International audience

While machine learning (ML) has been explored for beam prediction, many methods remain constrained by single-modality inputs, shallow temporal modeling, and limited robustness to interference and domain shift. We introduce Multimodal Semantic-Enhanced Real-World Beam Prediction via Temporal Modeling with Visual Foundation Models (MSET), a multimodal framework that couples visual semantics with positional priors in a causal, lightweight design. A visual foundation model (VFM), instantiated as a Swin-Transformer, learns rich spatial and semantic cues from RGB images and Segment Anything Model (SAM)-derived region masks, and a lightweight ResNet-18 student distills this knowledge for efficient inference. Short-horizon dynamics are captured by a causal Temporal Convolutional Network (TCN) with an adaptive receptive field, whose volatility-driven depth gate expands context under motion spikes and contracts it in calm periods. On top of semantic-aware frame embeddings, a Temporally Aware Cross-Attention (TACA) aligns original and semantic-enhanced tokens, while a Mambaconditioned GPS prior, implemented via a selective state-space model (SSM), issues a location-conditioned single query with positional bias to attend the fused tokens. We further extend inference to dense deployments with multiple proximate candidates and address target selection under ambiguity. Experiments on DeepSense 6G dataset show consistent Top-k improvements across single/multi-target and day/night scenarios, indicating reduced sweep reliance and strong generalization under realistic V2I dynamics.

We investigate coding schemes that map source symbols into multisets of an alphabet. Such a formulation of source coding is an alternative approach to the traditional framework and is inspired by an object tracking problem over proximity sensor networks. We define amultiset combinatorial Gray codeas a mulitset code with fixed multiset cardinality that possesses combinatorial Gray code characteristic. For source codes that are organized as a grid, namely an integer lattice, we propose a solution by first constructing a mapping from the grid to the set of symbols, which we referred to as colors. The codes are then defined as the images of rectangular blocks in the grid of fixed dimensions. We refer to the mapping as acolor mappingand the code as acolor multiset code. We propose the idea of product multiset code that enables us to construct codes for high dimensional grids based on 1-dimensional (1D) grids. We provide a detailed analysis of color multiset codes on 1D grids, focusing on codes that require the minimal number of colors. To illustrate the application of such a coding scheme, we consider an object tracking problem on 2D grids and show its efficiency, which comes from exploiting transmission parallelism. Some numerical results are presented to conclude the paper.

We focus on the Age of Information (AoI) performance in a system where each user generates packets periodically to send to a common access point (AP) for status updating. To avoid heavy overhead, we assume that channel sensing, feedback information from the AP, and time synchronization are not available in the system. We adopt a multi-access scheme called the sequence scheme, where each user is assigned a periodic binary sequence to schedule their transmissions. In our previous work, we have thoroughly studied the AoI performance under the sequence scheme when the sequence period,L, is equal to the status generating period,T. However, the case ofT̸=Lis not covered by the previous work. Therefore, in this paper, we aim at analyzing the AoI performance for general values ofTandL, which is more challenging and requires different approaches. We conduct in-depth analysis and develop a mathematical tool based on integer partitions to facilitate the analysis. We derive low-complexity closed-form expressions for two special cases. Based on the obtained analytical results, we propose an optimization method for parameter selection in sequence construction to minimize the average AoI. Finally, we compare our proposed sequence scheme with two commonly used baselines, and show that our proposed scheme outperforms the baselines in terms of AoI performance while consuming less energy.

Urban Air Mobility (UAM) offers a promising solution to urban congestion by utilizing low-altitude airspace, effectively alleviating pressure on ground transportation. The integration of air-taxi services with existing ground transport infrastructure enables streamlined, efficient door-to-door travel. However, current research on air-taxi systems often overlooks the role of passenger decision-making in selecting the optimal boarding vertiport, a factor that can greatly impact overall system efficiency and user satisfaction. To address this problem, we propose a Unified Air-Ground Mobility Coordination (UAGMC) framework. This framework, powered by deep reinforcement learning (RL) and Vehicle-to-Everything (V2X) communication, optimizes vertiport selection and dynamically plans air-taxi routes based on real-time air and ground traffic conditions. Experimental results show that our approach reduces average travel time by 32 % compared to traditional allocation methods using proportional distribution. This framework advances overall travel efficiency and provides novel insights for integrating multimodal transportation systems.

Traffic signal control (TSC) is a core challenge in urban mobility, where real-time decisions must balance efficiency and safety. Existing methods—ranging from rule-based heuristics to reinforcement learning (RL)—often struggle to generalize to complex, dynamic, and safety-critical scenarios. We introduce \textbf{VLMLight}, a novel TSC framework that integrates vision-language meta-control with dual-branch reasoning. At the core of VLMLight is the first image-based traffic simulator that enables multi-view visual perception at intersections, allowing policies to reason over rich cues such as vehicle type, motion, and spatial density. A large language model (LLM) serves as a safety-prioritized meta-controller, selecting between a fast RL policy for routine traffic and a structured reasoning branch for critical cases. In the latter, multiple LLM agents collaborate to assess traffic phases, prioritize emergency vehicles, and verify rule compliance. Experiments show that VLMLight reduces waiting times for emergency vehicles by up to 65% over RL-only systems, while preserving real-time performance in standard conditions with less than 1% degradation. VLMLight offers a scalable, interpretable, and safety-aware solution for next-generation traffic signal control.

As video transmission systems expand into various complex scenarios, real-time video coding methods are essential for maintaining low latency and high perceptual quality across varying network conditions. In this work, we propose service-aware Web real-time communication (WebRTC), a semantic-assisted WebRTC system built on scalable video coding (SVC). Specifically, this system is structured with three layers: 1)$\mathcal {L}_{1}$extracts and down-samples semantic information at the encoder, employing a novel super-resolution (SR) method named BUS-DDIM at the decoder to enhance the transmission efficiency and machine vision recognition rate; 2)$\mathcal {L}_{2}$adaptively compresses high-quality video by discarding frames with little motion at the encoder to address latency issues under poor network conditions, and utilize the adjacent frame-guided denoised interpolation model called the adjacent frame-guided denoised diffusion implicit model for restoring the video; and 3)$\mathcal {L}_{3}$transmits high-quality video tailored for users with high-definition video requirements and favorable network conditions. These layers dynamically enhance the visual experience and ensure low latency across various network environments. Experiments are conducted on diverse videos to validate the effectiveness of the proposed framework. The performance evaluation under real-time scenarios indicates significant enhancements in video quality and transmission efficiency, showcasing compatibility and versatility across various applications.

Symbiotic radio (SR), combining the advantages of cognitive radio and ambient backscatter communication (AmBC), stands as a promising solution for spectrum-and-energy-efficient wireless communications. In an SR network, backscatter devices (BDs) share the spectrum resources with the primary transmitter (PT) by utilizing the incident radio frequency (RF) signal from PT for uplink non-orthogonal multiple access (NOMA) transmission. The primary receiver (PR) decodes the signals of PT and BDs via the successive interference cancellation (SIC) technique. Our goal is to establish a long-term commensalistic relationship between PT and BDs. We address the problem of maximizing the long-term average sum rate of BDs while ensuring a minimum average rate for the PT by optimizing the power reflection coefficients of the BDs. We explicitly consider practical constraints such as the required power difference among signals for SIC decoding and the unknown future channel state information (CSI). We prove the NP-hardness of the offline version of the problem and subsequently employ the Lyapunov optimization technique to convert the original problem into a series of sub-problems in each individual time slot that can be solved in an online manner without relying on future CSI. We then utilize the successive convex optimization (SCO) technique to solve the non-convex sub-problems. Extensive simulations validate that our proposed Lyapunov-SCO algorithm achieves superior performance in terms of the average sum rate of BDs while ensuring PT’s required average rate. In addition, we provide discussions on extending the proposed solution to SR networks with multiple PT-PR pairs, high-mobility BDs, and enhancing fairness among BDs.

We consider the design of a positioning system where a robot determines its position from local observations. This is a well-studied problem of considerable practical importance and mathematical interest. The dominant paradigm derives from the classical theory of de Bruijn sequences, where the robot has access to a window within a larger code and can determine its position if these windows are distinct. We propose an alternative model in which the robot has more limited observational powers, which we argue is more realistic in terms of engineering: the robot does not have access to the full pattern of colours (or letters) in the window, but only to the intensity of each colour (or the number of occurrences of each letter). This leads to a mathematically interesting problem with a different flavour to that arising in the classical paradigm, requiring new construction techniques. The parameters of our construction are optimal up to a constant factor, and computing the position requires only a constant number of arithmetic operations.

We consider the tracking problem of an object that can randomly appear on a line of a fixed length. We want to determine the position of the object and rely on sensors that can detect objects with a predefined range and report that to a remote observer. Sensors are equipped with a transmitter that can transmit at a limited data rate. Once a sensor is triggered, it transmits its own ID to inform. A straightforward protocol is to label each sensor with different ID. However, this would require a large number of unique IDs and many bits to represent. As a result, higher data rate is required. We propose a newly defined protocol using multiset color coding with optimal design or efficiency in reusing a much smaller number of IDs for the whole system. We only require the minimal number of bits for labeling each sensor. We derive the factor of reduction and show its significance. We present some optimal constructions for the required multiset color coding sequence. Besides, we derive the general upper and lower bounds for the maximum length (size) of the system. Numerical examples have also demonstrated the effectiveness and improvement by the proposed new method.

In this paper, we study the problem of optimizing statistical beamforming design for each user in a two-antenna downlink multiple-input single-output (MISO) non-orthogonal multiple access (NOMA) system. The transmitter only possesses statistical information in the form of covariance matrices for each user’s link. The statistical beamforming designs are derived in maximizing the ergodic sum rate considering both low and high signal-to-interference-plus-noise ratio (SINR) extreme scenarios. In addition to the analytical investigations, we also conduct MonteCarlo simulations in comparison and to affirm the accuracy of the derived ergodic sum rate expressions. Results show that the ergodic sum rate increases with the total transmit power and decreases with the power coefficient of near users. Comparing the optimized MISO-NOMA system with conventional MISO-OMA scheme, it is demonstrated that MISO-NOMA can significantly improve the ergodic sum rate.

Cell-free massive MIMO (CFmMIMO) is a promising paradigm to provide uniform coverage in future wireless networks. However, a fully connected CFmMIMO system where all the access points (APs) serve every user equipment (UE) makes it challenging to deploy and scale in real-time due to high computational complexity and increased signaling overhead. In this work, we study the problem of downlink power allocation in partially connected CFmMIMO (p-CFmMIMO) systems using maximal ratio transmission (MRT). We utilize the underlying geometry of the problem to propose a graph representation of the CFmMIMO system and develop a graph neural network (GNN) based power allocation strategy to maximize the minimum SINR in the system. We demonstrate that the proposed GNN model has excellent generalizability to deployment size, radio propagation morphologies, and per-AP serving density1. Our GNN can address the power allocation problem in the fully connected case, the partially connected case, and even the cellular case with magnitudes lower computational complexity compared to the conventional numerical solvers. Notably, we show that over a wide range of service scenarios, the model achieves a median spectral efficiency that is within 10% of the optimal second-order cone programming (SOCP) solution while requiring 100 times fewer FLOPS.

We propose a framework for maximizing the number of machine-type devices connected in the uplink of a Narrow-band Internet of Things (NB-IoT) network using non-orthogonal multiple access (NOMA). The system is based on the fast-uplink grant (FUG), where the base station (BS) schedules the access for active devices requesting connection. This problem is a mixed-integer non-convex problem and real-time solutions using general solvers are computationally prohibitive. The proposed scheduling solution comprises efficient device clustering and optimum power allocation using a bipartite graph matching approach, termed connection throughput maximizing full matching with pruning (CTMBM). Different from the other solutions of state-of-the-art, our proposed scheme considers scheduling over multiple transmission time intervals while considering the transmission deadlines and quality of service (QoS) for the devices. Additionally, we provide a method for priority scheduling of a subset of devices. We compare our solution to the state-of-the-art schemes and analyze the achieved gains through Monte-Carlo computer simulations.

Unmanned aerial vehicles (UAVs) have been widely adopted for a variety of civilian and military applications. Despite their many advantages, most UAVs are not suitable for indoor missions due to the lack of global position system (GPS) and the large size of UAV with diverse sensors. In this work, a lightweight and cost-effective indoor multi-UAV surveillance system is presented for accurate target localization. The proposed system employs a vision-based architecture, leveraging ORB-SLAM for self-localization and YOLOv3 for object detection. The triangulation method is employed for target positioning, offering reliable performance with negligible time delays and improved detection compared to depth camera approaches. After that, the Riccati observer is introduced to address the stochastic nature of the system. Through a series of experiments involving real UAVs, the system demonstrates its ability to accurately localize and track targets in indoor environments, updating the UAVs’ positions in real-time with optimal performance.

By allowing multiple users to transmit using the same frequency band at the same time, non-orthogonal mul-tiple access (NOMA) can support more users as compared to orthogonal multiple access (OMA) given a fixed amount of time-frequency resources. In this paper, we study the resource allocation problem in the uplink of a single-cell network when NOMA is enabled, where the maximum completion time of serving all connected users is to be minimized. While such an objective function minimizes the required time to serve uplink users, the resource allocation problem is NP-hard. We propose a serial collaborative optimization framework based on simulated annealing (SA) to search for the optimal user pairing and scheduling solution. Simulation studies show that the proposed algorithm for NOMA scheduling can reduce the maximum completion time by more than 30% when compared against OMA scheduling and random NOMA user clustering.

This paper considers a system in which multiple users send periodically generated status information to a common access point (AP) over a collision channel. To avoid high overhead, there is no time synchronization and no feedback information from the AP to indicate whether a transmission is successful or not. The performance metric that we focus on is the age-of-information (AoI), which represents the freshness of the status information received at the AP. For this model, we propose a sequence based MAC scheme in which each user is pre-assigned a periodic sequence to schedule transmissions. This scheme guarantees each user at least one successful packet transmission within a sequence period, in the absence of time synchronization and feedback information from the AP. To the best of our knowledge, this is the first study investigating AoI performance under a sequence based MAC scheme. We derive the closed-form expressions for average AoI, average peak AoI and average age penalty under the sequence based scheduling. Besides, we derive several critical properties of the sequences to optimize the AoI performance. Comparison results show that our proposed sequence scheme outperforms slotted ALOHA and framed ALOHA in various settings.

Modern information and communication systems have become increasingly challenging to manage. The ubiquitous system logs contain plentiful information and are thus widely exploited as an alternative source for system management. As log files usually encompass large amounts of raw data, manually analyzing them is laborious and error-prone. Consequently, many research endeavors have been devoted to automatic log analysis. However, these works typically expect structured input and struggle with the heterogeneous nature of raw system logs. Log parsing closes this gap by converting the unstructured system logs to structured records. Many parsers were proposed during the last decades to accommodate various log analysis applications. However, due to the ample solution space and lack of systematic evaluation, it is not easy for practitioners to find ready-made solutions that fit their needs. This paper aims to provide a comprehensive survey on log parsing. We begin with an exhaustive taxonomy of existing log parsers. Then we empirically analyze the critical performance and operational features for 17 open-source solutions both quantitatively and qualitatively, and whenever applicable discuss the merits of alternative approaches. We also elaborate on future challenges and discuss the relevant research directions. We envision this survey as a helpful resource for system administrators and domain experts to choose the most desirable open-source solution or implement new ones based on application-specific requirements.

This paper studies the allocation of shared resources between ultra-reliable low-latency communication (URLLC) and enhanced mobile broadband (eMBB) in the emerging 5G and beyond cellular networks. In this paper, we design a unique queuing mechanism for the joint eMBB/URLLC system. The aim is to flexibly schedule URLLC traffic to enhance the total eMBB throughput and the reliability of URLLC packets (i.e., the probability of not dropping URLLC packets in each mini-slot) while maintaining a satisfactory transmission latency as per the 3GPP requirements. Precisely, by deriving the steady-state probabilities of URLLC queue backlog analytically, we formulate a stochastic optimization problem to maximize the total normalized eMBB throughput and the URLLC utility. Due to the stochastic nature of the objective function, it is expensive to evaluate it for any set of inputs, and thus the Bayesian optimization is applied to obtain the optimal results of such a black-box objective function. Numerical results demonstrate that the proposed queuing mechanism never violates the latency requirement of the URLLC services but improves the reliability. It also enhances the total normalized eMBB throughput as compared to the method without queuing.

Beyond 5G wireless technology Cell-Free Massive MIMO (CFmMIMO) downlink relies on carefully designed pre-coders and power control to attain uniformly high rate coverage. Many such power control problems can be calculated via second order cone programming (SOCP). In practice, several order of magnitude faster numerical procedure is required because power control has to be rapidly updated to adapt to changing channel conditions. We propose a Graph Neural Network (GNN) based solution to replace SOCP. Specifically, we develop a GNN to obtain downlink max-min power control for a CFmMIMO with maximum ratio transmission (MRT) beamforming. We construct a graph representation of the problem that properly captures the dominant dependence relationship between access points (APs) and user equipments (UEs). We exploit a symmetry property, called permutation equivariance, to attain training simplicity and efficiency. Simulation results show the superiority of our approach in terms of computational complexity, scalability and generaliz-ability for different system sizes and deployment scenarios.

We develop a framework for maximizing the number of transmitted packets for devices in a Narrowband Internet of Things (NB-IoT) network using non-orthogonal multiple access (NOMA) in the downlink. The base station (BS) chooses one of the multiple available physical resource blocks (PRBs) that are well separated in frequency for a device, giving them the advantage of exploiting frequency diversity. The scheduling strategy focuses on the two-fold problem involving efficient device clustering and optimum power allocation. This problem is a mixed-integer non-convex problem. We propose a bipartite graph matching approach, termed minimum weight full matching with pruning (MWFMP), to address the problem over multiple PRBs and solve it under the quality-of-service (QoS), allowable PRB, power budget, and interference constraints. Additionally, we provide a comparison with a greedy heuristic, the multi-PRB stratified device allocation (MPSDA), where we extend our previous work for a single PRB connectivity problem. Furthermore, we compare our algorithms to orthogonal multiple access (OMA) scheduling, which is prevalent in legacy LTE networks. We show that our algorithms steadily outperform the connectivity performance offered by OMA.

The 3D indoor positioning and indoor navigation (IPIN) system is of great significance for promoting and expanding indoor intelligent services and applications. The rapid development of unmanned aerial vehicles (UAVs) has provided new opportunities in this field. However, in contrast to their outdoor applications, IPIN for UAVs is more challenging since the Global Positioning System (GPS) is in general inaccessible in indoor environments. In this work, we propose a UAV-enabled 3D IPIN system based on visible light communication (VLC). Firstly, a novel VLC-based indoor positioning scheme is developed using a fusion algorithm based on the dynamic time warping (DTW) method with visible light intensity sequence (VLIS) and inertial measurement unit (IMU) data. To reduce the workload of fingerprint measurements, we propose to modularize a floor site using a standard symmetric structure for VLC positioning. In this manner, the navigation can be achieved by recognizing the edge of each module. Furthermore, since the sampling frequency of IMU is much higher than that of VLIS, discrete Kalman filter (KF) is introduced to correct the location measured by IMU when VLIS is unavailable. A proof-of-concept IPIN prototype is constructed. Field experiments confirm the effectiveness of our proposed IPIN system.

To maximize the throughput of wireless powered communication (WPC), it is critical for the device to decide when to harvest energy, when to transmit data and what transmit power to use. In this paper, we consider a WPC system with a single device using harvest-store-transmit protocol and aim to maximize the longterm average throughput with optimal allocation of the energy harvesting time, data transfer time and the device’s transmit power. With the consideration of many practical constraints including finite battery capacity, time-varying channels and non-linear energy harvesting model, we propose both deep Q-learning (DQL) and actor-critic (AC) approaches to solve the problem and obtain fully online policies. Simulation results show that the performance of our proposed AC approach comes close to that achieved by value iteration and is superior to DQL and other baseline algorithm. Meanwhile, its space complexity is 2-3 orders of magnitude less than that required by value iteration.

We address the issue of maximizing the number of connected devices in a Narrowband Internet-of-Things (NB-IoT) network using nonorthogonal multiple access (NOMA) in the downlink. We first propose an optimal joint subcarrier and power allocation strategy assuming perfect channel state information (CSI) called stratified device allocation (SDA), which maximizes the connectivity under data rate, power, and bandwidth constraints. Then, we generalize the connectivity maximization problem to the case of partial CSI, where only the distance-dependent path-loss component of the channel gain is available at the base station (BS). We introduce a novel framework called the stochastic connectivity optimization (SCO) framework. In this framework, we propose a heuristic improvement to SDA, namely, SDA with excess power (SDA-EP) algorithm for operation under partial CSI. Furthermore, we derive a concave approximation (SCO-CA) algorithm of near-optimal performance to SCO given the same amount of CSI. Through computer simulations, we show that SDA-EP and SCO-CA outperform conventional NOMA and OMA schemes in the presence of partial CSI over a wide range of service scenarios.

VLC based indoor positioning system is a natural choice thanks to LED technology and future trend. We can reuse existing LED lighting installations to design an appropriate scheme to provide indoor localization. By receiving basic information such as the light intensity and knowing the position of the transmitters for example through Visible Light Communication (VLC), one can estimate its position with the potential of high accuracy. In this paper, we use a multi-color sensor and build an indoor localization system based on the light fingerprint, which is suitable for smart cities and homes, health-care centers, hospitals or similar. There are, however, several practical problems that make it a technology that is not yet sufficiently developed to be used in our everyday life. In this paper, a new perspective is given by looking into indoor localization using multi-color fingerprinting and different machine learning methods. Experiment results have shown its effectiveness and potentials with mean localization errors of around ten centimeters.

We address the issue of maximizing the number of connected devices in a Narrowband Internet of Things (NB-IoT) network using non-orthogonal multiple access (NOMA). The scheduling assignment is done on a per-transmit time interval (TTI) basis and focuses on efficient device clustering. We formulate the problem as a combinatorial optimization problem and solve it under interference, rate and sub-carrier availability constraints. We first present the bottom-up power filling algorithm (BU), which solves the problem given that each device can only be allocated contiguous sub-carriers. Then, we propose the item clustering heuristic (IC) which tackles the more general problem of non-contiguous allocation. The novelty of our optimization framework is two-fold. First, it allows any number of devices to be multiplexed per sub-carrier, which is based on the successive interference cancellation (SIC) capabilities of the network. Secondly, whereas most existing works only consider contiguous sub-carrier allocation, we also study the performance of allocating non-contiguous sub-carriers to each device. We show through extensive simulations that non-contiguous allocation through IC scheme can outperform BU and other existing contiguous allocation methods.

Future wireless communication systems have been imposed high requirement on power efficiency for operator's profitability as well as to alleviate information and communication technology (ICT) global carbon emission. To meet these challenges, the power consumption minimization problem for a generic multi-cell multiple input and single output non-orthogonal multiple access (MISO-NOMA) system is studied in this work. The associated joint user grouping, beamforming (BF) and power control problem is a mixed integer non-convex programming problem, which is tackled by an iterative distributed methodology. Towards this end, the near-optimal zero-forcing (ZF) BF is leveraged, wherein the semiorthogonal user selection (SUS) strategy is applied to select BF users. Based on these, the BF vectors and BF users are determined for each cell using only local information. Then, two distributed user grouping strategies are proposed. The first one, called channel condition based user clustering (CCUC), performs user grouping in each cell based on the channel conditions. This is conducted independently of the power control part and has low computational complexity. Another algorithm, called power consumption based user clustering (PCUC), uses both the channel conditions and inter-cell interference information to minimize each cell's power consumption. In contrary to CCUC, PCUC is optimized jointly with the power control. Finally, with the obtained user grouping and BF vectors, the resultant power allocation problem is optimally solved via an iterative algorithm, whose convergence is mathematically proven given that the problem is feasible. We perform Monte-Carlo simulation and numerical results show that the proposed resource management methods outperform various conventional MISO schemes and the non-clustered MISO-NOMA strategy in several aspects, including power consumption, outage probability, energy efficiency, and connectivity efficiency.

In this paper, we analyze the outage probability of the multi-user multiple-input single-output (MISO) downlink system by combining the non-orthogonal multiple access (NOMA) scheme. We derive tractable closed-form outage expressions given a minimum target rate for the individual users for the case of two antennas, by modeling cumulative distribution function (CDF) of received signal-to interference plus noise ratio (SINR). Simulation results illustrate the outage performance for different power allocation scenarios and verify the accuracy of our outage probability analysis.

Non-orthogonal multiple access (NOMA) has received significant attention for future wireless networks. NOMA outperforms orthogonal schemes, such as OFDMA, in terms of spectral efficiency and massive connectivity. The joint subcarrier and power allocation problem in NOMA is NP-hard to solve in general, due to complex impacts of signal superposition on each users achievable data rates, as well as combinatorial constraints on the number of multiplexed users per sub-carrier to mitigate error propagation. In this family of problems, weighted sum-rate (WSR) is an important objective function as it can achieve different tradeoffs between sum-rate performance and user fairness. We propose a novel approach to solve the WSR maximization problem in multi-carrier NOMA with cellular power constraint. The problem is divided into two polynomial time solvable sub-problems. First, the multi-carrier power control (given a fixed subcarrier allocation) is non-convex. By taking advantage of its separability property, we design an optimal and low complexity algorithm (MCPC) based on projected gradient descent. Secondly, the single-carrier user selection is a non-convex mixed-integer problem that we solve using dynamic programming (SCUS). This work also aims to give an understanding on how each sub-problem's particular structure can facilitate the algorithm design. In that respect, the above MCPC and SCUS are basic building blocks that can be applied in a wide range of resource allocation problems. Furthermore, we propose an efficient heuristic to solve the general WSR maximization problem by combining MCPC and SCUS. Numerical results show that it achieves near-optimal sum-rate with user fairness, as well as significant performance improvement over OMA.

When deploying radio frequency (RF)-powered sensor networks for mission-critical applications such as security surveillance, it is often required to maximize or guarantee the quality of surveillance. Both placing and scheduling the charging/working modes of sensors are of key importance in order to continuously ensure a satisfying quality of surveillance. Traditionally, sensor placement and scheduling have been considered separately. The first decision regards where to place the sensors, and then when to activate them. In this paper, we study simultaneous sensor placement and charging/working scheduling problem for fusion-based detection in RF-powered sensor networks. The problem is formulated as a constrained optimization problem and proved to be NP-complete. Two greedy heuristic algorithms, joint optimization greedy algorithm with fixed fusion radius (JOGA-FFR) and joint optimization greedy algorithm with dynamic fusion radius (JOGA-DFR) based on fixed and dynamic fusion radiuses, respectively, are presented to solve the problem. We validate our approaches through extensive numerical simulations as well as simulations based on real data traces collected from a vehicle detection experiment. The results show that, our proposed algorithms always outperform two-stage greedy algorithm (TSGA), an algorithm that optimizes sensor placement and scheduling separately, in all the simulation scenarios, and are near optimal in small-scale networks. Besides, JOGA-DFR outperforms JOGA-FFR under certain specific sensing model settings, but more often has a comparable performance with JOGA-FFR. JOGA-FFR is thus more recommended for its lower complexity.

Non-orthogonal multiple access (NOMA) is a promising radio access technology for 5G. It allows several users to transmit on the same frequency and time resource by performing power- domain multiplexing. At the receiver side, successive interference cancellation (SIC) is applied to mitigate interference among the multiplexed signals. In this way, NOMA can outperform orthogonal multiple access schemes used in conventional cellular networks in terms of spectral efficiency and allows more simultaneous users. This paper investigates the computational complexity of joint subcarrier and power allocation problems in multi-carrier NOMA systems. We prove that these problems are strongly NP-hard for a large class of objective functions, namely the weighted generalized means of the individual data rates. This class covers the popular weighted sum-rate, proportional fairness, harmonic mean and max-min fairness utilities. Our results show that the optimal power and subcarrier allocation cannot be computed in polynomial time in the general case, unless P = NP. Nevertheless, we present some tractable special cases and we show that they can be solved efficiently.

In this paper, we investigate the distributed power allocation algorithm for the downlink of a two-cell multiple input and single output non- orthogonal multiple access (MISO-NOMA) system. The problem targets at minimizing the total power consumption of the base stations (BSs) while taking into consideration each user's data rate requirement. A distributed power control algorithm is devised. During each iteration, the BS updates the transmit power of its attached users according to the link gain vector and the inter-cell interference plus noise value at the users. For some special cases, we show that the proposed algorithm is guaranteed to converge to a unique fixed point that could be an optimal solution based on Yate's power control framework. Furthermore, some modifications are made for the iterative algorithm to enhance the convergence performance of the instances with feasible solutions. Simulation results demonstrate that the designed power allocation strategy can significantly improve system performance over conventional orthogonal multiple access (OMA) counterpart in terms of total transmit power and outage probability.

Non-orthogonal multiple access (NOMA) is an effective means to improve the spectral efficiency of a wireless communication system. When applied to cellular networks, cell edge users may suffer from low bit rate, or the associated base stations may need to use excessively high power to serve those users. In order to alleviate the problem, this paper considers the integration of NOMA with coordinated transmission techniques. A two-cell system is considered, in which there are two users near their associated base stations and a cell edge user served by both base stations. It is assumed that each user has a data rate requirement, and the system objective is to minimize the total transmit power. With a formal problem formulation, the feasibility of the problem is characterized by using Helly's theorem. When the problem is feasible, we design both centralized and distributed algorithms to solve it. Numerical results show that NOMA can significantly outperform an orthogonal multiple access scheme in terms of power consumption and outage probability.

This paper presents an online algorithm for mobile user speed estimation in 3GPP Long Term Evolution (LTE)/LTE-Advanced (LTE-A) networks. The proposed method leverages on uplink (UL) sounding reference signal (SRS) power measurements performed at the base station, also known as eNodeB (eNB), and remains effective even under large sampling period. Extensive performance evaluation of the proposed algorithm is carried out using field traces from realistic environment. The online solution is proven highly efficient in terms of computational requirement, estimation delay, and accuracy. In particular, we show that the proposed algorithm can allow for the first speed estimation to be obtained after 10 seconds and with an average speed underestimation error of 14 kmph. After the first speed acquisition, subsequent speed estimations can be obtained much faster (e.g., each second) with limited implementation cost and still provide high accuracy.

International audience

Software defined networking (SDN) and network function virtualization (NFV) are the embraced technologies for the backhauling of future 5G networks. Virtual Machine (VM) and Docker container based deployments have received much attention. This paper presents the virtualization of a prototyped software defined radio access network (RAN) architecture by using VMs and Docker containers. In addition, it provides an analytical model for the generalized software defined RAN architecture with the practice of VM based and Docker container based implementations. Using measurements obtained from the two testbeds and the introduced queuing model, we compare their performances and analyze the two different architectures. Results verify the superiority of the Docker technology. Some observations from the behavior of the testbeds are concluded for a better understanding of the VM and Docker container based technologies for the future development of 5G SDN controller.

In a visible light communications system (VLC), light sources are responsible for both illumination, communications and positioning. These light sources inevitably interfere each others at the receiver. To retain the appealing advantage that VLC systems can reuse existing lighting infrastructure, using an extra network to control or synchronize the light sources should be avoided. This paper proposes an uncoordinated multiple access scheme for VLC systems with positioning capability. The proposed scheme does not require a central unit to coordinate the transmission of the transmitters. Transmitters can be asynchronous with one another and with the receiver. Each transmitter is allocated a unique codeword with L chips for a system with up to L-1/2 transmitters where L is prime. Due to the linear growth in complexity with respect to number of transmitters, our proposed scheme is feasible for systems with large numbers of transmitters. Our novel decoder can minimize the effect of additive Gaussian noise at the receiver side. Simulation results show that the proposed decoder outperforms zero-forcing decoder.

Aiming to throughput enhancement in future mobile networks, dense deployment of wireless access points (APs) is intended. Spectral efficiency on the uplink can be improved via joint processing of users located in areas covered by several APs. Thanks to centralized processing of such users enabled by Cloud Radio Access Network (C-RAN) architecture, low-latency multi-cell cooperation becomes possible. However, the price to pay to benefit from its advantages is the cost of transferring data from distributed access points to the data center through limited-capacity fronthaul links. A mobile network operator using C-RAN would have the objective to maximize data transmission rates with lowest possible fronthaul rate. We consider in this work the optimal tradeoff between the amount of fronthaul allocated and the sum rate of each user group in a multiple access scheme, where the wireless resource is reused among the user groups. By performing low- complexity joint reception of signals quantized according to attributed fronthaul rate, we can maximize the benefit of uplink transmission in C- RAN despite constrained fronthaul links. With optimal fronthaul allocation, the net benefit of the uplink transmission improves by 10% in a usual configuration.

Cloud radio access network (C-RAN) is becoming more than ever timely in 5G for dense network deployments, but its design has to be adapted to 5G requirements. To ensure high-uplink throughput in cell-edge regions affected by inter-cell interference, C-RAN enables the multi-user reception techniques among cells. In this paper, we propose an efficient end-to-end uplink transmission scheme dealing with implementation and deployment constraints on both communication interfaces in C-RAN, i.e., the wireless one between the users and the remote radio heads (RRHs), and the fronthaul links between the RRHs and the central processing unit. Multi-cell non-orthogonal multiple access (NOMA) can improve spectral efficiency and cell-edge throughput, but its design needs to fit requirements regarding receiver complexity and delay. Optimizing the fronthaul rate allocation to maximize the benefit of the transmissions allows to exploit the fronthaul links effectively. Our model considers the throughput of NOMA transmissions in C-RAN and the expense related to fronthaul usage in various deployment scenarios. When the available fronthaul rate allows accurate transmission, optimizing fronthaul rate allocation results in 10% higher transmission benefit than uniform allocation. It also makes possible to involve less users in NOMA while keeping the benefit. The proposed strategy enables uplink multi-cell multi-user processing in a cost-effective manner in 5G C-RAN deployments.

As the demand for higher data rates is growing exponentially, homogeneous cellular networks have been facing limitations when handling data traffic. These limitations are related to the available spectrum and the capacity of the network. Heterogeneous networks (HetNets), composed of macro cells (MCs) and small cells (SCs), are seen as the key solution to improve spectral efficiency per unit area and to eliminate coverage holes. Due to the large imbalance in transmit power between MCs and SCs in HetNets, intelligent user association (UA) is required to perform load balancing and to favor some SCs attraction against MCs. As long term evolution (LTE) cellular networks use the same frequency sub-bands, user equipment may experience strong intercell interference (ICI), especially at cell edge. Therefore, there is a need to coordinate the resource allocation (RA) among the cells and to minimize the ICI. In this paper, we propose a generic algorithm to optimize UA and RA in LTE networks. Our solution, based on game theory, permits to compute cell individual offset and a pattern of power transmission over frequency and time domain for each cell. Simulation results show significant benefits in the average throughput and also cell edge user throughput of 40% and 55% gains respectively. Furthermore, we also obtain a meaningful improvement in energy efficiency.

To obtain good network performance in Long Term Evolution-Advanced (LTE-A) heterogeneous networks (HetNets), enhanced inter-cell interference coordination (eICIC) and further eICIC (FeICIC) have been proposed by LTE standardization bodies to address the entangled inter-cell interference and the user association problems. We propose the distributed algorithms based on the exact potential game framework for both eICIC and FeICIC optimizations. We demonstrate via simulations a 64% gain on energy efficiency (EE) achieved by eICIC and another 17% gain on EE achieved by FeICIC. We also show that FeICIC can bring other significant gains in terms of cell-edge throughput, spectral efficiency, and fairness among user throughputs. Moreover, we propose a downlink scheduler based on a cake-cutting algorithm that can further improve the performance of the optimization algorithms compared with conventional schedulers.

Software defined networking (SDN) is growing rapidly in telecommunications due to its capability to efficiently manage end-to-end networks by decoupling control plane and data plane. The scalability and flexibility can bring benefits to network management and maintenance. However, it has yet to happen for wireless networks since there is a lack of practical solutions for migrating Radio Access Networks (RAN) to software defined wireless networks. Here, we design a wireless SDN controller based on OpenDayLight (ODL). We show how our framework can support typical use case and Self-Optimized Network algorithm. The design is highly flexible and can be leveraged for future wireless networks such as C-RAN and 5G.

Due to Orthogonal Frequency Division Multiple Access (OFDMA) mechanism adopted in LTE cellular networks, intra-cell interference is nearly absent. Yet, as these networks are designed for a frequency reuse factor of 1 to maximize the utilization of the licensed bandwidth, inter-cell interference coordination remains an important challenge. In both homogeneous and heterogeneous cellular networks, there is a need for scheduling coordination techniques to efficiently distribute the resources and mitigate inter-cell interference. In this paper, we propose a dynamic solution of inter-cell interference coordination performing an optimization of frequency sub-band reuse and transmission power in order to maximize the overall network utility. The proposed framework, based on game theory, permits to dynamically define frequency and transmission power patterns for each cell in the coordinated cluster. Simulation results show significant benefits in average throughput and also cell edge user throughput of 40% and 55% gains when performing the frequency sub-band muting and power control. Furthermore, we also obtain a meaningful improvement in energy efficiency.

With the widely use of smart devices and rapid development of communication technologies, it becomes easier for base stations to obtain the context information of users. The context information can be utilized to optimize system resource allocation. This paper focuses on context-aware unicast cellular networks. Assuming that some channel state information (CSI) can be predicted based on user context such as user location and moving pattern, joint power control and transmission scheduling is applied to minimize the transmission energy consumption. By reducing the energy minimization problem to a semi-assignment problem, our proposed algorithm can find the optimal solution in polynomial time. Simulation results show that the proposed context-aware scheme outperforms the traditional round-robin scheduler and opportunistic scheduler, which do not consider the feature of context-awareness.

Estimating mobile user speed is a problematic issue which has significant impacts to radio resource management and also to the mobility management of Long Term Evolution (LTE) networks. This paper introduces two algorithms that can estimate the speed of mobile user equipments (UE), with low computational requirement, and without modification of neither current user equipment nor 3GPP standard protocol. The proposed methods rely on uplink (UL) sounding reference signal (SRS) power measurements performed at the eNodeB (eNB) and remain efficient with large sampling period (e.g., 40 ms or beyond). We evaluate the effectiveness of our algorithms using realistic LTE system data provided by the eNB Layer1 team of Alcatel-Lucent. Results show that the classification of UE's speed required by LTE can be achieved with high accuracy. In addition, they have minimal impact to the central processing unit (CPU) and the memory of eNB modem. We see that they are very practical to today's LTE networks and would allow a continuous and real-time UE speed estimation.

Coordination between neighboring cells is intended to be implemented in future mobile networks, since it promises significant performance gains. Despite low-latency cooperation made possible by Cloud Radio Access Networks (C-RAN), practical feasibility and improvements brought to a real system were still to be evaluated. We define in this paper an architecture based on the abstraction and scalability provided by Software Defined Networking (SDN) enabling multi-cell coordination both on the uplink and downlink. We also evaluate gains offered by the proposed coordination algorithms under practical conditions. The described proof-of-concept platform shows not only why multi-cell cooperation is useful, but also how to make it happen.

The major issue of indoor localization system is the trade-off between implementation cost and accuracy. A low-cost system which demands only few hardware devices could save the cost but often it turns out to be less reliable. Aiming at improving classical triangulation method that requires several reference points, this paper proposes a new method, called Two-Step Movement (2SM), which requires only one reference point (RP) by exploiting useful information given by the position change of a mobile terminal (MT), or the user movement. This method can minimize the number of reference points required in a localization system or navigation service and reduce system implementation cost. Analytical result shows that the user position can be thus derived and given in simple closed-form expression. Finally, simulation is conducted to demonstrate its effectiveness under noisy environment.

The co-existence of small cells and macro cells is a key feature of 4G and future networks. This heterogeneity, together with the increased mobility of user devices can generate a high handover frequency that could lead to unreasonably high call drop probability or poor user experience. By performing smart mobility management, the network can pro-actively adapt to the user and guarantee seamless and smooth cell transitions. In this work, we introduce an algorithm that takes as input sounding reference signal (SRS) measurements available at the base station (eNodeB in 4G systems) to estimate with a low computational requirement the mobility level of the user and with no modification at the user device/equipment (UE) side. The performance of the algorithm is showcased using realistic data and mobility traces. Results show that the classification of UE speed to three mobility classes can be achieved with accuracy of 87% for low mobility, 93% for medium mobility, and 94% for high mobility, respectively.

In this paper we propose a distributed algorithm for jointly optimizing almost blank subframe (ABS) and cell selection bias (CSB) patterns in Long Term Evolution- Advanced (LTE-A) heterogeneous networks (HetNets). We formulate the optimization problem as an exact potential game, where a Nash equilibrium point is guaranteed to be achieved within finite number of plays. Through simulations, we are able to demonstrate the fast convergence of the algorithm, an increase in average user rate, and a tremendous improvement on the service fairness of the users.

To optimize the network utility in 3GPP Long Term Evolution-Advanced (LTE-A) heterogeneous networks (HetNets), it is necessary to jointly consider inter-cell interference mitigation and user attachment. Based on potential game formulation, we optimize almost blank subframe (ABS) and/or cell selection bias (CSB) settings for both macrocells and picocells in a distributed manner. We demonstrate the need of joint ABS and CSB optimization via simulation case studies. Extensive simulations confirm that joint ABS and CSB optimizations can lead to a 20% improvement in spectral efficiency and a 46% improvement in energy efficiency while increasing the fairness of the achieved rates of users.

This paper proposes two novel and practical designs of angle diversity receivers to achieve multiple-input-multiple-output (MIMO) capacity for indoor visible light communications (VLC). Both designs are easy to construct and suitable for small mobile devices. By using light emitting diodes for both illumination and data transmission, our receiver designs consist of multiple photodetectors (PDs), which are oriented with different inclination angles to achieve high-rank MIMO channels and can be closely packed without the requirement of spatial separation. Due to the orientations of the PDs, the proposed receiver designs are named pyramid receiver (PR) and hemispheric receiver (HR). In a PR, the normal vectors of PDs are chosen the same as the normal vectors of the triangle faces of a pyramid with equilateral N-gon base. On the other hand, the idea behind HR is to evenly distribute the PDs on a hemisphere. Through analytical investigation, simulations and experiments, the channel capacity and bit-error-rate (BER) performance under various settings are presented to show that our receiver designs are practical and promising for enabling VLC-MIMO. In comparison to induced link-blocked receiver, our designs do not require any hardware adjustment at the receiver from location to location so that they can support user mobility. Besides, their channel capacities and BER performance are quite close to that of link-blocked receiver. Meanwhile, they substantially outperform spatially-separated receiver. This study reveals that using angle diversity to build VLC-MIMO system is very promising.

Heterogeneous network (HetNet) [1] is considered as an energy efficient system structure to alleviate the problem of rapidly increasing power consumption in the wireless communication system. Significant research on HetNet energy efficiency has been conducted. However, most of them only consider power consumption of Base Stations (BSs) while ignoring influence on energy efficiency of Mobile Devices (MDs) brought by new BSs deployment. In this work, we propose a novel power saving metric for HetNet. Under the coexisting scenario of a single macrocell and a single microcell, we analyze the changes in power consumption at both the BSs side and the MDs side with the deployment of a micro BS. Optimum microcell radii for maximum power saving at the MDs sides and for highest network energy efficiency are obtained through analytical studies. It is found that total power saving for microcell MDs is close to 18% with a proper deployment of a microcell. Finally, extensive simulations have been provided to establish the accuracy of our theoretical analyses.

Handover measurement is responsible for finding a handover target and directly decides the performance of mobility management. It is governed by a complex combination of parameters dealing with multicell scenarios and system dynamics. A network design has to offer an appropriate handover measurement procedure in such a multiconstraint problem. This paper proposes a unified framework for the network analysis and optimization. The exposition focuses on the stochastic modeling and addresses its key probabilistic events, namely: suitable handover target found; service failure; handover measurement triggering; and handover measurement withdrawal. We derive their closed-form expressions and provide a generalized setup for the analysis of handover measurement failure and target cell quality by the best signal quality and level crossing properties. Finally, we show its application and effectiveness in today's 3GPP-LTE cellular networks.

Heterogeneous cellular networks are composed of a mixture of macro and small (pico) cells embedded in the macro network. A greedy strategy is proposed to maximize network capacity. Picos placement and either macro or pico transmit power are optimized jointly, as picos are deployed. The scaling of area spectral efficiencies (ASE) as pico densities are increased is studied. Median ASEs grow linearly with density and are comparable to cell-splitting given fixed pico power -20dB EIRP relative to the macro. The effectiveness of the pico at such low power is due in part to the improvement in area coverage of an omni vs. a 3 sectored antenna.

This paper studies the problem of self-organizing heterogeneous LTE systems. We propose a model that jointly considers several important characteristics of heterogeneous LTE system, including the usage of orthogonal frequency division multiple access (OFDMA), the frequency-selective fading for each link, the interference among different links, and the different transmission capabilities of different types of base stations. We also consider the cost of energy by taking into account the power consumption, including that for wireless transmission and that for operation, of base stations and the price of energy. Based on this model, we aim to propose a distributed protocol that improves the spectrum efficiency of the system, which is measured in terms of the weighted proportional fairness among the throughputs of clients, and reduces the cost of energy. We identify that there are several important components involved in this problem. We propose distributed strategies for each of these components. Each of the proposed strategies requires small computational and communicational overheads. Moreover, the interactions between components are also considered in the proposed strategies. Hence, these strategies result in a solution that jointly considers all factors of heterogeneous LTE systems. Simulation results also show that our proposed strategies achieve much better performance than existing ones.

We consider coordination in a multi-user multiple input single output cellular system. In contrast with existing base station cooperation methods that rely on sharing CSI with or without user data to manage interference, we propose to share user data only. We consider a system where blind interference alignment (BIA) is applied to serve multiple users in each cell. We apply interference coordination through data sharing to mitigate other-cell interference at the cell-edge users. While BIA mitigates intra-cell interference in MU-MISO systems, it does not address the problem of inter-cell interference. We apply interference coordination through data sharing to mitigate inter-cell interference at the cell-edge users. We propose a new cooperative BIA scheme that takes into account the users whose data is being shared between adjacent base stations. We derive the achievable sum rate with interference mitigation and we compare it to achievable rates with the original BIA strategy. Numerical results show that the achievable sum rate of the cell-edge users with data sharing decreases with increasing number of served users in each cell and increasing number of antennas at the base stations.

We consider the problem of LTE network self organization and optimization of resource allocation. One particular challenge for LTE systems is that, by applying OFDMA, a transmission may use multiple resource blocks scheduled over the frequency and time. There are three key components involved in the resource allocation and network optimization: resource block scheduling, power control, and client association. We propose a distributed protocol that aims to achieve weighted proportional fairness (WPF) among clients by jointly consider them. The cross-layer design includes: (i) an optimal online policy for resource block scheduling, (ii) a heuristic for transmit power control, and (iii) a selfish strategy for client association. The proposed scheme only requires limited local information exchange and thus can be easily implemented for large networks. Simulation results have shown its effectiveness in both the system throughput and user fairness.

Small cell network is designed to provide mobile services to hot spots by deploying a large number of small access points (APs). As traditional network deployment requires costly AP location acquisition, cost-effective network deployment is necessary for small cell networks. We investigate this question by studying the network performance in terms of spatial outage and throughput of a completely random topology in comparison to that of a perfectly regular topology. Using a stochastic geometry model of user SINR in a random topology, our results show that the performance gap in terms of user SINR guarantee becomes narrow when the network density increases during the network densification. By a massive deployment, the loss is about 1 dB. Besides, it is at about 18% loss in user average throughput. These comparative results would provide helpful information to choose an appropriate deployment. In particular, as far as this relatively small performance loss can be compensated by other network control algorithms, the massive random deployment of a small cell network becomes attractive considering the cost reduction by the given deployment freedom.

This paper deals with the handover measurement in cellular networks. The work is dedicated to network modeling and performance evaluation. The exposition concentrates on temporal evolution of the scanning process with key events: (i) a suitable handover target found, (ii) call drop or service failure, and (iii) scan withdrawal. By observations on the stochastic properties of best signal quality and minimum-duration outage, we determine the probability of the key elements and provide a generalized framework for network design and analysis such as in scanning failure probability and target cell expected quality. Results obtained with LTE have also shown its effectiveness.

In this paper, we propose an autonomous radio resource allocation and optimization scheme that chooses the transmit power and precoding vector among codebooks for multiple antennas transmitters to improve spectral and power efficiency and provide user fairness. Network self-optimization is an essential feature for supporting the cell densification in future wireless cellular systems. The proposed self-optimization is inspired by Gibbs sampler. We show that it can be implemented in a distributed manner and nevertheless achieves system-wide optimization which improves network throughput, power utilization efficiency, and overall service fairness. In addition, we extend the work and include power pricing to parametrize and enhance energy efficiency further. Simulation results show that the proposed scheme can outperform today's default modes of operation in network throughput, energy efficiency, and user fairness.

We propose and analyze a class of distributed algorithms performing the joint optimization of radio resources in heterogeneous cellular networks made of a juxtaposition of macro and small cells. We show that within this context, it is essential to use algorithms able to simultaneously solve the problems of channel selection, user association and power control. In such networks, the unpredictability of the cell and user patterns also requires self-optimized schemes. The proposed solution is inspired from statistical physics and is based on Gibbs sampler. It can be implemented in a fully distributed way and nevertheless achieves minimal system-wide potential delay. Simulation results show that it outperforms today's default operational methods in both throughput and energy efficiency.

In this work, we develop mathematical and algorithmic tools for the self-optimization of mobile cellular networks. Scalable algorithms which are based on local measurements and do not require heavy coordination among the wireless devices are proposed. We focus on the optimization of transmit power and of user association. The method is applicable to both joint and separate optimizations. The global utility minimized is linked to potential delay fairness. The distributed algorithm adaptively updates the system parameters and achieves global optimality by measuring SINR and interference. It is built on Gibbs' sampler and offers a unified framework that can be easily reused for different purposes. Simulation results demonstrate the effectiveness of the algorithm.

Conflict-avoiding codes are used in the multiple-access collision channel without feedback. The number of codewords in a conflict-avoiding code is the number of potential users that can be supported in the system. In this paper, a new upper bound on the size of constant-weight conflict-avoiding codes is proved. This upper bound is general in the sense that it is applicable to all code lengths and all Hamming weights. Several existing constructions for conflict-avoiding codes, which are known to be optimal for Hamming weights equal to four and five, are shown to be optimal for all Hamming weights in general.

Protocol sequences are used for channel access in the collision channel without feedback. Each user is assigned a deterministic zero-one pattern, called protocol sequence. The zeros and ones in a protocol sequence are read out periodically, and a packet is sent if and only if it is one. A collision occurs if two or more users transmit at the same time. Due to the lack of feedback from the receiver and cooperation among users, the beginning of the protocol sequences cannot be synchronized and relative delay offsets are incurred. We study the design of protocol sequences from two different perspectives. Under the first one, called shift invariance, we aim at minimizing the fluctuation of throughput due to relative delay offsets. As for the second one, called user irrepressibility, we want to guarantee that each user can send at least one packet successfully in each period. For both design criteria, we derive a lower bound on sequence period and give an optimal construction that achieves this lower bound.

This paper deals with the weighted sum rate maximization problem in wireless networks consisting of multiple source-destination pairs. Since the optimization problem is non-convex, there are multiple local maxima. Here, we propose a simple iterative power control algorithm, namely round-robin (RR) power control, which has a low computational complexity. By comparing against benchmark problem instances, we show by simulation that the proposed algorithm converges to the global maximum with very high probability. Besides, a distributed implementation of the RR algorithm is established. The performance is satisfactory and the result is potential for practical use.

A two-hop neighborhood information-based routing protocol is proposed for real-time wireless sensor networks. The approach of mapping packet deadline to a velocity is adopted as that in SPEED; however, our routing decision is made based on the novel two-hop velocity integrated with energy balancing mechanism. Initiative drop control is embedded to enhance energy utilization efficiency, while reducing packet deadline miss ratio. Simulation and comparison show that the new protocol has led to lower packet deadline miss ratio and higher energy efficiency than two existing popular schemes. The result has also indicated a promising direction in supporting real-time quality-of-service for wireless sensor networks.

The authors consider collision channel without feedback in which collided packets are considered unrecoverable. For each user, the transmission of packets follows a specific periodical pattern, called the protocol sequence. Due to the lack of feedback, the beginning of the protocol sequences cannot be synchronized and nonzero relative offsets are inevitable. It results in variation of throughput. In this paper, we investigate optimal protocol sequence sets, in the sense that the throughput variance is zero. Such protocol sequences are said to be shift-invariant (SI). The characterizing properties of SI protocol sequences are presented. We also prove that SI sequences are identifiable, meaning that the receiver is able to determine the sender of each successfully received packet without any packet header. A general construction of SI sequences that meets the lower bound on sequence length is given. Besides, we study the least periods of SI sequences, and show that the least periods must be distinct in some cases. The throughput performance is compared numerically with other protocol sequences.

In this paper, a family of linear congruence sequences with interesting cross-correlation properties is investigated for potential applications in defining new multiple access protocols for distributed wireless systems. One can show that for any finite subset of the sequences with rate sum not exceeding a certain level, there cannot have enough collisions to completely block any particular user no matter how they are shifted with respect to one another. The user un-suppressibility and service guarantee can be exploited in many applications such as wireless sensor or impulse radio systems. To enhance the system's allowable rate sum while possessing the non-blocking property, new protocol sequences are designed. Besides, the throughput shift-invariant property is obtained.

This paper deals with a class of power control problems where the system link gains are assumed to be time varying and SIR estimates are allowed to be corrupted with bounded noises. A simple distributed algorithm of fixed-step power control is devised and the feedback requires only local information. As a generalization of the power control algorithm proposed by Sung and Wong, we have obtained a more robust solution which can handle time varying link gains and measurement noises. Convergence of the new algorithm is analyzed and numerical studies show that it is effective.

In integrated wireless multimedia service, isochronous traffic of different connections can be scheduled by using a most regular binary sequence (MRBS). Such a sequence schedules traffic in an evenly spaced manner to achieve any arbitrary rate asymptotically and while avoiding excessive delay or buffering requirement. Flexible slot assignment that can match requests exactly improves bandwidth efficiency in multirate operations. The most regular binary sequence provides a distributed solution for multiaccess control that is based on limited information exchange. As a generalization, the concept of a most regular code sequence (MRCS) is proposed to support variable rate transmission in wideband code division multiple access (CDMA) systems and to provide spreading factor (SF) optimization. This scheme improves channel utilization efficiency in supporting traffics of various classes and hence results in an overall capacity gain.