A Digital Twin (DT) is a virtual replica of a physical system (PS), such as an Internet-of-Things (IoT) device, enabling the analysis and prediction of the PS's dynamics. To maintain accuracy, a DT frequently synchronizes with its PS, by processing status updates sent from the PS. Therefore, the incurred synchronization latency is impacted by both, communication and computation resources. Due to being computationally demanding, DTs are commonly hosted on edge servers (ESs) in Mobile Edge Computing (MEC). To increase scalability and flexibility of MEC systems, hierarchical architectures distribute computation resources across multiple tiers of ESs between base stations and cloud. Hierarchical MEC gives rise to the DT placement problem, i.e., the selection of an ES for hosting the DT. The selection must consider the synchronization latency, which should remain below critical thresholds to ensure DT accuracy and thus, successful synchronization. DT placement is challenging, because the synchronization latency is impacted by the dynamic and statistically non-stationary MEC system characteristics, such as communication channels and server loads. Furthermore, additional overhead is caused by migrating DTs between ESs. To address these challenges, we propose a novel risk-aware Multi-Armed Bandit algorithm that adapts to piece-wise stationary environments. Our approach minimizes synchronization cost, defined as a weighted sum of synchronization latency and energy consumption, while reducing the risk of synchronization failures and limiting DT migration overhead. Simulation results demonstrate that our proposed algorithm achieves a similar synchronization cost, compared to state-of-the-art baselines, while achieving a significantly smaller synchronization failure rate and migration overhead.

Mobile crowdsensing (MCS) is a distributed sensing architecture that utilizes existing sensors on mobile units (MUs) to perform sensing tasks. A mobile crowdsensing platform (MCSP) publishes the sensing tasks and the MUs decide if they want to participate in their execution in exchange for money. The MCS system is characterized by its dynamic nature in which the task requirements, the MUs’ availability, and their available resources change over time. The MUs aim to find an efficient task participation strategy to maximize their income while the MCSP focuses on maximizing the number of completed tasks. As optimal task participation strategies require perfect non-causal information about the MCS system, which is unavailable in realistic scenarios, the main challenge in MCS is to find an efficient task participation strategy for the MUs under incomplete information. To this aim, a novel fully decentralized federated deep reinforcement learning algorithm, termed FDRL-PPO is proposed. FDRL-PPO enables every MU to learn its own task participation strategy based on its experiences, available resources, and preferences, without relying on perfect non-causal information about the MCS system. To replenish their batteries, the MUs rely on energy harvesting. As a result, their available energy varies over time, leading to varying availability and fragmented learning experiences. To mitigate these challenges, the proposed approach leverages federated learning, enabling MUs to collaboratively improve their models without having to share private raw data like their own experiences. By exchanging only learned models, MUs collectively compensate for individual limitations, and find more scalable, robust, and efficient task participation strategies. Comprehensive evaluations on both synthetic and real-world datasets show that FDRL-PPO consistently outperforms benchmark algorithms in terms of task completion ratio, fairness in task completion, energy consumption, and number of conflicting proposals.

The Satellite Internet of Things (S-IoT) enables global connectivity for remote sensing devices that must operate energy-efficiently over long time spans. We consider an S-IoT system consisting of a sender-receiver pair connected by a data channel and a feedback channel and capture its dynamics using a Markov Decision Process (MDP). To extend battery life, the sender has to decide on deep-sleep durations. Deep-sleep scheduling is the primary lever to reduce energy consumption, since sleeping devices consume only a fraction of their idle power. By choosing its deep-sleep duration online, the sender has to find a trade-off between energy consumption and data quality degradation at the receiver, captured by a weighted sum of costs. We quantify data quality degradation via the recently introduced Goal-Oriented Tensor (GoT) metric, which can take both age and content of delivered data into account. We assume a Markovian observed process and Markov channels with time-varying delay and erasure rates. The challenge is that content awareness of the GoT metric makes periodic transmissions inherently inefficient. Additionally, optimal sleep durations depends on the (unknown) future states of the observed process and the channels, both of which must be inferred online. We propose a novel algorithm using probabilistic simulation-based optimization (PSBO). With PSBO, the sensor forecasts future states based on estimated transition probabilities, and uses these forecasts to select the optimal deep-sleep duration. Extensive simulations demonstrate the strong performance of PSBO across diverse conditions. In S-IoT hardware experiments, PSBO reduces costs by 59% versus a threshold-based solution and by 89% versus Q-learning.

Device-to-Device (D2D) relaying is considered a promising technology to increase the data rates in next generation networks. We consider the D2D relay selection problem in which cell-edge mobile devices (CMDs), having bad channel conditions to the access point (AP), may forward their data to the AP via relaying mobile devices (RMDs) with better channel conditions. For this purpose, the RMDs sacrifice a fraction of their communication bandwidth and energy to relay the data of the CMDs. A key challenge in D2D relaying is to increase the willingness of RMDs to act as relays to CMDs. To overcome this challenge, considering the technical perspective of bandwidth allocation and transmit power optimization is not enough. In addition, the social perspective is important with the users' different individual motivations to participate, such as strong social relationships between CMDs and RMDs and an altruistic motivation to help CMDs. In this paper, we address the D2D relay selection problem with a socio-technical approach, i.e., we consider the RMDs as individual decision makers whose participation decision is influenced by its preferences regarding technical and social motivations. Furthermore, we formulate a relay selection problem to maximize the expected capacity under the a priori unknown decisions of the RMDs regarding their participation. To solve this problem, we propose a novel decentralized, preference-aware D2D relay selection algorithm, termed DPA-D2D, which is based on game theory. We show that the CMDs' capacity gain is more than 40 % higher compared to state-of-the-art D2D relay selection algorithms.

In this paper, we consider multiple multi-functional unmanned aerial vehicles (UAVs) with sensing, communication, and computation capabilities to simultaneously perform sensing and provide computing services to ground devices (GDs). The UAVs process both, the sensed data and the tasks offloaded by the GDs, onboard. To enable a scalable decentralized design, we formulate the task offloading problem as a matching game. In each time slot, each GD proposes to a UAV for task offloading, aiming to maximize its utility, defined as the time saved through offloading compared to local computing. Each UAV accepts to serve a subset of proposing GDs while handling its own sensing task as well. To balance resource allocation between offloaded and sensing tasks, each UAV aims to maximize its utility defined as a weighted sum of the total time saved for GDs minus the extra time incurred in processing its own sensing data when sharing partial computing resources with GDs compared to utilizing the entire computing resources for its sensing task. However, in practice, GDs may lack prior information about their channel conditions, the UAVs' communication and computing resources, and the task offloading strategies of other GDs, therefore, solving the task offloading matching game is challenging. To tackle this, we propose a novel gradient-based Multi-Armed-Bandit (GMAB) algorithm for task offloading, enabling GDs to learn and coordinate their offloading strategies in a decentralized manner. Simulation results show that the proposed GMAB algorithm outperforms several baseline task offloading schemes in terms of improving both, GD-side and UAV-side utility, by up to 34.1 % and 37.2 %, respectively.

This paper investigates UAV-aided wireless communications with multiple users while utilizing a rotatable dipole array at the UAV. We jointly optimize user scheduling, array steering, and beamforming to minimize the UAV's mission completion time, i.e., the time required to deliver a specified data volume from the UAV to each user. To tackle the resulting nonconvex mixed-integer nonlinear program (MINLP) problem, we identify a hidden convexity in the optimization of continuous variables for given values of the discrete variables. Leveraging this result, we reformulate the original problem as a multi-stage dynamic programming (DP) problem and characterize its optimal solution via the Bellman optimality equation. We further propose a novel low-complexity one-step lookahead rollout (OSLR) algorithm based on approximate DP and semidefinite programming (SDP) jointly to optimize the discrete and continuous variables, respectively. Simulation results demonstrate that our proposed algorithm achieves significant reductions in the UAV's mission completion time compared to two baseline schemes, even when only a small number of dipoles are deployed at the UAV.

In this paper, we investigate the efficient compression, transmission, and processing of high-volume sensor data collected from physical systems (PSs) to enable timely and accurate digital twin (DT) synchronization over resource-limited wireless networks. The sensors distributed in the PSs compress their sensed data prior to transmission to a base station (BS) for DT updating. However, due to the lack of a centralized decision-making unit, each sensor independently selects its own compression ratio to balance between its transmission time and compression overhead. To coordinate the compression across the sensors and ensure efficient DT updating globally, we formulate the problem as a Stackelberg game, where the BS acts as the leader for allocating communication/computing resources, while the sensors act as followers to optimize their data compression. By deriving each sensor’s best response (BR), we further propose a low-complexity iterative algorithm to compute the Stackelberg equilibrium. Simulation results show that incorporating data compression significantly reduces DT synchronization time. Furthermore, the proposed algorithm achieves near-optimal performance, closely matching the centralized joint optimization scheme with almost no price of anarchy.

Multi-access edge computing (MEC) refers to deploying computation resources, known as cloudlets or edge servers, near the edge of the mobile network. Services like augmented reality (AR) benefit from MEC by service placement, which refers to installing service-specific software and allocating resources on cloudlets. Service placement in MEC improves service quality and potentially reduces costs compared to centralized cloud computing approaches. The main stakeholders in MEC are infrastructure providers (IPs), who manage the MEC infrastructure, and service providers (SPs), who offer services to users. Both have unique technical and economic perspectives, such as resource demands, resource availability, and costs. Information asymmetries exist as only IPs have access to information about their resources, and only SPs have information about service usage and resource demands. This work addresses challenges of service placement in MEC from a multi-stakeholder, techno-economic perspective. We introduce a model including the stakeholders’ technical and economic goals and information asymmetries. To solve this problem efficiently, we propose a multi-stakeholder bargaining mechanism, termed Nash Backward Induction with Linear Equilibrium Strategies (NBI-LES). In a case study with 544 users and 16 SPs, we achieve$\text{79}{\%}$of the optimal reduction in traffic given by a centralized optimal service placement strategy.

In this paper, we consider bistatic integrated sensing and communication (ISAC) enabled by an unmanned aerial vehicle (UAV) and a ground sensing receiver. The UAV employs a rotatable array of patch antennas to communicate with multiple ground users and simultaneously probe multiple targets, while using the same transmit signals. To minimize the UAV’s load and transceiver complexity, the sensing receiver collects and processes echoes of the probing signals for e.g. detecting the activities of the targets. We jointly optimize transmit beamforming and array steering at the UAV to maximize the minimum received signal-to-interference-plus-noise ratio (SINR) for all targets while ensuring quality-of-service (QoS) for each communication user. Given the highly nonconvex nature of the formulated problem and the difficulty in obtaining its optimal solution, we propose a low-complexity suboptimal algorithm based on proximal block coordinate descent (BCD). This algorithm can exploit the underlying structure of the problem by decomposing it into several convex and manifold optimization subproblems, which are then alternately solved using off-the-shelf solvers. Simulation results demonstrate that by jointly optimizing transmit beamforming and array steering, the rotatble patch antenna array significantly enhances the sensing performance of UAV-aided bistatic ISAC while guaranteeing communication QoS, even in scenarios with limited number of transmit antennas and undesirable line-of-sight (LoS) interference from the ISAC transmitter.

Status Update Systems (SUSs) are central components in applications like environmental sensing or smart cities. They consist of a sender monitoring a remote process and sending the sensed information to a receiver. The sender aims to deliver fresh information about the monitored process's state to allow the receiver to timely respond to the process's changes. In SUSs, the sender is usually battery operated. Therefore, to increase the available energy we consider Energy Harvesting (EH). Moreover, as at the receiver the information transmitted by the sender is only relevant when the process's state changes, we measure the information's freshness using Age of Incorrect Information (AoII). Finding the optimal transmission strategy at the sender that minimizes the AoII requires perfect system knowledge, i.e., the behavior of the monitored process, the channel quality, and the available energy. However, in real applications this knowledge is usually not available. To overcome this challenge, we first establish the optimality of threshold-based policies for AoII minimization in SUSs with EH capabilities by proving that there exists an AoII value depending on the observed state of the monitored process, the battery level and the receiver's estimation of the monitored process's state beyond which transmitting is preferable over idling. Next, we exploit the threshold-based policies' structure and deploy a learning algorithm based on Finite-Difference Policy Gradient (FDPG). Our proposed approach finds the AoII thresholds without requiring perfect system knowledge. Simulations show that our approach outperforms reference algorithms by at least 20% and efficiently learns near-optimal policies for AoII minimization.

In Mobile Crowdsensing (MCS) a mobile crowd-sensing platform (MCSP) collects sensing data from mobile units (MUs) in exchange for payment. The MCSP broadcasts a list of available sensing tasks. Based on this list, each MU solves a task proposal problem to decide which task it is willing to perform and sends a proposal to the MCSP. Based on the MUs' proposals, the MCSP solves a task assignment problem. There are two challenges when finding efficient task proposal strategies for the MUs and an efficient task assignment strategy for the MCSP (i) The techno-economic perspective of MCS: From the technical perspective, MCS should maximize the data quality while minimizing time and energy consumption. From the economic perspective, there are two sides, the MUs and the MCSP which act as selfish decision-makers, who aim at maximizing their own income. (ii) Incomplete information at two sides: Initially, the MCSP does not know the expected data quality and the MUs do not know the expected effort required for task completion. To overcome these challenges, we propose a novel Two-Sided Learning (TSL) approach. At the MU side, TSL is based on an innovative gradient-based multi-armed bandit solution to maximize the MUs' utility under incomplete information about the strategies of other MUs. At the MCSP side, a learning strategy is used to find the task assignment strategy that maximizes its utility. Simulation results show that TSL achieves near-optimal social welfare, which is the sum of MUs' and MCSP's utilities, and a near-optimal energy efficiency.

In this paper, we investigate unmanned aerial vehicle (UAV)-aided integrated sensing and communication (ISAC) by employing a uniform linear array (ULA) of patch antennas onboard the UAV. The three-dimensional (3D) directional gain pattern of the patch antennas and the array beamforming are jointly exploited to facilitate efficient ISAC signal transmission for sensing multiple targets and communicating with multiple users. Assuming the positions of the targets and users are known, we jointly optimize the beamforming and 3D array-steering of the patch antenna array to maximize the sum of transmit beam-pattern gains towards the targets while guaranteeing quality-of-service (QoS) for each communication user. The formulated optimization problem is nonconvex and generally intractable. Exploiting the special structures underlying the problem, we propose a low-complexity iterative algorithm based on proximal block coordinate descent (BCD) to decompose the problem into several convex and manifold optimization subproblems and iteratively solve them. Simulation results verify the benefits of joint beamforming and 3D steering optimization for UAV-aided ISAC using patch antenna array, particularly when the communication QoS requirements are stringent.

Status Update System (SUS) are monitoring applications of Internet of Things (IoT). They are formed by a sender that monitors a remote process and sends status updates to a receiver over a wireless channel. For successful monitoring, the sender must keep the status updates at the receiver fresh. This freshness is generally measured using the Age of Information (AoI) metric. The aim of the sender is to find a monitoring and transmission strategy that minimizes the AoI. To find the optimal strategy, the sender needs to accurately track the AoI at the receiver, i.e., it needs to perfectly know whether a transmitted status update is correctly received or not. This knowledge can be achieved by using a feedback channel between receiver and sender to send acknowledge (ACK) or negative acknowledge (NACK) messages. However, in real applications, the feedback channel is not perfect, and the transmission of ACK/NACK messages might fail. This means, the monitoring and transmission decisions have to be made under uncertainty about the receiver's AoI. To overcome this challenge, we introduce the concept of a socalled belief distribution and propose a joint monitoring and transmission strategy at the sender based on reinforcement learning. Our approach, termed Belief Learning, exploits the belief distribution to minimize the AoI at the receiver. Through numerical simulations we show that Belief Learning enables the sender to achieve near-optimal performance with respect to the perfect feedback channel case.

Digital twin (DT) applications usually need to be synchronized in real time with the state of the physical system (PS). This process includes both synchronizing the data collected by sensors in the PS to a server and updating the DT at the server in adaptation to the dynamics of the PS. However, communicating with power- and rate-limited sensors and processing high-volume sensed data with limited computing resources present significant challenges for DT synchronization. In this paper, we tackle both bottlenecks by proposing a joint communication and computing design for DT synchronization. In particular, we exploit buffering at an aerial cluster head of the sensors and enable buffer-aided (BA) relaying to increase the communication throughput of the sensors during data synchronization. Moreover, we adopt a novel stream computing scheme, which allows for DT updating in parallel with data synchronization, to accelerate DT synchronization. To maximize the performance of the proposed approach, we jointly optimize the trajectory of the aerial relay and the communication and computing resource allocation for minimizing the DT synchronization time. The formulated problem is a mixed-integer nonconvex problem, which is generally intractable. To tackle this challenge, we propose a low-complexity two-layer iterative suboptimal algorithm. Our simulation results show that the DT synchronization time can be significantly reduced by up to 43.8%, through stream computing and the joint optimization of the relay's trajectory and the communication/computing resource allocation.

In this paper, we consider unmanned aerial vehicle (UAV)-aided downlink communication using a rotatable array of directional antennas such as half-wavelength dipoles. The antenna array is mounted onboard the UAV using a gimbal and can be flexibly rotated in the three-dimensional (3D) space. As such, the directional gain pattern of dipoles and the array beamforming can be both best exploited to facilitate efficient information transmission to multiple low-priority (or secondary) users while mitigating co-channel interference for another high-priority (or primary) user coexisting with but not served by the UAV. Assuming that the direction of the high-priority user is known, we jointly optimize the electrical beamforming and mechanical steering of the rotatable dipole array for maximizing the weighted sum-rate achievable by the low-priority users while minimizing the interference power radiated in the direction of the high-priority user. The formulated optimization problem is nonconvex and generally intractable. Exploiting its special problem structure, we decompose the problem into several convex and manifold optimization subproblems and further propose a low-complexity iterative algorithm based on proximal block coordinate descent for solution. Our simulation results verify the convergence of the proposed algorithm. Moreover, compared with systems employing non-rotatable and rotatable arrays of isotropic antennas, the rotatable dipole array can flexibly adjust the gain patterns in different azimuth and elevation angles to increase the communication throughput by up to 300% and 77%, respectively.

The problem of coordinated data collection is studied for a mobile crowdsensing (MCS) system. A mobile crowdsensing platform (MCSP) sequentially publishes sensing tasks to the available mobile units (MUs) that signal their willingness to participate in a task by sending sensing offers back to the MCSP. From the received offers, the MCSP decides the task assignment. A stable task assignment must address two challenges: the MCSP’s and MUs’ conflicting goals, and the uncertainty about the MUs’ required efforts and preferences. To overcome these challenges a novel decentralized approach combining matching theory and online learning, called collision-avoidance multi-armed bandit with strategic free sensing (CA-MAB-SFS), is proposed. The task assignment problem is modeled as a matching game considering the MCSP’s and MUs’ individual goals while the MUs learn their efforts online. Our innovative “free-sensing” mechanism significantly improves the MU’s learning process while reducing collisions during task allocation. The stable regret of CA-MAB-SFS, i.e., the loss of learning, is analytically shown to be bounded by a sublinear function, ensuring the convergence to a stable optimal solution. Simulation results show that CA-MAB-SFS increases the MUs’ and the MCSP’s satisfaction compared to state-of-the-art methods while reducing the average task completion time by at least 16%.

Enabling quantum switches (QSs) to serve requests submitted by quantum end nodes in quantum communication networks (QCNs) is a challenging problem due to the heterogeneous fidelity requirements of the submitted requests and the limited resources of the QCN. Effectively determining which requests are served by a given QS is fundamental to foster developments in practical QCN applications, like quantum data centers. However, the state-of-the-art on QS operation has overlooked this association problem, and it mainly focused on QCNs with a single QS. In this paper, the request-QS association problem in QCNs is formulated as a matching game that captures the limited QCN resources, heterogeneous application-specific fidelity requirements, and scheduling of the different QS operations. To solve this game, a swap-stable request-QS association (RQSA) algorithm is proposed while considering partial QCN information availability. Extensive simulations are conducted to validate the effectiveness of the proposed RQSA algorithm. Simulation results show that the proposed RQSA algorithm achieves a near-optimal (within 5%) performance in terms of the percentage of served requests and overall achieved fidelity, while outperforming benchmark greedy solutions by over 13%. Moreover, the proposed RQSA algorithm is shown to be scalable and maintain its near-optimal performance even when the size of the QCN increases.

Mobile Crowdsensing (MCS) is a promising distributed sensing architecture that harnesses the power of sensors on mobile units (MUs) to perform sensing tasks. The MCS is a dynamic system in which the requirements of the sensing tasks, the MUs' conditions and the available resources change over time. The performance of an MCS system depends on the selection of the MUs participating in each sensing task. However, this is not a trivial problem. An optimal task participation strategy requires non-causal knowledge about the dynamic MCS system, a requirement that cannot be fulfilled in real implementations. Moreover, centralized optimization-based approaches do not scale with increasing number of participating MUs and often ignore the MUs' preferences. To overcome these challenges, in this paper we propose a novel multi-agent federated deep reinforcement learning algorithm (FDRL-PPO) which does not need this perfect non-causal knowledge, but instead, enables the MUs to learn their own task participation strategies based on their own conditions, available resources, and preferences. Through federated learning, the MUs share their learned strategies without disclosing sensitive information, enabling a robust and scalable task participation scheme. Numerical evaluations validate the effectiveness and efficiency of FDRL-PPO in comparison with reference schemes.

Based on the Age of Information as an optimization criterion, proposals for further age-based metrics have been made in recent years in the Internet of Things (IoT) domain. The research community's great interest in age-based metrics for point-to-point wireless communication has led to a multitude of different scenarios being investigated, including energy optimization, sensing, and risk-sensitivity. All these scenarios involve a sender-receiver pair and revolve around finding appropriate times for the sender to communicate status updates to the receiver. We propose a unified and modular framework that represents the aforementioned options in various combinations and enables transferring solutions developed for specific cases to a variety of scenarios. We generalize an existing optimization approach, which decides to transmit based on a threshold for the age-based metric, using this framework. We develop a unified and extended Q-learning-based algorithm with mechanisms to learn suitable solutions for all scenarios derived from our framework. These mechanisms accelerate the learning process and result in improved algorithmic performance compared to traditional Q-learning. Furthermore, we demonstrate the effectiveness of our solution in numerical simulations. Our unified solution outperforms several reference schemes in terms of age-based metrics, energy consumption, and risk. We present our findings as a starting point to investigate transmission strategies for more general settings with a more efficient approach.

Base station (BS) selection for task offloading in Mobile Edge Computing (MEC) is a challenging problem due to the dynamic nature of MEC systems. The wireless channel as well as the load of BSs are stochastic quantities that can change in a statistically non-stationary fashion. Moreover, the computation capabilities of the BSs are heterogeneous. As the dynamic behaviour of a MEC system is, in practical scenarios, not known in advance, deciding where to offload has to be done under uncertainty about the MEC system and considering its non-stationary and heterogeneous characteristics. This paper investigates latency minimization in MEC with heterogeneous BSs. In order to meet low latency demands, a mobile unit (MU) has to quickly identify the best BS for offloading different computation tasks while facing uncertainty about the non-stationary system dynamics. To solve this problem, we propose a novel piece-wise stationary contextual Multi-Armed Bandit (MAB) algorithm that treats different task types as context and detects non-stationary changes in the BSs' performance. With the use of extensive simulations, we show that our proposed approach outperforms state-of-the-art algorithms, as it quickly adapts to changes in the MEC system and exhibits no penalty during stationary phases.

In this paper, we consider employing an unmanned aerial vehicle (UAV) equipped with an onboard radar transceiver to localize a ground target at an unknown position. Exploiting the UAV's mobility, we aim to gather line-of-sight (LoS) range measurements from favorable waypoints and improve the ensuing multi-lateration process while estimating the target's location. To this end, we introduce a novel localization error metric, characterized geometrically by the radius of a defined confidence region where the target resides at a predetermined confidence level. Additionally, we investigate robust dynamic optimization of the UAV's trajectory to minimize the defined localization error metric online, utilizing sequentially available but delayed range estimates. The formulated optimization problem belongs to a convex-nonconcave minimax problem, which is generally intractable. To solve this problem, we further propose two iterative online algorithms based on semidefinite programming (SDP) relaxation and alternating/sequential convex optimization techniques. Simulation results show that the proposed online schemes outperform several benchmarks, either in the final localization accuracy or in the rate of decreasing the localization error.

In multi-access edge computing (MEC), mobile users (MUs) can offload computation tasks to nearby computational resources, which are owned by a mobile network operator (MNO), to save energy. In this work, we investigate two important challenges of task offloading in MEC: (i) The techno-economic interactions of the MNO and the MUs. The MNO faces a profit maximization problem, whereas the MUs face an energy minimization problem. (ii) Limited information at the MUs about the MNO's communication and computation resources and the task offloading strategies of other MUs. To overcome these challenges, we model the task offloading problem as a matching game between the MUs and the MNO including their techno-economic interactions. Furthermore, we propose a novel Collision-Avoidance Task Offloading Multi-Armed-Bandit (CA-TO-MAB) algorithm, that allows the MUs to learn the amount of available resources at the MNO and the task offloading strategies of other MUs in an online, fully decentralized way. We show that by using CA-TO-MAB, the cumulative revenue of the MNO can be increased by 25% and, at the same time the energy consumption of the MUs can be reduced by 6% compared to state-of-the-art online learning algorithms for task offloading. Furthermore, the communication overhead can be reduced by 55% compared to a non-learning game-theoretic approach.

This paper considers a buffer-aided unmanned aerial vehicle (UAV) serving as an aerial relay for communication between a base station (BS) and multiple ground users (GUs). Thanks to its flexible mobility, the UAV can achieve high-rate communications with the GUs/BS by buffering the communication data and exploiting the favorable channel conditions on its flight trajectory for transmission and reception. However, the size of the buffer is limited in practice, which may severely restrict the throughput gains enabled by buffering. Whether it is beneficial to consider buffering at UAVs with a small buffer is an open research problem, which is tackled in this paper. Assuming a finite buffer mounted at the UAV, we consider joint optimization of the resource allocation, data buffering, and trajectory planning for minimizing the UAV's completion time required for delivering a given data volume from each GU to the BS, where the resource allocation contains power and bandwidth allocation. The formulated optimization problem is a mixed-integer nonconvex program, which is generally intractable. To solve this problem, we propose a novel low-complexity two-layer iterative suboptimal algorithm based on bisection search and penalty successive convex approximation (PSCA). Note that minimizing the completion time in turn maximizes the average throughput, i.e., the amount of data delivered from the GUs to the BS per unit of time. Simulation results show that the buffer with sufficiently large size can increase the UAV's average throughput by up to 123.8% compared to without buffering. Moreover, with our proposed scheme, 63.2% of the throughput gains can already be achieved using only a small buffer.

Wireless backhauling at millimeter-wave frequencies (mmWave) in static scenarios is a well-established practice in cellular networks. However, highly directional and adaptive beamforming in today’s mmWave systems have opened new possibilities for self-backhauling. Tapping into this potential, 3GPP has standardized Integrated Access and Backhaul (IAB) allowing the same base station to serve both access and backhaul traffic. Although much more cost-effective and flexible, resource allocation and path selection in IAB mmWave networks is a formidable task. To date, prior works have addressed this challenge through a plethora of classic optimization and learning methods, generally optimizing a Key Performance Indicator (KPI) such as throughput, latency, and fairness, and little attention has been paid to the reliability of the KPI. We propose Safehaul, a risk-averse learning-based solution for IAB mmWave networks. In addition to optimizing average performance, Safehaul ensures reliability by minimizing the losses in the tail of the performance distribution. We develop a novel simulator and show via extensive simulations that Safehaul not only reduces the latency by up to 43.2% compared to the benchmarks, but also exhibits significantly more reliable performance, e.g., 71.4% less variance in achieved latency.

We present a novel multi-hop data dissemination protocol for wireless networks that minimizes the total energy consumption across an entire network by minimizing the transmission power at each hop. It is based on a game-theoretic model, constructs a spanning tree topology in a decentralized manner, and is usable in practice. We evaluate the protocol via simulation and a pratical implementation on a testbed of 75 Raspberry Pis, demonstrating that a total energy reduction of up to 90% can be achieved compared to a simple broadcast protocol.

The optimal offloading of tasks in heterogeneous edge-computing scenarios is of great practical interest, both in the selfish and fully cooperative setting. In practice, such systems are typically very large, rendering exact solutions in terms of cooperative optima or Nash equilibria intractable. For this purpose, we adopt a general mean-field formulation in order to solve the competitive and cooperative offloading problems in the limit of infinitely large systems. We give theoretical guarantees for the approximation properties of the limiting solution and solve the resulting mean-field problems numerically. Furthermore, we verify our solutions numerically and find that our approximations are accurate for systems with dozens of edge devices. As a result, we obtain a tractable approach to the design of offloading strategies in large edge-computing scenarios with many users.

Mobile crowd-sensing (MCS) is an upcoming sensing architecture which provides better coverage, accuracy, and requires lower costs than traditional wireless sensor networks. It utilizes a collection of sensors, or crowd, to perform various sensing tasks. As the sensors are battery operated and require a mechanism to recharge them, we consider energy harvesting (EH) sensors to form a sustainable sensing architecture. The execution of the sensing tasks is controlled by the mobile crowd-sensing platform (MCSP) which makes task allocation decisions, i.e., it decides whether or not to perform a task depending on the available resources, and if the task is to be performed, assigns it to suitable sensors. To make optimal allocation decisions, the MCSP requires perfect non-causal knowledge regarding the channel coefficients of the wireless links to the sensors, the amounts of energy the sensors harvest and the sensing tasks to be performed. However, in practical scenarios this non-causal knowledge is not available at the MCSP. To overcome this problem, we propose a novel Deep-Q-Network solution to find the task allocation strategy that maximizes the number of completed tasks using only realistic causal knowledge of the battery statuses of the available sensors. Through numerical evaluations we show that our proposed approach performs only 7.8% lower than the optimal solution. Moreover, it outperforms the myopically optimal and the random task allocation schemes.

Mobile crowdsensing (MCS) is a novel approach to increase the coverage, lower the costs, and increase the accuracy of sensing data. Its main idea is to collect sensor data using mobile units (MUs). The sensing is controlled by a mobile crowdsensing platform (MCSP) through the assignment of delay-sensitive sensing tasks to the MUs. Although promising, research effort in MCS is still needed to find task assignment solutions that maximize the coverage while considering the cost incurred by the MCSPs, the preferences of the MUs and the limited communication resources available. Specifically, we identify two main challenges: (i) A task assignment problem which incorporates the MCSP’s utility and the preferences of the MUs. (ii) An underlying communication resource allocation problem formulating the requirement of the timely transmission of sensing results given the limited communication resources. To address these challenges, we propose a novel two-stage matching algorithm. In the first stage, potential MU-task pairs are constructed considering the preferences of the MUs and the utility of the MCSP. In the second stage, the communication resource allocation is done based on potential MU-task pairs from the first stage. Through numerical simulations, we show that our proposed approach outperforms state-of-the-art methods in terms of the MCSP’s utility, coverage and MU’s satisfaction.

In this paper, we consider optimal resource allocation for unmanned aerial vehicle (UAV)-assisted delay-sensitive communications, where a UAV flies to deliver time-critical messages to multiple ground users (GUs) as soon as possible. However, the GUs' locations cannot be perfectly known at the UAV, which may jeopardize the timeliness of message delivery to the GUs. To tackle this challenge, we consider a disk-based fixed-rate transmission scheme at the UAV, which can exploit the mobility of the UAV to facilitate timely communications despite uncertain user locations. Consequently, the system performance hinges on the UAV's flight trajectory and the scheduling of GUs, which are further optimized using a cost minimization approach. Thereby, a general class of delay-aware cost functions, referred to as the cost of delivery delay (CoDD), is defined taking into account the diverse delay-sensitivity requirements of the GUs, and we jointly optimize the user scheduling and the UAV's trajectory for minimization of the sum CoDD of all GUs incurred before the UAV's mission completes. The formulated optimization problem is a nonconvex mixed-integer nonlinear program. Exploiting the underlying structure of this problem, we further propose two novel low-complexity solutions based on approximate dynamic programming (DP). Simulation results show that the proposed schemes can flexibly adjust the UAV's flight trajectory and resource allocation according to the GUs' individual delivery delays, delay tolerance, and location uncertainty, which translates into significantly lower sum CoDD for the GUs than several benchmark schemes.

The importance of vehicular communications has grown significantly in recent years. Potential use cases of vehicular communications are manifold and range from sharing information for driver assistance to entertainment purposes. This means that each connected vehicle has an individual data requirement for the communication infrastructure. However, due to the dynamic wireless environment, the simultaneous fulfillment of such requirements cannot be guaranteed. Therefore, novel solutions should not only consider the requirements of each user but also the risk of not being able to fulfill them. In this paper, we consider a vehicular communication scenario consisting of a base station that serves the vehicles in its coverage area using 5G millimeter wave (mmWave) narrow beams. The problem boils down to finding an optimal policy for the selection of the narrow beams. This should be done carefully, as the choice of the used beams greatly impacts the performance. For this purpose, we propose a risk-aware contextual Multi-Armed Bandit (MAB) online learning algorithm. Using this algorithm, the base station autonomously learns its environment and selects the best set of beams based on the vehicles located in its coverage area. In order to achieve a large risk awareness, this work focuses on two pillars. Firstly, the notion of risk is integrated in the proposed contextual MAB algorithm by exploiting the concepts of Mean-Variance and Conditional Value at Risk for the evaluation of the decisions made by the algorithm. Secondly, we introduce mechanisms that can detect non-stationarities and swiftly adapt to them in order to make the proposed approach robust against volatile environments that violate stationarity assumptions. By using extensive simulations, the effectiveness of the aforementioned approaches are proven numerically.

Mobile edge computing based on cloudlets is an emerging paradigm to improve service quality by bringing computation and storage facilities closer to end users and reducing operating cost for infrastructure providers (IPs) and service providers (SPs). To maximize their individual benefits, IP and SP have to reach an agreement about placing and executing services on particular cloudlets. We show that a Nash Bargaining Solution (NBS) yields the optimal solution with respect to social cost and fairness if IP and SP have complete information about the parameters of their mutual cost functions. However, IP and SP might not be willing or able to share all information due to business secrets or technical limitations. Therefore, we present a novel iterative bargaining approach without complete mutual information to achieve substantial cost reductions for both IP and SP. Furthermore, we investigate how different degrees of information sharing impact social cost and fairness of the different approaches. Our evaluation based on the mobile augmented reality game Ingress shows that our approach achieves up to about 82% of the cost reduction that the NBS achieves and a cost reduction of up to 147% compared to traditional Take-it-or-Leave-it approaches, despite incomplete information.

This work studies the application of a reconfigurable intelligent surface (RIS) in a cloud radio access network (C-RAN) targeting the reduction of resource usage while providing adequate capacity. We investigate if an RIS can contribute to improve the trade-off between the downlink system spectral efficiency (SE) and energy consumption of a multi-base-station (BS) multi-user single-RIS setup by means of link activation, radiated power control, and operational power mode decisions that can benefit from RIS-enhanced radio channels. For this purpose, we optimize the activations jointly with BS and RIS beamforming for maximum energy efficiency (EE) under a centralized approach and subject to SE, power, fronthaul capacity, and RIS phase-shift constraints. The associated mixed-boolean non-linear problem is solved using monotonic and semidefinite relaxation methods integrated in a Branch-Reduce-and-Bound procedure. Simulations show that the RIS helps to increase the EE of a C-RAN w.r.t. its non-RIS-aided and fully-connected versions by 30% and 80%, respectively.

Ultra-Reliable Low-Latency Communications (URLLC) is an important part of emerging 5G and 6G networks which enables mission-critical applications like autonomous driving. These novel applications depend on the error-free delivery of short messages before an application-specific deadline, which is challenging in a fast-changing environment. In this work, we consider a wireless fading downlink channel shared for the transmission of periodically arriving messages for multiple mobile units (MUs). The message sizes, deadlines and the period of message arrival are MU-specific. The message for a MU can be split into smaller data packets, so that multiple unreliable transmissions can be combined to achieve a reliable transmission. We formulate an infinite time horizon Markov Decision Process (MDP) for the average timely throughput, and show that the MDP is periodic. We propose a novel two-timescale scheduling solution, which incorporates the uncertainty of the channel in an inter-frame problem and errors caused by short-packet coding in an intra-frame problem. Through numerical simulations, we show that the proposed approach outperforms State-of-the-Art scheduling algorithms in terms of timely throughput.

In this paper, the transmission energy for reliable communications with short packets and low latency requirements, e.g. for control applications, is minimized. Since the dynamics of the agents determine the allowed latencies for receiving control inputs, the requirements on latency and allowable packet error rate are individual, depending on the machine type. We consider a centralized environment with a single controller transmitting control commands wireless to multiple agents with given latency requirements. Also, the channel conditions are individual for each agent. Therefore, the optimal time-frequency resource allocation is derived for continuous time-frequency resource allocation. Since the resource allocation in OFDM systems like 5G is discrete, an algorithm to select the allocation from a resource grid with different resolutions is proposed and shown to achieve solutions with less than 0.5 dB increase in energy consumption compared to the continuous results. With numerical evaluation, the benefit of a channel-state- and deadline-aware solution is shown for a resource grid based on the 5G frame structure. On average, the gain of the proposed algorithm to an allocation only balancing the number of resources for each agent, as far as the deadlines allow, is about 50% energy saving.

The offloading decisions of K mobile users (MUs) aiming at minimizing the execution delay in a Mobile Edge Computing (MEC) scenario with non-orthogonal multiple access is considered. In this work, we assume a time-varying MEC server computing capacity which exploits additional computing resources that are freed over time, but are not known beforehand. In this setting, the optimal offloading decision depends on the different tasks of MUs, their channel fading processes and the MEC server computing capacity. We first formulate the optimization problem and identify two main challenges, namely, how to exploit the given incomplete knowledge to minimize the delay and how to handle the high dimensionality of the problem. To address these challenges, we propose a novel reinforcement learning (RL) algorithm, termed combinatorial offloading learning (COL). The name stands for its ability to handle the combinatorial nature of the solutions. Exploiting the available knowledge, we learn the offloading decision policy aiming at minimizing the delay. Furthermore, we handle the curse of dimensionality, typical of combinatorial problems, by splitting the learning task, solving K+1 smaller RL problems and using linear function approximation. Through numerical simulations, we show that COL could perform similar to a short term optimal solution with complete information and exhaustive search, and outperforms known strategies like the greedy approach.

The offloading decision making of multiple mobile units in a mobile edge computing (MEC) scenario with multiple access points, each with an attached cloudlet server, is investigated. This scenario bears a joint problem to be solved: the assignment of mobile units to access points, the allocation of the shared communication and computation resources and the offloading decision of each mobile unit. Additionally, the offloading decision is influenced by the availability of the required software for the computation of the task of the mobile unit that may not be available at the cloudlet and has to be downloaded before starting the computation. The proposed offloading decision making and resource allocation problem is formulated as a global optimization problem. To handle higher numbers of mobile units in the network and also achieve a greatly faster convergence, a game theoretic algorithm for the individual offloading decisions is introduced. In numerical simulations, the game based algorithm is shown to deliver results close to the optimum solution, while requiring only a small number of iterations until convergence to a Nash equilibrium.

In this work, an optimum communication resource allocation for sensor value transmission of wireless networked control systems consisting of independent linear subsystems is calculated. To use new service types of upcoming mobile radio standards in an optimal way, the communication resources used by each subsystem have to be adapted to the current individual subsystem requirements. In our case, the scheduling of the transmission of fresh sensor values to the controller of the subsystems is optimized. The quality of scheduling is directly related to the deviation of a subsystem from its control goal. A proof for the optimality of a regular updating scheme for minimizing this deviation is given. Based on this regularity, the optimum communication resource allocation for a limited amount of resources and a set of control systems with different given characteristics is derived. Using the resulting resource shares for the subsystems from this optimization, an algorithm to schedule the actual transmissions during runtime is given. This split approach saves computational effort during system operation. To simplify the evaluation of different scheduling policies, a deterministic calculation of the expected cost with mean absolute error cost function for each subsystem is presented, which removes the need of Monte-Carlo experiments for evaluation.

Millimeter-wave (mmWave) communication plays a vital role in disseminating large volumes of data in beyond-5G networks efficiently. Unfortunately, the directionality of mmWave communication significantly complicates efficient data dissemination, particularly in multicasting, which is gaining more and more importance in emerging applications (e.g., V2X, public safety, massive IoT). While multicasting for systems operating at lower frequencies (i.e., sub-6GHz) has been extensively studied, they are sub-optimal for mmWave systems as mmWave has significantly different propagation characteristics, i.e., using the directional transmission to compensate for the high path loss and thus promoting spectrum sharing. In this paper, we propose novel multicast scheduling algorithms by jointly exploiting relaying and spatial sharing gains while aiming to minimize the multicast completion time. We first characterize the problem with a comprehensive model and formulate it with an integer linear program (ILP). We further design a practical and scalable semi-distributed algorithm named mmDiMu, based on gradually maximizing the transmission throughput over time. Finally, we carry out validation through extensive simulations in different scales, and the results show that mmDiMu significantly outperforms conventional algorithms with around 95% reduction on multicast completion time.

We study multi-hop broadcast in wireless networks with one source node and multiple receiving nodes. The message flow from the source to the receivers can be modeled as a tree-graph, called broadcast-tree. The problem of finding the minimum-power broadcast-tree (MPBT) is NP-complete. Unlike most of the existing centralized approaches, we propose a decentralized algorithm, based on a non-cooperative cost-sharing game. In this game, every receiving node, as a player, chooses another node of the network as its respective transmitting node for receiving the message. Consequently, a cost is assigned to the receiving node based on the power imposed on its chosen transmitting node. In our model, the total required power at a transmitting node consists of (i) the transmit power and (ii) the circuitry power needed for communication hardware modules. We develop our algorithm using the marginal contribution (MC) cost-sharing scheme and show that the optimum broadcast-tree is always a Nash equilibrium (NE) of the game. Simulation results demonstrate that our proposed algorithm outperforms conventional algorithms for the MPBT problem. Besides, we show that the circuitry power, which is usually ignored by existing algorithms, significantly impacts the energy-efficiency of the network.

We consider a multicellular system in the downlink where at each base station (BS), massive multiple-input multiple-output (MIMO) is utilized via large antenna array. However, big numbers of antennas and users result in a huge channel state information (CSI) overhead and big computational complexity. To cope with these challenges, we propose a two-stage beamforming with power allocation using the signal to leakage and noise ratio (SLNR) as a performance metric since it is known to decouple the optimization problems compared to conventional design methods, allowing independent, distributed processing at each BS. As a first contribution, we derive a deterministic equivalent of the SLNR in the distributed two-stage beamforming context using random matrix theory which provides very accurate approximations in closed-form. The second contribution consists of beamforming design and power allocation. More precisely, in the first stage of the proposed beamforming, an outer beamformer is designed using the deterministic equivalent of the SLNR which requires only statistical CSI and produces an effective system of lower dimension. In the second stage, an inner beamformer applies regularized zero forcing on the low-dimensional effective channel to combat the interference. Additionally, to improve the user fairness, power allocation which maximizes the minimum effective SLNR is proposed and shown to be a convex problem. Simulation results confirm that both the proposed beamforming as well as the combination of the proposed beamforming with power allocation reduce the system dimensionality without degradation of system performance in terms of throughput and user fairness.

Many opportunistic routing protocols for wireless multi-hop networks rely on a fixed channel rate and a fixed priority order to manage the access to the channel by the involved nodes. Thereby, the actual channel capacities in the network are not considered and the diversity of links is not fully exploited. Furthermore, the data buffer of the nodes is not taken into account. In this work, we consider a wireless multihop scenario consisting of multiple cooperative nodes within each hop that share channel resources and adapt their channel rates based on local channel knowledge. A Markov Decision Process (MDP) model is used to derive an optimal resource allocation policy that minimizes the number of required time slots to forward all data packets to the next hop. Furthermore, we propose a state approximation technique that limits the required number of states, but captures the most important features of the problem. Simulation results demonstrate that the proposed policy achieves throughput gains of up to 25% compared to a fixed order transmission policy and up to 49% compared to a unipath approach.

The considered hierarchical multi-level offloading scenario consists of multiple mobiles units (MUs), an access point (AP) with attached cloudlet for mobile edge computing (MEC) and a cloud server. Each user has an arbitrarily splittable task and three possible options for the computation of fractions of this task, which are local computation, offloading to the cloudlet and offloading to the cloud server. We decompose a non-linear central energy minimization problem into subproblems and propose a distributed algorithm that separates the allocation of shared communication and computation resources by the AP from the offloading decisions by the MUs. The AP assigns fractions of the shared bandwidth of the radio access channel, the shared backhaul transmission link to the cloud server and the shared computation frequency at the cloudlet according to offloading decisions of the MUs by solving closed-form expressions which are derived in this paper. Given the available resources, each MU solves a linear optimization problem to calculate the optimal fractions of its task to be computed locally or offloaded. In numerical simulations, the algorithm is proven to be stable and reaching results close to the optimal policy.

A scenario of multiple agents working together to accomplish a common task is considered. Consensus control facilitates the coordination among the agents over time till they accomplish the task. In this paper, we consider formation control using consensus in which agents coordinate to form a circle. To make the coordination possible, agents need to periodically exchange their positions using orthogonal transmissions through a band-limited wireless channel which encounters different transmission delays on different links. To guarantee over all agents stability and convergence, we optimize the bandwidth allocation for equal rate transmission, which maintains the synchronization among agents. Moreover, we minimize the convergence time by optimizing the weights of the consensus algorithm. An ultra-reliable low latency communication between agents is guaranteed by transmitting short packets. The simulation results show that jointly optimizing the confidence weights and bandwidth allocation greatly reduces the convergence time as compared to conventional schemes.

The complexity of the mode selection and resource allocation (MS&RA) problem has hampered the commercialization progress of Device-to-Device (D2D) communication in 5G networks. Furthermore, the combinatorial nature of MS&RA has forced the majority of existing proposals to focus on constrained scenarios or offline solutions to contain the size of the problem. Given the real-time constraints in actual deployments, a reduction in computational complexity is necessary. Adaptability is another key requirement for mobile networks that are exposed to constant changes such as channel quality fluctuations and mobility. In this article, we propose an online learning technique (i.e., CBMoS) which leverages combinatorial multi-armed bandits (CMAB) to tackle the combinatorial nature of MS&RA. Furthermore, our two-stage CMAB design results in a tight model, which eliminates the theoretically feasible but practicality invalid options from the solution space. We prototype the first SDR-based D2D testbed to verify the performance of CBMoS under real-world conditions. The simulations confirm that the fast learning speed of CBMoS leads to outperforming the benchmark schemes by up to 132%. In experiments, CBMoS exhibits even higher performance (up to 142%) than in the simulations. This stems from the adaptability/fast learning speed of CBMoS in presence of high channel dynamics which cannot be captured via statistical channel models used in the simulators.

Countless novel approaches to communication protocols, overlay networks, and distributed middleware are published every year, yet the adoption of such novel findings in the global Internet landscape progresses at a slow pace. Many of such new communication mechanisms excel (only) under specific deployment conditions, while user mobility and application usage patterns lead to dynamic operation conditions. This mismatch is one reason that makes a wide deployment of new specialized mechanisms particularly hard as observed, for example, for multipath transport protocol extensions until the emergence of multipath transmission control protocol (TCP). This paper formalizes the concept of Transitions, i.e., a method to instrumentalize adaptivity at runtime in communication systems. It allows to exchange communication mechanisms in a running system to optimize the communication quality. In the following, we describe the building blocks required to: 1) capture the features and relations within a communication system and 2) express and optimize the decision making process in such a system. We show how this concept maps intuitively to the Internet model which makes a protocol-independent deployment of applications feasible in the future Internet.

A data dissemination scenario is considered. The transmitter harvests energy from the environment and uses it to transmit individual data to multiple receivers. We consider a realistic scenario in which only causal knowledge regarding the energy harvesting, the channel fading and the data arrival processes is available. Our goal is to find a power allocation policy aiming at maximizing the throughput. We propose a two-layer reinforcement learning algorithm which divides the learning task into two sub-tasks, namely, how much power to use in each time interval and how to split the power among the data to be transmitted. By dividing the task, we increase the learning speed as compared to the standard reinforcement learning algorithms Q-Iearning and SARSA. Moreover, the proposed algorithm outperforms reference policies that deplete the battery in every time interval.

In this paper, we propose a hierarchical beamforming approach for the downlink of a multicellular multiuser system with cloud radio access network (CRAN) which requires reduced fronthaul transmissions while benefiting from the centralized functionality of the cloud. We design the hierarchical beamforming as a concatenation of two beamformers. The first one is called inner beamformer and it is designed at the base station (BS) based on the instantaneous channel state information (CSI) of its own users. For this beamformer, the BSs apply regularized zero-forcing (RZF). The second one is called outer beamformer and it is defined at the cloud based on the global but only statistical CSI. All outer beamformers are designed together at the cloud to allow coordination among the BSs. This hierarchical approach provides a combination of distributed and centralized precoding and so it manages the intra- cell as well as the inter-cell interference. Because the cloud has only statistical channel knowledge, we apply random matrix theory to obtain deterministic approximations of the useful power, intra-cell and inter-cell interference power at every user. These approximations are closed-form expressions and allow the cloud to optimize the outer beamformers for diverse objectives. In this work, we propose a low complexity iterative outer beamformer design based on block diagonalization which maximizes the system sum rate. Simulations show that the deterministic approximations are tight and that the proposed hierarchical beamforming achieves a higher sum rate compared to the conventional distributed RZF.

Millimeter-Wave (mmWave) bands have become the de-facto candidate for 5G vehicle-to-everything (V2X) since future vehicular systems demand Gbps links to acquire the necessary sensory information for (semi)-autonomous driving. Nevertheless, the directionality of mmWave communications and its susceptibility to blockage raise severe questions on the feasibility of mmWave vehicular communications. The dynamic nature of 5G vehicular scenarios, and the complexity of directional mmWave communication calls for higher context-awareness and adaptability. To this aim, we propose the first online learning algorithm addressing the problem of beam selection with environment-awareness in mmWave vehicular systems. In particular, we model this problem as a contextual multi-armed bandit problem. Next, we propose a lightweight context-aware online learning algorithm, namely FML, with proven performance bound and guaranteed convergence. FML exploits coarse user location information and aggregates received data to learn from and adapt to its environment. We also perform an extensive evaluation using realistic traffic patterns derived from Google Maps. Our evaluation shows that FML enables mmWave base stations to achieve near-optimal performance on average within 33 minutes of deployment by learning from the available context. Moreover, FML remains within ~ 5% of the optimal performance by swift adaptation to system changes such as blockage and traffic.

Multicasting popular contents (e.g. HD video streaming) with high data rates is a typical scenario in the future 5G system. In this paper, we investigate the downlink design of multicast Fog Radio Access Network (F-RAN), where each Remote Radio Head (RRH) is equipped with a cache module and has some limited signal processing functionalities. Caching some popular contents at the off-peak hours might not only greatly decrease the service delay, but also, as we are going to show, reduce the energy consumption of the network. Hence, the minimization of network energy consumption is considered within this work. In particular, information compression based (soft) fronthauling and information sharing based (hard) fronthauling are discussed. Based on only imperfect CSI, we propose algorithms for robust network design, where RRH deactivation, power and fronthauling traffic control, beamformer and precoder construction, etc., are jointly optimized for soft and hard fronthauling, respectively.

Millimeter-Wave (mmWave) bands have become the de-facto candidate for 5G vehicle-to-everything (V2X) since future vehicular systems demand Gbps links to acquire the necessary sensory information for (semi)-autonomous driving. Nevertheless, the directionality of mmWave communications and its susceptibility to blockage raise severe questions on the feasibility of mmWave vehicular communications. The dynamic nature of 5G vehicular scenarios and the complexity of directional mmWave communication calls for higher context-awareness and adaptability. To this aim, we propose an online learning algorithm addressing the problem of beam selection with environment-awareness in mmWave vehicular systems. In particular, we model this problem as a contextual multi-armed bandit problem. Next, we propose a lightweight context-aware online learning algorithm, namely fast machine learning (FML), with proven performance bound and guaranteed convergence. FML exploits coarse user location information and aggregates the received data to learn from and adapt to its environment. Furthermore, we demonstrate the feasibility of a real-world implementation of FML by proposing a standard-compliant protocol based on the existing architecture of cellular networks and the forthcoming features of 5G. We also perform an extensive evaluation using realistic traffic patterns derived from Google Maps. Our evaluation shows that FML enables mmWave base stations to achieve near-optimal performance on average within 33 mins of deployment by learning from the available context. Moreover, FML remains within ~ 5% of the optimal performance by swift adaptation to system changes (i.e., blockage, traffic).

In this paper, a multi-group multi-way relaying network is considered. Multiple half-duplex nodes form a group and each node wants to share its message with all other nodes in its group via an intermediate half-duplex relay. The nodes of the whole network are equally distributed over the groups. In this paper, we propose a multicast interference alignment algorithm for a multi-group multi-way relay network, in which the minimum required number of antennas at the relay is independent of the number of nodes per group. This is an important property, because physical antenna resources are limited in general. In order to achieve this, we consider a transmission scheme with several multiple access phases and several multicast phases. In each of the multicast phases we create a MIMO interference multicast channel, by separating the antennas of the relay into clusters. Each of these clusters serves a specific group of nodes and transmits in such a way that the signals transmitted from different clusters are aligned at the non-intended multicast groups. It is shown that the proposed multicast algorithm outperforms a reference algorithm from the literature for a broad range of Signal-to-Noise Ratio (SNR) values, while still requiring less antennas at the relay.

Mobile-edge computation offloading (MECO) is a promising solution for enhancing the capabilities of mobile devices. For an optimal usage of the offloading, a joint consideration of radio resources and computation resources is important, especially in multiuser scenarios where the resources must be shared between multiple users. We consider a multi-user MECO system with a base station equipped with a single cloudlet server. Each user can offload its entire task or part of its task. We consider parallel sharing of the cloudlet, where each user is allocated a certain fraction of the total computation power. The objective is to minimize the completion time of users' tasks. Two different access schemes for the radio channel are considered: Time Division Multiple Access (TDMA) and Frequency Division Multiple Access (FDMA). For each access scheme, we formulate the corresponding joint optimization problem and propose efficient algorithms to solve it. Both algorithms use the bisection-search method, where each step requires solving a feasibility problem. For TDMA, the feasibility problem has a closed-form solution. Numerical results show that the performance of offloading is higher than of local computing. In particular, MECO with FDMA outperforms MECO with TDMA, but with a small margin.

In this work, we investigate cooperative multihop communications based on OFDMA and fountain codes. Data packets are forwarded between clusters of simultaneously transmitting nodes which exploit the diversity of links by sharing the available subcarriers based on local channel conditions. Furthermore, to enable distributed MISO transmissions between clusters, we investigate the potential of distributing data packets within the cluster by single node transmissions prior to the common transmissions aiming to increase the achievable throughput. We present two novel forwarding schemes that utilize distributed MISO transmissions while taking advantage of the properties of fountain codes. The first scheme relies on a full distribution of all data packets within each cluster while the second scheme adapts the extent to which data packets are distributed within the cluster according to local channel conditions. The proposed schemes are compared to an exclusive SISO forwarding scheme in which no additional distribution within each cluster is used.

We study video dissemination in a multi-hop wireless network with a source and several users. The source intends to stream a video to the users of the network. For the sake of energy-efficiency, the video is disseminated through the whole network by the help of some users that forward the video to who other users. In such networks, designing a proper incentive for the forwarding users who consume energy for forwarding the video to others is of high importance. In this paper, we design an incentive mechanism based on a game-theoretic model in which a user is paid by its receiving users in case of forwarding the video to them. The video is layered and a higher quality of experience (QoE) at a receiving user is possible by receiving more layers of the video. A utility function is proposed for every user that captures the perceived QoE at the user and the cost she pays for the video. Moreover, it captures the reward the user receives from others in exchange for forwarding the video to them. The utility function is designed in a way that the users who contribute more in the network, in terms of forwarding the video to others, are paid more. A non-cooperative game is formulated in which every user selfishly maximizes its own utility and determines the number of video layers she prefers to receive. The game is iterative and converges to the Nash equilibrium point. The simulation results demonstrate that the proposed game theoretic model results in a higher QoE at the users as compared to that of a non-incentive video dissemination model.

One of the key challenges in wireless communications is handling the ever growing traffic demand. A large fraction of this traffic is induced by popular content. One way to face this challenge is mobile content caching which improves the system performance by caching content closer to the user. The benefit of content caching depends on the applied content delivery strategy. In this paper, we investigate a scenario where multiple destinations are concurrently requesting a content, which is already cached at mobile devices and then delivered over a wireless multihop network. We propose a content delivery framework which jointly exploits content already cached at mobile devices as well as switching between mechanisms at the physical layer and the network layer in order to optimally deliver the content to all destinations under changing network conditions. In our framework, we use a unified graph model to jointly model the network, the cached content and different mechanisms at the lower three layers. From the unified graph model, an optimization problem is formulated, which is used to find the optimal content delivery strategy. In our numerical evaluation, we show the combined gain of caching and the capability of switching between mechanisms by comparing with conventional schemes which either cannot switch between mechanisms or do not exploit caching.

Content caching in small base stations or wireless infostations is considered to be a suitable approach to improve the efficiency in wireless content delivery. Placing the optimal content into local caches is crucial due to storage limitations, but it requires knowledge about the content popularity distribution, which is often not available in advance. Moreover, local content popularity is subject to fluctuations, since mobile users with different interests connect to the caching entity over time. Which content a user prefers may depend on the user's context. In this paper, we propose a novel algorithm for context-aware proactive caching. The algorithm learns context-specific content popularity online by regularly observing context information of connected users, updating the cache content and observing cache hits subsequently. We derive a sublinear regret bound, which characterizes the learning speed and proves that our algorithm converges to the optimal cache content placement strategy in terms of maximizing the number of cache hits. Furthermore, our algorithm supports service differentiation by allowing operators of caching entities to prioritize customer groups. Our numerical results confirm that our algorithm outperforms state-of-the-art algorithms in a real world data set, with an increase in the number of cache hits of at least 14%.

Energy harvesting (EH) two-hop communications are considered. The transmitter and the relay harvest energy from the environment and use it exclusively for transmitting data. A data arrival process is assumed at the transmitter. At the relay, a finite data buffer is used to store the received data. We consider a realistic scenario in which the EH nodes have only local causal information, i.e., at any time instant, each EH node only knows the current value of its EH process, channel state and data arrival process. Our goal is to find a power allocation policy to maximize the throughput at the receiver. We show that because the EH nodes have local causal information, the two-hop communication problem can be separated into two point-to-point problems. Consequently, independent power allocation problems are solved at each EH node. To find the power allocation policy, reinforcement learning with linear function approximation is applied. Moreover, to perform function approximation two feature functions which consider the data arrival process are introduced. Numerical results show that the proposed approach has only a small degradation as compared to the offline optimum case. Furthermore, we show that with the use of the proposed feature functions a better performance is achieved compared to standard approximation techniques.

Computation offloading is a promising approach for reducing the computational load and extending the battery lifetime of mobile nodes. A network consisting of several wireless nodes accessing the cloud in a multi-hop fashion is considered. In multi-hop networks, offloading a computational task requires relaying the task by the intermediate nodes along the path towards the cloud. If the nodes are autonomous and rational, the intermediate nodes need to be incentivized for forwarding the tasks of other nodes. In this paper, a distributed decision algorithm which determines the set of tasks to be offloaded and the set of tasks to be locally computed for total energy minimization is proposed. Since a task needs to be sequentially forwarded by multiple nodes, each of which decides independently, decision conflicts on forwarding a task can occur. Accordingly, a novel coordination mechanism is proposed by which the forwarding nodes resolve their decision conflicts. In this coordination mechanism, nodes need only to exchange their forwarding decisions to resolve the conflicts. The results show that the proposed distributed algorithm achieves a performance close to the performance of the centralized algorithm.

We consider a data dissemination scenario in a wireless network with selfish nodes. A message available at a source node has to be disseminated through the network in a multi-hop manner. In order to incentivize a node to forward the source's message to others, a forwarding cost is paid to a forwarder by its respective receiver. In the case of multicast transmission, the cost is shared among the receivers using the Shapley value (SV). Moreover, a node may exploit the maximal ratio combining (MRC) technique to receive the message from multiple transmitting nodes. In this paper, we show that in a game theoretic framework, the optimal decision of a node for receiving the message with minimum cost can be achieved by solving a linear optimization problem. In addition, we propose an algorithm by which truthfulness is a dominant strategy for the nodes and thus, fair cost allocation is guaranteed. Simulation results show that our proposed algorithm shares the cost of data dissemination among the nodes of a network in a fair manner. Compared to previous algorithms, the proposed algorithm can reduce the total cost paid by the nodes in the network for receiving messages.

A promising architecture for content caching in wireless small cell networks is storing popular files at small base stations (sBSs) with limited storage capacities. Using localized communication, an sBS serves local user requests, while reducing the load on the macro cellular network. The sBS should cache the most popular files to maximize the number of cache hits. Content popularity is described by a popularity profile containing the expected demand of each file. Assuming a fixed popularity profile of which the sBS has complete knowledge, the optimal content placement problem reduces to ranking the files according to their expected demands and caching the highest ranked ones. Instead, we assume that the popularity profile is varying, for example depending on fluctuating types of users in the vicinity of the sBS, and it is unknown a priori. We present a novel algorithm based on contextual multi-armed bandits, in which the sBS regularly updates its cache content and observes the demands for cached files in different contexts, thereby learning context-dependent popularity profiles over time. We derive a sub-linear regret bound, proving that our algorithm learns smart caching. Our numerical results confirm that by exploiting contextual information, our algorithm outperforms reference algorithms in various scenarios.

Energy harvesting point-to-point communications are considered. The transmitter harvests energy from the environment and stores it in a finite battery. It is assumed that the transmitter has always data to transmit and the harvested energy is used exclusively for data transmission. As in practical scenarios prior knowledge about the energy harvesting process might not be available, we assume that at each time instant only information about the current state of the transmitter is available, i.e., harvested energy, battery level and channel coefficient. We model the scenario as a Markov decision process and we implement reinforcement learning at the transmitter to find a power allocation policy that aims at maximizing the throughput. To overcome the limitations of traditional reinforcement learning algorithms, we apply the concept of function approximation and we propose a set of binary functions to approximate the expected throughput given the state of the transmitter. Numerical results show that the performance of the proposed approach, which requires only causal knowledge of the energy harvesting process and channel coefficients, has only a small degradation compared to the optimum case which requires perfect non-causal knowledge. Additionally, the proposed approach outperforms naïve policies that assume only causal knowledge at the transmitter.

We consider the two-user multiple-input multiple-output X-channel where the transmitters 1, 2 have M1,M2antennas and the receivers 1, 2 have N1,N2antennas, respectively, and study the achievable number of degrees of freedom (DoF) of this network under the assumption of delayed channel state information at the transmitters. For this scenario, Kao and Avestimehr proposed in [1] a transmission scheme, which was conjectured to achieve the number of DoF of this network under the assumption of linear encoding strategies at the transmitters. In our paper, we show that the number of DoF achieved using this scheme is less than previously reported due to the fact that the transmitted information symbols are not always decodable at the receivers. This is shown by performing linear independence analysis of the received linear combinations, where new decodability constraints on the parameters of the transmission scheme are identified for the region of antenna configurations where N1+ N2> max{M1,M2}, M1+ M2> max{N1,N2} and min{M1,M2} > min{N1,N2} hold. Based on the identified constraints, we propose a new transmission scheme, which for the case where the identified constraints are active, achieves the number of DoF greater than that achieved by the transmission scheme proposed in [1], where only the number of decodable information symbols is transmitted.

Dynamic Vertical Sectorization (VS) is a flexible way of cell densification used to enhance system capacity in temporally and spatially varying overload situations. An automated control mechanism in the context of Self Optimizing Network (SON) is required to execute the sectorization only when there is a demand for extra capacity and the sectorization brings benefit in a dynamically varying traffic condition. The SON mechanism requires proper real-time modeling of the system performance with respect to VS activation/deactivation via monitoring the traffic situation and commonly used system parameters.

Video streaming in wireless multihop networks is a challenge due to different capabilities of end-user devices and changing network conditions. This challenge is addressed at the application layer with adaptive video streaming schemes like dynamic adaptive streaming over HTTP (DASH), which is widely applied by content providers. DASH copes with diverse end-user device capabilities by storing several representations of the same video such that DASH can offer a video in multiple qualities to users. Nevertheless, adjustments in DASH are solely taking place at the application layer. Especially in wireless multihop networks, adaptions on the lower layers are of particular importance. Therefore, we propose a novel application-aware cross-layer framework which adapts network support structures at the network layer, performs resource allocation at the medium access layer, switches between communication types at the physical layer and takes into account the properties and requirements of DASH at the application layer. Furthermore, we present a unified graph model, which takes into account the application layer, the network layer, the medium access layer and the physical layer jointly. We formulate a binary linear problem which chooses the optimal video representation for each user and finds the best combination of mechanisms on the lower three layers to optimally distribute the video content through the wireless multihop network. We show that our application- aware cross-layer framework which utilizes transitions leads to gains between 15-83 % compared to conventional approaches that do not switch between different mechanisms.

A major challenge for data transmission in wireless multihop networks is to efficiently utilize the present channel capacity provided by the variety of links within the network. To achieve this, we combine the concept of opportunistic routing with rateless coding and Orthogonal Frequency Division Multiple Access (OFDMA). Opportunistic routing exploits the broadcast nature of wireless transmissions by considering multiple nodes as potential next forwarder of a certain data packet and by selecting the next forwarder after transmission instead of prior to it. Rateless coding enables mutual information accumulation at subsequent nodes of a multihop transmission path which overhear the transmissions. Using a predefined selection of nodes as a support structure, consisting of stages of fully connected transmitters and receivers, allows for a local cooperation among the nodes. Thereby, an unnecessary and wasteful forwarding of data duplicates can be avoided. Furthermore, based on OFDMA, the nodes can exploit the diversity of links within each stage by an adaptive local resource allocation. For the operation of this concept, we propose suitable algorithms for scheduling of data packets and resource allocation and show that the proposed scheme provides significant throughput gains compared to forwarding without overhearing at subsequent nodes and forwarding along a unipath.

In this paper, a partially connected network consisting of multiple subnetworks is considered. Each subnetwork includes a single relay and all nodes connected to this relay. The two-way relaying protocol is employed to achieve a bidirectional communication between the nodes of a communication pair. Only relays which have a connection to both nodes of a communication pair can assist the communication. Throughout the paper, it is assumed that all nodes are served by at least one relay. If a single node of a communication pair is in addition connected to a relay which cannot assist the communication, this node receives only interference and no useful signal from this relay. Such a node suffers from inter-subnetwork interference. In this paper, a closed form algorithm is proposed that minimizes the inter-subnetwork interference power in the whole network. The nodes which suffer from inter-subnetwork interference have to design their transmit filters in order to minimize the inter- subnetwork interference and to achieve a reliable communication with their intended partner node. The simulation results show that the proposed algorithm is able to minimize the inter- subnetwork interference and therefore to improve the sum rate. Under certain conditions, which are derived in this paper, the proposed algorithm achieves an interference free communication and maximizes the degrees of freedom.

In this paper, we propose a novel algorithm to maximize the sum-rate in interference-limited scenarios where each user decodes its own message with the presence of unknown interferences and noise. The problem of adapting the transmit and receive filters of the users to maximize the sum-rate with a transmit power constraint is nonconvex. Our novel approach is to formulate the sum-rate maximization problem as an equivalent multiconvex optimization problem by adding two sets of auxiliary variables. An iterative algorithm, which alternatingly adjusts the system variables and the auxiliary variables is proposed to solve the multiconvex optimization problem and we show that the algorithm converges to a stationary point. The proposed algorithm is applied to a downlink cellular scenario consisting of several cells each of which contains a base station serving several mobile stations. We examine the two cases, with or without several half-duplex amplify-and-forward relays assisting the transmission. A sum power constraint at the base stations and at the relays are assumed. The applicability of our approach to the individual power constraints case is also shown. Finally, we show that the proposed multiconvex formulation of the sum-rate maximization problem is applicable to many other wireless systems in which the estimated data symbols are multiaffine functions of the system variables.

The handover from WiFi to mobile networks is known to lead to TCP connection drops due to changing IP addresses. Multipath TCP (MPTCP), a recent TCP extension, enables a transparent mobile handover by combining subflows on multiple interfaces, such as WiFi and LTE, to one logical connection. MPTCP provides multiple handover modes, which differ in their energy consumption and the performance during the handover. The Full-MPTCP mode uses permanently both WiFi and the mobile network, which increases energy consumption. The Single-Path mode establishes the mobile network connection after the WiFi connection broke, which leads to a short performance degradation. In this paper, we argue that this trade-off is not necessary. We propose to use the available (sensor) information to forecast the mobile handover. This allows switching to the Full-MPTCP mode before the WiFi connection breaks, providing both low energy consumption and high performance during the handover. For a first experimental evaluation, we use a declining WiFi link quality to forecast a handover. Our real world measurements show that both low energy consumption and high performance during the handover are possible at the same time.

Video streaming constitutes the dominant portion of today’s traffic on the Internet and will grow in the coming years. In order to provide for a low cost distribution of bulky video content, Peer-to-Peer (P2P) approaches are a viable way to cut down server bandwidth cost by utilizing user’s upstream bandwidth to redistribute data. However, users need an incentive to participate in such a system. The related work on incentive schemes has focused on using bandwidth contribution as a measure for contribution to the system’s performance, ignoring that user perceived Quality of Experience (QoE) is not necessarily maximized by maximizing bandwidth, but by delivering the right data in the right order. Consequently, this work presents QTrade, a topology agnostic incentive scheme for adaptive P2P video streaming systems based on user-validated video quality metrics. QTrade is evaluated on top of an existing adaptive streaming overlay. The results show that QTrade utilizes bandwidth more efficiently by providing incentive to distribute parts of the video with a high QoE. Moreover, up to 70% less and shorter rebuffering events are observed for cooperative peers while maintaining a 10 to 11 times worse performance in terms of rebuffering events for non-cooperative peers.

Scalable video coding (SVC) can overcome the user heterogeneity issue, e.g., different screen resolutions or different connectivities, in video-streaming. In wireless multihop networks, the performance of SVC is not adequate, because SVC cannot adapt the lower layers. In order to adapt to changing environmental conditions, e.g., network topology, available resources or channel conditions, a cross-layer framework is required. We propose a new application-aware cross-layer framework which utilizes SVC, network structures and communication types at APP, NET, DLL and PHY layers together. Further, our application-aware cross-layer framework performs transitions at different layers to find the best combination of mechanisms. This is achieved by the following steps. First, we apply a graph-based approach to integrate all mechanisms on the different layers in a single graph. Secondly, video layers are modeled in the graph as virtual sources. Thirdly, we perform an optimal mapping from video layer data rates to physical layer rates. Fourthly, we formulate a multi-source sum rate optimization problem which chooses the best video layer distribution among users and the best combination of mechanisms at all layers. Finally, we demonstrate that our application-aware cross-layer framework outperforms current approaches.

A wireless Ad Hoc network consisting of a source and multiple receiving nodes is considered. The source wants to transmit a common message throughout the whole network. The message has to be spread in a multi-hop fashion, as the transmit powers at the source and the nodes are limited. The goal of this paper is to find the multi-hop broadcast tree with a minimum energy consumption in the network. To reach this goal, a new decentralized game theoretic approach is proposed which considers the following two aspects jointly for the first time: Firstly, it optimizes the transmit powers at the source and at the individual intermediate nodes. Secondly, it employs maximum ratio combining at the receiving nodes following the fact that a node can receive several copies of the message from different sources in different time slots. The game is modeled such that the nodes are incentivized to forward the message to their neighbors. In terms of the total transmit energy, the results show that the proposed algorithm outperforms other conventional algorithms.

Computation offloading is an upcoming approach to increase battery life of mobile devices overburdened by resource-consuming applications. In multi-hop networks, computation offloading poses new challenges since intermediate devices are required to relay tasks of others along the path to the server. The decision of a device about whether to offload or not depends thus on the provided energy of relay devices and on the decisions of other offloading devices since relay resources need to be shared. This also implies that for energy minimization, optimal decisions are topology-dependent. This paper introduces a novel theoretical framework for energy minimization of computation offloading in multi-hop wireless networks which formulates the energy minimization problem as a binary linear problem. Proving its equivalence to a multi-dimensional knapsack problem allows us to specify a greedy heuristic, which shows very good performance, with a maximal deviation of less than 5% from the optimal results. From simulations and analytical results for different topologies, we derive under which conditions computation offloading in multi-hop networks is beneficial.

In this paper, a partially connected ad-hoc network with relays is considered. Partially connected means that not all nodes are connected to all relays, but each node may be connected to one or multiple relays. This leads to multiple partially connected subnetworks, where each subnetwork includes a single relay and all nodes connected to this relay. The most challenging part of such a partially connected network is the handling of the nodes which are connected to multiple relays. In this paper, a new closed-form solution to achieve interference alignment in partially connected networks with relays is proposed. It is shown that local channel state information (CSI) is sufficient to perform interference alignment in such a network. The properness condition for the proposed algorithm is derived using the method of counting the dimensions of signal spaces. The new algorithm to perform interference alignment is decomposed into what we call simultaneous signal alignment, simultaneous channel alignment and transceive zero forcing. The simulation results show that the degrees of freedom increase for the considered network in comparison with our reference scheme.

Cell densification is a typical means for capacity enhancement in a certain area. A flexible and dynamic way of cell densification can be provided via sectorization by Active Antenna Systems (AAS). By means of flexible beam forming capabilities, sectorization employs new sub-sector(s) reusing the same frequency band. The higher resource gain has to be paid off with more cell borders and cell edge users suffering from inter-sector interference. In this paper work, enhanced Intercell Interference Coordination (eICIC) technique is applied to coordinate the intra-site co-channel interference between the inner/outer sector in Vertical Sectorization (VS). Simulation results have shown that, eICIC brings significant system performance gain by improving the Signal to Interference plus Noise Ratio (SINR) experience of the users close to the inner/outer sector border regions; mainly, the severely affected regions of the outer sector.

Non-regenerative multi-antenna multi-way relaying is considered. The scenario consists of a group of single-antenna nodes that want to communicate using multiple subcarriers. Each node has a message which every other node in the group should receive. The communications between the nodes are performed via a half-duplex multi-antenna relay station. It is assumed that the nodes have individual resource requirements. A new transmit strategy, in which the individual resources requirements of the nodes are considered, is proposed. To ensure that the relay station has enough spatial dimensions to separate the incoming signals, it is proposed that the number of transmitting nodes per subcarrier is equal to the number of antennas at the relay station. Moreover, it is proposed that the required numbers of subcarriers are calculated according to the buffer level of the nodes, reflecting the amount of data a node has to transmit. To allocate the required number of subcarriers to each node, the proposed transmit strategy performs an efficient subcarrier allocation. Numerical results show that the proposed strategy outperforms existing transmit strategies especially when the number of antennas at the relay station is smaller than the number of nodes.

For transmitting data in scenarios showing a high user density, infrastructure based and multihop Ad hoc communication can be combined to benefit from the reliability of a stable backbone network and the increased coverage of multihop communication. Such scenarios have been investigated from a cross layer perspective in the recent years mainly focusing on pure performance optimization. However, the question of providing incentives to nodes to forward data has largely been ignored in the cross layer domain, even though providing incentives is vital for the network: each node represents a user comparing his or her satisfaction and the cost to decide on his or her participation. A likely reason for the gap in cross layer incentive research is the necessity to model users as well as the network in order to express a user's utility, which requires knowledge in both fields. In order to foster future research in the area of cross layer incentive schemes, this work proposes a general cross layer simulation model combining user and network models. Moreover, an instantiation of the simulation model for the use case of live video broadcasting is presented.

In this paper, multi-group multi-way relaying is considered. There are L groups with K nodes in each group. Each node wants to share d data streams with all the other nodes in its group. A single MIMO relay assists the communications. The relay does not have enough antennas to spatially separate the data streams. However, the relay assists in performing interference alignment at the receivers. In order to find the interference alignment solution, we generalize the concept of signal and channel alignment developed for the MIMO Y channel and the two-way relay channel to group signal alignment and group channel alignment. In comparison to conventional multi-group multi-way relaying schemes [1, 2], where at least R ≥ LKd - d antennas are required, in our proposed scheme, exploiting the multiple antennas at the nodes, only R ≥ LKd - Ld antennas are needed. The number of antennas required at the nodes to achieve this is also derived. It is shown that the proposed interference alignment based scheme achieves more degrees of freedom than the reference schemes without interference alignment.

Active Antenna System (AAS) is an advanced antenna technology that features the ability of advanced beam-forming techniques to provide a great flexibility in cellular network deployment which enables improvements in network capacity and coverage. Conventionally, network dimensioning is done based on busy hour traffic leading to cost-intensive over-dimensioning for most of the time via deploying additional macro and small cells. In AAS, however, varying traffic concentrations can be flexibly handled by dynamic cell densification, e.g. by splitting a sector into smaller “sub-sectors”. Vertical sectorization is a well-known approach where a conventional sector is split vertically in to two, inner and outer sectors, resulting in 3×2 sectors per site for AAS-based tri-sectorized site. In this paper work, an alternative vertical sectorization deployment configuration is presented where the inner sectors build a so called super-cell resulting from transmitting the same cell information in all inner sectors. Investigation results show that the super-cell configuration can mitigate unwanted back and side lobe effects in close proximity of the site and, therefore, provides a significant gain for users in this coverage area.

Corridor-based Routing enables advanced physical layer schemes in Wireless Multihop Networks (WMNs). It widens paths in order to span multiple nodes per hop. As a result, groups of nodes cooperate locally at each hop to forward packets. Recent theoretical work suggests using Orthogonal Frequency-Division Multiple Access (OFDMA) in combination with Corridor-based Routing to improve throughput in WMNs. However, results focus on achievable capacity and do not consider practical issues such as modulation and coding schemes. In this paper, we study OFDMA for corridors in practice and implement it on software-defined radios. We show that OFDMA corridors provide significantly larger throughput gains when considering realistic modulation and coding, achieving up to 2x throughput gain compared to traditional routing not based on corridors.

Corridor-based Routing widens traditional hop-by-hop paths to enable advanced physical layer mechanisms in Wireless Multihop Networks. The key concept is to let groups of nodes cooperate to jointly forward data. Instead of establishing a path formed by a fixed sequence of nodes, the network layer builds a "corridor" from source to destination to allow for such an approach. The corridor is divided into stages, which consist of the aforementioned groups of nodes. Existing mechanisms for Corridor-based Routing focus on the physical layer and assume a routing protocol that builds the corridor. In this paper, we design a corridor construction algorithm and a practical protocol that implements it. In particular, we address in detail the overhead introduced by our protocol, since this is crucial for performance and an open issue of Corridor-based Routing. We implement our approach both in simulation and practice. We obtain the turning point at which corridors become profitable and show that our protocol builds corridors, which enable throughput gains up to 74%.

Today, the performance of routing mechanisms in Wireless Multihop Networks (WMNs) is still limited by the lower layers. While recent cross-layer approaches take advantage of the characteristics of the medium, they are often based on traditional physical layers such as OFDM. State-of-the-art techniques used in one-hop scenarios, such as OFDMA or MIMO, pose a significant challenge in practical multihop networks, since typically Channel State Information (CSI) at the transmitter is required. Due to its volatile nature, disseminating timely CSI in the network is often infeasible. We generalize Corridor-based Routing to enable advanced physical layers in WMNs. Instead of routing packets from node to node, we forward them along fully-connected groups of nodes. As a result (1) CSI only needs to be exchanged locally to enable cooperation in a group, and (2) groups can adaptively choose the best physical layer technique according to CSI. We investigate the benefits of Corridor-based Routing and present a protocol design that enables operation of corridors in WMNs.

Interference alignment (IA) is often considered in the spatial domain in combination with MIMO systems. In contrast, aligning interference in the frequency domain among multiple subcarriers can also benefit single-antenna OFDM-based access networks. It allows for flexible operation on a per-subcarrier basis. We investigate the gains achievable by frequency IA in practice for a scenario with multiple access points and clients. Previous work is predominantly theoretical and focuses on idealized cases where all nodes have the same average signal-to-noise ratio (SNR). On the contrary, in practical networks, nodes typically have heterogeneous SNRs depending on channel conditions, which might have a significant impact on IA performance. We tackle this problem by designing mechanisms that adaptively choose which nodes shall perform IA on which subcarriers depending on current channel conditions. We implement and validate our approach on software-defined radios. To the best of our knowledge, this is the first practical implementation of IA in the frequency domain. Our measurements show that (1) frequency IA is feasible in practice, and (2) choosing appropriate nodes and subcarriers overcomes the main limitations due to heterogeneous SNRs. Our mechanisms enable IA in scenarios where it would be infeasible otherwise, achieving throughput gains close to the 33% theoretical maximum.

In Digital Subscriber Line (DSL) systems, the concept of Virtual Noise (VN) was introduced to improve the protection against fluctuating crosstalk and to increase link stability. In our previous work, we have presented an algorithm that estimates the VN mask and the initialization signal-to-noise-ratio (SNR) margin from noise measurements. In however, perfect bitswapping was assumed. In practical DSL systems bitswapping might be too slow which makes the link sensitive to sudden noise increases. Hence, the outage probability achieved in can only be realized in slowly changing channels. This paper investigates the optimization of the VN mask and the initialization SNR margin presented in in order to improve the robustness against sudden noise increases in terms of outage probability, especially for modems with slow bitswapping procedures.

The Long Term Evolution (LTE) is a new radio access technology (RAT) which is currently being deployed on top of the second generation (2G) or third generation (3G) mobile networks. As a result, user equipments (UEs) will be handed over from one RAT to another. To improve the robustness of the inter-RAT handovers and reduce cost, self-organizing networks (SONs) are used to configure the inter-RAT handover thresholds in an automatic and autonomous way replacing the current manual optimization methods. The handover thresholds can be configured cell-specifically or cell-pair specifically where a dedicated handover threshold is configured with respect to each target cell of handover. However, both optimization paradigms can fail to resolve all mobility failure events in some cells where radio conditions are not stationary along the cell border. In this paper, we propose a new paradigm for configuring and optimizing the inter-RAT handover thresholds based on the locations of UEs in the cell. Simulation results show that the new paradigm outperforms cell-specific and cell-pair specific schemes by resolving additional numbers of mobility failure events.

In this paper, a bi-directional communication between K node pairs is considered. Each of the 2K nodes has multiple antennas. There is no direct link between the nodes. Q half-duplex relays each with R antennas support the communication. Two-way relaying is assumed. The linear transmit filters, relay filters and receive filters are designed iteratively to minimize the mean square error (MMSE) subject to power constraints at the nodes and at the relays. It is guaranteed that the proposed scheme achieves a local minimum of the objective function. The proposed iterative MMSE algorithm can also be applied to a unidirectional communication based on one-way relaying. Simulation results show that at low and moderate signal to noise ratios, the proposed iterative MMSE scheme performs better than other MMSE based and interference alignment based two-way and oneway relaying schemes known in the literature.

A scenario consisting of several single antenna source-destination node pairs communicating through multiple single antenna relays is considered. A two time-slot transmission scheme is considered. In the first time-slot, the source nodes transmit to both the relays and the destination nodes. Both the source nodes and the relays retransmit to the destination nodes in the second time-slot. As the relays cannot decode the received signals, an amplify and forward relaying strategy is assumed. In the present paper, the sum rate maximization problem is tackled. Due to the two transmissions of the source nodes and the two receptions of the destination nodes, there are temporal transmit and receive filters which can be optimized together with the relays' coefficients aiming at maximizing the sum rate. By partially adapting the filters and by introducing two sets of scaling factors, the sum rate maximization problem is reformulated as a tri-convex optimization problem. An iterative algorithm is proposed which maximizes the sum rate and guarantees a local optimum achievement. The results show that the proposed algorithm outperforms the previously proposed interference alignment scheme in all SNRs.

Performance and efficiency of a cellular network can be enhanced by properly adjusting the antenna tilt setting. Antenna tilt is one of the most important radio parameters that determines the service coverage boundary and level of inter-cell-interference in cellular systems. Moreover, tilt tuning is an effective technique in radio network optimization to effect a better load balance among cells for efficient utilization of spare radio resources. The variability of user traffic distribution makes it more challenging to operators to ensure the required service capacity and quality with acceptable capital and operational expenditures. Active Antenna System (AAS) features promise the ability to flexibly handle system capacity by adapting the orientation of the antenna beam. In this paper, the potential performance gains of tilt optimization for differently placed user traffic concentrations are investigated. Simulation results show that users at traffic hot spot areas suffering from resource sharing can achieve significant performance gains from traffic oriented tilt optimization.

A scenario consisting of single antenna source- destination node pairs communicating using multiple single antenna relays is considered. A transmission scheme utilizing two subsequent time-slots assuming a time invariant channel throughout the transmission period is applied to this scenario. In the first time-slot, the source nodes transmit to both the relays and the destination nodes. Both the source nodes and the relays retransmit to the destination nodes in the second time-slot. As the relays can not decode the received signals, amplify and forward relaying is used. By exploiting the direct links between the source and the destination nodes, each destination node observes a two dimensional signal space. In the present paper, the sum mean square error can be written as a convex quadratic function of the relays' gain and part of the temporal filters at the source and the destination nodes. Moreover, closed form solutions for the minimum sum mean square error with interference zero forcing constraints and a total energy constraint are obtained. The results show that the proposed schemes outperform the interference alignment scheme at low and moderate SNRs.

A multi-group multi-way relaying scenario is considered. Each node has to transmit an individual message and has to receive the messages of all other nodes within its group. These multi-way communications between the multi-antenna nodes are performed via an intermediate non-regenerative multi-antenna relay station. Self- as well as known-interference cancellation are exploited at the nodes and are considered for the derivation of the relay transceive filter. Furthermore, to achieve high spectral efficiency, a transmit strategy which exploits the ideas of network coding is proposed. The proposed transmit strategy combined with the derived relay transceive filter requires less antennas at the relay station and achieves higher sum rates compared to conventional approaches which do not fully exploit the interference cancellation capabilities of the nodes.

With active antenna systems (AAS) cell deployment changes can be handled rather flexible to meet the local and momentary capacity demands. Antenna tilt is one of the important parameters that should be properly adjusted in order to enhance the performance and efficiency of a cellular network. Simulative performance evaluation of these gains currently assumes radio propagation models where the shadowing map remains unchanged with respect to tilt changes. The assumption, however, may not be ralistic in particular to dense urban and urban scenarios. This paper investigates the impact of tilt on the shadowing map with regard to the current 3GPP assumptions. Moreover, the work tries to check if the shadowing process can be really assumed as stationary and independent from beam changes. The investigations are based on shadowing maps isolated from real world ray tracing propagation maps and has been carried out for urban scenario with various beam tilt settings.

In this paper, we investigate the feasibility conditions for relay-aided interference alignment in a class of partially connected networks. The considered scenario consists of several single-antenna communicating node pairs and multiple single-antenna amplify-and-forward relays. Some of the direct links between the source and the destination nodes may have zero gain. A two-hop transmission scheme is applied. The relays' scaling factors, along with the transmit and receive filters, are adapted to the channel to null the interference signals at every destination node. However, this would also null the desired useful signal at certain destinations if the number of relays is insufficient. To avoid this, the required number of relays is studied. We show that the required number of relays depends on the rank of the incidence matrix of a graph defined by the topology of the direct links.

In this paper, bidirectional pair-wise communication between 2K nodes is considered. Each node has N antennas and wants to transmit d data streams to its communication partner. A single non-regenerative half-duplex relay with R antennas supports the communication. In this scenario, the process of interference alignment can be decomposed into partial signal alignment (PSA), partial channel alignment (PCA) and zero forcing (ZF). PSA and PCA are dual problems and we focus on PSA in this paper. PSA is a bilinear problem. A closed form solution is possible only when there is a sufficient number of variables in the system. In this paper, a closed form solution is proposed and the condition for the feasibility of the closed form solution is derived in terms of K;N;R; and d. Besides this, in order to improve the performance at low and medium signal to noise ratios (SNRs), a gradient based algorithm to maximize the useful signal power is also proposed. It is shown through simulations that in some cases, it is better to serve less node pairs and utilize the additional degrees of freedom in the system to maximize the useful signal power.

In multi-hop networks, conventional forwarding along a unicast route forces the data transmission to follow a fixed sequence of nodes. In previous works, it has been shown that widening this path to create a corridor of forwarding nodes and applying OFDMA to split and merge the data as it travels through the corridor towards the destination node leads to considerable gains in achievable throughput compared to the case forwarding data along a unicast route. However, the problem of selecting potential nodes to act as forwarding nodes within the corridor has not been addressed in the literature, as in general a rather homogeneous network topology with equally spaced relay clusters per hop between source and destination node has been assumed. In this paper, a more realistic heterogeneous network is considered where the nodes in the area between source and destination are randomly distributed instead of being clustered with equal distance. A node selection scheme is presented which selects the forwarding nodes within the corridor based on a given unicast route between source and destination node. In simulations, it is shown that with the proposed node selection scheme, considerable throughput gains of up to 50 % compared to forwarding along unicast route can be achieved applying corridor-based routing in heterogeneous networks especially in sparse networks.

This paper analyzes multicast (MC) as an efficient approach in transmitting the same information to multiple receivers (RXs). In each transmission time slot (TS), based on the channel realizations and on the specific MC strategy, a subset of RXs to be served is selected. The members of the served subset may change in the next TS. We assume that the channels from the transmitter (TX) to the RXs are independent and identically distributed (i.i.d.). This makes it possible for each RX to get, on average, the same amount of information. Herein, we find a closed form solution regarding the throughput of the Opportunistic Multicast strategy with fixed group size (OppMC-FS) [1] and present a new variant of this strategy called OppMC with optimal group size (OppMC-OS), providing an analytical solution regarding its throughput. Both variants, OppMC-FS and OppMC-OS, require instantaneous channel state information (CSI) at the TX. In addition, we also propose a new MC strategy, named MC based on statistical channel knowledge (StCSI-MC), and find the throughput of this strategy in a closed form. Results show that our strategies outperform broadcast or unicast and also that a good MC strategy can be found without the need of instantaneous CSI.

State-of-the-art physical layers such as OFDMA are widely used in infrastructure-based networks to enhance efficiency in one-to-many transmissions. Application to wireless mesh networks is highly promising, as the diversity increases in many-to-many scenarios. While theoretical work on OFDMA for this scenario exists, it has not yet been implemented in practice. In this demonstration, we show practical measurements of OFDMA in a topology with multiple transmitters and receivers, which represents a fully-connected segment of a mesh network. Moreover, we model our system analytically and in simulation. Our measurements show the validity of these models. We demonstrate over 90% reduction of the symbol error rate and a 29% channel capacity increase.

We consider a non-regenerative multi-group multi-way relaying scenario in which each group consists of multiple half-duplex nodes. Each node wants to share its data with all other nodes within its group. The transmissions are performed via an intermediate non-regenerative half-duplex multi-antenna relay station, termed RS, which spatially separates the different groups. In our proposal, all nodes simultaneously transmit to RS during a common multiple access phase and RS retransmits linearly processed versions of the received signals back to the nodes during multiple broadcast (BC) phases. We propose a novel transmit strategy which exploits analog network coding (ANC) and efficiently combines spatial transceive processing at RS with joint receive processing at each node over multiple BC phases. A closed-form solution for an ANC aware relay transceive filter is introduced and closed-form solutions for the joint receive processing filters at the nodes are presented. Furthermore, self-interference cancellation and successive interference cancellation are exploited at the nodes to improve the joint receive processing. By numerical results, it is shown that the proposed transmit strategy significantly outperforms existing multi-way strategies.

In this paper, K bidirectionally communicating node pairs with each node having N antennas and one amplify and forward relay having R antennas are considered. Each node wants to transmit d data streams to its communication partner. Taking into account that each node can perform self interference cancellation, a new scheme called Pair-Aware Interference Alignment is proposed. In this scheme, the transmit precoding matrices and the relay processing matrix are chosen in such a way that at any given receiver all the interfering signals except the self interference are within the interference subspace and the useful signal is in a subspace linearly independent of the interference subspace. If the number of variables is larger than or equal to the number of constraints in the system, the system is classified as proper, else as improper. Through simulations it is shown that for a proper system (2Kd≤2N+R-d), interferences can be perfectly aligned and the useful signals can be decoded interference-free at the receivers. An iterative algorithm to achieve the interference alignment solution is proposed. Also for the proper system fulfilling a certain additional condition, which will be derived in this paper, a closed form solution is proposed.

In Digital Subscriber Line (DSL) systems, the concept of Virtual Noise was introduced to improve the protection against fluctuating crosstalk and to increase link stability. This paper investigates the joint optimization of the Virtual Noise power spectral density and the initialization SNR margin. A new algorithm is developed for modems to estimate the optimum Virtual Noise power spectral density and the initialization SNR margin from crosstalk measurements. Using this algorithm leads to both a better stability in terms of outage probability and an improvement in the achieved data rates compared to the traditional SNR margin approach. Moreover, it makes the use of the Low Power mode in VDSL2 systems possible.

The Long Term Evolution (LTE) network will be at first deployed in some areas with high user traffic overlaying with legacy radio access technologies (RATs) such as the second generation (2G) or third generation (3G) mobile system. Consequently, the user equipments (UEs) will be handed over from LTE to 3G network and vice versa. Having robust and error-free handovers between different RATs is one of the most prominent self-organizing network (SON) use cases where the handover thresholds of the base stations in both networks have to be automatically optimized. Currently, the thresholds of measurement event B2 triggering the handover of the UEs from LTE to another RAT are defined by the 3rdGeneration Partnership Project (3GPP) as cell-specific, and therefore, they cannot be differentiated with respect to each neighboring target cell. In this paper, we analyze the difference between optimizing a single handover threshold in a cell-specific and cell-pair specific ways. Based on this analysis, we investigate three new alternative configuration paradigms for the inter-RAT handover thresholds along with those considered by 3GPP. The performance of the SON-based algorithm optimizing the inter-RAT handover thresholds is evaluated for each configuration paradigm. The simulation results have shown that the three alternative configuration paradigms outperform those used by 3GPP. Among the three configuration paradigms, we propose the one which extends the current 3GPP approach and configures the threshold corresponding to the handover target cell in measurement event B2 as cell-pair specific instead of cell-specific.

In this paper, a pilot transmission scheme and a robust self-interference aware relay transceive filter for multipair two-way relaying are introduced. The bidirectional pairwise communications of multiple single-antenna nodes are simultaneously performed via an intermediate non-regenerative multi-antenna relay station and a pilot transmission scheme is proposed to obtain channel state information at the relay station and at the nodes. It is assumed that the nodes can use the available channel state information to subtract the back-propagated self-interference and the cases of perfect and imperfect self-interference cancellation are investigated. The relay station performs linear signal processing based on the estimated channels and a robust relay transceive filter approach is introduced which utilizes the fact that the nodes can perform self-interference cancellation. The proposed pilot transmission scheme requires less resources for channel estimation than conventional schemes and the proposed robust filter design increases the achievable sum rate in case of imperfect channel state information.

In this paper, unidirectional communication between K half-duplex node pairs is considered. The source nodes have N antennas each and the destination nodes have M antennas each. There is no direct link between the source and the destination nodes. Q half-duplex relays, each with R antennas, assist in the communication. It is assumed that the relays do not have enough antennas to spatially separate the data streams and hence, transceive zero forcing cannot be performed at the relays. In this paper, we propose a scheme in which the source nodes and the relays cooperate in choosing their precoding matrices and the filter coefficients, respectively, to perform a cooperative zero forcing. A closed form solution is proposed and the feasibility condition is derived. Simulation results show that the proposed cooperative zero forcing scheme achieves more degrees of freedom and hence, achieves higher sum rate as compared to reference schemes.

In multi-hop networks, conventional unipath routing approaches force the data transmission to follow a fixed sequence of nodes. In this paper, we widen this path to create a corridor of forwarding nodes. Within this corridor, data can be split and joined at different nodes as the data travels through the corridor towards the destination node. To split data, decode-and-forward OFDMA is used since with OFDMA, one can exploit the benefits of opportunistically allocating different subcarriers to different nodes according to their channel conditions. To avoid interference, each subcarrier is only allocated once per hop. For the presented scheme, the problem of optimizing the network throughput by means of resource and power allocation is formulated and two suboptimal algorithms are proposed to solve this problem with feasible effort. Simulations show that in multi-hop networks corridor-based routing using opportunistic forwarding outperforms conventional unipath routing approaches in terms of achievable throughput.

Interference alignment is proposed for achieving the maximum degrees of freedom in interference channels. In the present paper, a scenario consisting of several pairs of multiple antenna nodes and a single MIMO relay helping the interference alignment is considered. The interference alignment is performed in two subsequent transmission phases assuming a time-invariant channel during the two transmission phases. Firstly, the relay and the destination nodes receive the signals from all the source nodes, i.e., also the direct links between the communicating node pairs are exploited. In the second transmission phase, both the source nodes and the relay retransmit the signals to the destination nodes. By adapting the relay's linear signal processing and partially adapting both the transmit and receive filters to the channel, a closed form solution for interference alignment is obtained. The performance of the proposed scheme is investigated by simulations. The results show that the proposed scheme achieves the maximum degrees of freedom and outperforms conventional relaying schemes at high SNRs.

A multi-pair two-way relaying scenario with multi-antenna mobile stations is considered. The bidirectional communications between the mobile stations are supported by an intermediate non-regenerative multi-antenna relay station. It is assumed that the mobile stations can subtract the back-propagated self-interference. Different relay transceive filter design approaches which utilize the fact that the mobile stations can perform self-interference cancellation are introduced. First, a self-interference aware MMSE transceive filter is derived. Second, a multi-antenna MMSE extension of the zero-forcing block-diagonalization based approaches is proposed and an upper bound for these approaches is given. The proposed transceive filters require less antennas at the relay station and achieve higher sum rates compared to conventional relay transceive filter approaches which do not exploit the capability of the mobile stations to perform self-interference cancellation.

The deployment of Long Term Evolution (LTE) system will be at first concentrated on areas with high user traffic overlaying with legacy second (2G) or third generation (3G) mobile system. Consequently, the limited LTE coverage will result in many inter-radio access technology (RAT) handovers from LTE to 2G/3G and vice versa. Trouble-free operation of inter-RAT handovers is very important for mobile operators. However, the joint parameter optimization of different RATs is complex and difficult with traditional optimization means. Thus, an autonomous inter-RAT mobility robustness optimization (MRO) is required. In this work, inter-RAT mobility problems and related inter-RAT key performance indicators (KPIs) are identified. Based on simulative investigations, the impact of the mobility parameters on KPIs is analyzed. Results show that the performance of the network highly depends on the configuration of the mobility parameters and there is a potential for developing a self-organizing inter-RAT MRO algorithm.

A promising approach to optimize coverage and capacity in the cellular network is the adjustment of antenna azimuth orientation and tilt. Tuning both antenna parameters without considering a realistic user traffic distribution in the network might result in a cellular layout having cells covering large areas when small areas are expected and vice-versa. In real life scenarios, mobile users are distributed in the network where some areas are concentrated more than others. Therefore, the adjustment of antenna parameters should yield a cellular layout that is compatible with the distribution of the user traffic in the network. In this work, a new mathematical model for user traffic is presented in coverage and capacity optimization. The antenna azimuth orientations and tilts are configured jointly for a predefined user traffic using an optimization procedure based on Taguchi's method applying nearly orthogonal array. The proposed model for user traffic is validated in long term evolution downlink where results show that coverage and capacity are optimized and the resulting network layouts are fully compliant with the assumed user traffic distributions.

One of the primary aims of radio network planning is to place and configure the transmit antennas of the base stations such that the deployment achieves the required quality of service. Long term evolution (LTE) systems are operated with frequency reuse one and, therefore, a proper configuration of the antenna azimuth orientations and tilts is essential to mitigate the inter-cell interference. Various algorithms have been proposed to adjust these two antenna parameters, but only a few are exploiting the mutual dependencies that exist between them. In this paper, we propose a new algorithm based on Taguchi's method that jointly optimizes the antenna azimuth orientations and tilts. LTE downlink simulations show that the joint optimization of the two antenna parameters outperforms the independent optimization methods. Moreover, the joint optimization reduces the computational complexity by a factor of two.

In this paper, we consider a bidirectional communication between K node pairs, where each node is equipped with multiple antennas and has a message to be transmitted to its communication partner. There is no direct link between the 2K nodes and a relay is employed to enable the communication. It is assumed that the relay does not have enough antennas to perform receive and transmit zero forcing. As interferences are unavoidable at each receiver, we call this channel K user pair symmetric relay interference channel. We show that the Degrees of Freedom (DoF) achievable by the multiple input multiple output (MIMO) Interference Alignment in such a K user pair symmetric relay interference channel are the same as in a K user symmetric interference channel without a relay. Besides this, the presence of a relay simplifies the algorithm for computing the alignment solution. We propose a two step closed form solution for the alignment problem based on the two way relaying protocol.

In this work, the combination of one-way and two-way relaying is examined in a scenario where two multi-antenna nodes exchange information under an asymmetric rate constraint via a half-duplex non-regenerative multi-antenna relay. Two-way relaying overcomes the multiplexing loss of one-way relaying, but additional optimizations are required to fulfill the asymmetric rate constraint. To enable the consideration of suboptimal low-complexity approaches, a hybrid one-way / two-way scheme is suggested which ensures that the asymmetric rate constraint can always be fulfilled. The optimization problem of maximizing the overall sum rate for the considered scenario is formulated and an approach for transceive filter and power optimization considering the asymmetric rate constraint is derived. Simulation results for different link qualities and rate constraints confirm the theoretical considerations and show that the proposed algorithm performs close to the upper bound in case of high asymmetric rate constraints or highly asymmetric channels.

In non-regenerative two-way relaying, two communicating nodes transmit simultaneously to the Relay Station (RS). The RS processes the received signal and forwards it to both nodes simultaneously. In order to obtain its partner's information, each node performs self-interference cancellation by subtracting its transmitted signal from its received signal. However, in multi-user two-way relaying, the interference at each node is not only the a priori self-interference, but also the interference from the other two-way pairs' nodes. Using Zero Forcing (ZF) transceive beamforming, the RS spends unnecessary energy to cancel the interference that can be cancelled by the nodes. Therefore, we propose to apply pair-aware transceive beamforming (PATB) at the RS to cancel only the other-pair interference and let each node cancels its self-interference. Two PATB schemes are proposed, namely, pair-aware (PA) matched filter (PA-MF) and PA semidefinite relaxation of maximising the minimum signal to noise ratio (PA-SDR). From sum rate analysis, PA-SDR outperforms PA-MF, and both outperform non-PA ZF.

Game theory provides an adequate methodology for analyzing topics in communication systems that include trade-offs such as the subject of load balancing. As a means of balancing the load in the network, users are handed over from highly loaded cells to lower loaded neighbors increasing the capacity usage and the Quality of Service (QoS). The algorithm that calculates the amount of the load that each cell should decide either to accept or to offload might differ if the base stations are from distinct vendors, which in-turn may have an impact on the performance of the network. In this paper, we study the load balancing problem using a game-theoretic approach where, in the worst case, each cell decides independently on the amount of load that maximizes its payoff in an uncoordinated way and investigate whether the resulting Nash equilibrium would exhaust the gains achieved. Moreover, we alter the behavior of the players using the linear pricing technique to have a more desirable equilibrium. The simulation results for the Long Term Evolution (LTE) network have shown that the Nash equilibrium point can still provide a remarkable increase in the capacity when compared to a system without load balancing and has a slight degradation in performance with respect to the equilibrium achieved by linear pricing.

In this paper, we introduce non-regenerative multi-group multi-way relaying. A half-duplex non-regenerative multi-antenna relay station (RS) assists multiple communication groups. In each group, there are multiple nodes that want to communicate with each other. Each node has a message and wants to decode the messages only from other nodes in its group. The required number of communication phases for each group is equal to the number of nodes in the group. In the first phase, all nodes transmit simultaneously to the RS and in the following phases the RS applies transceive beamforming and transmits to all nodes. We derive the achievable sum rate of non-regenerative multi-group multi-way relaying for asymmetric and symmetric traffic and for two different cases, namely, with and without analog network coding (ANC). We specially design an ANC-aware transceive beamforming for non-regenerative multi-group multi-way relaying. The ANC-aware transceive beamforming is designed using two criteria, namely, matched filter (MF) and semidefinite relaxation (SDR) of a maximisation of minimum signal to noise ratio problem. From sum rate analysis, it is shown that applying ANC at the RS improves the performance. Regarding the ANC-aware transceive beamforming, the ANC-SDR outperforms ANC-MF at the cost of computational complexity and additional signaling from the nodes to the RS.

An extended Kalman filter-based interacting multiple model algorithm (IMM-EKF) is proposed for mobile terminal tracking in cellular networks based on time of arrival estimates. The proposed IMM-EKF is able to cope with line-of-sight (LOS) and non-line-of-sight (NLOS) conditions modeled by a Markov chain, where the LOS and NLOS errors are described by different noise models. Road-constraints are included into the IMM-EKF to improve performance. Simulation results show that the IMM-EKF outperforms conventional methods. A comparison to the posterior Cramer-Rao lower bound is given to demonstrate the effectiveness of the IMM-EKF.

To dimension cellular wireless networks, accurate estimates of the cell throughput have to be available. Consideration of all requirements of future packet-switched networks, especially the impact of scheduling, leads to extensive system level simulations that have to be performed in order to obtain the cell throughput. A semi-analytical methodology is proposed in this paper that is able to give average cell throughput estimates for given network layouts without performing extensive system level simulations. The signal to interference ratio (SIR) probability density function (pdf) which reflects the behavior that is achieved after scheduling of users is derived. Based on the SIR pdf, probabilities for the usage of modulation and coding schemes are obtained and the average cell throughput is calculated.Results are presented for well known scheduling algorithms and a channel model that is valid for networks utilizing frequency diversity by distributing the subcarriers over the whole system bandwidth like the partial usage of subchannels (PUSC) mode in IEEE 802.16e. It is shown that the average cell throughput estimates are within 10 % of the average cell throughput that is obtained by system level simulations which model all the properties including scheduling in detail. Due to the usage of analytical expressions, results can be obtained in less than a minute for the proposed methodology while a system level simulation usually takes several hours.

In this paper, we introduce non-regenerative multi-way relaying. A half-duplex multi-antenna relay station (RS) assists multiple nodes which want to communicate to each other. Each node has a message and wants to decode the messages from all other nodes. The number of communication phases is equal to the number of nodes, N, such that when N = 2, we have the well known two-way relaying. In the first phase, all nodes transmit simultaneously to the RS and in the following (N-1) phases the RS applies transceive beamforming and transmits to all nodes. The achievable sum rate for asymmetric traffic and symmetric traffic cases are derived for N-phase multi-way relaying. Three low complexity linear transceive beamformers based on Zero Forcing (ZF), Minimum Mean Square Error (MMSE) and Maximisation of Signal to Noise Ratio (MSNR) criteria are designed for N-phase multi-way relaying. From sum rate analysis, MMSE outperforms the other beamformers at the expense of using feedback channel to obtain the noise variance of the nodes. If interference cancellation is performed at all nodes, MSNR achieves the highest performance gain.

Providing quality of service in broadband wireless access systems is an important issue. Fair throughput scheduling is one scheduling algorithm that is able to guarantee equal throughput in the long term for all users within one cell by allocating a resource to the user with the lowest throughput averaged over a past interval. The throughput that can be obtained by a user depends on the signal to interference plus noise ratio achieved by the users within the cell. Usually, system level simulations are necessary to get results on the user throughput for a specific cell layout, user distribution and the scheduling algorithm. This paper presents an analytical performance analysis for cellular systems using fair throughput scheduling. Derivations are presented for the number of allocated resources and the user throughput that can be achieved at a certain position within the network as well as the cell throughput that can be obtained. The investigation can be done for arbitrary user distributions and different network layouts. Results from system level simulations show that the analytical estimates achieve good accuracy with only a fraction of the computational complexity compared to a system level simulation.

Future mobile radio networks are expected to witness an increase in capacity demand. Since the spectrum suited for mobile radio application is scarce, the spectrum efficiency of future mobile radio networks has to be increased in order to be able to meet the capacity demand. In networks applying adaptive modulation and coding, both, transmit power and bandwidth can be considered as resources. In order to achieve high spectrum efficiency, the adaptation of the allocation of transmit power and bandwidth to the time-varying capacity demand is an important topic. In this paper, an approach that adjusts the allocation of transmit power to the cells in order to adapt the network to changing capacity demands is proposed. A mathematical model that relates transmit power and the probability of outage in the cells is presented and used in an approach for the minimsation of the outage probability in the cells using convex optimisation techniques. The performance of the presented approach is evaluated and its suitability for the adaptation of the network to capacity hotspots is shown. Due to the use of a mathematical model and convex optimisation techniques, the presented approach is suited for self-organising optimisation.

In this paper, Discrete Fourier Transform (DFT) precoded Orthogonal Frequency Division Multiple Access (OFDMA) with interleaved subcarrier allocation per user is considered which is denoted as Interleaved Frequency Division Multiple Access (IFDMA). In order to estimate the channel for IFDMA, in general, each subcarrier of an IFDMA symbol is used for pilot transmission to fulfill the sampling theorem in frequency domain. If the data of different users is additionally separated by a time division multiple access component, a limited number K of successive IFDMA symbols is transmitted and at least two of these K symbols are used for pilot transmission if the channel is estimated with the help of interpolation filters. In this paper, we propose to estimate the channel with an iterative decision directed channel estimation with Wiener filtering in order to reduce the pilot symbol overhead in time domain. We additionally introduce the combination of this approach with semiblind subspace based channel estimation in order to reduce the pilot symbol overhead in frequency domain. By this means the channel can be estimated even if the sampling theorem is not fulfilled. The new approach outperforms the iterative decision directed channel estimation with Least-Squares estimation on each subcarrier and the Wiener interpolation filter for Signal-to-Noise Ratios larger than 20 dB and for velocities up to 15 km/h.

The knowledge of the impact of imperfect channel state information (CSI) on data transmission techniques is helpful to improve the performance of wireless communication systems. In this paper, based on the proposed cooperative downlink (DL) transmission scheme applying adaptive scheduling of mobile stations (MSs), significant channel selection, and joint transmission (JT) with partial CSI, i.e., JT considering only part of the useful channels and the interference channels, a novel performance assessment is performed taking imperfectness of the CSI into consideration. The performance degradation caused by the imperfectness of the CSI which is used in the adaptive scheduling, the significant channel selection and the JT with partial CSI is investigated. The question about how much CSI should be taken into consideration as a function of the extent of imperfectness of the CSI in order to achieve optimum system performance is answered based on numerical results.

Discrete Fourier transform (DFT) preceding is a promising approach for reduction of the envelope fluctuations of orthogonal frequency division multiple access (OFDMA) signals. However, the envelope fluctuations of DFT precoded OFDMA signals strongly depend on the subcarrier allocation used. In this work, the envelope fluctuations of DFT precoded OFDMA signals with different subcarrier allocations are analyzed based on various metrics. The mutual dependency of the metrics is addressed and a set of suitable metrics for the design of appropriate DFT precoded OFDMA solutions for the uplink of future mobile radio systems is proposed. New results for oversampled signals considering pulse shaping and windowing are presented and analyzed.

The ability to specify outage probability for cellular mobile radio networks is important in order to be able to assure a certain quality of service (QoS). In packet switched networks applying adaptive transmission, users have varying resource demands due to the adaptability. Furthermore, the medium, or a certain part of it, is not assigned exclusively to a user for the duration of a call or session. The determination of outage is therefore difficult. Time consuming simulations can be applied in order to determine a suitable network design capable of providing the required service. Alternatively, analytic or semi-analytic approaches can be applied. This paper presents an analytic model based on random variables (RVs) for the determination of the bandwidth demand of the downlink in packet switched cellular mobile radio networks applying adaptive transmission. The application of the model for determining outage probability is shown, an example of the application of the model in network design is given and the properties of the model are discussed. Comparisons with simulations are made in order to show the accuracy of the model.

The assignment of resources to the cells of a cellular mobile radio network is usually done by assigning certain parts of the total bandwidth to the cells. With the use of adaptive transmission, power can be considered as a resource and taking power into account in the assignment of resources to cells offers additional flexibility, especially in achieving efficient assignments and in adapting the network to varying capacity demands. Considering power and bandwidth jointly requires the knowledge of their interdependence since they are exchangeable. Concerning a single link, this interdependence has been stated by Shannon and is well known. For the assignment of resources to cells, however, this interdependence has to be known for the whole cell. In this paper, an analytic approach to modelling the interdependence of transmit power and cell bandwidth considering outage probability is presented. The GammaB-Characteristic, where Gamma is a term considering the power of signal, noise and interference, is introduced as a representation of this interdependence and a method for the on-line measurement of GammaB-Characteristics for practical application is presented. The applicability of the proposed approach is validated using simulations and a broad spectrum of applications for operation and optimisation of mobile radio networks based on GammaB-Characteristics is proposed.

In this paper, the interleaved frequency division multiple access (IFDMA) scheme is considered as a special case of Discrete Fourier Transform (DFT)-precoded Orthogonal Frequency Division Multiple Access (OFDMA) with interleaved subcarrier allocation. The redundancy that is characteristic for an IFDMA-signal is exploited in order to estimate the channel without pilot symbols and, thus, overcome the drawback of increased pilot symbol overhead that arises for IFDMA-systems. Two well-known blind channel estimation algorithms based on Second Order Statistics (SOS) are adapted to the application in an IFDMA-system and compared in terms of performance and convergence. Further on, the influence of multi-user transmission is illustrated for the two proposed blind channel estimation algorithms and the differences between multi-user uplink and downlink transmission in terms of channel estimation performance are investigated and emphasized by numerical results.

This paper considers the two-hop relaying case with bi-directional communication of two multiple-antenna nodes S1 and S2 via an intermediate multiple-antenna node, namely the relay station (RS). A general framework for the sum rate maximization in linear, non-regenerative two-way relaying with multiple-antenna nodes is proposed assuming all nodes have perfect channel state information (CSI) in order to perform linear beamforming (BF). There may be application cases in which the system cannot provide perfect CSI to all nodes or the nodes cannot be such complex. Hence, three different cases of system complexity are investigated which are shown to be special cases of the introduced framework. Firstly, all nodes can perform BF, secondly only S1 and S2 can perform BF, and thirdly only the RS can perform BF. The optimum performances of all three cases are compared to each other by means of numeric optimizations, and approaches to solve the resulting optimization problems are proposed. It is shown that applying BF exclusively at the RS provides the same performance as applying BF at all nodes.

The assignment of resources to cells of a cellular mobile radio network is an important task in design and operation of a network. In practice, the number of resource units that are available for assignment to the cells is limited. As a consequence, not all cells can generally receive as many resource units as demanded and outage, defined as the number of resource units requested but not assigned, occurs. In this paper, the dynamic assignment of resources to the cells of a cellular network is discussed under the assumption that the number of resource units available for the assignment is not in all cases sufficient to fulfil the resource demand of every cell and that the occurring outage has to be minimised. An efficient method of determining the number of resource units required for outage-free resource assignment is presented. This method is used to determine outage probability and a lower bound for the amount of outage. Finally, a policy based algorithm for the assignment of resources with very low complexity is presented and its performance in terms of amount of outage compared to the lower bound evaluated.

In this paper, the throughput of an adaptive multiuser SISO-OFDMA/FDD system with channel quality information (CQI) signalled digitized over a feedback channel to the transmitter is investigated, where the instantaneous signal-to-noise ratio (SNR) of the different subcarriers is used as CQI to exploit multi-user diversity using adaptive subcarrier allocation. The CQI available at the transmitter is assumed to be imperfect due to estimation errors and quantization at the receiver side, time delay and feedback errors. In this paper, a closed form expression of the average throughput of an adaptive multi-user OFDMA system using imperfect CQI and uncoded M-QAM modulation is derived. Furthermore, a closed form expression of the average throughput of an OFDMA system exploiting frequency diversity, which does not require CQI at the transmitter, is presented. Both throughput performances are compared in order to identify the optimal transmission strategy depending on the grade of CQI imperfectness.

Space Division Multiple Access (SDMA) is a promising solution to improve the spectral efficiency of future mobile radio systems. However, finding the group of MSs that maximizes system capacity using SDMA is a complex combinatorial problem, which can only be assuredly solved through an Exhaustive Search (ES). Because an ES is usually too complex, there are several sub- optimal SDMA grouping algorithms to solve this problem. Such algorithms, however, usually depend on the preceding matrices of candidate SDMA groups and are also considerably complex. In this work, an SDMA grouping algorithm is proposed for the downlink of multi-user multiple input multiple output systems. It is based on the spatial correlation and gains of the MSs' channels in the SDMA group, thus not depending on preceding and having low complexity. The proposed algorithm is formulated as a convex quadratic optimization problem and is efficiently solved by convex optimization methods. It is analyzed considering zero-forcing precoding and it is shown to almost achieve the performance of an ES for the SDMA group that maximizes the system capacity.

Since the provision of both unicast and multicast services is expected for the next generation of multi-carrier wireless systems, the allocation of resources for this combination of services is a relevant topic, which may have a significant impact on the system performance. The focus of this work lies on the allocation of power to the different downlink subchannels of a multi-carrier system containing both unicast and multicast users. The power allocation problem is analyzed considering on the one hand the maximization of the sum throughput and on the other hand the maximization of the minimum SNR. The solution of the former is presented, which depends on numerical optimization, and an algorithm similar to the waterfilling hypothesis testing is proposed for reducing the processing time, while for the latter a closed-form solution is demonstrated. A simplified power allocation algorithm based on multicast group quality criteria is also evaluated and shown to approximate the maximal achievable performance under certain circumstances.

Mobile networks based on the IEEE 802.16e standard are promising candidates for providing broadband wireless access to mobile users. Due to the flexibility of the physical layer definition based on Orthogonal Frequency Division Multiple Access (OFDMA), it is possible to adjust the networks according to IEEE 802.16e to meet different requirements, e.g., system bandwidth. However, these networks cannot guarantee a reliable transmission in scenarios with frequency reuse of 1 to users at the cell border which achieve only poor Signal to Interference plus Noise Ratio (SINR) conditions due to the high amount of interference from neighbouring cells. Link level simulations provided in this paper show that transmission with a sufficiently low block error probability is achieved for SINR conditions above 4 dB. To improve the SINR conditions at the cell border, a system design is proposed that coordinates the resource allocation among the cells. The available subcarriers shall not be used in an omnidirectional way within the cell, but instead shared among the sectors of a cell that neighbouring sectors do not utilise the same subcarriers and interference can be reduced. System level simulations show that the sector and user throughput can be double with the proposed system design compared to an omnidirectional transmission, although the average amount of allocated radio resource units to each user is equal in both scenarios.

Recently, a new multiple access (MA) scheme denoted Block-Interleaved Frequency Division Multiple Access (B-IFDMA) has been proposed which can be regarded as a generalization of Interleaved Frequency Division Multiple Access (IFDMA). Compared to IFDMA, B-IFDMA provides higher flexibility, increased robustness to carrier frequency offsets and additional power savings at the mobile terminal at the expense of increased envelope fluctuations and higher complexity. In this paper, different variants of B-IFDMA are presented and their properties are discussed. Moreover, new algorithms for a low complexity implementation of B-IFDMA providing low envelope fluctuations are introduced. The complexity of B-IFDMA is shown to be lower than for OFDMA. New performance results for coded transmission over a mobile radio channel are given.

In this paper, the performance of an adaptive multi-user OFDM system with imperfect channel knowledge at the transmitter is investigated, where the ergodic capacity is taken as performance criterion. This performance is then compared to the performance achievable with the use of diversity, where no channel knowledge at the transmitter is required. For that reason, different models of imperfect channel knowledge are introduced: noisy, outdated and quantised channel knowledge, where we provide an analytical derivation of the ergodic capacity for the combinations of the different models. By means of the ergodic capacity it is shown to which grade of channel knowledge imperfectness the use of adaptive subcarrier allocation with imperfect channel knowledge is still better than the use of diversity.

The provision of multicast services is a relevant feature in the context of the further evolution of cellular communication systems. The scope of this paper lies on the analysis and comparison of linear and non-linear downlink precoding techniques for separating users of both unicast and multicast services in space. The paper investigates and derives a non-linear algorithm based on Tomlinson-Harashima precoding (THP) for this unicast/multicast scenario. It is seen that, differently from the linear case, the application of a non-linear multicast-aware algorithm is not capable of providing significant gains. Additionally, a hybrid linear/non-linear algorithm is proposed, which is shown to achieve a good trade-off between performance and complexity.

This paper considers the two-hop relaying case with bidirectional communication of two nodes 51 and 52 via an intermediate relay station (RS). The RS is equipped with multiple antennas and channel state information is available at the RS. Either the multiple antennas can be used to achieve spatial diversity by applying receive and transmit maximum ratio combining (MRC) at the RS in a one-way relaying approach where up- and downlink are transmitted on orthogonal channel resources, or they can be used to apply the recently introduced multiple input multiple output (MHVIO) two-way relaying. In MIMO two-way relaying, the number of required channel resources is reduced since up- and downlink are transmitted on the same channel resources. In this paper, it is investigated which approach provides a better average and outage performance, respectively. Concerning the average performance, MIMO two-way relaying always outperforms MRC one-way relaying. However, the outage performance in MIMO two-way relaying significantly depends on the choice of the linear filter at the RS. MIMO two-way relaying with a linear zero forcing filter provides a worse outage performance than MRC one-way relaying while MIMO two-way relaying with a linear minimum mean square error filter outperforms MRC one-way relaying.

Wireless mobile radio systems have to serve users with different quality of service (QoS) requirements. Scheduling algorithms like weighted proportional fair (WPF) have been proposed considering channel gains to improve the system capacity in terms of sector throughput as well as user priorities to fulfill QoS requirements. These scheduling algorithms allow assigning different priority factors to users and services so that different QoS requirements can be fulfilled. This paper provides an analysis of the influence of users with different priorities on the sector and user throughput. It is shown analytically and by system level simulations that the average user throughput can be adjusted by choosing a specific priority factor. Furthermore, it is shown that the sector throughput decreases if WPF scheduling for users with different priorities and full buffer model are considered compared to a scenario with users having equal priority. If QoS requirements for realistic traffic models, e.g. FTP, have to be fulfilled, for each high priority user low priority users have to be removed from the system in order not to exceed an acceptable number of unsatisfied users. It is shown that the decrease in sector throughput with a realistic traffic model and QoS requirements is higher than for the full buffer model.

In this work, a sub-optimal space division multiple access (SDMA) grouping strategy is proposed for the downlink of multi-user MIMO systems. It uses a new spatial compatibility check metric to estimate the grouping efficiency without needing to compute MIMO filter weights, thus reducing the complexity of SDMA grouping. Moreover, an iterative variant of the block diagonalization for throughput maximization algorithm is proposed, which can be employed to select the number of streams allocated to each user as to increase the sum capacity of the SDMA group. The proposed SDMA strategy is compared through simulation with single-user transmission, random grouping, and exhaustive search, and it is shown to have a good performance-complexity trade-off

This paper evaluates and compares different adaptive antenna techniques applied in the context of multicast services and presents a methodology for performing the spatial multiplexing of both unicast and multicast users. It is seen that adaptive beamforming is able to provide good results even for large groups of multicast users. A fair multicast beamforming algorithm is proposed, which focuses on the performance of the worst multicast user and particularly benefits from scenarios with strong line-of-sight. Additionally, grouping strategies that allow the allocation of the same resources to unicast and multicast users, and which make use of the proposed spatial multiplexing procedure, are shown to be more efficient than allocating separate resources to unicast and multicast users.

In this paper, space-time block codes (STBCs), which gain from spatial transmit diversity, are applied in a distributed fashion at several cooperating relay stations (RSs) with multiple transmit antennas. It is well known that non-distributed STBCs exhibit a degraded bit error rate (BER) performance in spatially correlated MIMO channels. Applying distributed STBCs in cooperative relay networks reduces the probability of correlated channel coefficients as the RSs are spatially separated. In this paper, the Chernoff bound of the BER in Rayleigh fading channels is extended to the case of correlated channel coefficients at the same relay station and different receive powers from different cooperating RSs. It is shown that the BER performance has a higher sensitivity to spatial correlation in MIMO channels than to different receive powers at the receiver from several cooperating RSs for distributed space-time coding. The theoretical results are confirmed by means of simulations

The interleaved frequency division multiple access (IFDMA) scheme is based on compression, repetition and subsequent user dependent frequency shift of a modulated signal. Multiple access is enabled by the assignment of overlapping but mutually orthogonal subcarriers to each user. In this paper it is shown that IFDMA can be regarded as unitary precoded OFDMA with interleaved subcarriers. On the other hand, IFDMA is shown to be a CDMA variant with frequency domain orthogonal signature sequences and chip interleaving. Thus, it combines the advantages of single and multi-carrier transmission such as low peak to average power ratio, orthogonality of the signals of different users even for transmission over a time dispersive channel and low complexity. Simulation results show the good performance of coded IFDMA transmission over a mobile radio channel for different data rates

The interleaved frequency division multiple access (IFDMA) scheme is a promising candidate for next generation mobile radio systems. IFDMA is based on compression, repetition and subsequent user dependent frequency shift of a modulated signal. As in OFDMA, multiple access is enabled by the assignment of overlapping but mutually orthogonal subcarriers to each user. It combines the advantages of single and multicarrier transmission such as low peak to average power ratio, orthogonality of the signals of different users even for transmission over a time dispersive channel and low complexity. In this paper, a linear low complexity frequency domain equalizer for IFDMA is presented and extended by subsequent decision feedback with initialization. Simulation results show the good performance for data transmission using IFDMA with frequency domain equalization over a mobile radio channel

The third-generation mobile radio system UTRA that has been specified in the Third Generation Partnership Project (3GPP) consists of an FDD and a TDD mode. This paper presents the UTRA TDD mode, which is based on TD-CDMA. Important system features are explained in detail. Moreover, an overview of the system architecture and the radio interface protocols is given. Furthermore, the physical layer of UTRA TDD is explained, and the protocol operation is described.

The main requirements to be met by third generation mobile radio systems are high cellular spectrum efficiency and high flexibility. The authors focus on high cellular spectrum efficiency, which is difficult to achieve due to the time variance and frequency selectivity of the mobile radio channel and due to interference. It is known that the degrading effects of these adverse characteristics of the mobile radio channel and of interference can be mitigated by diversity. The way how diversity influences cellular spectrum efficiency is derived in general. As a reference point, the types of diversity used in GSM are analyzed. In GSM, the potential for diversity enhancement inherent in code-division multiple-access (CDMA) is not exploited. A joint detection code-division multiple-access (JD-CDMA) system concept aimed at third generation mobile radio systems has been proposed which introduces a CDMA feature into systems based on time-division multiple-access (TDMA) and frequency-division multiple-access (FDMA) like GSM and also advanced TDMA (ATDMA). The gains achievable by different types of diversity in GSM as well as in the JD-CDMA system concept are investigated. It is shown that considerable gains can be achieved by different types of antenna diversity and by exploiting the additional diversity potential of CDMA. Therefore, third generation standards should be flexible in order to allow the use of as many types of diversity as possible to enhance the cellular spectrum efficiency.

Presently, standards for third generation mobile radio systems are being developed. The main requirements to be met by third generation mobile radio systems are high cellular spectrum efficiency and high flexibility. In this paper, the focus is on high cellular spectrum efficiency, which is difficult to achieve due to the time variance and frequency selectivity of the mobile radio channel and due to interference. It is known that the degrading effects of these adverse characteristics of the mobile radio channel and of interference can be mitigated by diversity. The way how diversity influences cellular spectrum efficiency is derived in general. Diversity techniques applied in mobile radio systems realize combinations of different types of diversity. As a reference point, the types of diversity used in GSM are analyzed since GSM is the most successful standard of second generation mobile radio systems worldwide. In GSM, the potential for diversity enhancement inherent in code division multiple access (CDMA) is not exploited. Within COST 231, a joint detection code division multiple access (JD-CDMA) system concept aiming at third generation mobile radio systems has been developed which introduces a CDMA feature into systems based on TDMA and FDMA like GSM and also advanced TDMA (ATDMA). The gains achievable by different types of diversity in the JD-CDMA system concept are investigated. It is shown that considerable gains can be achieved by different types of antenna diversity and by exploiting the additional diversity potential of CDMA. Therefore, third generation standards should be flexible in order to allow the use of as many types of diversity as possible to enhance the cellular spectrum efficiency.

For future mobile radio systems, an appropriately chosen multiple access technique is a critical issue. Multiple access techniques presently under discussion are code division multiple access (CDMA), time division multiple access (TDMA), and hybrids of both. In the paper, a hybrid C/TDMA system using joint detection (JD-C/TDMA) with coherent receiver antenna diversity (CRAD) at the base station (BS) receiver is proposed. Some attractive features of the JD-C/TDMA system are the possibility to flexibly offer voice and data services with different bit rates, soft capacity, inherent frequency and interferer diversity, and high system capacity due to JD. Furthermore, due to JD, a cluster size equal to 1 can be realized without needing soft handover. The single cell E/sub b//N/sub 0/ performance and the interference situation in a cellular environment of the uplink of a JD-C/TDMA mobile radio system with CRAD is investigated in detail. It is shown that the cellular spectrum efficiency is remarkably high, taking values up to 0.2 bit/s/Hz/BS in the uplink, depending on the actual transmission conditions.>

A low-cost linear algorithm for unbiased separation of not necessarily orthogonal signals is proposed. A new view on the suitability of signals is opened which increases the variety of candidate signals for e.g. CDMA applications. The proposed algorithm can be advantageously implemented by a modified matched filter bank.>

Data estimation in the uplink of a synchronous mobile radio system applying code-division multiple access (CDMA) is considered. In mobile radio systems applying CDMA, multipath propagation leads to intersymbol interference (ISI) and together with time variance, to cross interference between the signals of different users regardless of whether the user codes are chosen orthogonal or not. A linear unbiased data estimation algorithm is presented that eliminates both ISI and cross interference perfectly by jointly detecting the different user signals, leading to unbiased estimates of the transmitted data symbols. By theoretical analysis and simulation, the performance of the linear unbiased data estimation algorithm is examined under the assumption that the radio channel impulse responses are known at the receiver. The price to be paid for the interference elimination are SNR degradations, which are calculated for typical mobile radio situations in urban areas. The resulting average uncoded bit error probabilities lead to the conclusion that systems applying the linear unbiased data estimation algorithm are well suited for mobile radio applications.>

In this paper, data estimation in the uplink of a synchronous mobile radio system applying CDMA is considered. In mobile radio systems applying CDMA, multipath propagation leads on the one hand to ISI and on the other hand, together with time-variance, to cross interference between the signals of different users, regardless whether the user codes are chosen orthogonal or not. A linear unbiased data estimation algorithm is presented which cancels both ISI and cross interference perfectly by jointly detecting the different user signals, thus leading to unbiased estimates of the transmitted data symbols. This algorithm is less complex than MLSE performed e.g. by the Viterbi algorithm and furthermore, the complexity does not depend on the kind of the applied linear modulation scheme, which opens a simple way to offer services with different data rates in one system. By theoretical analysis and simulation, the performance of the unbiased data estimation algorithm is examined under the assumption that the radio channel impulse responses are known at the receiver. The price to be paid for the interference cancellation is SNR-degradations, which are calculated for typical mobile radio situations in urban areas. The resulting average uncoded bit error probabilities lead to the conclusion that systems applying the unbiased data estimation algorithm are well-suited for mobile radio applications.>