Emerging industrial and massive Internet of Things (IoT) applications increasingly rely on private 5G New Radio (NR) deployments operating in the 3.5 GHz Citizens Broadband Radio Service (CBRS) band. General Authorized Access (GAA) provides a low-cost spectrum opportunity for IoT-specific NR networks, but the absence of interference protection makes uplink reliability highly vulnerable to co-channel interference (CCI), especially in dense IoT deployments. Ensuring dependable IoT connectivity requires interference-aware channel pattern allocation coordinated by the Spectrum Access System (SAS) while accounting for IoT traffic characteristics and gateway constraints. This paper presents a game-theoretic optimization framework for channel pattern allocation for IoT NR small cells operating under the CBRS GAA tier. We first formulate CCI minimization as a binary quadratic program (BQP) and show its nonconvex and NP-hard nature. To address this complexity, we develop two complementary solutions: (i) a cooperative hedonic coalition formation algorithm that enables SAS-assisted clustering of IoT NR gateways, and (ii) a distributed potential game approach where IoT gateways autonomously select channel patterns using log-linear learning. Numerical evaluations under realistic IoT deployment and propagation models demonstrate significant gains in packet delivery ratio (PDR), reduced energy consumption of IoT devices through fewer retransmissions, and improved spectrum reliability compared to existing CBRS coexistence mechanisms. The proposed framework is shown to be robust under asymmetric interference conditions and achieves more equitable per-user reliability than existing coexistence mechanisms. The proposed approaches provide practical and scalable solutions for interference-aware private IoT NR networks in shared mid-band spectrum.

This paper presents a comprehensive study on random access (RA) design for multi-channel Ambient IoT (A-IoT) networks enabled by uplink frequency division multiple access (FDMA). We introduce and unify the three-step RA procedure (RAP) specified in ongoing 3GPP standardization, which serves as a useful reference for future research. To fully exploit the multi-channel capability, we propose enhanced frame structures, including a novel multi-round Msg1 collection (MRMC) scheme, in which Msg1 carries a randomly generated ID from each device and is transmitted in the first step of the three-step RAP. We also develop new analytical models that capture the unique characteristics of multi-channel A-IoT, providing closed-form expressions for RA efficiency that serve as a foundation for network optimization. Furthermore, we design online control algorithms that estimate the device population in real time and dynamically adjust frame configurations for optimal operation under device uncertainty. The proposed Boosted Estimation with Early Frame Termination (BE2FT) algorithm effectively handles bursty traffic conditions and improves resource utilization. Extensive simulations validate the accuracy of our analytical models and demonstrate significant gains in RA efficiency and service time. The proposed scheme achieves performance close to the ideal performance, where the number of devices is assumed perfectly known and the frame length is optimally configured.

Combining integrated sensing and communication (ISAC) and an unmanned aerial vehicle (UAV) can not only save the wireless resource but also enhance the air-ground coverage. However, the high-quality air-ground link of ISAC network is more prone to exposure, and its security is challenging. In this paper, we design a covert air-ground transmission scheme for ISAC, where the sensing signal can be utilized as a mask to disrupt the detection of communication by Willie. Since it is difficult to obtain the accurate knowledge about Willie’s location, we employ the norm-bounded model to describe the uncertainty of location at Willie. To further enhance the covertness, a full-duplex (FD) UAV user is considered to receive the covert signal while transmitting the artificial jamming to confuse Willie. We first calculate the minimum detection error probability (MDEP) by deriving the optimal detection threshold, and we obtain the analytic expression of average MDEP. Then, the covert transmission rate is maximized by controlling beamforming vectors and the UAV trajactory while satisfying the target detection constraint, the covertness constraint as well as the transmit power constraint, which can be resolved by an alternating optimization algorithm. Finally, we present simulation results to verify that the proposed scheme with the FD jamming can better guarantee the covertness of air-ground ISAC.

In this paper, we propose a covert air-ground transmission scheme for integrated sensing and communication (ISAC), where the sensing signal can be utilized as a mask to disrupt the detection of communication by Willie. To further enhance the covertness, a full-duplex (FD) unmanned aerial vehicle (UAV) user is deployed to receive the covert signal while transmitting the artificial jamming to confuse Willie. The minimum detection error probability (MDEP) is first calculated by deriving the optimal detection threshold, and the analytic expression of average MDEP is obtained. Then, the covert transmission rate is maximized while satisfying the target detection constraint, the covertness constraint as well as the transmit power constraint, which can be resolved by an alternating optimization algorithm. Finally, simulation results are presented to demonstrate that the proposed scheme with the FD jamming can better guarantee the covertness of air-ground ISAC.

ALOHA has become an essential random access protocol for low-power wide-area networks (LPWANs) due to its compatibility with low-cost, low-power consumption and long-range communication requirements. However, its inherent throughput limitation of approximately 0.183 packets per packet transmission time for a large user population significantly restricts its scalability and suitability for the rapidly growing number of IoT devices. To surpass this fundamental bound, we propose a non-orthogonal multiple access (NOMA)-based collision resolution scheme that enables devices to initiate immediate contention-resolving retransmissions following a collision event. This approach requires only an additional collision timer for each user to determine collision resolution participation and appropriate transmission power selection, thereby substantially enhancing throughput without incurring additional hardware costs. We present an analytical framework to evaluate the achievable system throughput and develop an online backoff algorithm that leverages an extended Kalman filter (EKF)-based backlog estimator to dynamically maximize system performance. Numerical results confirm the reliability of the proposed scheme, demonstrating that the system throughput can be improved to approximately 0.5 packets per packet transmission time under ideal conditions. Moreover, the EKF-based approach closely approximates optimal throughput and delay performance under both Poisson and bursty traffic conditions.

This study investigates ALOHA with listen-before-talk (LBT) to enhance the scalability of low-power wide-area networks (LPWANs), such as long range (LoRa). The LBT allows devices to sense the channel prior to accessing so that it can mitigate interference by preventing devices from transmitting during ongoing transmissions. However, its effectiveness is compromised by inherent imperfections in channel sensing, such as false negatives and false positives. A false negative occurs when devices incorrectly find the channel idle while it is actually in use. Thus, this leads devices to unintended interferences with ongoing transmissions. A false positive arises when the channel is erroneously sensed as busy, despite the fact that it is free. This deprives devices of access opportunities. This work analyzes the impact of these imperfections of LBT on the performance of ALOHA in terms of throughput, access delay, and system stability. Additionally, we propose an online backoff control algorithm to optimize system performance under imperfect LBT. The results show that even when devices falsely identify the channel as idle or mistakenly detect it as busy nearly half the time, the throughput still outperforms that of ALOHA without LBT. The proposed backoff control algorithm is also shown to be essential to maximize the throughput in the presence of sensing errors. To demonstrate our analysis and algorithm, we incorporate LoRa’s physical layer parameters into simulations and validate the results accordingly.

Machine learning in the application of Industrial Internet of Things (IIoT) has great potential. However, traditional machine learning methods require collecting user data for centralized training, which may disclose sensitive information of enterprises. To solve this problem, federated learning (FL) technology with distributed training has emerged. However, FL is not infallible, as attackers retain the capability to launch inference attacks on the local models, thereby deducing sensitive information about the original data. To address this issue, this paper proposes an accurate and efficient FL solution for IIoT (AEPPFL). It employs blinding techniques to safeguard local model parameters and encrypts the masks using lightweight keys generated from the Computational Diffie-Hellman (CDH) problem. Without obtaining any precise local models from clients, the server can still perform accurate aggregation. Furthermore, this solution operates independently of secure channels and eliminates the need for shared keys. It can protect clients’ local model parameters from inference attacks with minimal precision loss while maintaining low computation and communication overheads. For example, our proposed method demonstrates significant improvements over recent state-of-the-art approaches, achieving at least a 42.3× reduction in runtime when the number of clients is 500 and the data vector size is 50K.

ALOHA can be a viable solution as a light-weight medium access control (MAC) protocol in low power wide area networks (LPWANs) for Internet of Things (IoT). However, the maximum throughput of traditional ALOHA is too low to accommodate a large number of IoT devices. To address this limitation, this work proposes an enhanced ALOHA, where successive interference cancellation (SIC) aids in collision resolution. In the proposed system, each user measures the time interval from their transmission epoch to the end of a collision using a collision timer. Upon a collision, the access point (AP) with SIC and the users’ collision timer work jointly to resolve the collision. This work characterizes the throughput of the proposed system and further proposes an online backoff algorithm to maximize the throughput. Numerical results demonstrate that the proposed ALOHA with SIC-aided collision resolution (SACR) can offer significantly improved throughput compared to slotted ALOHA and the other systems.

The freshness of information is crucial for IoT applications, such as remote sensing systems and real-time status updates. The overabundance of stale information at the destination can potentially compromise the accuracy and reliability of system decision-making processes. To address this concern, a new metric termed the Age of Information (AoI) has been proposed to capture the freshness of status updates. In this article, we present an analytical framework to establish a dual-action guideline for minimizing the average AoI in random access networks. We then utilize this guideline to propose two online activation control protocols: 1) the age-dependent activation control (ADAC) algorithm and 2) the threshold-based ADAC (T-ADAC) algorithm. The former prioritizes the activation of devices with higher instantaneous AoI, while the latter enables a device to be active with a dynamic probability only when its instantaneous AoI is beyond a predetermined threshold. Extensive simulations demonstrate the effectiveness of the proposed ADAC and T-ADAC, showing that our proposed algorithms outperform the state-of-the-art approaches in random access networks. Specifically, the proposed methods can achieve maximum throughput and minimum average AoI, showcasing their superior performance in real-world scenarios.

Task offloading can meet users’ demands for the latency and energy consumption by offloading tasks from resource-constrained Internet of Things devices to relatively resource-rich edge servers. Traditional task offloading usually makes use of fixed base stations or servers as edge servers. This would lead to limited range of services and increased costs due to large-scale deployment of edge servers. Therefore, deploying unmanned aerial vehicles (UAVs) as mobile edge servers for task offloading in complex terrains (e.g., forest, desert, etc.) is a worthwhile research problem. To this end, this article proposes a UAV-assisted task offloading mechanism. The mechanism aims to minimize the weighted sum of latency and energy consumption through jointly optimizing resource allocation, offloading decision, and UAV trajectory. We first transform the nonconvex optimization problem into convex optimization subproblems to obtain the optimal resource allocation. Second, we use an improved particle swarm optimization algorithm to find the optimal offloading decision. Finally, we present the deep determination policy gradient algorithm to optimize the UAV trajectory which is a kind of deep reinforcement learning algorithm. Through simulation experiments, we show that the proposed mechanism can efficiently reduce the weighted sum of latency and energy consumption.

Grasping the intrinsic properties of protein structure is crucial for comprehending relevant biological mechanisms, with protein flexibility standing out as a critical aspect. Therefore, the prediction of protein flexibility is of great importance in understanding molecular mechanisms. We propose a deep learning method named ResaPred, which extracts diverse features from protein sequences, such as secondary structure, torsion angle, solvent accessibility, etc. ResaPred is a novel deep network based on a modified 1D residual module and a self-attention mechanism, which effectively extracts deep key features related to flexibility. The modified 1D residual module consists of three convolution layers, with batchnorm and relu layers added after each layer to prevent gradient explosion or vanishing. Incorporating self-attention mechanisms into neural network architectures introduces a significant advantage in capturing long-range dependencies within sequential data. We conduct experiments on the non-strict and strict cases, and achieve state-of-the-art results in predicting flexibility compared to existing methods. Furthermore, we extended our analysis to explore the correlation between protein secondary structure and solvent accessibility with flexibility. Finally, we used two important viral proteins as case studies, confirming the effectiveness of our method in recognizing the flexibility of protein structures.

The rapid expansion of Internet of Things (IoT) applications has led to significant access challenges for radio access networks, potentially causing bottlenecks and degrading service quality. This paper proposes Dual-Core, a novel dual collision resolution mechanism for the four-step random access (RA) procedure of 5G communication systems. Dual-Core integrates slotted ALOHA protocol for random access preamble (RAP) transmission and a custom splitting algorithm for bandwidth request message (Msg 3) transmissions, resolving collisions in both PRACH and PUSCH. Additionally, we introduce an online controlled access class barring (ACB) to maximize the throughput of Dual-Core, with the optimal ACB rate determined by our proposed analytical model. Numerical results demonstrate that the proposed Dual-Core significantly reduces service time compared to the conventional RA procedure operating at its optimal performance, while utilizing the same amount of PUSCH resources. Furthermore, when additional PUSCH resources are available, Dual-Core efficiently utilizes them to serve more devices, a capability that conventional systems lack.

Along with the rapid growth of Internet of Things (IoT), low power wide area networks (LPWANs) based on unslotted ALOHA, without the need for strict synchronization among IoT devices, becomes one of the cost-effective solutions to support wireless access for sensors requiring long-range connectivity, low cost, and low-power consumption. However, the increasing number of IoT devices poses potential congestion for ALOHA with a low throughput limit of 0.183 (data frames per frame transmission time). This work addresses the throughput limitations of ALOHA by implementing immediate collision resolution (ICR) and integrating an online Bayesian backoff algorithm. The focus is on lifting the throughput limit to enhance LPWANs performance. The work further details translating MAC layer modeling into practical implementation, emphasizing the integration of additional functionalities like uplink local sensing (ULS) and receive window. Our implemented ALOHA with ICR shows a substantial 29% throughput improvement, validating theoretical predictions.

Learning multi-scale feature representations is essential for dense prediction tasks in autonomous driving. Most existing works are based on U-shaped architectures, where high-resolution representations are progressively recovered by connecting different levels of the decoder with low-resolution representations from the encoder. We observed that rich details from low-level representation and high semantic information from high-level representations are not fully utilized in the cross-stage fusion process. Additionally, current architectures often struggle to extract efficient discriminative feature along object boundaries. To address this issue, we propose CSNet, a generic cross-stage subtraction network that extracts spatial and semantic multi-scale representations through guided contextual feature. This approach allows fine-grained features to refine deeper layers, capturing discriminative high-resolution features while filtering out redundant information. Specifically, we introduce a cross-stage subtraction module (CSM), which consists of three sub-modules: 1) a Short Path Unit, focusing on capturing complementary adjacent information; 2) Medium Path Unit for effective middle-stages features aggregation; and 3) Long Path Unit for redundant information masking and long-range context modeling. Additionally, we propose the Semantic Guided Context Reasoning (SGCR) module to reason and model contextual relations between different subtraction units. CSNet demonstrates consistent performance gains across various semantic segmentation datasets. Our model, CSNet-M, achieves 82.2% mIoU on the Camvid dataset, while CSNet-S and CSNet-M attain 79.6% and 80.5% mIoU accuracy, respectively, on the Cityscapes dataset. These results show that the proposed CSNet has the potential for enhancing real-time semantic segmentation in autonomous driving applications, offering improved accuracy and efficiency in diverse urban scenarios. The source code for this work will be published athttps://github.com/mohamedac29/CSNet.

This work proposes a system-level multi-beam setup for an integrated sensing and communication (ISAC) system with an active intelligent reflecting surface (IRS). The system includes a multi-antenna base station (BS), active IRS, communication users, and targets in both line-of-sight (LoS) and non-line-of-sight (NLoS) regions. Signal processing is centralized at the BS to reduce costs, given the lack of dedicated sensing receivers in the IRS. While the communication beam consistently targets the user equipment (UE), the system dynamically directs the sensing beam towards areas of interest for environmental monitoring. LoS targets are directly detected using the sensing beam, while the active IRS aids in NLoS target detection by intelligently reflecting the sensing beam, thus creating a virtual LoS link via the BS-IRS-target-IRS-BS path. This innovative approach extends the sensing capabilities of the system to previously inaccessible NLoS areas. Finally, the performance of the system is evaluated by computing the root mean square error (RMSE) of the estimated distance and radial velocity of targets in both LoS and NLoS scenarios, while considering various frequency bands and antenna configurations.

When offloading computing tasks of sensory data to the edge network, it is necessary to consider whether the idle resources such as CPU frequency and memory, meet the task processing requirements. However, even if edge collaboration is used to improve offloading performance, most studies assume homogeneity in hardware configuration across all edge servers, discarding the impact of the differentiated resource allocation among heterogeneous edge servers. Therefore, resource allocation and offloading decisions in a collaborative heterogeneous edge network are comprehensively considered in this study. Firstly, the offloading problem of heterogeneous edge servers is expressed as a joint optimization problem associated with delay and energy consumption constrained by CPU frequency and storage resources. Secondly, dynamic collaboration clusters are constructed based on distance, position and workload correlation to identify distinct collaboration regions and balance the load within edge servers. And then, a distributed alternating direction multiplier method (ADMM) based on constraint projection and variable splitting is proposed to solve the optimization problem. Additionally, a cooperative path selection algorithm, which takes into account length and throughput of return paths, is proposed to alleviate network congestion and minimize energy consumption loss. Finally, the proposed algorithm for Collaborative offloading and resource Configuration (CooCo) is demonstrated to be effective and rapidly converging based on a real dataset from Shanghai Telecom. The simulation results also show that compared to the DRAOA, no-cooperation, single-hop, and other state-of-the-art collaborative algorithm, CooCo can significantly reduce the sum of the system costs by 26%, 35%,11% and 8%, respectively.

Tourism is an important industry sector that requires tour companies to plan multiple routes for different tour groups, which is called tour multi-route planning. This paper focuses on tour multi-route planning, which can improve the economic benefit and allocation efficiency of tour resources. The main contributions of this paper are threefold. First, we propose a novel multiple routes planning model that captures the real-world tourism scenario and practical constraints. We also define four typical constraints for tourism planning and classify them into soft and hard constraints. Second, we develop a matrix-based differential evolution algorithm to jointly optimize multiple routes that can efficiently handle the high-dimensional optimization under various constraints. Third, we collect real-world data to construct problem instances and compare the performance of our algorithm with the conventional differential evolution algorithms in terms of runtimes. The experimental results show that our algorithm can effectively solve tour multi-route planning problems and achieve excellent runtimes performance, suitable for large-scale transportation network optimization.

Due to flexibility, ad-hoc networking is an attractive structure for future emerging networks, such as Internet of Things (IoT), wireless sensor networks and vehicular networks. However, due to infrastructure-less, optimizing transmission control to avoid collision in ad-hoc networks is challenging. In this paper, we proposed an adaptive slotted-contention-based media access control (A-SCMAC) protocol which can adapt to network dynamics so that it can reduce collision and improve throughput. With A-SCMAC, each time slot consists of two periods: reservation period (RP) and transmission period (TP). By observing the channel outcomes in the RP the nodes learn the network status such as the number of nodes who have packets to transmit and control the transmission optimally for reserving the TP. Simulation results show that the proposed A-SCMAC can achieve the maximum throughput even the network traffic load changes.

Internet of Things (IoT) represents one of the most significant paradigm shifts recently, with several heterogeneous services and applications to the ultimate realization of connected living. In many status-sensitive IoT services, information usually has a higher value when it is fresher. A new metric, termed the age of information (AoI), was proposed to capture the freshness of status updates. In this paper, we propose a novel online control weight-based age-dependent activation control (W-ADAC) algorithm that prioritizes the activation of the devices that are divided into different classes possibly according to different AoI requirements. With the proposed W-ADAC, we introduce the concept of weighted AoI which is applied to control the activation probability of each device. In particular, higher weights are given to the devices that manifest higher priority requirements. As it is hard to obtain the system weighted AoI due to a lack of information on the distributed devices, we further introduce the capability of estimating the system weighted AoI into the proposed W-ADAC. Extensive simulations show the effectiveness of the proposed W-ADAC over multiple priorities and confirm that the proposed algorithm not only improves the overall system throughput but also provides the near-minimum AoI.

In machine type communication (MTC), diverse applications requiring high reliability and low latency lead to the consideration of ultra-reliable and low-latency MTC (uMTC) communication scenarios. Consequently, two-step random access procedure (RAP), as an alternative to the four-step RAP, is introduced into the 5th generation (5G) communication systems to reduce the unnecessary latency caused by the multi-round transmissions in the wireless medium. In this paper, we first analyze the delay performance of the two-step RAP based on which we further propose an algorithm to control the number of preambles allocated for the two-step RAP to meet a given average delay requirement. In particular, our proposed algorithm estimates the number of active devices in an online manner and controls the number of preambles to be allocated. Through extensive simulations, we show the effectiveness of our proposed algorithm in satisfying the delay requirement as well as minimizing the preamble resource usage.

To improve the throughput per channel in multichannel nonorthogonal multiple access (NOMA) random access (RA) system, users (re)transmit their packet to one of the channels using transmit power control such that the receive power of the packets at the base station (BS) can be one of the predefined levels called target receive power (TRP). The BS decodes the received packets in the descending order of the TRPs at each slot using successive interference cancellation (SIC). This work proposes the real-time transmission algorithm for users to (re)transmit their packet for maximization of the system throughput. To do this, the BS estimates the number of backlogged users in real time and adjusts and broadcasts the throughput-optimal (re)transmission probability in the algorithm. We analyze the average RA delay performance of the proposed algorithm and demonstrate its performance even with time-varying traffic.

Millimeter wave (mmWave) spectrum promises unprecedented data rates in 5G New Radio (NR). However, mmWave links are susceptible to severe path and propagation losses. Implementation of directional antennas that can manifest multiple beams becomes necessary at 5G base stations (gNBs) and User Equipments (UEs). Random access procedure (RAP), which is the first stage to establish connections to gNB, in its current state would be inadequate for such systems. To support RAP, 5G NR introduced mapping of different subsets of preambles in Physical Random Access Channel (PRACH) to different beams. We explore changes in RAP due to preamble-beam mapping and present a novel online algorithm to dynamically distribute preambles amongst beams to achieve fair access opportunities. The proposed algorithm can also eliminate overload problem of the beams with large UE population especially for high arrival rates. We extend the proposed algorithm to be applicable for RAP with access class barring mechanism which enables UEs to transmit a preamble with an optimal probability and, therefore, reduce access congestion. Through extensive simulations, we validate the efficiency of our proposed algorithms in providing fair channel access opportunities, improving throughput (or stability region), and reducing access delay when the system has high rate of UE arrivals and has unbalanced UE arrivals over different beams.

In slotted random access systems, when multiple users transmit packets simultaneously, owing to the successive interference cancellation (SIC) technique, the access point (AP) is able to decode them through SIC-enabled resolution procedures (SRPs), which may occupy multiple consequent slots. While such an SRP could potentially improve the system throughput, how to fully exploit it in practical systems is still questionable when SIC capability is limited. Moreover, the number of active users contending for the channel varies over time which complicates the random access algorithm design. In order to fully exploit the potential of such limited SIC capability and maximize the system throughput, a novel online estimation based on Bayesian approach is introduced to estimate the number of active users in real-time and controls each user's transmission accordingly. It is shown that the throughput of the proposed algorithm can reach up to 0.693 packets/slot under practical assumptions, which is the first result achieving the throughput limit proved by Yu-Giannakis. It is further shown that the system throughput of 0.594 packets/slot (85.7% of the throughput limit) is achievable even when the SIC capability is restricted by two. It is also shown that the proposed online estimation and adaptation framework can be extended to further exploit collision information and the imperfect SIC.

Internet-of-Things (IoT) applications for real-time control gradually increase and become computationally demanding. To provide better quality-of-service (QoS) in random access (RA) system based on slotted ALOHA (S-ALOHA), this work proposes a novel S-ALOHA system with cross-slot successive interference cancellation (SIC). To facilitate SIC, we design each slot with several time offsets (TOs) and one packet transmission time, where the length of overall TOs is a fraction of a packet transmission time. Users (re)transmit at the boundary of a TO randomly selected. This enables the base station (BS) to distinguish who makes the first and last transmissions in a collision slot and ask immediate retransmissions from them in the subsequent one or two slots. With these retransmitted packets, the BS performs SIC for the previously collided packets. We analyze the system throughput and the distribution of RA delay. The results show that the proposed system can achieve throughput from 0.5 (packets per packet transmission time) at minimum to 0.856 at maximum, depending on the number of TOs and the length of TO. In addition, to run this system stably, we propose a Bayesian-optimized backoff algorithm that enables users to use throughput-optimal (re)transmission probability. It is demonstrated that the proposed backoff algorithms can achieve the throughput close to genie-aided (GA) system.

This work considers a bulk transmission scheme for slotted ALOHA (S-ALOHA) systems with a finite population of unsaturated terminals. In the system, once a user makes a successful random access (RA) via S-ALOHA, it can transmit C packets to the traffic channel. We examine an effective capacity, which is defined as the maximally admissible mean arrival rate to the system, subject to the constraint that each user’s queue does not overflow. To obtain this capacity, we use the large deviation theory on the user’s queueing process. For comparison, we also use the dominant pole method in order to obtain the butter overflow probability. We then show that results from the method of using the large deviation theory are asymptotically identical to those from the dominant pole method due to the same decaying rate, and that keeping the traffic load within the effective capacity prevents a user’s queue from overflowing in practice.

A larger capacity of random access (RA) channel is demanded to cope with massive accesses from Internet of Things (IoT) devices. To do this, this work proposes multipacket reception (MPR) S-ALOHA with reservation: When making a successful RA, the user can reserve the channel for the next slot transmission with probability$r$, if its queue is not yet empty. This reservation can be continued with probability$r$until its queue becomes empty. In this system, as the number of the reserved channels grows, the number of available capacity for RA reduces. Thus, the number of RA attempting users needs to be controlled to avoid collision, whereas the unbounded growth of other users’ queue should be prevented. This work analyzes the throughput and stability condition of the proposed system and designs a throughput-optimal backoff algorithm based on the backlog size estimation. In the numerical studies, MPR S-ALOHA without reservation and time division multiple access (TDMA) are compared as benchmarks. As a result, it is proven that as$r\rightarrow 1$, the MPR channel capacity is fully utilized if the proposed RA algorithm is jointly used with the reservation scheme. Moreover, it is demonstrated that the system can be also stabilized by the proposed backoff algorithm.

Unslotted ALOHA has been often employed by several low-power wide-area networks (LPWANs) for Internet of Things (IoT) as a random access (RA) protocol. This work analyzes the performance of unslotted ALOHA systems in terms of throughput and RA delay, and investigates their optimization. Our analysis consists of: 1) two-heterogeneoususer case, whose backoff rate and packet length are different; 2)$N$-homogeneoususer case, whose backoff rate and packet length are identical; and 3) homogeneous users of infinite population model. In the two-user case, we investigate the throughput region of unslotted ALOHA by using a multiobjective optimization problem (MOOP) and derive the Laplace Stieltjes transform (LST) of the probability density function (PDF) of RA delay. For$N$-homogeneous user case, we show how the throughput behaves according to the population size, packet length, and backoff rate. Our work may provide a comprehensive analytical framework for unslotted ALOHA systems.

In recent years, overlapping community detection algorithms have been paid more and more attention, which not only reveal the real social relations, but also expose the possible communication channels between communities. Those individuals (or people) in the overlapping area are very important to the communities that can promote communication between two or more communities. On the other hand, from the privacy perspective, some people may not want to be found out in the overlapping areas. With this in mind, we raise a question “Can individuals modify their relationships to avoid the community discovery algorithms locating them into overlapping areas?” If this problem could be solved, these people may not need to worry about being disturbed. In particular, we first give three heuristic hiding strategies, i.e., Random Hiding(RH), Based Degree Hiding(DH) and Betweenness Hiding(BH), as comparison, utilizing the randomly the node, information of node degree and node betweenness centrality, respectively. And then, we propose a novel hiding algorithm by exploiting the importance degree of nodes in communities based on which the corresponding social connections are added or deleted called nameBIH. Through extensive experiments, we show the effectiveness of the proposed algorithm in moving out a target node from overlapped areas.

High-dimensional problems are ubiquitous in many fields, yet still remain challenging to be solved. To tackle such problems with high effectiveness and efficiency, this article proposes a simple yet efficient stochastic dominant learning swarm optimizer. Particularly, this optimizer not only compromises swarm diversity and convergence speed properly, but also consumes as little computing time and space as possible to locate the optima. In this optimizer, a particle is updated only when its two exemplars randomly selected from the current swarm are its dominators. In this way, each particle has an implicit probability to directly enter the next generation, making it possible to maintain high swarm diversity. Since each updated particle only learns from its dominators, good convergence is likely to be achieved. To alleviate the sensitivity of this optimizer to newly introduced parameters, an adaptive parameter adjustment strategy is further designed based on the evolutionary information of particles at the individual level. Finally, extensive experiments on two high dimensional benchmark sets substantiate that the devised optimizer achieves competitive or even better performance in terms of solution quality, convergence speed, scalability, and computational cost, compared to several state-of-the-art methods. In particular, experimental results show that the proposed optimizer performs excellently on partially separable problems, especially partially separable multimodal problems, which are very common in real-world applications. In addition, the application to feature selection problems further demonstrates the effectiveness of this optimizer in tackling real-world problems.

For industrial Internet of Things network operators, undesired and abrupt network failure is a critical problem to be resolved quickly. In order to provide reliable communication services to devices in faulty cells, in this article, we propose a distributed autonomous self-healing mechanism that allows a random-access-based instantaneous communication to the neighbor cells. The design of the self-healing mechanism is challenged by the diverse device locations and the different available number of channels provided by the neighbor cells due to their intracell traffic load. By estimating the number of devices communicating with each neighbor cell in an online manner, our proposed mechanism can control the channel access probability of each cell to maximize throughput. In addition, the devices can reselect their serving cells in a distributed manner while realizing maximum but fair throughput among devices. Through extensive simulations, we show that our proposed mechanism can provide effective performance for autonomous self-healing.

Proliferation of the urban Internet-of-Things (IoTs) for smart cities has fuelled massive amounts of data over wireless cellular networks. Random access (RA) system of wireless cellular networks, e.g., 5G New Radio (NR), based on S-ALOHA system should cope with ever-growing IoT traffic. This work proposes S-ALOHA system with time offsets (TOs), where one slot consists of K TOs and one packet transmission time. The length of the overall TOs is a fraction of a packet transmission time. In the system users (re)transmit to the boundary of a TO randomly selected. This enables the base station (BS) to inform the users of who transmits the first and the last packets in the slot with collision so that the two users can retransmit successfully in the following two slots respectively. Our throughput analysis compared to simulations shows that adopting even with three and four TOs surpasses the throughput limit of S-ALOHA system without TOs. Additionally, we propose two Bayesian-optimized backoff algorithms for S-ALOHA system with TOs, with which users can apply throughput-optimal (re)transmission probability or uniform backoff window even in unsaturated traffic scenarios. Numerical results demonstrate that the proposed backoff algorithms can achieve the throughput close to an ideal system and drastically reduce the access delay compared to S-ALOHA system.

A massive number of Internet-of-Things (IoT) and machine-to-machine (M2M) communication devices generate various types of data traffic in cellular IoT networks: periodic or nonperiodic, bursty or sporadic, etc. In particular, bursty and nonperiodic traffic may cause an unexpected network congestion and temporary lack of radio resources. In order to effectively accommodate such bursty and nonperiodic traffic, we propose a novel recursive access class barring (R-ACB) technique to optimally utilize the available resources associated with the random access procedure (RAP) that consists of multiple steps in cellular IoT networks, while existing ACB schemes only considered the resource of the first step of RAP, i.e., the number of available preambles. The proposed R-ACB technique consists of two main parts: 1) online estimation of the number of active IoT/M2M devices who have data to transmit to an eNodeB and 2) adjustment of the ACB factor that indicates the probability that an active device sends a preamble to eNodeB. It is notable that the estimation and the adjustment recursively affect each other when R-ACB operates. In addition, we also propose mathematical models to analyze the performance of R-ACB in terms of total service time, average access delay, resource efficiency, and energy efficiency (EE). Through extensive computer simulations, we show that the proposed R-ACB technique outperforms the conventional ACB schemes.

Unslotted ALOHA protocol has been adopted as a channel access mechanism in commercial low-power wide-area networks (LPWANs), such as Sigfox and long-range (LoRa) alliance. This work examines the throughput and random access (RA) delay distribution of unslotted ALOHA systems by considering exponential random backoff (ERB) or uniform random backoff (URB) algorithm. We further characterize the operating region of the systems as unsaturated stable, bistable, and saturated regions in terms of the new packet arrival and retransmission rates. To run the system stably with the maximum throughput, we propose a Bayesian online backoff algorithm that estimates the number of backlogged devices. Its performance is compared with other algorithms, such as particle filter (PF)-based algorithm, binary exponential backoff (BEB) algorithm, and the algorithm of exploiting exact backlog size information. Through extensive simulations, it is demonstrated that the performance of the proposed algorithm is very close to the upper bound and robust to time-varying traffic condition.

Internet of Things (IoT) imbued with several heterogeneous services and applications is integral to the ultimate realization of connected living. However, massive connectivity would pose huge challenges not only due to immense scalability but also due to diverse characteristics of periodicity, delay-criticality, and quality-of-service (QoS) requirement. One important trend of this diversity is that the connected devices would manifest different priorities. Random access procedure (RAP) is the first step by which most devices establish connection to the base station. A major transformation is urgently needed in RAP for cellular IoT as the volume of connected devices would far exceeds human oriented connections of the legacy networks. To prioritize the RAP for IoT devices, we propose novel online control algorithm that enables the most likely successful preamble transmission for the devices that manifest higher priority requirements. In the proposed algorithm, the number of active devices in each priority is recursively estimated based on Bayesian rule and then the preambles to each priority are accordingly allocated. We extend our proposal by adopting access class baring (ACB) to optimize the algorithm and subsequently, enhance it further by incorporating access delay requirements. Extensive simulations show the effectiveness of proposed algorithms over multiple priorities and confirm that the proposed algorithms are able to resolve congestions for massive activation of IoT devices.

In slotted-ALOHA (S-ALOHA) system, users should contend for the channel to transmit a packet in their queue every single time, irrespective of how many packets they have in their queue. The maximum throughput of S-ALOHA system is known as$e^{-1}\approx 0.3679$(packets/slot) for a large number of users. This work proposes a novel S-ALOHA system, where the user with a successful (re)transmission of the packet at the head of queue is allowed to keep on transmitting his packet with some reservation probability if his queue is not empty. We call this system S-ALOHA system with successive transmission (ST) and show that the throughput of the proposed system becomes one packet per slot as the reservation probability is raised. We analyze the stability region of S-ALOHA systems with ST for two heterogeneous users and also investigate the stability condition of the system with$N$symmetric users. Finally, we develop a Bayesian-learning backoff algorithm, with which users can control their (re)transmission with real-time estimation on the backlogged users. It is shown that the system with the proposed backoff algorithm can achieve the theoretical limit of the throughput.

Large-scale optimization has become a significant and challenging research topic in the evolutionary computation (EC) community. Although many improved EC algorithms have been proposed for large-scale optimization, the slow convergence in the huge search space and the trap into local optima among massive suboptima are still the challenges. Targeted to these two issues, this article proposes an adaptive granularity learning distributed particle swarm optimization (AGLDPSO) with the help of machine-learning techniques, including clustering analysis based on locality-sensitive hashing (LSH) and adaptive granularity control based on logistic regression (LR). In AGLDPSO, a master-slave multisubpopulation distributed model is adopted, where the entire population is divided into multiple subpopulations, and these subpopulations are co-evolved. Compared with other large-scale optimization algorithms with single population evolution or centralized mechanism, the multisubpopulation distributed co-evolution mechanism will fully exchange the evolutionary information among different subpopulations to further enhance the population diversity. Furthermore, we propose an adaptive granularity learning strategy (AGLS) based on LSH and LR. The AGLS is helpful to determine an appropriate subpopulation size to control the learning granularity of the distributed subpopulations in different evolutionary states to balance the exploration ability for escaping from massive suboptima and the exploitation ability for converging in the huge search space. The experimental results show that AGLDPSO performs better than or at least comparable with some other state-of-the-art large-scale optimization algorithms, even the winner of the competition on large-scale optimization, on all the 35 benchmark functions from both IEEE Congress on Evolutionary Computation (IEEE CEC2010) and IEEE CEC2013 large-scale optimization test suites.

The resource-constrained project scheduling problem (RCPSP) is a basic problem in project management. The net present value (NPV) of discounted cash flow is used as a criterion to evaluate the financial aspects of RCPSP in many studies. But while most existing studies focused on only the contractor's NPV, this article addresses a practical extension of RCPSP, called the payment scheduling negotiation problem (PSNP), which considers both the interests of the contractor and the client. To maximize NPVs of both sides and achieve a win-win solution, these two participants negotiate together to determine an activity schedule and a payment plan for the project. The challenges arise in three aspects: 1) the client's NPV and the contractor's NPV are two conflicting objectives; 2) both participants have special preferences in decision making; and 3) the RCPSP is nondeterministic polynomial-time hard (NP-Hard). To overcome these challenges, this article proposes a new approach with the following features. First, the problem is reformulated as a biobjective optimization problem with preferences. Second, to address the different preferences of the client and the contractor, a strategy of multilevel region interest is presented. Third, this strategy is integrated in the nondominated sorting genetic algorithm II (NSGA-II) to solve the PSNP efficiently. In the experiment, the proposed algorithm is compared with both the double-level optimization approach and the multiobjective optimization approach. The experimental results validate that the proposed method can focus on searching in the region of interest (ROI) and provide more satisfactory solutions.

Surrogate-assisted evolutionary algorithms (SAEAs) have become one popular method to solve complex and computationally expensive optimization problems. However, most existing SAEAs suffer from performance degradation with the dimensionality increasing. To solve this issue, this article proposes a classifier-assisted level-based learning swarm optimizer on the basis of the level-based learning swarm optimizer (LLSO) and the gradient boosting classifier (GBC) to improve the robustness and scalability of SAEAs. Particularly, the level-based learning strategy in LLSO has a tight correspondence with the classification characteristic by setting the number of levels in LLSO to be the same as the number of classes in GBC. Together, the classification results feedback the distribution of promising candidates to accelerate the evolution of the optimizer, while the evolved population helps to improve the accuracy of the classifier. To select informative and valuable candidates for real evaluations, we devise an${L}1$-exploitation strategy to extensively exploit promising areas. Then, the candidate selection is conducted between the predicted${L}1$offspring and the already real-evaluated${L}1$individuals based on their Euclidean distances. Extensive experiments on commonly used benchmark functions demonstrate that the proposed optimizer can achieve competitive or better performance with a very small training dataset compared with three state-of-the-art SAEAs.

For slotted random access systems, the slotted ALOHA protocol provides the maximum throughput of 0.368 (packets/slot) while in the category of splitting (or tree) algorithms, the maximum achievable throughput can reach up to 0.487 with the first-come first-serve (FCFS) algorithm. It has been so far demonstrated that the FCFS algorithm can achieve this maximum throughput only for Poisson traffic. This may limit its application in practical systems, where packet arrivals may not be Poissonian. In this paper, we propose a novel online transmission control framework that introduces random splitting upon collisions and controls the transmission probabilities optimally at each slot by estimating the number of active users in the system. The proposed algorithm is said to be online as it estimates the number of active users slot by slot recursively, and thus can adapt to network dynamics. We first show that the splitting algorithm of our interest can achieve the throughput of 0.532 if the number of users involved in a collision could be known, which serves as a guideline for the upper limit for the random access systems with splitting. Then, when the information on the number of collided users is not available, we show that the proposed algorithm can achieve the maximum throughput of 0.487 for Poisson arrivals while achieving shorter access delay than FCFS. When more bursty traffic than Poisson process is applied, the proposed algorithm shows much better throughput and delay performance than FCFS.

Due to the increasing complexity of optimization problems, distributed differential evolution (DDE) has become a promising approach for global optimization. However, similar to the centralized algorithms, DDE also faces the difficulty of strategies' selection and parameters' setting. To deal with such problems effectively, this article proposes an adaptive DDE (ADDE) to relieve the sensitivity of strategies and parameters. In ADDE, three populations called exploration population, exploitation population, and balance population are co-evolved concurrently by using the master-slave multipopulation distributed framework. Different populations will adaptively choose their suitable mutation strategies based on the evolutionary state estimation to make full use of the feedback information from both individuals and the whole corresponding population. Besides, the historical successful experience and best solution improvement are collected and used to adaptively update the individual parameters (amplification factor F and crossover rate CR) and population parameter (population size N), respectively. The performance of ADDE is evaluated on all 30 widely used benchmark functions from the CEC 2014 test suite and all 22 widely used real-world application problems from the CEC 2011 test suite. The experimental results show that ADDE has great superiority compared with the other state-of-the-art DDE and adaptive differential evolution variants.

By efficiently building and exploiting surrogates, data-driven evolutionary algorithms (DDEAs) can be very helpful in solving expensive and computationally intensive problems. However, they still often suffer from two difficulties. First, many existing methods for building a single ad hoc surrogate are suitable for some special problems but may not work well on some other problems. Second, the optimization accuracy of DDEAs deteriorates if available data are not enough for building accurate surrogates, which is common in expensive optimization problems. To this end, this article proposes a novel DDEA with two efficient components. First, a boosting strategy (BS) is proposed for self-aware model managements, which can iteratively build and combine surrogates to obtain suitable surrogate models for different problems. Second, a localized data generation (LDG) method is proposed to generate synthetic data to alleviate data shortage and increase data quantity, which is achieved by approximating fitness through data positions. By integrating the BS and the LDG, the BDDEA-LDG algorithm is able to improve model accuracy and data quantity at the same time automatically according to the problems at hand. Besides, a tradeoff is empirically considered to strike a better balance between the effectiveness of surrogates and the time cost for building them. The experimental results show that the proposed BDDEA-LDG algorithm can generally outperform both traditional methods without surrogates and other state-of-the-art DDEA son widely used benchmarks and an arterial traffic signal timing real-world optimization problem. Furthermore, the proposed BDDEA-LDG algorithm can use only about 2% computational budgets of traditional methods for producing competitive results.

In this paper, we propose a novel access control (AC) mechanism for cellular internet of things (IoT) networks with massive devices, which effectively satisfies various performance metrics such as access throughput, access delay, and energy efficiency. Basic idea of the proposed AC mechanism is to adjust access class barring (ACB) factor according to performance targets. For a given performance target, we derive the optimal ACB factors by considering not only the conventional preamble collision detection technique but also the early preamble collision detection technique, respectively. In addition, the proposed AC mechanism considers overall radio resources to optimize the ACB factor, which includes the number of preambles, random access response (RAR) messages, and physical-layer uplink shared channels (PUSCHs), while most conventional ACB schemes consider only the number of preambles. In particular, the proposed AC mechanism is illustrated with two representative performance metrics: latency and energy efficiency. Through extensive computer simulations, it is shown that the proposed versatile AC mechanism outperforms the conventional ACB schemes in terms of various performance metrics under diverse resource constraints.

For slotted random access systems with a single channel, the slotted ALOHA (S-ALOHA) protocol shows 0.368 (packets/slot) as the maximum throughput, whereas some splitting (or tree) algorithms exhibit 0.487 (packets/slot). The S-ALOHA protocol has been widely adopted even for multi-channel systems such as Long-Term Evolution (LTE), as it is more practically implementable. However, the throughput of each channel in multi-channel S-ALOHA is limited to 0.368. In order to overcome this limit and some implementational drawbacks of the existing splitting algorithms, this paper proposes a novel splitting algorithm for multi-channel slotted systems which can also adapt to dynamic system situations through estimating the number of users who have packet to transmit. We analyze the throughput of our proposed algorithm and show that the proposed algorithm is more practical than the first-come first-serve (FCFS) algorithm and shows smaller access delay than the FCFS algorithm even for a single channel system. For M-channel systems, the proposed algorithm yields the maximum throughput of 0.487M. Extensive simulations validate our analytical results.

With rapid growth of the number of mobile smart devices, the resource usage for video streaming, as one of the mostly popular mobile applications, also increases drastically. Thus, understanding the practical resource usage in current LTE-A systems becomes more important, which may serve for the future deployment of mobile communication systems. In this paper, we first introduce a methodology to measure the practical resource usage pattern of the mobile video streaming by taking YouTube service as a representative example. Then, we analyze the data measured from the LTE-A systems in South Korea to investigate the required data rate to support high definition (HD) video streaming and its relation to spectral efficiency, signal to interference and noise ratio (SINR), etc. In addition, we also analyze the resource assignment pattern in the time domain when streaming.

This paper considers non-orthogonal multiple access (NOMA) systems in which users randomly transmit packets in each slot while the transmit power is adjusted so that the receive power of each packet at the base station can be one of two predetermined values. While the NOMA random access system can support simultaneous transmissions from up to two users with different target receive powers, it complicates the access protocol design as each user not only has to optimize the transmission probability but also has to suitably choose the target receive power. By considering the fact that users may have different capability of choosing the target receive power due to their diverse locations in a cell, we propose an online distributed channel access algorithm which enables the users to adaptively choose the target receive power and control the transmission probability over time through observing channel outcomes such as idle, success and collision. Numerical results demonstrate that the proposed algorithm can optimize the system performance in terms of throughput and access fairness.

In massive cellular internet-of-things (IoT) networks, periodic and sporadic traffic may be well processed, while bursty traffic may cause an unexpected network congestion or overload problem. Thus, in this paper, we propose a generalized random access (RA) control mechanism considering whole steps of RA procedure and available resources at each step for handling bursty traffic in cellular IoT networks. The proposed RA control mechanism mainly consists of the estimation method for the number of backlogged nodes and the computation method for access class barring (ACB) factors. Through extensive computer simulations, the proposed RA control mechanism shows the enhanced performance in terms of the total service time, the average access delay, and the energy efficiency, compared to the conventional RA control mechanism, which only focuses on controlling preamble transmissions at the first step of the RA procedure.

Despite extensive studies on optimal power allocation, how to design an efficient joint user scheduling and power allocation scheme for uplink multiuser networks remains largely unexplored. This study investigates joint opportunistic user scheduling and power allocation in uplink multiuser networks to maximise user throughput subject to the power and resource sharing constraints . By exploiting the cumulative distribution function‐based scheduling method, the authors first characterise the optimal power allocation subject to both long‐term and short‐term power constraints. Instead of calculating the transmit power in an iterative and central manner, users can independently decide their instantaneous transmit power in the proposed scheme, which facilitates the algorithm implementation for each user in uplink networks. The closed‐form throughput of the proposed scheme is also derived, which can provide an efficient way to estimate and evaluate user performance. Numerical results reveal that compared with several benchmark schemes, the proposed scheme improves throughput performance significantly.

The authors propose an adaptive cell‐breathing (ACB) technique to improve the energy efficiency (EE) of a downlink cellular network consisting of small‐cell base stations (BSs), wherein each BS adaptively adjusts its transmission power such that the received signal strength of the worst‐case user is larger than a pre‐defined threshold. They also propose an aggressive BS on–off (ABO) technique in which the small‐cell BSs having a number of users smaller than a certain value, , are turned off, whereas conventional techniques only turn off the empty BSs. They adopt a stochastic geometry for modelling the locations of both BSs and users. Simulation results show that the ACB technique yields a much better EE than the power on–off technique with a fixed power, including the ABO technique. In particular, the EE of the ACB technique is proportional to , where denotes the BS density and the exponent c denotes the increasing ratio of the EE to in the domain. The EE of the ABO technique tends to increase as increases.

This paper investigates joint user association (UA) and user scheduling (US) for load balancing over the downlink of a wireless heterogeneous network by formulating a network-wide utility maximization problem. In order to efficiently solve the problem, we first approximate the nonconvex throughput achieved with US to a concave function, and demonstrate that the gap for such an approximation approaches zero when the number of users is sufficiently large. Then, by exploiting a distributed convex optimization technique known as alternating direction method of multipliers, a joint UA and US algorithm, which can be implemented on each user's side and base station (BS)'s side separately, is proposed to obtain the single-BS association and resource allocation solutions. A remarkable feature of the proposed algorithm is that apart from load balancing, multiuser diversity is exploited in the association process to further improve system performance. We also extend the algorithm design to multi-BS association, whereby a user is associated with multiple BSs. The simulation results show the superior performance of the proposed algorithms and underscore the significant benefits of jointly exploiting multiuser diversity and load balancing.

This paper investigates the joint user association (UA) and user scheduling (US) for load balancing in a wireless downlink heterogeneous network by formulating a network-wide utility maximization problem. In order to efficiently solve the problem, we first approximate the original non-convex throughput function to a concave function, and demonstrate that the gap for such approximation approaches zero when the number of users is sufficiently large. Then, a distributed algorithm is further proposed to obtain the UA and US solutions by exploiting the convex optimization technique known as alternating direction method of multipliers. A remarkable feature of the proposed algorithm is that apart from load balancing, multiuser diversity is exploited in the association time to further improve system performance. The simulation results show the superior performance of the proposed algorithm and underscore the significant benefits of jointly exploiting multiuser diversity and load balancing.

In this paper, we propose a novel user scheduling that achieves the optimal multi-user diversity gain in multi-cell uplink networks with multiple eavesdroppers. In the proposed scheduling, each base station (BS) selects a certain user based on two pre-determined thresholds (i.e., scheduling criteria). The first threshold is related to the amount of generating interference of the users to other BSs and the second threshold is related to the maximum amount of information overheard by the eavesdroppers in the network. Simulation results show that the proposed scheduling significantly outperforms the other scheduling algorithms in terms of secrecy throughput. Note that the proposed scheduling can operate with a distributed manner when time division duplex is adopted, i.e., no coordination among different BSs is needed.

This paper investigates physical layer security with opportunistic scheduling in a downlink wireless network with multiple asymmetrically located legitimate users (LUs) and eavesdroppers. We employ the cumulative distribution function (CDF)-based scheduling policy to guarantee fairness among LUs in arbitrary fading channels while exploiting multiuser diversity. Under this scheduling framework, the closed-form expressions for the secrecy throughput and secrecy outage probability are derived, illustrating the interplay among the system parameters such as the channel statistics and the number of LUs and eavesdroppers. In order to investigate the exploited multiuser diversity gain, the normalized secrecy throughput, i.e., the secrecy throughput for a given LU normalized by the probability of it being selected, is analyzed and is proved to achieve a double-logarithmic growth when the number of LUs in the network increases to infinity. In addition, we derive the secrecy diversity order through an asymptotic analysis of intercept probability and prove that the secrecy diversity order is equal to the number of LUs in the system, implying that full diversity is achieved by the CDF-based scheduling.

Wireless local area networks (WLANs) are becoming denser and thus interference-limited due to heavy traffic from a number of adjacent access points (APs) and stations (STAs). We propose a novel interference management technique for overlapping basic service sets (OBSSs) in such WLANs, which intelligently applies an opportunistic interference alignment (OIA) concept to WLANs. Each BSS has an AP and multiple STAs, and operates with a carrier sensing multiple access (CSMA) protocol as commercial IEEE 802.11 WLANs. Both APs and STAs are assumed to have multiple antennas. Specifically, the proposed OIA framework consists of physical (PHY) and medium access control (MAC) layer techniques: transmit beamforming and opportunistic medium access, respectively. First, each STA performs transmit beamforming which minimizes generating interference to other BSSs at the PHY layer. Second, each STA sends packets to its serving AP only when its generating interference to other BSSs is smaller than a pre-determined threshold at the MAC layer. Through extensive simulations, we show that proposed OIA scheme significantly outperforms existing schemes in terms of system throughput. Note that the OIA scheme operates with a distributed manner based on local channel state information at each STA and does not require any coordination among BSSs, leading an easier implementation in practice.

In this paper, we propose two opportunistic scheduling algorithms for uplink wiretap networks with multiple legitimate users (LUs) and eavesdroppers. Different from the existing works on scheduling algorithms with secrecy considerations, we focus on the practical scenario where LUs experience diverse path-loss to the base station (BS). Hence, both secrecy throughput and fairness among LUs are crucial design considerations. In the proposed scheduling algorithms, the feedback information generated by each LU is designed to reduce the probability that the LU is selected by the BS when the eavesdroppers overhear much information. It is proved that fairness among LUs can be achieved in arbitrary fading channels. In order to investigate the efficiency of our proposed scheduling algorithms, the normalized secrecy throughput, i.e., the secrecy throughput for a given LU normalized by the probability of it being selected, is analyzed and proved to achieve double-logarithmic growth when the number of LUs in the network increases to infinity.

This paper proposes efficient backoff algorithms for uplink multi-packet reception (MPR) capable IEEE 802.11 systems in order to maximize the system throughput. According to the proposed algorithms, each station (STN) estimates, in a Bayesian manner under an unsaturated channel traffic condition, the number of backlogged STNs sharing the multiple access channel to obtain an optimal (re)transmission probability. Additionally, an access point and associated STNs cooperate by exchanging information piggybacked in transmitted data packets and the corresponding acknowledgment packets. The mean and variance of the queuing delays of the proposed algorithms are extensively evaluated via simulations under various environments such as time-varying populations and various asymmetric traffic conditions and compared to those of the conventional binary exponential backoff (BEB) algorithm. Furthermore, the queuing performance of the proposed algorithms is compared to the queuing delay lower bound obtained from a system that has perfect knowledge of the backlog size. Numerical results demonstrate the robustness of the proposed algorithms in various environments, and that they outperform the BEB algorithm.

In this paper, we investigate fundamental performance limits of cumulative distribution function (CDF)-based scheduling (CS) in downlink cellular networks. CS is known as an efficient scheduling method that can assign different time fractions for users or, equivalently, satisfy different channel access ratio (CAR) requirements of users while exploiting multiuser diversity. We first mathematically analyze the throughput characteristics of CS in arbitrary fading statistics and data rate functions. It is shown that the throughput gain of CS increases as the CAR of a user decreases or the number of users in a cell increases. For Nakagami-m fading channels, we obtain the average throughput in closed form and investigate the effects of the average signal-to-noise ratio, the shape parameter m, and the CAR on the throughput performance. In addition, we propose a threshold-based opportunistic feedback technique in order to reduce feedback overhead while satisfying the CAR requirements of users. We prove that the average feedback overhead of the proposed technique is upper-bounded by -lnp, where p is the probability that no user satisfies the threshold condition in a cell. Finally, we adopt a novel fairness criterion, called qualitative fairness, which considers not only the quantity of the allocated resources to users, but also the quality of the resources. It is observed that CS provides a better qualitative fairness than other scheduling algorithms designed for controlling CARs of users.

In this paper, we propose a novel cumulative-distribution-function-based scheduling (CS) with flexible-beam transmissions (CSFB) as an opportunistic downlink scheduling with fair resource sharing for distributed antenna systems (DASs). Taking advantage of the spatially distributed nature of remote antenna units (RAUs) in DASs, CSFB dynamically assigns a different weight for each user-RAU pair and adjusts the number of beams for transmissions based on the channel conditions, in order to increase the probability that each RAU can contribute to the throughput of users located near it by efficiently exploiting spatial multiplexing gain. Furthermore, by simply setting a single threshold for each RAU, which is applied for user selection, CSFB maintains fair resource sharing constraints. This simple setting greatly facilitates the scheduler design. Thus, our proposed CSFB can exploit the multiuser diversity gain provided by the independent channel fading of multiple users, as well as the spatial multiplexing gain through the effective utilization of distributed RAUs. Simulation results demonstrate that CSFB achieves a better throughput performance than CS with all-beam transmissions and CS with single-beam transmissions, while satisfying the fair resource sharing constraints.

In this paper, we propose a cumulative distribution function (CDF)-based scheduling for multi-cell uplink networks in order to exploit multi-user diversity, while satisfying fair resource sharing among users. In the proposed scheduling, each user adjusts its transmit power to reduce the amount of generating interference to other cells, based on a pre-determined threshold. Then, each user calculates CDF of an uplink signal-to-noise ratio with the adjusted transmit power, and feeds the CDF value back to its serving base station (BS). In each time slot, the BS selects the user having the largest CDF value. The proposed scheduling operates with a distributed manner even though it effectively copes with inter-cell interference. As a main result, we prove that the proposed scheduling achieves the double-logarithmic growth of normalized user throughput which is defined as the ratio of user throughput to the probability of the user being selected. Moreover, we observe that a fixed threshold is enough to accommodate diverse network scenarios with different population sizes and user locations in the proposed scheduling.

This paper introduces the Cloud-based Radio Access Network (C-RAN) architecture into heterogeneous network (HetNet), in which distributed antennas are connected to a cloud-based baseband processing unit through optical fibers. Among various opportunities realized by this architecture, our focus in this paper is on the spectral efficiency (SE) advantages achieved by cooperative transmission and its associated power consumption that may affect the energy efficiency (EE) of the system. A simple but efficient pre-coding scheme is proposed to reduce the computation complexity of cooperative transmission, thus lowering the associated power consumption, and a detailed power model is then developed to benchmark the various sources of energy consumption in C-RAN. Through detailed simulation, an early performance evaluation of the potentially energy efficient C-RAN implementation was demonstrated.

When multiple users are communicating with an access point based on a random access, the stability region is known as all possible combinations of the mean packet arrival rates to keep their queue lengths bounded. This paper investigates the stability region of p-persistent carrier sense multiple access (CSMA) systems, where two users have different mean packet arrival rates and (re)transmission probabilities. We then extend our results to p-persistent CSMA systems with N users and discuss applicability of our results to IEEE 802.11 systems with the basic and request-to-send/clear-to-send access mechanisms. In numerical studies, we show the stability region by varying system parameters such as packet arrival rates, (re)transmission probabilities, and radio channel errors, and how the stability region of p-persistent CSMA systems gets close to that of a time-sharing system.

Cumulative distribution function (CDF)-based scheduling is known as an efficient scheduling method that can assign different time fractions for user access or, equivalently, satisfy different channel access ratio requirements of users in cellular downlink while exploiting multi-user diversity. In this paper, we propose CDF-FR, a feedback reduction technique for CDF-based scheduling that reduces feedback overhead from users in a cell. Although several threshold based feedback reduction schemes have been proposed for various scheduling algorithms, none of them considers users' different channel access ratio requirements for which CDF-based scheduling is designed. In the proposed technique, a single threshold is used for all users who have different channel access ratio requirements. We show that this simple setting is sufficient for CDF-FR to satisfy users' diverse channel access ratio requirements. It is proved that the average feedback overhead of CDF-FR is upper-bounded by - lnp for an arbitrary number of users in a cell, where p represents the probability that no user satisfies the threshold condition. Furthermore, the normalized throughput loss due to feedback reduction is upper-bounded by p in fading channels with arbitrary statistics.

In this paper we propose a new Markovian model for p-persistent carrier sense multiple access (CSMA) systems with a finite population of unsaturated single-buffered terminals. Focused on the distribution of the number of backlogged terminals in the steady state, our model allows the optimal persistent probability p from the number of backlogged terminals, which enables us to determine the throughput upper-bound (or mean access delay lower-bound) of slotted CSMA systems. We compare the performance of slotted CSMA systems with binary exponential backoff (BEB) algorithm and with p-persistent protocol against the throughput upper-bound and examine the stability of these systems. We show how closely slotted CSMA systems with BEB algorithm or p-persistent protocol approaches the throughput upper-bound in accordance with the minimum contention window size or the persistent probability p. Further, we propose a generalized Bertsekas' (backoff) algorithm (GBA) based on backlog size estimation, which is a generalization of the existing algorithm proposed by Bertsekas, in order to achieve the throughout upper-bound. Our study shows that in slotted CSMA systems, the access fairness of BEB algorithm is worse than those of p-persistent protocol and GBA algorithm, while the BEB and GBA algorithms show throughput performance close to optimality.

Cumulative distribution function (CDF)-based scheduling (CS) is known to be effective in meeting the different channel access ratio (CAR) requirements of users in a multi-user wireless system. In this paper, we propose a one-bit-feedback scheme for CS (OBCS) to reduce the feedback overhead from users in a cell. In OBCS, each user sets its individual threshold to decide whether to send one-bit feedback to the base station (BS). The BS randomly generates numbers for all users based on their feedback behavior and selects a user who is assigned with the largest value. We further propose OBCS with reduced complexity, OBCS-RC, which employs a universal threshold for all users, and relieves the BS to generate random numbers only for the users who have sent feedback. Both OBCS and OBCS-RC inherit the properties of CS in meeting diverse CAR requirements of users in arbitrary fading channels. Extensive analytical and simulation results indicate that simply setting the OBCS-RC threshold to 0.1 is adequate for good throughput performance compared to OBCS with the optimal threshold for each user. Although OBCS and OBCS-RC induce a throughput loss due to the reduced feedback overhead, their throughput still grows in a double-logarithmic manner as CS in Nakagami-m channels when the number of users increases to infinity.

Distributed antenna systems (DAS) have been studied extensively to improve spectral efficiency and extend coverage. Different from the previous studies that focused mostly on the enhancement of downlink or uplink performance through cooperation of distributed antennas, this paper proposes and investigates a novel communication configuration in which a fiber-connected DAS is employed as a distributed relay antenna system (DRAS). We investigate that such a DRAS has a self-interference cancellation property that leads to the support of simultaneous transmissions and receptions. The DRAS also allows the number of transmit and receive antennas to be controlled to achieve a balance between the signal-to-noise ratios of the source-to-relay and relay-to-destination links. Consequently, a higher spectral efficiency can be achieved compared to conventional half-duplex relay systems. For Rayleigh fading channels, the throughput and the diversity order of the DRAS are analyzed and numerical results show that the full-duplex DRAS exhibits better throughput performance than half-duplex relay systems with large number of relay antennas and in high SNR regions.

IEEE 802.11ac standard has newly adopted a downlink multi-user multiple-input and multiple-output (DL-MU-MIMO) scheme. For user multiplexing in downlink WLAN, we can also use a frame aggregation scheme for multiplexing multiple users' data with space-time block coding (STBC) for achieving spatial diversity. We compare the performance of the two downlink user multiplexing schemes: multi-user MIMO and frame aggregation in IEEE 802.11ac. If each user's encoded data stream has a similar length, the multi-user MIMO scheme yields better average throughput than the frame aggregation scheme. On the other hand, if each user's encoded data stream has a different length, the frame aggregation scheme outperforms the multi-user MIMO scheme in terms of average throughput. In a fast-varying channel, the multi-user MIMO scheme yields worse throughput due to the channel feedback overhead, compared to that with the frame aggregation scheme. We also observe that the multi-user frame aggregation scheme with STBC always outperforms a single-user transmission scheme with STBC in terms of average throughput due to enhanced MAC layer efficiency through frame aggregation.

We propose two scheduling protocols which are applicable to two-hop relay networks based on mobile WiMAX system: the first protocol adopts a persistent scheduling (PS) scheme in both hops, called the PS-PS protocol, while the second protocol adopts the PS scheme only in the first hop and the dynamic scheduling (DS) scheme in the second hop, called the PS-DS protocol. We optimize the above two protocols by minimizing their average resource usage (ARU) through appropriate selection of the modulation and coding scheme (MCS) level for given delay and packet error requirements. Taking into account different characteristics in both a source-relay (SR) link and a relay-destination (RD) link, resource usage due to hybrid automatic repeat request (HARQ) retransmissions, and the accompanying signaling overhead, we compare the performance of the proposed optimization methods with that of the conventional method in terms of ARU. The PS-PS protocol outperforms the PS-DS protocol and the conventional method considered in most channel case, while the PS-DS protocol outperforms other schemes in a harsh channel condition.

Due to high spectral-efficiency of multiple-input multiple-output (MIMO) transmission techniques, IEEE 802.11n WLAN system adopted a single-user MIMO (SU-MIMO) scheme in which multiple symbol streams are transmitted from a single station (STA) to enhance the system performance. On the other hand, recently, adoption of a multi-user MIMO (MU-MIMO) scheme for multi-packet reception (MPR) in uplink WLAN has also attracted attention. The SU-MIMO scheme achieves a MIMO multiplexing gain at physical (PHY) layer while the MU-MIMO scheme achieves a MIMO multiplexing gain at medium access control (MAC) layer. Thus, there is a fundamental question which scheme is a better solution for uplink WLANs and, in this paper, we analyze and compare these two schemes with random STA distribution scenarios. Moreover, with the adaptation of MAC layer parameters, we also analyze and compare the maximum throughput performance of both the SU- and MU-MIMO schemes in uplink WLANs and we find a proper decision criterion to select the MIMO mode in uplink WLANs.

The IEEE 802.11 standard supports multiple PHY rates. However, the IEEE 802.11 DCF in a multiple-rate environment may cause a performance anomaly. There have been many studies about the performance analysis and improvement of single- and multi-rate WLAN systems. However, there were a few studies about a generalized analysis on throughput and channel utilization for successful transmissions in multi-rate WLAN systems with different frame parameters and channel errors. In particular, the performance anomaly problem of the multi-rate WLANs still needs to be solved. We propose a more generalized mathematical model for each station with a different data transmission time and mathematically analyze the throughput and channel utilization for successful transmission in error-prone multi-rate WLANs. Moreover, we propose a contention window size adjustment scheme and a payload adjustment scheme to resolve the well-known performance anomaly problem in multi-rate WLANs by achieving temporal fairness. Numerical results show that the proposed scheme is very effective in achieving temporal fairness.

Collision mitigation is one of classical research issues for wireless local area networks (WLANs). Recently, multiple-input multiple-output (MIMO) transmission techniques have been widely deployed in wireless systems, while a multi-user MIMO-based collision mitigation scheme in uplink WLANs was proposed by authors, and we showed the scheme is very efficient for the uplink performance. However, for an infrastructure-based WLAN, we observe a significant performance unbalance problem between uplink and downlink, compared to the conventional WLANs. Moreover, access point (AP) yields lower throughput performance than each contending station(STA). In order to solve this unbalance problem between uplink and downlink, we adopt a modified minimum contention window (CWmin) adjustment scheme and a random piggyback scheme to the multi-user MIMO-based WLANs. We also develop an analytical model to evaluate the performance of multi-user MIMO-based WLANs in a saturated traffic environment. The result shows that the random piggyback scheme performs more efficiently for the multi-user MIMO-based WLANs.

In this paper, we compare the performance of wireless local area networks (WLANs) with single-user MIMO (SU-MIMO) and multi-user MIMO (MU-MIMO) in terms of collision probability, average throughput, and delay. In the SU-MIMO scheme, multiple antennas are used for transmitting multiple data streams of a single user and this MIMO technique increases link capacity at physical (PHY) layer. In the MU-MIMO scheme, however, multiple antennas at different users are used for transmitting data streams of multiple users. The MU-MIMO scheme reduces the collision probability at medium access control (MAC) layer and increases the link capacity. Both MIMO schemes yield different collision, throughput, and delay performance at the MAC layer of WLANs. Numerical results show that the MU-MIMO scheme yields lower collision probability and shorter delay performance than the SU-MIMO scheme. Furthermore, the SU-MIMO scheme yields better throughput performance for high SNR values and a small number of contending stations. In other cases, the MU-MIMO scheme yields better throughput performance.

In orthogonal code hopping multiplexing (OCHM) systems, hopping pattern (HP) collisions may degrade the system performance. Previous studies on the effect of HP collisions in OCHM systems were mainly based on computer simulations and there was no rigorous mathematical analysis of bit error rate (BER) performance. The HP collisions in OCHM systems differ from the hits in frequency-hopping (FH) systems or intracell interference in DS-CDMA systems because it can be effectively controlled through synergy and perforation techniques. In this paper, we introduce a received signal model for OCHM systems and analyze the BER performance for OCHM systems. Through the analysis of the BER performance, OCHM systems can be characterized more clearly and the allocated power at base station can be estimated. Furthermore, the user capacity is analyzed for a given channel coding scheme.