links (SSL) algorithm. Numerical results exhibit significant improvement over scheduling non-interfering links with full diversity gain.

In this paper we address the problem of interference-aware routing that tightly couples the design of the lower three layers of the ISO Open Systems Interconnection (OSI) protocol stack. This is primarily motivated by the observation that shortest path routing could potentially lead to degrading the single-hop throughput which constitutes an upper bound on the end-to-end multi-hop throughput. We introduce the concept of set-based routing in an attempt to incorporate interference into the routing decision as well as reduce the problem complexity. Towards this objective, we propose a novel algorithm that takes routing, scheduling and power control decisions for a set of interference-coupled transmitters. Furthermore, we discuss set coordination schemes for combating inter-set interference. Finally, we conduct a simulation study that shows considerable throughput improvement over a reference system that uses minimum hop routing and collision-free scheduling.

In this paper we explore the design of multi-modal MAC for wireless ad hoc and sensor networks that dynamically adapt its behavior in order to minimize the energy to delivery ratio under a wide variety of network loads. The prime motivation is to balance the inherent trade-off between the energy wasted in collisions and the energy expended by collision avoidance handshake mechanisms. Towards this objective, the study goes through two phases. First, we explore the space of MAC modes subject to the constraint that different access schemes can inter-operate. Accordingly, we limit our attention to modes within the non-slotted random access paradigm. Second, we analyze, with the aid of detailed network simulations, the energy performance trade-offs of four variations of the CSMA/CA access scheme. Finally, we shed some light on the problem of dynamically switching between different modes depending on the network loading conditions and application QoS requirements. Initial results reveal interesting observations related to the energy/delivery contribution of channel reservation and single-hop acknowledgment packets under a wide variety of temporal network loads

In this paper we explore the design of multi-modal MAC for wireless ad hoc and sensor networks that dynamically adapt its behavior in order to minimize the energy to delivery ratio under a wide variety of network loads. The prime motivation is to balance the inherent trade-off between the energy wasted in collisions and the energy expended by collision avoidance handshake mechanisms. Towards this objective, the study goes through two phases. First, we explore the space of MAC modes subject to the constraint that different access schemes can inter-operate. Accordingly, we limit our attention to modes within the non-slotted random access paradigm. Second, we analyze, with the aid of detailed network simulations, the energy performance trade-offs of four variations of the CSMA/CA access scheme. Initial results reveal interesting observations related to the energy/delivery contribution of channel reservation and single-hop acknowledgment packets under a wide variety of temporal network loads

In this paper we characterize the scaling laws of the generated traffic and scheduling delays associated with the broadcast problem in dense multihop sensor networks where sample measurements are highly correlated. More specifically, we assess the benefits, and possibly the trade-offs, of exploiting sample correlations via cooperatively compressing the data as it hops around the network. First, we determine, with the aid of basic information theory, the transport traffic and schedule length growth rates under the no cooperation and network-wide cooperation extremes. We observe that network-wide cooperation significantly improves the transport traffic growth rate, without any degradation in the linear schedule length growth rate. Second, we propose a novel two-phase cooperation strategy that localizes cooperation within regions of the network in an attempt to optimize the schedule length for a given network size. We demonstrate the role of the cooperation set size in trading transport traffic for schedule length, or vice versa, and how the two extreme strategies turn out to be special cases of the two-phase cooperation framework.

In this paper we study the problem of information dissemination in dense multi-hop sensor networks characterized by highly correlated sample measurements. In particular, we investigate the benefits, and trade-offs, of exploiting correlations via cooperatively compressing the data as it hops around the network. First, we study two extreme cooperation strategies, namely no cooperation and network-wide cooperation. We show that network-wide cooperation achieves logarithmic growth rate for the transport traffic with the network size whereas the schedule length growth rate remains linear. Next, we analyze a two-phase cooperation strategy which localizes cooperation within regions of the network in an attempt to assess the performance of strategies bounded by the two aforementioned extremes. Finally, we extend two-phase cooperation to a multi-phase hierarchical cooperation strategy where the number of phases depends on the number of nodes and the size of the cooperation set. The rationale behind this strategy is to achieve logarithmic scaling laws at the expense of more complexity in coordinating nodes' cooperation. In addition, hierarchical cooperation opens room for optimizing the transport traffic and schedule length for a given network size.

In this paper, we introduce a cross-layer design framework to the multiple access problem in contention-based wireless ad hoc networks. The motivation for this study is twofold, limiting multiuser interference to increase single-hop throughput and reducing power consumption to prolong battery life. We focus on next neighbor transmissions where nodes are required to send information packets to their respective receivers subject to a constraint on the signal-to-interference-and-noise ratio. The multiple access problem is solved via two alternating phases, namely scheduling and power control. The scheduling algorithm is essential to coordinate the transmissions of independent users in order to eliminate strong levels of interference (e.g., self-interference) that cannot be overcome by power control. On the other hand, power control is executed in a distributed fashion to determine the admissible power vector, if one exists, that can be used by the scheduled users to satisfy their single-hop transmission requirements. This is done for two types of networks, namely time-division multiple-access (TDMA) and TDMA/code-division multiple-access wireless ad hoc networks.

In this paper, we introduce a novel reservation based multiple access protocol for ad hoc networks using directional antennas. First, we investigate the limitations of the extreme reservation schemes, namely omni-directional and directional reservations. We highlight the trade-off between spatial reuse (favors directional reservation) and control/data packet collisions (favors omni-directional reservation). Next, we show that the so-called hybrid reservation schemes fail to balance the trade-off as well. Therefore, we introduce a novel algorithm that balances the aforementioned trade-off via sending reservation messages that carry information about the required direction of transmission, in all unblocked directions. In addition, we introduce candidate techniques for handling new types of collisions inherent to directional antennas. Finally, we conduct a simulation study that shows considerable performance gains of the proposed scheme over the omni-directional, directional, and hybrid reservation paradigms.

In this paper we introduce power control as a solution to the multiple access problem in contention-based wireless ad-hoc networks. The motivation for this study is two fold, limiting multi-user interference to increase single-hop throughput, and reducing power consumption to increase battery life. We focus on next neighbor transmissions where nodes are required to send information packets to their respective receivers subject to a constraint on the signal-to-interference-and-noise ratio. The multiple access problem is solved via two alternating phases, namely scheduling and power control. The scheduling algorithm is essential to coordinate the transmissions of independent users in order to eliminate strong interference (e.g. self-interference) that can not be overcome by power control. On the other hand, power control is executed in a distributed fashion to determine the admissible power vector, if one exists, that can be used by the scheduled users to satisfy their single-hop transmission requirements. This is done for two types of networks, namely TDMA and TDMA/CDMA wireless ad-hoc networks.

We introduce the notion of power management within the context of wireless ad-hoc networks. More specifically, we investigate the effects of using different transmit powers on the average power consumption and end-to-end network throughput in a wireless ad-hoc environment. This power management approach would help in reducing the system power consumption and hence prolonging the battery life of mobile nodes. Furthermore, it improves the end-to-end network throughput as compared to other ad-hoc networks in which all mobile nodes use the same transmit power. The improvement is due to the achievement of a tradeoff between minimizing interference ranges, reduction in the average number of hops to reach a destination, reducing the probability of having isolated clusters, and reducing the average number of transmissions (including retransmissions due to collisions). The protocols would first dynamically determine an optimal connectivity range wherein they adapt their transmit powers so as to only reach a subset of the nodes in the network. The connectivity range would then be dynamically changed in a distributed manner so as to achieve the near optimal throughput. Minimal power routing is used to further enhance performance. Simulation studies are carried out in order to investigate these design approaches. It is seen a network with such a power managed scheme would achieve a better end-to-end throughput performance (about 10% improvement with a slotted aloha MAC protocol) and lower transmit power (about an 80% Improvement) than a network without such a scheme.

We consider the extension of a cellular system by means of satellite channels. Specifically, we consider an area covered by a number of cells, that is also covered by a number of spot beams. We consider connection-oriented service, and call durations are assumed to be exponentially distributed. Also, users are mobile and, as such, they may cross cell and/or spot-beam boundaries, thus necessitating handoffs. We incorporate the possibility of call dropping due to unsuccessful handoff attempts, in addition to satellite propagation delays along with the probability of new call blocking, and formulate a specific multifaceted cost function that must be ultimately minimized. The minimization is to be carried out by choosing: (1) the optimal partitioning of channels between the cellular and the satellite systems, and (ii) the call admission and assignment policy, subject to the constraints of a demand vector that consists of an exogenous (new-call) generation process and an internal (handoff-based) process that results from the mobility model. Two subproblems of this complex optimization problem are solved by means of numerical techniques and by means of so-called standard clock simulation techniques. In this solution method, we employ the ordinal optimization approach which focuses on preserving the performance rank, rather than the performance prediction of the different control policies. We find that the "double" coverage, through both cellular and satellite resources, results in substantial improvement over pure terrestrial or pure satellite systems for parameter values that correspond to practical environments.