Statistical Delay Quality-of-service Research Articles

Proportionally fair load balancing with statistical quality of service provisioning for aerial base stations

AbstractAerial base stations (ABSs) seem promising to enhance the coverage and capacity of fifth‐generation and upcoming networks. With the flexible mobility of ABSs, they can be positioned in air to maximize the number of users served with a guaranteed quality of service (QoS). However, ABSs may be overloaded or underutilized given inefficient placement, and user association has not been well addressed. Hence, we propose a three‐dimensional ABS placement scheme with a delay‐QoS‐driven user association to balance loading among ABSs. First, a load balancing utility function is designed based on proportional fairness. Then, an optimization problem for joint ABS placement and user association is formulated to maximize the utility function subject to statistical delay QoS requirements and ABS collision avoidance constraints. To solve this problem, we introduce an efficient modified gray wolf optimizer for ABS placement with a greedy user association strategy. Simulation results demonstrate that the proposed scheme outperforms baselines in terms of load balancing and delay QoS provisioning.

Uplink Non-Orthogonal Multiple Access With Statistical Delay Requirement: Effective Capacity, Power Allocation, and α Fairness

The proliferation of delay-sensitive Internet-of-Things (IoT) applications has ushered in a need for the statistical delay quality-of-service (QoS) guarantee for the applications. In this paper, we first derive an upper bound for the queuing delay violation probability (UB-QDVP) in uplink non-orthogonal multiple access (NOMA) by applying stochastic network calculus (SNC) to the Mellin transforms of service processes. A closed-form asymptotic approximation of the UB-QDVP is developed by proving the asymptotic convergence of the Mellin transform and its finite-length truncations. Given the closed-form asymptotic UB-QDVP, we propose two power allocation schemes. The first scheme minimizes the transmit power of a NOMA user pair while guaranteeing the statistical delay QoS of the pair. The second maximizes the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> -utility function of the effective capacity of the user pair, striking a balance between the energy efficiency and user fairness of uplink NOMA systems. Simulations validate the UB-QDVP and show the superiority of the proposed schemes to conventional power allocation schemes in terms of energy efficiency and fairness.

Dynamic Power Allocation for Uplink NOMA With Statistical Delay QoS Guarantee

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.

Energy Efficiency Maximization for UAV‐Assisted Emergency Communication Networks

Using unmanned aerial vehicles (UAVs) in emergency communications is a promising technology because of their flexible deployment, low cost, and high mobility. However, due to the limited energy of the onboard battery, the service duration of the UAV is greatly limited. In this paper, we study an emerging energy‐efficient UAV emergency network, where a UAV works as an aerial base station to serve a group of users with different statistical quality‐of‐service (QoS) constraints in the downlink. In particular, the energy efficiency of the UAV is defined as the sum effective capacity of the downlink users divided by the energy consumption of the UAV, which includes the energy consumed by communication and the energy consumed by hovering. Then, we formulate an optimization problem to maximize the energy efficiency of the UAV by jointly optimizing the UAV’s altitude, downlink transmit power, and bandwidth allocation while meeting a statistical delay QoS requirement for each user. The formulated optimization problem is a nonlinear nonconvex optimization problem of fractional programming, which is difficult to solve. In order to deal with the nonconvex optimization problem, the following two steps are used. First, we transform the fractional objective function into a tractable subtractive function. Second, we decompose the original optimization problem into three subproblems, and then, we propose an efficient iterative algorithm to obtain the energy efficiency maximization value by using the Dinkelbach method, the block coordinate descent, and the successive convex optimization technique. Extensive simulation results show that our proposed algorithm has significant energy savings compared with a benchmark scheme.

Asymptotic Performance Analysis of NOMA Uplink Networks Under Statistical QoS Delay Constraints

In this article, we study the performance of an uplink non-orthogonal multiple access (NOMA) network under statistical quality of service (QoS) delay constraints, captured through each user's effective capacity (EC). We first propose novel closed-form expressions for the EC in a two-user NOMA network and show that in the high signal-to-noise ratio (SNR) region, the “strong” NOMA user, referred to as U <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> , has a limited EC, assuming the same delay constraint as the “weak” user, referred to as U1. We demonstrate that for the weak user U1, OMA and NOMA have comparable performance at low transmit SNRs, while NOMA outperforms OMA in terms of EC at high SNRs. On the other hand, for the strong user U <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> , NOMA achieves higher EC than OMA at small SNRs, while OMA becomes more beneficial at high SNRs. Furthermore, we show that at high transmit SNRs, irrespective of whether the application is delay tolerant, or not, the performance gains of NOMA over OMA for U <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> , and OMA over NOMA for U <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> remain unchanged. When the delay QoS of one user is fixed, the performance gap between NOMA and OMA in terms of total EC increases with decreasing statistical delay QoS constraints for the other user. Next, by introducing pairing, we show that NOMA with user-pairing outperforms OMA, in terms of total uplink EC. The best pairing strategies are given in the cases of four and six users NOMA, raising once again the importance of power allocation in the optimization of NOMA's performance.

QoS Driven Power Allocation in Secure Multicarrier Full-Duplex Relay Systems

In this paper, we propose the quality-of-service (QoS) driven robust power allocation policy with channel uncertainty in the secure multicarrier full-duplex (FD) relay communication system, where the statistical delay QoS is given by the secure effective capacity (SEC). To maximize the SEC under the constraints of both the total system power and the residual loopback interference (LI) power of the FD relay, an optimization problem is formulated and the corresponding power allocation strategy has been presented in the case of perfect channel state information (CSI) and imperfect CSI. Specially, considering the imperfect CSI, we propose a robust power allocation scheme, where the channel uncertainty is modeled by a bounded region. Furthermore, the worst-case method and the Bernstein approximations are utilized to deal with the uncertainty. After the Taylor approximation based simplification of the formulated optimization problem, the optimal solution is obtained by subgradient descent algorithm based on Lagrangian dual method and Karush-Kuhn-Tucker (KKT) conditions. With known error range of CSI, numerical results and analysis illustrate that the proposed robust power allocation strategy can satisfy the statistical delay QoS requirement very well. Furthermore, the trade-off between the SEC and the robustness can be achieved with channel uncertainty.

Analysis of effective capacity for visible light communication systems with mobility support

Visible light communications (VLCs) are competitive and attractive candidates for next generation indoor communication networks which are required to support mobility and guarantee delay QoS (Quality of Service) for users. User movement in VLC systems changes the propagation path, including the distance, the angle of incidence and irradiance, all of which have a significant impact on channel gain and link capacity. In this paper, we present a user mobility model for indoor VLC systems with the help of discrete time Markov chain theory. Considering a service process with temporal correlation caused by user mobility, we derive the effective capacity (EC) for VLC systems which characterizes the capacity performance subject to the user’s statistical delay QoS. Moreover, the effect of the user’s mobility on effective capacity is revealed.

Delay-QoS-Aware Adaptive Modulation and Power Allocation for Dual-Channel Coherent OWC

Statistical-delay quality of service (QoS) provides bounded link-layer delay over wireless fading channels with a certain delay-bound violation probability. We propose statistical-delay-QoS-aware adaptive modulation (AM) and power allocation for a dual-channel coherent optical wireless communication system over the atmospheric turbulence fading channels. For given statisticaldelay constraints and target bit-error-rate requirements, our proposed AM and power allocation maximize the effective spectral efficiency subject to the transmit-power constraints. We develop delay-QoS-aware adaptive transmission schemes by employing independent and joint channel optimizations subject to average transmit-power constraints. We also consider independent, joint, and successive channel optimizations for developing delay- QoS-aware adaptive transmission schemes subject to peak transmit-power constraints. Numerical results demonstrate that our proposed AM and power allocation significantly outperform the conventional adaptive transmission schemes in the strict statistical-delay constraints. Numerical results also depict superiority of the joint channel optimization in the strong turbulence fading and strict statistical-delay constraints.

Fair Resource Allocation with QoS Guarantee in Secure Multiuser TDMA Networks

We investigate a secure multiuser time division multiple access (TDMA) system with statistical delay quality of service (QoS) guarantee in terms of secure effective capacity. An optimal resource allocation policy is proposed to minimize the β‐fair cost function of the average user power under the individual QoS constraint, which also balances the energy efficiency and fairness among the users. First, convex optimization problems associated with the resource allocation policy are formulated. Then, a subgradient iteration algorithm based on the Lagrangian duality theory and the dual decomposition theory is employed to approach the global optimal solutions. Furthermore, considering the practical channel conditions, we develop a stochastic subgradient iteration algorithm which is capable of dynamically learning the intended wireless channels and acquiring the global optimal solution. It is shown that the proposed optimal resource allocation policy depends on the delay QoS requirement and the channel conditions. The optimal policy can save more power and achieve the balance of the energy efficiency and the fairness compared with the other resource allocation policies.

Performance Assessment for Cognitive Cooperative Multiple Relays Network (CCMRN) With Imperfect Channel State Information

In the underlay spectrum sharing approach, cognitive radios have limited coverage area to keep interference protection for the licensed users. Multiple cooperative relays have shown benefit for allowing reliable communication with an increase in the radio coverage area. The performance of an underlay cognitive cooperative multiple relays network (CCMRN) is evaluated in case of imperfect channel state information (CSI). In this paper, CCMRN will be treated as a secondary network with multiple decode and forward relays, whereas the secondary user (SU) transmitter will select a single relay from the available set of relays. The relay selection process is based on obtaining maximum end-to-end effective capacity ( $C_{\text {eff}}$ ) as seen by the SU. By assuming cooperation between the licensed primary network and the unlicensed secondary network, the SU and the selective secondary relay can share the spectrum with occupying two orthogonal channels of two different primary users (PUs). According to the average interference power constraint of the PUs, the SU allowed maximum power transmission will be obtained. Network performance is assessed in terms of the SU maximum end-to-end $C_{\text {eff}}$ as well as outage probability ( $P_{\text {out}}$ ). Mathematical expressions for $C_{\text {eff}}$ and $P_{\text {out}}$ are derived for practical multipath Rayleigh faded channel. The analytical results illustrate the effect of the imperfect CSI on the performance metrics with applying various constraints such as available multiple relays group size, statistical delay quality of service, and average interference power.

Statistical Delay QoS Provisioning for Energy-Efficient Spectrum-Sharing Based Wireless Ad Hoc Sensor Networks

In this paper, we develop the statistical delay quality-of-service (QoS) provisioning framework for the energy-efficient spectrum-sharing based wireless ad hoc sensor network (WAHSN), which is characterized by thedelay-bound violation probability. Based on the established delay QoS provisioning framework, we formulate the nonconvex optimization problem which aims at maximizing the average energy efficiency of the sensor node in the WAHSN while meeting PU’s statistical delay QoS requirement as well as satisfying sensor node’s average transmission rate, average transmitting power, and peak transmitting power constraints. By employing the theories offractional programming,convex hull, andprobabilistic transmission, we convert the original fractional-structured nonconvex problem to theadditively structured parametric convex problemand obtain the optimal power allocation strategy under the given parameter via Lagrangian method. Finally, we derive the optimal average energy efficiency and corresponding optimal power allocation scheme by employing theDinkelbach method. Simulation results show that our derived optimal power allocation strategy can be dynamically adjusted based on PU’s delay QoS requirement as well as the channel conditions. The impact of PU’s delay QoS requirement on sensor node’s energy efficiency is also illustrated.

Optimal Power Allocation for Underlay-Based Cognitive Radio Networks With Primary User's Statistical Delay QoS Provisioning

Due to the highly-stochastic nature of wireless channels, how to provide efficient delay quality-of-service (QoS) provisioning for primary users (PU) while optimizing the performance of secondary users (SU) is a critically important task for cognitive radio networks (CRN). To address the above issue, we investigate the optimal power allocation strategy for underlay-based CRN with PU's statistical delay QoS protection. Instead of utilizing the widely-used interference power constraint to protect PU's transmission, we aim at satisfying PU's statistical delay QoS requirement characterized by the queue-length bound violation probability. By applying the theory of effective capacity, we further convert PU's queue-length bound violation probability constraint to the equivalent maximum sustainable traffic load requirement. Then, we formulate the optimization problem to maximize SU's average throughput while meeting PU's statistical delay QoS requirement as well as SU's average and peak transmit power constraints, which can be proved as a nonconvex problem. By employing the theories of convex hull and probabilistic transmission, we convert the original nonconvex problem to the equivalent strictly convex problem and then obtain the optimal power allocation strategy, which adapts to both PU's delay QoS requirements and channel conditions. Moreover, we also develop for comparison a fixed power allocation scheme that only adjusts with PU's delay QoS requirements. Simulation results are provided which demonstrate that both the optimal and fixed schemes can flexibly allocate the upperbounded transmit power budget according to PU's delay QoS requirements, but the proposed optimal power allocation strategy can also efficiently exploit the time-varying nature of wireless channels and thus significantly outperforms the fixed power allocation scheme.

A Pricing Power Control Scheme with Statistical Delay QoS Provisioning in Uplink of Two-tier OFDMA Femtocell Networks

Femtocell is a promising technique to enhance indoor coverage and improve network capacity. Nevertheless, because of the random and co-channel deployment of femtocells, the macrocell will suffer serious cross-tier interference from femtocells in two-tier femtocell networks. Thus, interference mitigation in femtocell networks has been an indispensable task. Meanwhile, with the explosive popularity of smart terminals, especially smart phones and tablets, the wireless networks have loaded a mount of data services with diverse delay quality of service (QoS) requirements. However, due to the stochastically varying nature of wireless physical channel, it is extremely difficult to offer a deterministic delay guarantee in wireless networks. Therefore, the effective capacity of femtocell users (FU) has been introduced to provide a statistical delay QoS provisioning. For that reason, in this paper, we will study the interference mitigation with statistical delay QoS guarantee in uplink two-tier orthogonal frequency division multiple access (OFDMA) femtocell networks. In order to mitigate the cross-tier interference at macrocell base station (MBS), we adopt a price-based power control strategy, in which the MBS protects itself by pricing the interference from FU. Additionally, to guarantee the statistical delay QoS for each FU, effective capacity is introduced into their utility functions. Then, a Stackelberg game is formulated to study the joint utility maximization of the MBS and the FUs subject to a maximum tolerable interference power constraint at the MBS. Subsequently, based on the mathematical analysis of the equilibrium of the formulated Stalkeberg game, a particle swarm optimization (PSO) aided power allocation (PSOPA) algorithm is proposed to solve this optimization problem. At last, simulation results show that our proposed PSOPA algorithm can not only improve significantly the average effective capacity of each FU and guarantee their statistical delay QoS, but also converge successfully.

Statistical Delay QoS Protection for Primary Users in Cooperative Cognitive Radio Networks

In cooperative cognitive radio networks, where secondary users (SU) can relay the signal of primary user (PU) in exchange for PU's licensed spectrum, most existing works employ deterministic quality-of-service (QoS) guarantee in terms of minimum required transmission rate for PU's protection. In this letter, we adopt the statistical QoS requirement characterized by queue-length bound violation probability for PU's delay QoS provisioning. By applying the effective capacity theory, we further convert PU's queue-length bound violation probability requirement to the equivalent effective capacity constraint and formulate the corresponding SU's throughput maximization problem. Then, we obtain the optimal joint time-slot allocation scheme, where the time-slot division adapts to both channel conditions and PU's delay QoS requirements, such that not only PU's statistical delay QoS requirement can be well guaranteed, but also SU's throughput can be optimized. Moreover, we also develop a fixed time-slot allocation scheme that only varies with PU's delay QoS requirements. Simulation results show that both the optimal and fixed allocation schemes can flexibly perform time-slot divisions according to the delay QoS requirements of PU's traffic, but the developed optimal scheme outperforms the fixed time-slot allocation scheme.

Statistical quality of service provisioning in multi‐user centralised networks

Statistical quality of service (QoS) provisioning is a performance metric that determine the user satisfaction as the main purpose of future wireless networks. Effective capacity (EC) is a valuable quantity which helps analysing delay QoS performance as the most popular QoS metric. In this study, the authors concentrate on characterising the delay QoS performance via the EC of uplink transmission mode of individual users in multiuser centralised networks in which the user's transmitted signals may interfere with each other. To reduce the computational complexity of finding EC, the authors first propose closed form expressions for the EC of each user, with or without considering the inter-symbol interference (ISI). Then, two general algorithms are presented to clarify the relation of EC and QoS guarantee performance of a system in which the way of analysing QoS guarantee performance is explained step-by-step. By the use of these algorithms and the obtained closed form EC functions, the authors investigate the statistical delay QoS performance of individual users and the effects of channel conditions and ISI on their performance. The numerical results validate our analytical outcomes in deriving closed form equations and evaluate the offered schemes in characterising delay QoS performance.

Effective Capacity of Statistical Delay QoS Guarantees with Imperfect Channel Information in Spectrum-Sharing Networks

This paper considers a spectrum-sharing cognitive network in which a secondary user shares spectrum with an existing primary user as long as the interference caused by the secondary user to the primary user is below permissible levels. We investigate the delay quality-of-service (QoS) guaranteed capacity gains with imperfect channel state information (CSI) between the secondary transmitter and the primary receiver in Rayleigh fading environments. In particular, we quantify the relation between the secondary link effective capacity and the interference inflicted on the primary user. The impacts of channel estimation error on the performance of the secondary link are studied. We further evaluate and compare the capacity gains using proposed power allocation policies and truncated channel inversion with fixed rate (TIFR) transmission policies under different constraint conditions. Our results indicate channel estimation errors, especially in the smaller value range, lead to considerably degradation of capacity gains for the looser delay QoS requirements. Numerical simulations are conducted to corroborate our theoretical analysis. 

Characterization of effective capacity in antenna selection MIMO systems

In this paper, the effective capacity of a multiple-input multiple-output (MIMO) system in two different cases with receive antenna selection (RAS) and transmit antenna selection (TAS) schemes is investigated. A closed-form solution for the maximum constant arrival rate of this network with statistical delay quality of service (QoS) constraint is extracted in the quasi-static fading channel. This study is conducted in two different cases. When channel state information (CSI) is not available at the MIMO transmitter, implementation of TAS is difficult. Therefore, RAS scheme is employed and one antenna with the maximum instantaneous signal to noise ratio is chosen at the receiver. On the other hand, when CSI is available at the transmitter, TAS scheme is executed. In this case, one antenna is selected at the transmitter. Moreover, an optimal power-control policy is applied to the selected antenna and the effective capacity of the MIMO system is derived. Finally, this optimal power adaptation and the effective capacity are investigated in two asymptotic cases with the loose and strict QoS requirements. In particular, we show that in the TAS scheme with the loose QoS restriction, the effective capacity converges to the ergodic capacity. Then, an exact closed-form solution is obtained for the ergodic capacity of the channel here.

Resource allocation for OFDM relay systems with statistical QoS guarantees

SUMMARYBy integrating the concept of effective capacity, we propose the resource allocation schemes for subcarrier‐pair based OFDM decode‐and‐forward and amplify‐and‐forward relay systems for quality‐of‐service (QoS) guarantees. The objective is to maximize the system throughput subject to a given statistical delay QoS constraint. First, we pair the subcarriers over the source‐relay channel and the relay‐destination channel by the descending order of the subcarriers' channel gains. Second, by making use of the derived equivalent end‐to‐end channel gains of the subcarrier pairs, we apply joint water‐filling power allocation over the subcarrier pairs and then partition the power of the subcarrier pairs between the source and the relay. We prove that as the equivalent end‐to‐end channel gains of the subcarrier pairs are given, the combination of sorted subcarrier pairing and joint water‐filling power allocation is jointly optimal to maximize the effective capacity. The simulation results show that our proposed schemes achieve the highest effective capacity for OFDM decode‐and‐forward and amplify‐and‐forward relay systems as compared with other existing schemes. The results also verify that our proposed schemes can efficiently provide different levels of delay QoS guarantees, even if under the stringent delay QoS constraints. Copyright © 2012 John Wiley &amp; Sons, Ltd.

Optimal Resource Allocation for Spectrum Sensing Based Cognitive Radio Networks with Statistical QoS Guarantees

Resource allocation under spectrum sensing based dynamic spectrum sharing strategy is a critically important issue for cognitive radio networks (CRNs), because they need to not only satisfy the interference constraint caused to the primary users (PUs), but also meet the quality-of-service (QoS) requirements for the secondary users (SUs). In this paper, we develop the optimal spectrum sensing based resource allocation scheme for the delay QoS constrained CRNs. Specifically, we aim at maximizing the maximum constant arrival rate of the SU that can be supported by the time-varying service process subject to the given statistical delay QoS constraint. In our derived power allocation scheme, not only the average transmit and interference power constraints are considered, but also the impact of the PUs’ transmission to the CRNs and the PUs’ spectrum-occupancy probability are taken into consideration. Moreover, the spectrum sensing errors are also taken into consideration. Simulation results show that, (1) the effective capacity of the secondary link decreases when the statistical delay QoS constraint becomes stringent; (2) given the QoS constraint, the effective capacity of the secondary link varies with the interference power constraint and the SNR of the primary link.

Adaptive Modulation in Spectrum-Sharing Channels Under Delay Quality-of-Service Constraints

We propose a variable-rate variable-power M-level quadrature amplitude modulation (MQAM) scheme employed under delay quality-of-service (QoS) constraints over spectrum-sharing channels. An underlay cognitive radio system, with one primary user and a secondary user, with constraints on interference leakage imposed by the primary receiver is considered. The transmission parameters of the secondary user are set optimally such that the successful communications for the primary user in terms of a minimum rate to be supported is satisfied, irrespective of co-existence with the secondary user. We obtain interference constraints that, when satisfied by the secondary users, is sufficient to satisfy the service outage requirement of the primary user. We further study the performance of the secondary user's link employing an adaptive MQAM scheme when, on top of the aforementioned interference constraint, the secondary user is also required to satisfy a statistical delay QoS constraint. Considering two modulation schemes, i.e., continuous MQAM and discrete MQAM with restricted constellations, we obtain the effective capacity of the secondary user's link and derive the optimum power allocation that maximizes the effective capacity of the secondary user.