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Massive Multi-Input Multi-Output (MIMO) and Coordinated MultiPoint (CoMP) technologies in Cloud-RAN (C-RAN) architecture become inevitable trend due to the advent of next-generation mobile applications, which are traffic-intensive, such as ultra high definition (UHD) video. In this paper, we study a joint beam activation and user scheduling problem in a 5G cellular network with massive MIMO and CoMP utilizing orthogonal random beamforming technique. This paper aims to minimize total Remote Radio Heads’ (RRHs’) energy expenditure in a dynamic C-RAN architecture while ensuring finite service time for all user traffic arrivals in the communication coverage. We leverage Lyapunov drift-plus-penalty framework to transform an original long-term average problem into a series of per-slot modified problems. Since the provided per-slot problem is combinatorial and nonlinear optimization problem, we are inspired by a greedy algorithm to design energy and delay guaranteed joint beam activation and user scheduling policy, namely BEANS. We prove that the proposed BEANS ensures finite upper bounds of average RRH energy consumption and average queue backlogs for all traffic arrival rates within constant ratio of capacity region and all energy-delay tradeoff parameters. These proofs are the first attempt to theoretically demonstrate guarantees of energy and queue bounds in a framework consisting of possibly negative submodular objective function and non-matriod constraints. Finally, via extensive simulations, we compare the capacity region and energy-queue backlog tradeoff of BEANS with optimal and existing algorithms, and show that BEANS attains up to 65% of energy saving for the same average queue backlog compared to the algorithms which do not take traffic dynamics and energy consumption into considerations. [ABSTRACT FROM AUTHOR]

Most existing optimization objectives considered in non-orthogonal multiple access (NOMA) power allocation schemes are non-delay-sensitive metrics. In order to apply NOMA to various Internet of Things scenarios, the delay must be considered. The effective capacity of users, which characterizes the capacity under specific expiration probabilities, can potentially be a performance metric of statistical delay quality of service (QoS). In this paper, we propose two novel dynamic power allocation schemes with statistical delay QoS guarantee in the uplink NOMA system with paired users. One of the schemes maximizes the sum effective capacity (SEC) of the strong and weak users, which is a non-convex nonlinear optimization problem and is solved by Lagrangian dual decomposition and successive convex approximation (SCA). The other one maximizes the effective energy efficiency (EEE) of uplink NOMA, which is a fractional optimization problem and is solved by integrating the Dinkelbach method, SCA, and Lagrangian dual decomposition. Numerical results show that the SEC and EEE can be significantly improved by the proposed schemes, compared to the existing NOMA and orthogonal multiple access power allocation schemes. [ABSTRACT FROM AUTHOR]

Low earth orbit (LEO) satellite-assisted communications have been considered as one of the key elements in beyond 5G systems to provide wide coverage and cost-efficient data services. Such dynamic space-terrestrial topologies impose an exponential increase in the degrees of freedom in network management. In this paper, we address two practical issues for an over-loaded LEO-terrestrial system. The first challenge is how to efficiently schedule resources to serve a massive number of connected users, such that more data and users can be delivered/served. The second challenge is how to make the algorithmic solution more resilient in adapting to dynamic wireless environments. We first propose an iterative suboptimal algorithm to provide an offline benchmark. To adapt to unforeseen variations, we propose an enhanced meta-critic learning algorithm (EMCL), where a hybrid neural network for parameterization and the Wolpertinger policy for action mapping are designed in EMCL. The results demonstrate EMCL’s effectiveness and fast-response capabilities in over-loaded systems and in adapting to dynamic environments compare to previous actor-critic and meta-learning methods. [ABSTRACT FROM AUTHOR]

Providing backhaul connectivity for macro and pico base stations (BSs) constitutes a significant share of infrastructure costs in future heterogeneous networks (HetNets). To address this issue, the emerging idea of flexible backhaul is proposed. Under this architecture, not all the pico BSs are connected to the backhaul, resulting in a significant reduction in the infrastructure costs. In this regard, pico BSs without backhaul connectivity need to communicate with their nearby BSs in order to have indirect accessibility to the backhaul. This makes the radio resource management (RRM) in such networks more complex and challenging. In this paper, we address the problem of cross-layer RRM in HetNets with flexible backhaul. We formulate this problem as a two-timescale non-convex stochastic optimization, which jointly optimizes flow control, routing, interference mitigation, and link scheduling in order to maximize a generic network utility. By exploiting a hidden convexity of this non-convex problem, we propose an iterative algorithm which converges to the global optimal solution. The proposed algorithm benefits from low complexity and low signaling, which makes it scalable. Moreover, due to the proposed two-timescale design, it is robust to the backhaul signaling latency as well. Simulation results demonstrate the significant performance gain of the proposed solution over various baselines. [ABSTRACT FROM AUTHOR]

In the spectrum auction, if a buyer locates in a “critical” place, interfering with a lot of other buyers, his occupancy of the spectrum may deprive many other transmission opportunities. In this paper, we propose a Location-aware Online Truthful doUble auction Scheme (LOTUS), which incorporates the buyers' location information into auction mechanism design. In the online auction, the biggest challenge is how to allocate the spectrums based on the knowledge in the current time slot, without knowing the spectrum requests that may come afterward. To solve this problem, we propose considering the opportunity cost of allocating the spectrum to a buyer based on his local interference conditions. We introduce the “interference discount” to markdown a buyer's bid if he induces a wide range of interference. Furthermore, we take into account the spectrum heterogeneity and design mechanisms that guarantee the economic robustness of the auction. The simulation results show that LOTUS outperforms the existing online auction mechanism, significantly improving buyers' and sellers' utility. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a joint dynamic power control and user pairing algorithm for power-efficient and delay-constrained hybrid multiple access systems. In a hybrid multiple access system, user pairing determines whether the transmitter serves as a certain user by orthogonal multiple access (OMA) or non-orthogonal multiple access (NOMA). The proposed optimization framework minimizes the long-term time-average transmit power expenditure while reducing the queuing delay and guaranteeing the minimum time-average data rates. The proposed technique observes both channel and queue state information and adjusts queue backlogs to avoid an excessive queueing delay by appropriate user pairing and power allocation. Furthermore, the flexible use of resources is captured in the proposed algorithm by employing NOMA. The data-intensive simulation results show that the proposed scheme for power allocation and user scheduling achieves a balance among multiple performance goals, i.e., power efficiency, queueing delay, and data rate. [ABSTRACT FROM AUTHOR]

Wireless powered backscatter communications (WPBC) is capable of implementing ultra-low-power communication, thus promising in the Internet of Things (IoT) networks. In practice, however, it is challenging to apply WPBC in large-scale IoT networks because of its short communication range. To address this challenge, this paper exploits an unmanned ground vehicle (UGV) to assist WPBC in large-scale IoT networks. In particular, we investigate the joint design of network planning and dynamic resource allocation of the access point (AP), tag reader, and UGV to minimize the total energy consumption. Also, the AP can operate in either half-duplex (HD) or full-duplex (FD) multiplexing mode. Under HD mode, the optimal cell radius is derived and the optimal power allocation and transmit/receive beamforming are obtained in closed form. Under FD mode, the optimal resource allocation, as well as two suboptimal ones with low computational complexity, is developed. Simulation results disclose that dynamic power allocation at the tag reader rather than at the AP dominates the network energy efficiency while the AP operating in FD mode outperforms that in HD mode concerning energy efficiency. [ABSTRACT FROM AUTHOR]

The dramatic growth of machine-to-machine (M2M) communication in cellular networks brings the challenge of satisfying the quality of service (QoS) requirements of a large number of M2M devices with limited radio resources. In this paper, we propose an optimization framework for the semi-persistent scheduling of M2M transmissions based on the exploitation of their periodicity with the goal of reducing the overhead of the signaling required for connection initiation and scheduling. The goal of the optimization problem is to minimize the number of frequency bands used by the M2M devices to allow fair resource allocation of newly joining M2M and human-to-human communications. The constraints of the problem are delay and periodicity requirements of the M2M devices. We first prove that the optimization problem is NP-hard and then propose a polynomial-time heuristic algorithm employing a fixed priority assignment according to the QoS characteristics of the devices. We show that this heuristic algorithm provides an asymptotic approximation ratio of 2.33 to the optimal solution for the case where the delay tolerances of the devices are equal to their periods. Through extensive simulations, we demonstrate that the proposed algorithm performs better than the existing algorithms in terms of frequency band usage and schedulability. [ABSTRACT FROM AUTHOR]

Simultaneous random access attempts from massive machine-type communications (mMTC) devices may severely congest a shared physical random access channel (PRACH) in mobile networks. This paper presents a novel two-phase random access (TPRA) procedure to deal with the congestion caused by mMTC devices accessing the PRACH. During the first phase, the TPRA splits the mMTC devices into smaller groups according to a preamble selected randomly by the devices. Then, in the second phase, each group of devices is assigned with a dedicated channel to complete the random access procedure. The proposed concept allows a base station to adjust the number of dedicated channels in real-time according to the actual network load. We then present an analytical model to estimate the access success probability and the average access delay of the TPRA. Finally, we propose a simple formula to determine the optimal number of random access resources for the second phase of the proposed TPRA. Simulations are carried out to validate the analytical models and to demonstrate the benefits of the TPRA compared to competitive techniques. [ABSTRACT FROM AUTHOR]

Spectrum auction is considered a suitable approach to efficiently allocate spectrum among unlicensed users. In a typical spectrum auction, secondary users (SUs) bid to buy spectrum bands from a primary owner (PO) who acts as the auctioneer. Existing spectrum auctions assume that SUs have static and known values for the channels. However, in many real world settings, the SUs do not know the exact value of channel access at first, but they learn it and adapt it over time. In this paper, we study spectrum auctions in a dynamic setting where SUs can change their valuations based on their experiences with the channel quality. We propose ADAPTIVE, a dynAmic inDex Auction for sPectrum sharing with TIme-evolving ValuEs that maximizes the social welfare of the SUs. ADAPTIVE is based on multi-armed bandit models where for each user an allocation index is independently calculated in polynomial time. Then we generalize ADAPTIVE to Multi-ADAPTIVE that auctions multiple channels at each time. We provide a sufficient condition under which Multi-ADAPTIVE achieves the maximum social welfare. Both ADAPTIVE and Multi-ADAPTIVE have some desired economic properties that are formally proven in the analysis. Also, we provide a numerical performance comparison between our proposed mechanisms and the well known static auctions, namely the Vickrey second price auction and the VCG mechanism. [ABSTRACT FROM PUBLISHER]

By fully exploiting the cooperative communication capacities among mobile terminals (MTs), the MTs can adapt the offloading designs well to the practical network with dynamic features. In this paper, joint multi-user cooperative partial offloading, transmission scheduling and computation allocating is discussed for device-to-device (D2D) underlay mobile edge computing (MEC). By considering stochastic application requests, unpredictable MTs states, time-varying channel states and computation resources, a customized application offloading model, which aims to minimize the network-wide response latency and energy consumption simultaneously, is formulated. In order to solve this non-convex and non-smooth optimization problem, an online resource coordinating and allocating scheme (ORCAS) is proposed by exploiting Lyapunov optimization theory, variable substitution technique and resource provisioning priority mechanism. Both theoretical analyses and simulation results demonstrate that the proposed ORCAS can 1) drive the application response cost converge to the minimum; 2) achieve superior performance (e.g., the average network-wide response cost under ORCAS is approximately 19.14% lower than that under partial offloading directly); 3) adapt to dynamic situations in terms of stochastic user demands and channel states. [ABSTRACT FROM AUTHOR]

In this paper, we study the resource slicing problem in a dynamic multiplexing scenario of two distinct 5G services, namely Ultra-Reliable Low Latency Communications (URLLC) and enhanced Mobile BroadBand (eMBB). While eMBB services focus on high data rates, URLLC is very strict in terms of latency and reliability. In view of this, the resource slicing problem is formulated as an optimization problem that aims at maximizing the eMBB data rate subject to a URLLC reliability constraint, while considering the variance of the eMBB data rate to reduce the impact of immediately scheduled URLLC traffic on the eMBB reliability. To solve the formulated problem, an optimization-aided Deep Reinforcement Learning (DRL) based framework is proposed, including: 1) eMBB resource allocation phase, and 2) URLLC scheduling phase. In the first phase, the optimization problem is decomposed into three subproblems and then each subproblem is transformed into a convex form to obtain an approximate resource allocation solution. In the second phase, a DRL-based algorithm is proposed to intelligently distribute the incoming URLLC traffic among eMBB users. Simulation results show that our proposed approach can satisfy the stringent URLLC reliability while keeping the eMBB reliability higher than 90%. [ABSTRACT FROM AUTHOR]

This paper aims to produce an effective online scheduling technique, where a base station (BS) schedules the transmissions of energy harvesting-powered Internet-of-Things (IoT) devices only based on the (differently outdated) in-band reports of the devices on their states. We establish a new primal-dual learning framework, which learns online the optimal proactive schedules to maximize the time-average throughput of all the devices. Batch gradient descent is designed to enable stochastic gradient descent (SGD)-based dual learning to learn the network dynamics from the outdated reports. Replay memory is deployed to allow online convex optimization (OCO)-based primal learning to predict channel conditions and prevent over-fitting. We also decentralize the online learning between the BS and devices, and speed up learning by leveraging the instantaneous knowledge of the devices on their states. We prove that the proposed framework asymptotically converges to the global optimum, and the impact of the outdated knowledge of the BS diminishes. Simulation results confirm that the proposed approach can increasingly outperform state of the art, as the number of devices grows. [ABSTRACT FROM AUTHOR]

The recent development in mobile edge computing necessitates caching of dynamic contents, where new versions of contents become available around-the-clock, thus timely update is required to ensure their relevance. The age of information (AoI) is a performance metric that evaluates the freshness of contents. Existing works on AoI-optimization of cache content update algorithms focus on minimizing the long-term average AoI of all cached contents. Sometimes, user requests that need to be served in the future are known in advance and can be stored in user request queues. In this paper, we propose dynamic cache content update scheduling algorithms that exploit the user request queues. We consider a use case, where the trained neural networks (NNs) from deep learning models are being cached in a heterogeneous network (HetNet), as a motivating example. A queue-aware cache content update scheduling algorithm based on constrained Markov decision process (CMDP) is developed to minimize the average AoI of the dynamic contents delivered to the users. By using enforced decomposition technique and deep reinforcement learning, we propose two low-complexity suboptimal scheduling algorithms. Simulation results show that our proposed algorithms outperform the periodic cache content update scheme and reduce the average AoI by up to 30%. [ABSTRACT FROM AUTHOR]

To facilitate the deployment of private industrial Internet-of-Things (IoT), applying long-term-evolution (LTE) over unlicensed spectrum (LTE-U) is a promising technology, which can deal with the licensed spectrum scarcity problem and the stringent quality-of-service (QoS) requirement via centralized control. In this paper, we investigate the computing offloading problem for LTE-U-enabled IoT, where computing tasks on an IoT device are either executed locally or offloaded to the edge server on an LTE-U base station. Considering a constrained edge computing cost (e.g., operation power consumption) for offloaded tasks, the task scheduling problem is formulated as a constrained Markov decision process (CMDP) to maximize the long-term average reward, which integrates both task completion profit and task completion delay. In order to address the uncertainty of task arrivals and channel availability, a constrained deep Q-learning-based task scheduling algorithm with provable convergence is proposed, where an adaptive reward function can appropriately bound the average edge computing cost. Extensive simulation results show that the proposed scheme considerably enhances the system performance. [ABSTRACT FROM AUTHOR]

New short-length single-direction frame structures are proposed for 5G time division duplex (TDD) systems, where the transmit direction [i.e., either downlink (DL) or uplink (UL)] can be independently chosen at each cell in every frame. Accordingly, high flexibility is provided to match the per-cell DL/UL traffic asymmetries and full exploitation of dynamic TDD is allowed. As a downside, interference management becomes crucial. In this regard, this paper proposes a procedure for dynamic TDD in dense multiple-input multiple-output small cell networks, where the transmit direction selected per small cell (SC) is dynamically optimized together with the user scheduling and transmit precoding. We focus on the maximization of a general utility function that takes into account the DL/UL traffic asymmetries of each user and the interference conditions in the network. Although the problem is non-convex, it is decomposed thanks to the interference-cost concept and then efficiently solved in parallel. Simulation results show gains in DL and UL average rates for different traffic asymmetries and SC/user densities as compared to existing dynamic TDD schemes thanks to the proposed joint optimization. The gains become more significant when there is high interference and limited number of antennas. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a spectrum auction mechanism for secondary spectrum access in cognitive radio networks. Different from existing works in the literature, the time-dependent buyer valuation information is employed in the proposed mechanism so that the primary spectrum owner (PO) can determine more favorable spectrum allocations and pricing functions in order to maximize the expected auction revenue. In addition, to exploit the temporal spectrum reusability, the proposed mechanism allows each secondary wireless user to declare its specific time preferences, including service starting time, delay tolerance, and service length. By further considering the heterogeneities in secondary wireless service provisioning, the proposed mechanism is able to support heterogeneous forms (continuous or disjointed spectrum usages) of secondary spectrum requests. Specifically, at the beginning of the auction frame, secondary wireless users report their different spectrum usage requests along with the bidding prices, while the PO decides a single-step spectrum allocation and calculates the payment for each winner based on not only the received bids but also the known time-dependent valuation information. Theoretical analyses and simulation results show that the proposed auction mechanism can satisfy all desired economic properties, and can improve the spectrum allocation efficiency and auction revenue compared with counterparts. [ABSTRACT FROM PUBLISHER]

Cloud radio access network (C-RAN) as a promising and cost-efficient cellular architecture has been proposed to meet the increasing demand of wireless data traffic. The main concept of C-RAN is to decouple the baseband unit (BBU) and the remote radio head (RRH), and place the BBUs in a data center for centralized control and processing. In this paper, we study the resource sharing problem in a fronthaul constrained C-RAN, where multiple service providers lease radio resources from a network operator to serve their subscribers. To provide isolation among different service providers, we introduce a threshold-based policy to control the interference among RRHs, and define a new metric to provide minimum resource guarantee for service providers. By leveraging a mobility prediction method, the user locations are predicted for traffic demand estimation and interference control. We propose a multi-timescale resource sharing mechanism, which consists of a global resource allocation process and multiple local resource allocation processes that are performed at different time scales. Simulation results show that the proposed mechanism achieves efficient resource sharing and isolation among service providers. [ABSTRACT FROM PUBLISHER]

Power saving-semipersistent scheduler (PS-SPS) for voice over LTE (VoLTE) traffic was proposed in the downlink of LTE-A cell to reduce the energy consumption of the eNodeB. PS-SPS introduces the energy saving ability through a modification of traditional SPS (T-SPS) in allocating downlink Physical Resource Block (PRBs) to VoLTE users. In this paper, we first identify the drawbacks of PS-SPS that might curtail its energy saving capability and also degrade its capacity (maximum number of calls that can be supported simultaneously) compared with T-SPS. We then propose a novel SPS scheduler called enhanced-PS-SPS (E-PS-SPS) to allocate downlink PRBs to VoLTE users more efficiently compared with PS-SPS. Next, we build analytical models to derive expressions for capacity and eNodeB energy consumption for both PS-SPS and E-PS-SPS. Our analytical results show that E-PS-SPS not only outperforms PS-SPS in capacity by a substantial margin, but also considerably improves the energy saving. The analysis results are validated using simulations. Furthermore, owing to the modeling complexity of T-SPS, we bound its performance in terms of PS-SPS and E-PS-SPS through heuristic arguments. Simulation results validate our analytical bounds and also show that the proposed E-PS-SPS outperforms T-SPS also both in capacity and energy consumption, unlike PS-SPS whose capacity is lower than T-SPS. [ABSTRACT FROM PUBLISHER]

Energy harvesting with combination of multiple cooperating radio access technologies (multi-RAT) is regarded as a promising network paradigm to improve the energy efficiency of 5G networks. In this paper, we propose a resource-on-demand energy scheduling strategy for multi-RAT wireless networks, where the varying energy demand of the network can be satisfied by both grid power and harvested energy. Due to the high sensitivity to uncertainties of energy harvesting, a dynamic network energy queue model is designed first considering the inherently stochastic and intermittent nature of the harvested energy. Then, to minimize time-averaged grid power consumption and make effective utilization of harvested energy, the energy scheduling is formulated as a stochastic optimization problem subject to data queue stability and harvested energy availability, considering the high ynamics of wireless channel states and renewable energy sources. Following the Lyapunov optimization framework, the stochastic grid power minimization problem is decomposed into a network flow control subproblem, a network energy management subproblem, and a network resource allocation subproblem, respectively. In order to solve these subproblems, we develop a dynamic adaptive resource-on-demand (DAROD) algorithm to effectively reduce the grid power consumption cost by allocating the resource efficiently based on the dynamic demands of multi-RAT networks. Finally, the tradeoff between grid power consumption cost and network delay is achieved, in which the increase of network delay is approximately linear with the network control parameter $V$ and the decrease of grid power consumption cost is at the speed of $1/V$. Extensive simulations are conducted to verify the theoretical analysis and show the effectiveness of our proposed algorithm. [ABSTRACT FROM AUTHOR]

Dynamic spectrum access (DSA) is a promising platform to solve the problem of spectrum shortage for which the most challenging issue is spectrum allocation under uncertain availability information, which is referred as a nondeterministic spectrum allocation problem. The nature of such a problem is due to inaccurate spectrum sensing results, which are induced by that power or energy based sensing can be greatly impacted by thermal and environmental noise. For spectrum allocation, auction-based mechanisms have been extensively studied because of channel allocation efficiency, and its potential to achieve bidding truthfulness for secondary uses (SUs). However, most existing spectrum auction mechanisms focus on realizing the truthfulness under certain spectrum availability information. In this paper, we propose FORTUNE, the first truthful online auction mechanism for nondeterministic spectrum allocation by considering uncertain spectrum availability and dynamic spectrum requests. Specifically, we take limited information to compute expected income and losses when interference between primary users (PUs) and SUs occurs, and present a virtual request method for changing of spectrum’s actual state. Thorough theoretical analysis proves the truthfulness of FORTUNE. Furthermore, given a sample set with 5%-30% noise in spectrum sensing, FORTUNE achieves not only truthfulness, but also up to 50% higher channel utilization than existing spectrum auction mechanisms. [ABSTRACT FROM AUTHOR]

In this paper, we introduce dynamic time-frequency-division duplex (D-TFDD), which is a novel duplexing scheme that combines time-division duplex (TDD) and frequency-division duplex (FDD). In D-TFDD, a user receives from the base station (BS) on the downlink in one frequency band and transmits to the BS on the uplink in another frequency band, as in FDD. Next, the user shares its uplink transmission (downlink reception) on the corresponding frequency band with the uplink transmission or the downlink reception of another user in a D-TDD fashion. Hence, in a given frequency band, the BS communicates with user 1 (U1) and user 2 (U2) in a D-TDD fashion. The proposed D-TFDD scheme does not require inter-cell interference (ICI) knowledge and only requires channel state information (CSI) of the local BS-U1 and BS-U2 channels. Thereby, it is practical for implementation. The proposed D-TFDD scheme increases the throughput region between the BS and the two users in a given frequency band, and significantly decreases the outage probabilities on the corresponding BS-U1 and BS-U2 channels. Most importantly, the proposed D-TFDD scheme doubles the diversity gain on both the corresponding BS-U1 and the BS-U2 channels compared to the diversity gain of existing duplexing schemes, which results in very large performance gains. [ABSTRACT FROM AUTHOR]

The ever-increasing demand for high-quality video contents over mobile networks is pushing Telco operators towards the design of new delivery solutions. 3GPP has defined Multimedia Broadcast/Multicast Service Single Frequency Network (MBSFN) to enable the simultaneous transmission of the same content within multiple cells over the same radio resources, with improved network scalability and spectral efficiency. This paper proposes a Dynamic MBSFN Area Formation (DMAF) algorithm that suitably selects the adjacent synchronized cells to include in any MBSFN Area of a 5G New Radio (NR) system to the purpose of increasing the system Aggregate Data Rate while avoiding user outage. The proposed algorithm dynamically creates MBSFN Areas by leveraging the multicast “subgrouping” paradigm for Scalable Video Coding (SVC) traffic delivery. Cells are grouped into different MBSFN Areas and any cell can be part of Disjoint MBSFN Area or Overlapping MBSFN Area. Each Overlapping MBSFN Area broadcasts a Base video flow at a given quality level to all users, whereas Enhancement Layers are only delivered to users with better channel conditions. Simulation results testify to the better performance achieved by the proposed algorithm w.r.t. other schemes available from the literature. [ABSTRACT FROM AUTHOR]

To promote the revolution of Industrial Internet of Things, the next generation communication system is expected to provide latency critical services in industry. However, for the traditional downlink-centric cellular systems, the timely delivery of packets cannot be guaranteed by the default dynamic access scheme due to complex signaling procedure. A promising solution to low-latency access is the resource pre-allocation scheme based on the semi-persistent scheduling (SPS) technique, however at the expense of low spectrum utilization. Aiming to make those pre-allocated resources more rewarding, a so-called DPre, a predictive pre-allocation scheme based on learning for low-latency uplink access in industrial wireless networks, is proposed in this paper. It intelligently explores the correlation of devices’ access behavior and device utility diversity through sequential learning. Thus, flexible and judicious per-allocation decisions in both time and frequency domains can be made in an on-demand manner. Moreover, with the proposed temporal-spatial utility metric, DPre is guaranteed to reserve for more informative devices. Both theoretical analysis and simulation validate its high spectrum utilization through accurate prediction and the potential to pre-allocate for valuable packets. [ABSTRACT FROM AUTHOR]

Device-to-device (D2D) communication has recently gained much attention for its potential to boost the capacity of cellular systems. D2D enables direct communication between devices while bypassing a base station (BS), hence decreasing the load of BSs and increasing the network throughput via spatial reuse of radio resources. However, the cellular system is highly dynamic, an optimal allocation plan of radio resource to D2D links at one time point can easily become suboptimal when devices move. Thus, to maximize spatial reuse in cellular systems, it is crucial to update the resource allocation adaptively to reflect the current system status. In this paper, we develop the first adaptive solution framework to the dynamic resource problem for maximizing spatial reuse. At the core of the framework, we present the two algorithms for the adaptive set multicover problem with approximation ratio $f$ and $\log n$ , respectively, where $f$ is the frequency of the most frequent element and $n$ is the total number of elements. The experimental results not only show that our solutions have a significant improvement in running time, compared with optimal or approximated offline methods, but also demonstrate their good performance through the resource usage, network throughput, and other metrics. [ABSTRACT FROM AUTHOR]

With the shortage of spectrum in conventional cellular frequencies, millimeter-wave (mmWave) bands are being widely considered for use in next-generation networks. Multihop relaying is likely to play a significant role in mmWave cellular systems for self backhauling, range extension and improved robustness from path diversity. However, designing scheduling policies for these systems is challenging due to the need to account for both adaptive directional transmissions and dynamic time-division duplexing schedules, which are key enabling features of mmWave systems. This paper considers the problem of joint scheduling and congestion control in a multihop mmWave network using a Network Utility Maximization (NUM) framework. Interference is modeled with an exact model and two auxiliar simplified models: actual interference (AI), with a graph-based calculation of the Signal to Interference plus Noise Ratio (SINR) depending on dynamic link activity and directivity, as well as upper and lower bounds computed from worst-case interference (WI) and interference free (IF) approximations. Throughput and utility optimal policies are derived for all interference models (AI, WI and IF) with both deterministic Maximum Weighted and randomized Pick and Compare scheduling algorithms, jointly with decentralized Dual Congestion Control. Results are evaluated with numerical simulations, using accurate mmWave channel and beamforming gain approximations based on measurement campaigns. [ABSTRACT FROM PUBLISHER]

In this paper, we study the performance of mode selections in device-to-device (D2D) communications in terms of end-to-end average throughput, average delay, and dropping probability, considering dynamic data arrival with non-saturated buffers. We first introduce a general framework that includes three canonical routing modes, namely D2D mode, cellular mode, and hybrid mode, which can be combined with different resource allocation restrictions to represent the semi-static and dynamic selections of the three resource sharing modes. A queuing model is developed when the routing mode for every D2D connection is chosen, and an exact numerical analysis and an approximate decomposition and iteration approach are proposed. The performance measures are obtained from the decomposition approach and validated by means of simulation. We further introduce a mode selection scheme that adaptively chooses to semi-statically or dynamically select the resource sharing modes according to the estimated performance measures. [ABSTRACT FROM PUBLISHER]

In this paper, we study downlink scheduling with transmission strategy selection in multi-cell multiple-input multiple-output (MIMO) systems. Depending on the level of inter-cell interference experienced by a user, the scheduler can choose between two MIMO transmission strategies, namely, spatial multiplexing and interference alignment. We formulate an optimization problem which aims to jointly select a user and the corresponding transmission strategy for each base station in order to maximize the overall system utility while stabilizing all transmission queues. We first develop a centralized dynamic scheduling scheme with transmission strategy selection by using a stochastic network optimization approach. To reduce the communication overhead, we then propose a distributed scheduling algorithm which only requires limited message exchange between the base stations. We also consider the impact of imperfect channel state information on the scheduling schemes and propose an efficient rate adjustment method to improve the performance for this case. Simulation results show that the performance of the proposed distributed scheduling scheme is close to that of the centralized scheduling scheme, and both schemes achieve a better performance than schemes employing a single transmission strategy. [ABSTRACT FROM PUBLISHER]

To meet the increasing demand for wireless capacity, future networks are likely to consist of dense layouts of small cells. The number of users in each cell is thus reduced, which results in diminished gains from opportunistic scheduling, particularly under dynamic traffic loads. We propose a user-initiated base station (BS)-transparent traffic spreading approach that leverages user–user communication to increase BS scheduling flexibility. The proposed scheme can increase opportunistic gain and improve user performance. For a specified tradeoff between performance and power expenditure, we characterize the optimal policy by modeling the system as a Markov decision process and also present a heuristic algorithm that yields significant performance gains. Our simulations show that, in the performance-centric case, average file transfer delays are lowered by up to 20% even in homogeneous scenarios and up to 50% with heterogeneous users. Further, we show that the bulk of the performance improvement can be achieved with a small increase in power expenditure, e.g., in an energy-sensitive case, up to 78% of the performance improvement can be typically achieved at only 20% of the power expenditure of the performance-centric case. [ABSTRACT FROM PUBLISHER]

Dynamic spectrum access (DSA) can be leveraged by introducing external spectrum sensing for secondary users (SUs) to overcome the hidden primary users (PUs) problem and improve spectrum utilization. In this paper, we investigate the DSA networks with external sensors, i.e., external sensing agents, to utilize spectrum access opportunities located in cellular frequency bands. Considering the diversity of SUs' demands and the secondary bandwidths discovered by external sensors, it is critical to manage the detected spectrum resources in an efficient way. To this end, we formulate the resource management problem in the DSA networks as a dynamic resource demand-supply matching problem, and propose a cooperative matching solution. Specifically, spectrum access opportunities are classified into two types by the resource block size: massive sized blocks and small sized blocks. For the former type, SUs are encouraged to share the whole time-frequency block via forming coalitional groups with a "wholesale" sharing approach. For the latter type, the resource "aggregation" sharing approach is proposed to meet the time-frequency demand of individual SUs. To further reduce the delay in the spectrum allocation and compress the matching process, we develop a distributed fast spectrum sharing (DFSS) algorithm, which can deal with both two aforementioned types of resource sharing cases. Simulation results show that the DFSS algorithm can adapt to the dynamic spectrum variations in the DSA networks and the average utilization of detected spectrum access opportunities reaches nearly 90%. [ABSTRACT FROM PUBLISHER]

Scheduling different types of packets, such as real-time and non-real-time data packets, at sensor nodes with resource constraints in Wireless Sensor Networks (WSN) is of vital importance to reduce sensors' energy consumptions and end-to-end data transmission delays. Most of the existing packet-scheduling mechanisms of WSN use First Come First Served (FCFS), non-preemptive priority and preemptive priority scheduling algorithms. These algorithms incur a high processing overhead and long end-to-end data transmission delay due to the FCFS concept, starvation of high priority real-time data packets due to the transmission of a large data packet in non-preemptive priority scheduling, starvation of non-real-time data packets due to the probable continuous arrival of real-time data in preemptive priority scheduling, and improper allocation of data packets to queues in multilevel queue scheduling algorithms. Moreover, these algorithms are not dynamic to the changing requirements of WSN applications since their scheduling policies are predetermined. In this paper, we propose a Dynamic Multilevel Priority (DMP) packet scheduling scheme. In the proposed scheme, each node, except those at the last level of the virtual hierarchy in the zone-based topology of WSN, has three levels of priority queues. Real-time packets are placed into the highest-priority queue and can preempt data packets in other queues. Non-real-time packets are placed into two other queues based on a certain threshold of their estimated processing time. Leaf nodes have two queues for real-time and non-real-time data packets since they do not receive data from other nodes and thus, reduce end-to-end delay. We evaluate the performance of the proposed DMP packet scheduling scheme through simulations for real-time and non-real-time data. Simulation results illustrate that the DMP packet scheduling scheme outperforms conventional schemes in terms of average data waiting time and end-to-end delay. [ABSTRACT FROM PUBLISHER]

The emerging distributed satellite cluster network (DSCN) holds great promise in various practical fields, including earth observation, disaster rescue, and tracking of forest fires. In the DSCN environment, it is essential to achieve the best data delivery performance by coordinating multi-dimensional heterogeneous and dynamic resources. However, in real-world applications, the distribution of long-term data arrival is not often fully known. Motivated by this fact, we propose a distributionally robust two-stage stochastic optimization framework with considering the dynamic network resources and the partially known distribution information of long-term data arrival. Aiming at maximizing the total network reward, we formulate a two-stage stochastic flow optimization problem based on the extended time expanded graph. Then, we introduce an ambiguity set for the uncertain distribution of the long-term random data arrival inspired by the idea from the distributionally robust optimization. On the basis of the proposed ambiguity set, we further propose a data arrival distribution robust two-stage recourse (DADR-TR) algorithm by converting the original stochastic optimization problem into a deterministic cone optimization problem, which is computationally tractable. The extensive simulations have been conducted to evaluate the impact of various network parameters on the algorithm performance and further validate that the proposed DADR-TR algorithm can achieve high data delivery performance without full distribution information of the long-term data arrival. [ABSTRACT FROM AUTHOR]

The fifth-generation (5G) air interface, namely, dynamic multiple access (MA) based on multiuser superposition transmission (MUST) and orthogonal MA (OMA), may require a complicated scheduling and heavy signaling overhead. To address these challenges, we propose a unified MA scheme for future cellular networks, which we refer to as structured MUST (S-MUST). In S-MUST, we apply complex power allocation coefficients (CPACs) over multiuser legacy constellations to generate a composite constellation. In particular, the in-phase ($I$) and quadrature ($Q$) components of the legacy constellation of each user are separately multiplied by those of the CPACs. As such, the CPACs offer an extra degree of freedom for multiplexing users and guarantee fairness in symmetric broadcast channels. This new paradigm of superposition coding allows us to design $IQ$ separation at the user side, which significantly reduces the decoding complexity without degrading performance. Hence, it supports low-complexity frequency-selective scheduling that does not entail dynamical switching between MUST and OMA. We further propose to quantize the CPACs into complex numbers where $I$ and $Q$ components of each quantized coefficient are primes, facilitating parallel interference cancellation at each user via modulo operations; last but not least, we generalize the design of S-MUST to exploit the capabilities of multiantenna base stations. The proposed S-MUST exhibits an improved user fairness with respect to conventional MUST (134% spectral efficiency enhancement) and a lower system complexity compared with dynamically alternating MUST and OMA. [ABSTRACT FROM AUTHOR]

We jointly optimize resource allocation with the dynamic activation of energy harvesting base stations in a two-tier orthogonal frequency-division multiple access-based heterogeneous network. We consider both energy harvesting constraints and interference constraints along with time-variation in channel condition, user activity, and energy arrival. We optimize the trade-off between throughput performance of the small cell (or hotspot) users and the associated power cost by maximizing the net reward, where positive reward is associated with achievable throughput of the hotspot users and negative reward with the corresponding non-renewable power consumption. Quality-of-service requirements of hotspot users as well as macrocell users are considered in the optimization problem. Assuming the availability of non-causal information, we propose offline resource allocation algorithm using discrete binary particle swarm optimization and dual decomposition technique. Assuming the availability of statistical information of future values, we propose dynamic programming-based online algorithm. Finally, we propose simple and greedy online algorithm assuming lack of any kind of future information. Numerical results demonstrate the performances of the proposed offline, dynamic programming-based online, and greedy online algorithms and highlight the scenarios, where the performance of the proposed algorithms is significantly better than the baseline schemes. [ABSTRACT FROM PUBLISHER]

This paper proposes a novel joint resource allocation algorithm combining dynamic user grouping, multi-cell cooperation, and resource block (RB) allocation for single carrier-frequency division multiple access uplink in multi-cell virtual MIMO systems. We first develop the dynamic multi-cell user grouping criteria using minimum mean square error equalization and adaptive modulation with bit error rate (BER) constraint. Then, we formulate and solve a new throughput maximization problem whose resource allocation includes cell selection, dynamic user grouping, and RB pattern assignment. Furthermore, to reduce the computational complexity significantly, especially in the case of large numbers of users and RBs, we present an efficient iterative Hungarian algorithm based on user and resource partitions to solve the problem by decomposing the large scale problem into a series of small scale sub-problems, which can obtain close-to-optimal solution with much lower complexity. The simulation results show that our proposed joint resource allocation algorithm with dynamic multi-cell user grouping scheme achieves better system throughput with BER guarantee than fixed user grouping algorithm and other proposed schemes in the literature. [ABSTRACT FROM PUBLISHER]

The potential of intelligent reflecting surfaces (IRSs) is investigated as a promising technique for enhancing the energy efficiency of wireless networks. Specifically, the IRS enables passive beamsteering by employing many low-cost individually controllable reflect elements. The resulting change of the channel state, however, not only increases the signal quality but also the interference at the users. To counteract this negative side effect, we employ rate splitting (RS), which inherently is able to mitigate the impact of interference. We facilitate practical implementation by considering a Cloud Radio Access Network (C-RAN) at the cost of finite fronthaul-link capacities, which necessitate the allocation of sensible user-centric clusters to ensure energy-efficient transmissions. Dynamic methods for RS and the user clustering are proposed to account for the interdependencies of the individual techniques. Numerical results show that the dynamic RS method establishes synergistic benefits between RS and the IRS. Additionally, the dynamic user clustering and the IRS cooperate synergistically, reflected by increased individual gains for the dynamic clustering. Interestingly, with an increasing fronthaul capacity, the gain of the dynamic user clustering decreases, while the gain of the dynamic RS method increases. Around the resulting intersection, both methods affect the system concurrently, improving the energy efficiency drastically. [ABSTRACT FROM AUTHOR]

Securing necessary resources for edge computing processes via effective resource trading becomes a critical technique in supporting computation-intensive mobile applications. Conventional onsite spot trading could facilitate this paradigm with proper incentives, which, however, incurs excessive decision-making latency/energy consumption, and further leads to underutilization of dynamic resources. Motivated by this, a hybrid market unifying futures and spot is proposed to facilitate resource trading among an edge server (seller) and multiple smart devices (buyers) by encouraging some buyers to sign a forward contract with seller in advance, while leaving the remaining buyers to compete for available resources with spot trading. Specifically, overbooking is adopted to achieve substantial utilization and profit advantages owing to dynamic resource demands. By integrating overbooking into futures market, mutually beneficial and risk-tolerable forward contracts with appropriate overbooking rate can be achieved relying on analyzing historical statistics associated with future resource demand and communication quality, which are determined by an alternative optimization-based negotiation scheme. Besides, spot trading problem is studied via considering uniform/differential pricing rules, for which two bilateral negotiation schemes are proposed by addressing both non-convex optimization and knapsack problems. Experimental results demonstrate that the proposed mechanism achieves mutually beneficial player’s utilities, while outperforming baseline methods on critical indicators, e.g., decision-making latency, resource usage, etc. [ABSTRACT FROM AUTHOR]

For cognitive radio (CR) networks with user hierarchy, the sensing strategy with "listen-before-talk" (LBT) policy plays a key role in spectrum utilization and primary user (PU) protection. However, existing sensing strategies do not handle satisfactorily the randomness of both user locations and channel conditions in the network environment, resulting in inefficient spectrum utilization. To cope with such randomness, this paper develops three dynamic sensing strategies that can adaptively schedule the sensing slots/cycles according to the online link conditions without assuming knowledge of the PU traffic model. The proposed strategies can improve the efficiency of spectrum utilization while being robust with respect to the uncertainty in the PU traffic pattern. To maximize spectrum utilization, the proposed strategies are formulated through closed-form expressions. Efficient methods are introduced to compute the optimal values of the parameters used in the strategies, such as sensing time and sensing threshold. Simulations verify that the proposed sensing strategies offer an evident improvement on the spectrum utilization of the CR network. [ABSTRACT FROM PUBLISHER]

Orthogonal frequency division multiple access (OFDMA) systems, such as IEEE 802.16e/m and 3GPP Long Term Evolution (LTE), require significantly large signaling overhead for delivering small-sized delay-sensitive traffic such as voice over IP (VoIP) service. It may result in a significant decrease in the spectral efficiency. In order to overcome this drawback, recently, a persistent scheduling scheme has been standardized in standard bodies. In order to reduce the signaling overhead, the modulation and coding scheme (MCS) is set to be fixed during a burst period for the persistent scheduling procedure and an incomplete transmission, if it occurs, is recovered by a hybrid automatic repeat request (HARQ) scheme. In this paper, considering the HARQ retransmissions, we propose a rate selection scheme for the persistent scheduling satisfying given quality of service (QoS) requirements. In our proposed rate selection scheme, we consider two important factors which were not considered in previous HARQ-related work: time-correlations of the wireless channel in the HARQ retransmissions and the resource usage of signaling overhead. Numerical results show that the proposed rate selection scheme can efficiently enhance the utilization of radio resources compared to the conventional schemes. [ABSTRACT FROM PUBLISHER]

Extrinsic information transfer (EXIT) charts are utilized to optimize the iterative multiuser detector receiver in a multiuser turbo-coded CDMA system. The (receive) power levels are optimized for the system load using a constrained nonlinear optimization approach. The optimal decoding schedule is derived dynamically using the power optimized EXIT chart and a Viterbi search algorithm. Dynamic scheduling is shown to be a more flexible approach which results in a more stable QoS for a typical system configuration than one-shot scheduling, and large complexity savings over a receiver without scheduling. We verify through simulations that complexity savings of over 50% and power savings of over 8dB can be achieved. We show that the optimized power levels combined with adaptive scheduling allows for efficient utilization of receiver resources for heavily loaded systems. [ABSTRACT FROM PUBLISHER]

Satellite networks have been regarded as a promising architecture for supporting the Internet of remote things (IoRT) due to their advantages of wide coverage and high communication capacity in remote areas, which further promotes the development of the satellites for IoRT networks (SIoRTNs). The effectiveness of multi-dimensional resource collaboration has significant impacts on the IoRT data downloading performance. However, the environment ’ s dynamics, e.g., channel conditions and solar infeed process, are unknown in practical scenarios, which poses daunting challenges in making efficient utilization of multi-dimensional resources. Motivated by this fact, we model the joint resource scheduling and IoRT data scheduling problem with the aim of maximizing the amount of the IoRT data of the overall network by applying the model-free reinforcement learning framework. To overcome the limitations of traditional reinforcement learning algorithms, we propose several feature functions by investigating the natural attributes of the multi-dimensional resources of the SIoRTNs, and further exploit the concept of function approximation to approximate the expected downloaded IoRT data given the network state. Furthermore, we propose a state-action-reward-state-action (SARSA) based actor-critic reinforcement learning (SACRL) resource allocation strategy to achieve the optimal resource allocation and IoRT data scheduling with casual information at LEO satellites. Simulations validate the convergence property and the effectiveness of the proposed SACRL algorithm in terms of the amount of the downloaded IoRT data. Particularly, we investigate the impact of typical network parameters on network performance to further provide guidance for future SIoRTN system design. [ABSTRACT FROM AUTHOR]

Non-orthogonal multiple access (NOMA) exploits the potential of the power domain to enhance the connectivity for the Internet of Things (IoT). Due to time-varying communication channels, dynamic user clustering is a promising method to increase the throughput of NOMA-IoT networks. This article develops an intelligent resource allocation scheme for uplink NOMA-IoT communications. To maximise the average performance of sum rates, this work designs an efficient optimization approach based on two reinforcement learning algorithms, namely deep reinforcement learning (DRL) and SARSA-learning. For light traffic, SARSA-learning is used to explore the safest resource allocation policy with low cost. For heavy traffic, DRL is used to handle traffic-introduced huge variables. With the aid of the considered approach, this work addresses two main problems of fair resource allocation in NOMA techniques: 1) allocating users dynamically and 2) balancing resource blocks and network traffic. We analytically demonstrate that the rate of convergence is inversely proportional to network sizes. Numerical results show that: 1) Compared with the optimal benchmark scheme, the proposed DRL and SARSA-learning algorithms have lower complexity with acceptable accuracy and 2) NOMA-enabled IoT networks outperform the conventional orthogonal multiple access based IoT networks in terms of system throughput. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) are capable of serving as aerial base stations (BSs) for providing both cost-effective and on-demand wireless communications. This article investigates dynamic resource allocation of multiple UAVs enabled communication networks with the goal of maximizing long-term rewards. More particularly, each UAV communicates with a ground user by automatically selecting its communicating user, power level and subchannel without any information exchange among UAVs. To model the dynamics and uncertainty in environments, we formulate the long-term resource allocation problem as a stochastic game for maximizing the expected rewards, where each UAV becomes a learning agent and each resource allocation solution corresponds to an action taken by the UAVs. Afterwards, we develop a multi-agent reinforcement learning (MARL) framework that each agent discovers its best strategy according to its local observations using learning. More specifically, we propose an agent-independent method, for which all agents conduct a decision algorithm independently but share a common structure based on Q-learning. Finally, simulation results reveal that: 1) appropriate parameters for exploitation and exploration are capable of enhancing the performance of the proposed MARL based resource allocation algorithm; 2) the proposed MARL algorithm provides acceptable performance compared to the case with complete information exchanges among UAVs. By doing so, it strikes a good tradeoff between performance gains and information exchange overheads. [ABSTRACT FROM AUTHOR]

M-LWWF/M-LWDF scheduling schemes have attracted much interest due to their ability to both stabilize queues whenever possible and control delay through parameter selection. However, a good implementation of these schedulers would require a mechanism to minimize the required fraction of the bandwidth while satisfying its stability and delay requirements. To the best of our knowledge, previous works on these scheduling policies did not address the problem of minimizing the bandwidth utilization while satisfying delay constraints. In this paper, we explore the solution of this problem using a joint bandwidth and weight adaptation approach. We characterize the problem solution space for M-LWWF and M-LWDF scheduling, assuming time-varying traffic. We also show that, starting from any point in the solution space, simple dynamic bandwidth and weight updates can surely lead to the convergence to the optimal operation point in this space. Based on these characteristics, we propose a dynamic parameter adaptation algorithm that is able to track the time-varying optimal operation points for dynamic traffic and channel conditions. Simulation results show the efficiency of our proposed algorithm in tracking the optimal operation points in dynamic traffic and channel settings. [ABSTRACT FROM PUBLISHER]

We propose algorithms for cloud radio access networks that not only provide heterogeneous quality of-service (QoS) for rate- and, importantly, delay-sensitive applications, but also jointly optimize the frequency reuse pattern. Importantly, unlike related works, we account for random arrivals, through queue awareness and, unlike the majority of works focusing on a single frame only, we consider QoS measures averaged over multiple frames involving a set of closed loop controls. We model this problem as multi-cell optimization to maximize a sum utility subject to the QoS constraints, expressed as minimum mean-rate or maximum mean-delay. Since we consider dynamic interference coordination jointly with dynamic user association, the problem is not convex, even after integer relaxation. We translate the problem into an optimization of frame rates, amenable to a decomposition into intertwined primal and dual problems. The solution to this optimization problem provides joint decisions on scheduling, dynamic interference coordination, and, importantly, unlike most works in this area, on dynamic user association. Additionally, we propose a novel method to manage infeasible loads. Extensive simulations confirm that the design responds to instantaneous loads, heterogeneous user and AP locations, channel conditions, and QoS constraints while, if required, keeping outage low when dealing with infeasible loads. Comparisons to the baseline proportional fair scheme illustrate the gains achieved. [ABSTRACT FROM PUBLISHER]

A dynamic optimization algorithm is proposed for the joint allocation of subframes, resource blocks, and power in the Type 1 inband relaying scheme mandatory in the LTE-Advanced standard. Following the general framework of Lyapunov optimization, we decompose the original problem into three sub-problems in the forms of convex programming, linear programming, and mixed-integer programming. We solve the last sub-problem in the Lagrange dual domain, showing that it has zero duality gap, and that a primal optimum can be obtained with probability one. The proposed algorithm dynamically adapts to traffic and channel fluctuations, it accommodates both instantaneous and average power constraints, and it obtains arbitrarily near-optimal sum utility of each user's average throughput. Simulation results demonstrate that the joint optimum can significantly outperform suboptimal alternatives. [ABSTRACT FROM PUBLISHER]

A class of dynamic fair scheduling schemes based on the generalized processor sharing (GPS) fair service discipline, under the generic name code-division GPS (CDGPS), is proposed for a wideband direct-sequence code-division multiple-access (CDMA) cellular network to support multimedia traffic. The CDGPS scheduler makes use of both the traffic characteristics in the link layer and the adaptivity of the wideband CDMA physical layer to perform fair scheduling on a time-slot by time-slot basis, by using a dynamic rate-scheduling approach rather than the conventional time-scheduling approach. Soft uplink capacity is characterized for designing an efficient CDGPS resource allocation procedure. A credit-based CDGPS (C-CDGPS) scheme is proposed to further improve the utilization of the soft capacity by trading off the short-term fairness. Theoretical analysis shows that, with the C-CDGPS scheme, tight delay bounds can be provided to delay-sensitive traffic, and short-term unfairness can be bounded so that long-term weighted fairness for all users can still be satisfied. Simulation results show that bounded delays, increased throughput, and long-term fairness can be achieved for both homogeneous and heterogeneous traffic. [ABSTRACT FROM PUBLISHER]