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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 Key Performance Indicators (KPIs) 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 the 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 latency.

Intelligent reflecting surfaces (IRSs) are being widely investigated as a potential low-cost and energy-efficient alternative to active relays for improving coverage in next-generation cellular networks. However, technical constraints in the configuration of IRSs should be taken into account in the design of scheduling solutions and the assessment of their performance. To this end, we examine an IRS-assisted time division multiple access (TDMA) cellular network where the reconfiguration of the IRS incurs a communication cost; thus, we aim at limiting the number of reconfigurations over time. Along these lines, we propose a clustering-based heuristic scheduling scheme that maximizes the cell sum capacity, subject to a fixed number of reconfigurations within a TDMA frame. First, the best configuration of each user equipment (UE), in terms of joint beamforming and optimal IRS configuration, is determined using an iterative algorithm. Then, we propose different clustering techniques to divide the UEs into subsets sharing the same suboptimal IRS configuration, derived through distance- and capacity-based algorithms. Finally, UEs within the same cluster are scheduled accordingly. We provide extensive numerical results for different propagation scenarios, IRS sizes, and phase shifters quantization constraints, showing the effectiveness of our approach in supporting multi-user IRS systems with practical constraints.

The remote wireless control of industrial systems is one of the major use cases for 5G and beyond systems: in these cases, the massive amounts of sensory information that need to be shared over the wireless medium may overload even high-capacity connections. Consequently, solving the effective communication problem by optimizing the transmission strategy to discard irrelevant information can provide a significant advantage, but is often a very complex task. In this work, we consider a prototypal system in which an observer must communicate its sensory data to a robot controlling a task (e.g., a mobile robot in a factory). We then model it as a remote Partially Observable Markov Decision Process (POMDP), considering the effect of adopting semantic and effective communication-oriented solutions on the overall system performance. We split the communication problem by considering an ensemble Vector Quantized Variational Autoencoder (VQ-VAE) encoding, and train a Deep Reinforcement Learning (DRL) agent to dynamically adapt the quantization level, considering both the current state of the environment and the memory of past messages. We tested the proposed approach on the well-known CartPole reference control problem, obtaining a significant performance increase over traditional approaches.

Future wireless networks and sensing systems will benefit from access to large chunks of spectrum above 100 GHz, to achieve terabit-per-second data rates in 6th Generation (6G) cellular systems and improve accuracy and reach of Earth exploration and sensing and radio astronomy applications. These are extremely sensitive to interference from artificial signals, thus the spectrum above 100 GHz features several bands which are protected from active transmissions under current spectrum regulations. To provide more agile access to the spectrum for both services, active and passive users will have to coexist without harming passive sensing operations. In this paper, we provide the first, fundamental analysis of Radio Frequency Interference (RFI) that large-scale terrestrial deployments introduce in different satellite sensing systems now orbiting the Earth. We develop a geometry-based analysis and extend it into a data-driven model which accounts for realistic propagation, building obstruction, ground reflection, for network topology with up to 105 nodes in more than 85 km2. We show that the presence of harmful RFI depends on several factors, including network load, density and topology, satellite orientation, and building density. The results and methodology provide the foundation for the development of coexistence solutions and spectrum policy towards 6G.

Over the past few years, the concept of Virtual Reality (VR) has attracted increasing interest thanks to its extensive industrial and commercial applications. Currently, the 3D models of the virtual scenes are generally stored in the VR visor itself, which operates as a standalone device. However, applications that entail multi-party interactions will likely require the scene to be processed by an external server and then streamed to the visors. However, the stringent Quality of Service (QoS) constraints imposed by the VR's interactive nature require Network Slicing (NS) solutions, for which profiling the traffic generated by the VR application is crucial. To this end, we collected more than 4 hours of traces in a real setup and analyzed their temporal correlation, focusing on the Constant Bit Rate (CBR) encoding mode, which should generate more predictable traffic streams. From the collected data, we then distilled two prediction models for future frame size, which can be instrumental in the design of dynamic resource allocation algorithms. Our results show that even the state-of-the-art H.264 CBR mode may have significant frame size fluctuations, impacting NS optimization. We then exploited the models to dynamically determine requirements in an NS scenario, providing the required QoS while minimizing resource usage.

Massive Internet of Things systems, e.g., Low Power Wide Area Networks, aim at connecting very large numbers of low-cost devices with multi-year battery life requirements, a goal that is hard to achieve with current technologies. In this paper, a novel asynchronous spread spectrum technique, called Golden Modulation, is introduced. This modulation provides a vast family of equivalent waveforms with very low cross-correlation even in asynchronous conditions, hence enabling naturally massive multiuser operation without the need for inter-user synchronization or interference cancellation receivers. The basic modulation principles, which rely on spectrum spreading via direct Zadoff-Chu sequences modulation, are presented and the corresponding theoretical bit error rate performance in additive white Gaussian noise and multi-path channels is derived and compared by simulation with realistic receiver performance. The demodulation of the Golden Modulation is described, and its performance in the presence of uncoordinated multiple users is characterized and compared against LoRa in a variety of scenarios. Various higher-layer and backward compatibility issues are also discussed.

With the introduction of Orthogonal Frequency Division Multiple Access (OFDMA) in 802.11ax, the role of the Access Point (AP) in Wi-Fi networks changes significantly, thanks to the opportunity of implementing more complex scheduling logic to handle Downlink (DL) traffic flows and simultaneously act as coordinator of Multi User (MU) Uplink (UL) transmissions. In this context, it becomes necessary to develop reliable network analysis and simulation tools that allow for an in-depth investigation of the trade-offs involved in the usage of OFDMA, especially considering that the standard leaves much of the actual scheduling algorithmic details to vendor-specific implementation. In this work we present a series of results highlighting how several network settings have an impact on throughput and Head-of-Line Delay, in a network that employs multiple 802.11ax features such as OFDMA and the MU Enhanced Distributed Channel Access (EDCA) Parameter Set, while also containing legacy devices. The results are obtained via both the newly re-designed ns-3 wifi module and an original analytical framework, based on the well-established Bianchi 802.11 model.

Network slicing is a key feature of 5G and beyond networks, allowing the deployment of separate logical networks (network slices), sharing a common underlying physical infrastructure, and characterized by distinct descriptors and behaviors. The dynamic allocation of physical network resources among coexisting slices should address a challenging trade-off: to use resources efficiently while assigning each slice sufficient resources to meet its service level agreement (SLA). We consider the allocation of time-frequency resources from a new perspective: to design a control algorithm capable of learning over the operating network, while keeping the SLA violation rate under an acceptable level during the learning process. For this purpose, traditional model-free reinforcement learning (RL) methods present several drawbacks: low sample efficiency, extensive exploration of the policy space, and inability to discriminate between conflicting objectives, causing inefficient use of the resources and/or frequent SLA violations during the learning process. To overcome these limitations, we propose a model-based RL approach built upon a novel modeling strategy that comprises a kernel-based classifier and a self-assessment mechanism. In numerical experiments, our proposal, referred to as kernel-based RL, clearly outperforms state-of-the-art RL algorithms in terms of SLA fulfillment, resource efficiency, and computational overhead.

Current advancements in waterborne autonomous systems, together with the development of cloud-based service-oriented architectures and the recent availability of low-cost underwater acoustic modems and long-range above-water wireless devices, enable the development of new applications to support ships and port activities. Autonomous surface vehicles can, for instance, be used to perform bathymetry and environmental data collection tasks to ensure under keel clearance and to monitor the quality of the water. Similarly, remotely operated vehicles can be deployed to inspect ship hulls and typical port infrastructure elements, such as quay and sheet pilling walls. In this article, we present the complete system deployed for the small-scale demonstrations of the Robotic Vessels as-a-Service project, which introduces an on-demand service-based cloud system that dispatches unmanned vehicles capable of performing the required service either autonomously or piloted. These vessels are able to interact with sensors deployed in the port and with the shore station through an integrated underwater and above-water network. The developed system has been validated through sea trials and showcased through an underwater sensor data collection use case. The results of the test presented in this article provide a proof of concept of the system design and indicate its technical feasibility. It also shows the need for further developments for a mature technology allowing on-demand robotic maritime assistance services in real operational scenarios.

Non-Terrestrial Networks (NTNs) are expected to be a key component of 6th generation (6G) networks to support broadband seamless Internet connectivity and expand the coverage even in rural and remote areas. In this context, High Altitude Platforms (HAPs) can act as edge servers to process computational tasks offloaded by energy-constrained terrestrial devices such as Internet of Things (IoT) sensors and ground vehicles (GVs). In this letter, we analyze the opportunity to support Vehicular Edge Computing (VEC) via HAP in a rural scenario where GVs can decide whether to process data onboard or offload them to a HAP. We characterize the system as a set of queues in which computational tasks arrive according to a Poisson arrival process. Then, we assess the optimal VEC offloading factor to maximize the probability of real-time service, given latency and computational capacity constraints.

The development of small low-cost autonomous underwater and surface vehicles has increased the need for underwater wireless communication and ranging to support swarm operations for collaborative data collection efforts. Specifically, newly available low-cost underwater and surface vehicles make the realization of swarm formation cost effective. However, their mission coordination involves the use of expensive acoustic modems whose price may be higher than that of the vehicle itself. In this paper, we describe and evaluate a low-cost software-defined acoustic modem developed with only off-the-shelf components, able to perform one-way travel-time ranging. The modem is specifically designed for dense mobile networks deployed in shallow water environments, such as rivers, lagoons and lakes. The modular software design of the modem allows us to easily configure parameters such as modulation and coding schemes, scheduling algorithm, source power, carrier frequency and bandwidth. In this paper we evaluate, in a shallow environment, the modem performance in terms of packet detection ratio, packet delivery ratio, and one-way travel-time ranging.

Accurate scene understanding from multiple sensors mounted on cars is a key requirement for autonomous driving systems. Nowadays, this task is mainly performed through data-hungry deep learning techniques that need very large amounts of data to be trained. Due to the high cost of performing segmentation labeling, many synthetic datasets have been proposed. However, most of them miss the multi-sensor nature of the data, and do not capture the significant changes introduced by the variation of daytime and weather conditions. To fill these gaps, we introduce SELMA, a novel synthetic dataset for semantic segmentation that contains more than 30K unique waypoints acquired from 24 different sensors including RGB, depth, semantic cameras and LiDARs, in 27 different weather and daytime conditions, for a total of more than 20M samples. SELMA is based on CARLA, an open-source simulator for generating synthetic data in autonomous driving scenarios, that we modified to increase the variability and the diversity in the scenes and class sets, and to align it with other benchmark datasets. As shown by the experimental evaluation, SELMA allows the efficient training of standard and multi-modal deep learning architectures, and achieves remarkable results on real-world data. SELMA is free and publicly available, thus supporting open science and research.

In telecommunications, network service accessibility as a requirement is closely related to equitably serving the population residing at locations that can most appropriately be described as remote. Remote connectivity, however, would have benefited from a more inclusive consideration in the existing generations of mobile communications. To remedy this, sustainability and its social impact are being positioned as key drivers of the sixth generation's (6G) research and standardization activities. In particular, there has been a conscious attempt to understand the demands of remote wireless connectivity, which has led to a better understanding of the challenges that lie ahead. In this perspective, this article overviews the key challenges associated with constraints on network design and deployment to be addressed for providing broadband connectivity to rural areas, and proposes novel approaches and solutions for bridging the digital divide in those regions.

Shark attacks are a rare but ever-present danger for swimmers and surfers in some regions of the world, and the threat of sharks is embedded in popular culture. Traditionally, shark attack mitigation involved the culling of massive numbers of sharks, which has significant environmental and ethical downsides. More recent systems for mitigating the risk of shark attacks involve the manual or automated detection of sharks close to the shore, alerting water users to the potential danger when it occurs and evacuating the water if the shark gets too close. In this work, we present the design of a Shark Warning Acoustic Network (SWAN) that exploits underwater acoustic sensing and communication to automate the spotting, providing a highly accurate and relatively low-cost alternative to visual spotting. We analyze the performance of the SWAN in terms of communication performance and accuracy in alerting water users to dangerous situations, and compare different medium access schemes to identify the most effective network design.

We describe a scheduling protocol for multimodal networks of relatively limited size, whose nodes encompass various underwater communication technologies. For such a case, we show that significant improvement in the network operations is possible when the transmission schedule is set to jointly utilize all communication technologies. Our solution is based on per-technology time-division multiple access frames, whose time slots are determined optimally to maximize the overall channel utilization while preserving flow limitations and maintaining fairness in resource allocation. Our numerical simulations and experimental results for multimodal networks with several acoustic technologies show that, while maintaining a fair resource allocation, our scheduling solution provides both high throughput and low packet delivery delay.

Future 5G networks will likely include mmWave radio access communication links, because of their potential multi-gigabit-per-second capacity. However, these frequencies are characterized by highly dynamic channel conditions, which lead to wide fluctuations in the received signal quality. This article explains how the end-to-end user experience in mobile mmWave networks could be affected by a suboptimal interaction between the most widely used transport protocol, TCP, and mmWave links. It also provides insights on the throughput-latency tradeoff when Multipath TCP (MP-TCP) is used judiciously across various links, such as Long-Term Evolution (LTE) and mmWave.

We propose a scheme that opportunistically exploits received power diversity across different packets to both favor spatial reuse in underwater acoustic networks and limit the exposed terminal problem. Since the power attenuation in the underwater acoustic channel is large, such power differences are common. This situation, also referred to as the near-far effect and often considered a problem, is converted into a resource by means of multipacket reception (MPR). Yet, even without MPR capability our scheme offers performance benefits. Our scheme is simple, lightweight and distributed, and can be easily implemented over any protocol based on carrier-sense multiple access with collision avoidance. Our results show that higher throughput and lower transmission delay are achieved compared to a benchmark channel access protocol. Our results are validated and demonstrated in a lake experiment. To allow reproducibility, the implementation of our scheme is publicly available.

Connectivity is probably the most basic building block of the IoT paradigm. Up to now, the two main approaches to provide data access to things have been based on either multihop mesh networks using short-range communication technologies in the unlicensed spectrum, or long-range legacy cellular technologies, mainly 2G/GSM/GPRS, operating in the corresponding licensed frequency bands. Recently, these reference models have been challenged by a new type of wireless connectivity, characterized by low-rate, long-range transmission technologies in the unlicensed sub-gigahertz frequency bands, used to realize access networks with star topology referred to as low-power WANs (LPWANs). In this article, we introduce this new approach to provide connectivity in the IoT scenario, discussing its advantages over the established paradigms in terms of efficiency, effectiveness, and architectural design, particularly for typical smart city applications.

This letter studies the interference-constrained performance of spectrum sharing networks using adaptive power allocation. The throughput of the secondary user is derived in different cases with constraints on the primary user average or instantaneous received interference power. The analysis is based on some recent results on the achievable rates of finite-length codes and shows the effect of the codeword length on the performance of spectrum sharing networks. The results indicate that, with codewords of finite length and different primary user's interference constraints, there is potential for spectrum sharing, if the transmission power is properly adapted.

Coordination and cooperation among transmitters are fundamental paradigms to improve the performance of next-generation mobile networks. By grouping multiple transmitters into clusters, interference can be managed to improve the performance at the end user's side. In this letter, we present a novel distributed clustering algorithm that adapts the cluster configuration according to the users distribution and the average cluster size. We compare the performance obtained with other clustering solutions in an LTE scenario using the open-source network simulator ns3. Our proposed algorithm is shown to significantly outperform the other approaches, particularly for the users with low signal-to-noise ratio (SNR), with a 26% improvement of throughput for the worst 10% of the low-SNR users compared with greedy clustering.

Multipath routing protocols trade off the effort of replicating data packets through multiple routes for improved delivery ratio or end-to-end delay. These advantages are especially valuable for those underwater networking applications where reliable data delivery justifies a higher resource consumption. In this letter, we argue that choosing multipath routes according only to the node- and link-disjoint paradigms may lead to excessive interference in underwater networks, even in the presence of MAC protocols based on interference avoidance. We show that it is more convenient to directly choose multipath routes that cause little interference to one another, and propose a multipath routing protocol that distributedly implements this concept. We simulate our solution in underwater network scenarios, and show that it achieves better packet delivery ratio and fewer interference-induced packet losses with respect to standard multipath routing approaches, even when the latter are stacked on top of interference-avoiding MAC protocols.

This letter studies the power-limited throughput of a communication system utilizing incremental redundancy (INR) hybrid automatic repeat request (HARQ). We use some recent results on the achievable rates of finite-length codes to analyze the system performance. With codewords of finite length, we derive closed-form expressions for the outage probabilities of INR HARQ and study the throughput in the cases with variable-length coding. Moreover, we evaluate the effect of feedback delay on the throughput and derive sufficient conditions for the usefulness of the HARQ protocols, in terms of power-limited throughput. The results show that, for a large range of HARQ feedback delays, the throughput is increased by finite-length coding INR HARQ, if the sub-codeword lengths are properly adapted.

In this article, we present the current status of the Internet of Things, and discuss how the current situation of many "Intranets" of Things should evolve into a much more integrated and heterogeneous system. We also summarize what in our opinion are the main wirelessand mobility-related technical challenges that lie ahead, and outline some initial ideas on how such challenges can be addressed in order to facilitate the IoT's development and acceptance in the next few years. We also describe a case study on the IoT protocol architecture.

Cognitive radios operate in a particularly challenging wireless environment. Besides the strict requirements imposed by opportunistic coexistence with licensed users, cognitive radios may have to deal with other concurrent (either malicious or selfish) cognitive radios that aim at gaining access to the available spectrum resources with no regard to fairness or other behavioral etiquettes. By taking advantage of their highly flexible RF front-ends, they are able to mimic a licensed user's behavior or simply jam a given channel with high power. This way these concurrent users (jammers) are capable of interrupting or delaying the neighbor discovery process initiated by a cognitive radio, which is interested in using a portion of the available spectrum for its own data communications. To solve this problem we propose a Jamming Evasive Network-coding Neighbor-Discovery Algorithm (JENNA), which ensures complete neighbor discovery for a cognitive radio network in a distributed and asynchronous way. We compare the proposed algorithm with baseline schemes that represent existing solutions, and validate its feasibility in a single-hop cognitive radio network.

This paper deals with some of the main problems that affect directional antenna ad hoc networks, especially deafness and collisions. By means of a thorough study of the possibilities offered by an antenna array, we propose cooperation as a novel technique for deafness suppression, together with a low complexity multiuser detector and a collision avoidance algorithm tailored for this type of networks. Building on these ideas, a new family of protocols (called Cooperative MAC) is designed. A lower and an upper bound on its performance are compared with the Circular RTS/CTS protocol (CRCM), a known benchmark in this field. We are able to achieve interesting improvements.

Underwater sensor networks are a very interesting case of wireless communication in extreme conditions. They exploit acoustic communication in the water and are nowadays used in surveillance and monitoring applications. These networks present very challenging aspects, such as low data rates and large delays, as well as the special propagation characteristics of the underwater medium. We propose an integer-linear programming approach to jointly optimize routing, link-scheduling and node placement in such a scenario. Accounting for these special aspects of underwater wireless communications leads to re-thinking traditional approaches; this results in original solutions, which highlight novel directions for further research in this area.

Multicellular networks based on the IEEE 802.16 standard appear to be very promising candidates to provide end users with broadband wireless access. However, they also pose interesting challenges in terms of radio resource management, where several design choices are not specified in the standard, intentionally left open to implementors. For this reason, we focus in this article on scheduling and resource allocation, and investigate how they could operate in a cross-layer fashion. In particular, we describe the principles of joint scheduling and resource allocation for IEEE 802.16 operating in AMC mode, and discuss the critical role played by physical layer considerations, especially intercell interference estimation and channel state awareness, in the obtained performance. This leads to identifying key open issues and possible general solutions

We investigate Wireless LAN hot-spots based on the IEEE 802.11b protocol, considering technical and economic issues of the Radio Resource Allocation. Firstly, we discuss how to model the trade-off between perceived QoS and paid price in the users' request, so as to represent the users as choosing the most satisfactory allocation, determined by service requirements and willingness to pay. After the setup of the users' requests, the multiple medium access mechanism is considered and the network performance is evaluated and discussed. Thus, we investigate the provider's task of having a suitable price policy which gives a satisfactory income and efficiently exploit network capacity. This is also dependent on a price setting that is accepted by the users and optimises resource usage. Finally, we study how the multiple access scheme specified in the IEEE 802.11b protocol combines users' requests to a final allocation, and identify possibilities of improvement for the inherent inefficiencies arising from overload.

In this paper, distributed power control is proposed as a means to improve the energy efficiency of routing algorithms in ad hoc networks. Each node in the network estimates the power necessary to reach its own neighbors, and this power estimate is used both for tuning the transmit power (thereby reducing interference and energy consumption) and as the link cost for minimum energy routing. With reference to classic routing algorithms, such as Dijkstra and Link State, as well as more recently proposed ad hoc routing schemes, such as AODV, we demonstrate by extensive simulations that in many cases of interest our scheme provides substantial transmit energy savings while introducing limited degradation in terms of throughput and delay.

In this article we provide a comprehensive review of the existing literature on techniques and protocols for in-network aggregation in wireless sensor networks. We first define suitable criteria to classify existing solutions, and then describe them by separately addressing the different layers of the protocol stack while highlighting the role of a cross-layer design approach, which is likely to be needed for optimal performance. Throughout the article we identify and discuss open issues, and propose directions for future research in the area

The focus of this paper is to analyze the energy consumption performance of various versions of TCP, namely, Tahoe, Reno and NewReno, for bulk data transfer in an environment where channel errors are correlated. We investigate the performance of a single wireless TCP connection by modeling the correlated packet loss/error process (e.g., as induced by a multipath fading channel) as a first-order Markov chain. Based on a unified analytical approach, we compute the throughput and energy performance of various versions of TCP. The main findings of this study are that (1) error correlations significantly affect the energy performance of TCP (consistent with analogous conclusions for throughput), and in particular they result in considerably better performance for Tahoe and NewReno than iid errors, and (2) the congestion control mechanism implemented by TCP does a good job at saving energy as well, by backing off and idling during error bursts. An interesting conclusion is that, unlike throughput, the energy efficiency metric may be very sensitive to the TCP version used and to the choice of the protocol parameters, so that large gains appear possible.

In this paper, a modified form of the ARQ selective repeat protocol with timer control is studied. Transmissions on both the forward and the reverse channels are assumed to experience Markovian errors and therefore the feedback is unreliable. Feedback error recovery is made possible through the use of time diversity. Using results from renewal theory, exact results for the throughput of the protocol are evaluated. In order to overcome the complexity of the exact analytical technique, lower and upper bounds are developed. Simulation results, that confirm the analysis and document the tightness of the bounds are also shown, and a number of implementation issues are discussed. An application to mobile radio is also presented. It is shown that the use of an appropriate degree of time diversity makes it possible to approach the ideal protocol performance obtained with perfect feedback.

In this paper, the slotted ALOHA protocol in a mobile radio environment, in the presence of Ricean fading, twofold antenna diversity and multiple reception capability, is considered. The capture probabilities and the average throughput are computed, and the issue of stability is addressed. It is shown that diversity can improve the system performance, although only to a certain extent. Some extensions are also considered: in particular, the use of a higher degree of diversity is investigated.

In this paper, an ARQ Go-Back-N protocol with time-out mechanism is studied. Transmissions on both the forward and the reverse channels are assumed to be subject to Markovian errors. A recently developed approach, based on renewal theory, is further extended and the steady state average number of packets in the ARQ system is evaluated. This allows us to determine analytically both throughput and transmission delay of the system. Simulation results, that confirm the analysis, are also presented. Based on the analysis, the trade-off involved in the choice of the time-out parameter is identified and discussed.

To enhance the throughput of a slotted random access protocol in a radio communication system, we describe the use of a scheme in which multiple power levels are used at the transmitters. We first consider a situation in which ntransmitters are simultaneously trying to send a packet to a central receiving station using a time‐slotted access protocol, like slotted ALOHA. Each of these transmitters randomly chooses one of mdiscrete power levels during each attempt to send a packet. One of the simultaneously sent packets can often be successfully received due to the power capture effect. We consider two types of capture models: (1) one in which the transmitter with the largest received power captures the channel, and (2) one in which the transmitter captures the channel only if its signal‐to‐interference ratio is above some threshold when received at the central station. In this paper, we determine the optimal transmit probabilities for the power levels as well as the optimal values of the power levels themselves, when their range is constrained and for cases both with and without Rayleigh fading. After determining the precise optimal power levels and probabilities for maximizing the capture probabilities (i.e., for a given n), we propose a less complex, but nearly optimal, approximate approach based on using logarithmically equi‐spaced levels. After demonstrating the closeness of our suboptimal results to the optimal results, we apply our approach to the problem of optimizing the throughput of the slotted ALOHA protocol for a case in which the input traffic is generated according to a Poisson process. Several numerical examples are presented along with a demonstration of how the optimal choice of power levels and probabilities can enhance throughput relative to previous ad hoc methodologies.

The paper compares the performance of two channel-access schemes suitable for the cellular environment, which, in particular, allow the packet capture and can deal with inter-cell interference. The first scheme is the well known S-ALOHA while the second one is the Capture Division Packet Access, recently proposed. The comparison is analytically performed over a common system with a common analytical model. Despite the many analyses appeared on S-ALOHA, the one we develop is new because a throughput density uniformly distributed on the plane is considered in a multiple cell environment. The analysis clearly shows the effect of intra-cell and inter-cell interference on the ALOHA system and quantifies the throughput gain achieved by CDPA, which completely avoids intra-cell interference. Our analysis also provides insight about the effectiveness of power control on both systems.

In this paper, the capture performance of a random access scheme in the presence of Rayleigh fading, shadowing and diversity is studied. The conditional throughput Cn, i.e., the average number of packets which are correctly received per slot, given the number of colliding packets, n, is computed, as well as its limit as n⊒∞. Some different diversity schemes are compared. Also, retransmission control is considered as a means to enhance the system performance. The stability of the controlled system is directly proved. Finally, the effect of long-term attenuations on the system performance and stability is discussed.

Recently, a new method for achieving spectrum reuse in cellular systems, called Capture‐Division Packet Access (CDPA), has been introduced. The method uses a single frequency in all cells, and exploits packet switching and packet retransmission as a means to overcome destructive cochannel interference. As the CDPA key factor is packet retransmission, it is also very effective in fighting lognormal shadowing attenuation as long as this attenuation can be considered independent from slot to slot. In this paper we analyze CDPA in presence of “slow shadowing”, to account for situations in which obstacles obscure the receiver for several transmission periods. The results show a severe throughput impairment with respect to the “fast shadowing” model. A variation of CDPA, Macrodiversity CDPA, that uses three corner fed antennas and three packet receivers is analyzed. The numerical results show that M‐CDPA is more efficient than CDPA and that it is only slightly affected by both fast and slow shadowing. To further investigate its robustness, a non‐lognormal slow shadowing model, the hard shadowing model, is also analyzed.

When a new user enters a cell in a mmWave cellular system, the beamforming directions must be identified to initiate communication, a procedure known as initial access (IA). However, users are more likely to enter from some directions than others (eg, along streets), and beamforming directions (eg, those affected by blockage) may never be used. We exploit the unequal distribution of the entrance direction to speed up the IA procedure, exploring more often the directions wherein the probability of finding new users is higher. Two solutions are proposed: a memory‐less random illumination (MLRI) algorithm and a statistical and memory‐based illumination (SMBI) algorithm. While in MLRI, the direction to be explored is randomly generated according to an optimized distribution, and independent of previously explored directions, in SMBI a precise exploration sequence is designed, thus we take into account previously explored directions. In the analysis, we include the movement of the user within the cell during the IA process, described by a Markov chain whose states correspond to the beamforming directions associated to the user position at a given IA exploration time. We assess the performance of the proposed methods in terms of average discovery time. We exploit the unequal distribution of the entrance direction to speed up the initial access procedure in a mmWave cellular system, exploring more often the directions wherein the probability of finding new users is higher. Two solutions are proposed: a memory‐less random illumination (MLRI) algorithm and a statistical and memory‐based illumination (SMBI) algorithm. In MLRI directions are explored randomly and independently. In SMBI a precise exploration sequence is designed, thus we take into account previously explored directions.