Interference-aware radio resource management framework for the 3GPP LTE uplink with QoS constraints

LTE uses single carrier-frequency division multiple access (SC-FDMA) scheme for the uplink transmission which limits the flexibility of the allocation process as the sub carriers assigned to a single user have to be contiguous. We propose an LTE uplink interference-aware search-tree based scheduling scheme using the high interference indicator (HII) and the overload indicator (OI) messages that are exchanged over the X2-interface. HII indicates the potential uplink inter-cell interference (ICI) to other cells while OI indicates the detection of low, medium or high uplink ICI from other cells. The exchanged HII and OI messages are used to scale down the proportional-fair (PF) scheduling metric before being processed by the search-tree scheduler to make interfered physical resource blocks (PRBs) less favorable to cell-edge (CE) UEs. Unlike previous work which handles these problems separately, the proposed integrated RRM framework jointly takes into account 1) interference information exchanged between neighbor eNBs, 2) constraints on max power per UE and equal power on different PRBs, and 3) constraints on the contiguity in PRBs allocation due to SC-FDMA mechanism. It is also fully integrated with the LTE uplink open and closed-loop fractional power control for proper power allocation. The proposed interference-aware scheduler reduces the effect of the generated ICI mainly on CE users and also manages to provide fair resource sharing between CE and cell-center (CC) users. Simulation results show that the proposed framework improves CE users' throughput by around 5% compared to soft frequency reuse (SFR). It also reduces the probability of not being served to 1% -- compared to 10% in SFR.

We propose a framework for network-assisted radio resource management in wireless data networks. This type of radio resource management techniques offer implementation and capacity benefits compared to conventional, interference-measurement based, dynamic channel assignment (DCA) algorithms. The basic idea is to use interbase signaling to shift most of the burden of the resource allocation from the air interface to the backbone infrastructure. By exchanging channel assignment as well as other relevant information in real time through the backbone network, each base can calculate the impact of a resource assignment on the system. As a result, rapid interference measurements, which are typically needed to implement DCA schemes, are replaced by a limited amount of path loss measurements and the computation of interference conditions by the base stations. This significantly reduces the measurement and over-the-air signaling requirements, and can also provide an opportunity for a better optimization of the system performance. We focus on two specific algorithms: network-assisted least-interference-based dynamic packet assignment (NA-LI-DPA) and network-assisted dynamic packet assignment with throughput optimization (NA-DPA). NA-LI-DPA closely resembles a least-interference-based dynamic channel assignment algorithm, and NA-DPA attempts to further improve the overall system throughput. The algorithms, as defined, are appropriate for a best-effort data service, where the primary goal is to provide a higher throughput. However, it will be clear from the discussion that it is also feasible to alter the algorithms to optimize performance metrics other than throughput, e.g., to ensure a certain quality of service. We show through simulation that, for a system like enhanced general packet radio service (EGPRS) system, NA-DPA can provide a throughput that is 50% higher than random packet assignment, and 25% higher than that obtained by conventional DCA algorithms.

The interface management and resource allocation in backhaul (BH)/access networks for the cloud/centralized radio access networks (C-RAN) are two of the largest challenges to enable the C-RAN architecture deployment successfully in cellular wireless systems. From the PHY perspectives, one major advantage of C-RAN is the ease of implementation of multicell coordination mechanisms to manage the interference and improve the system spectral efficiency (SE). Theoretically, a large number of cooperative cells leads to a higher SE; however, it may also cause significant delay due to extra channel state information feedback and joint processing computational needs at the cloud data center, which is likely to result in performance degradation. In order to investigate the delay impact on the throughput gains, we divide the network into multiple clusters of cooperative small cells and formulate a throughput optimization problem. We model various delay factors and the sum-rate of the network as a function of cluster size, treating it as the main optimization variable. For our analysis, we consider both base stations' as well as users' geometric locations as random variables for both linear and planar network deployments. The output signal-to-interference-plus-noise ratio and ergodic sum-rate are derived based on the homogenous Poisson-point-processing model. The sum-rate optimization problem in terms of the cluster size is formulated and solved. From the radio resource management (RRM) perspective, we consider the problem of joint BH and access links optimization in dense small-cell networks with special focus on time division duplexing mode of operation in BH and access links transmission. Here, we propose a framework for joint RRM where we systematically decompose the problem in BH and access links. To simplify the analysis, the procedure is tackled in two stages. At the first stage, the joint optimization problem is formulated for a point-to-point scenario where each small cell is simply associated to a single user. In the second stage, the problem is generalized for multiaccess small cells. In addition, the chapter addressed thejoint routing and BH scheduling in a dense small-cell networks using 60 GHz multihop BH, coordinated by a local C-RAN central unit. The problem is formulated as a generalized vehicle routing problem and decoupled into two subproblems, channel-aware path selection and queue-aware link scheduling.

Multiple access techniques like single carrier frequency division multiple access (SC-FDMA) requires orthogonal subcarriers to be distributed among various active users by the base station (BS). Intelligent scheduling of available subcarriers can significantly improve the quality of service (QoS) by employing SC-FDMA for uplink mobile communication. The BS scheduler must efficiently utilize the available frequency and multi-user diversity with limited side information exchange. In this regard, a simplified mathematical approach is first discussed for symbol error rate (SER) analysis of SC-FDMA system over a generalized multipath channel utilizing the statistical characterization of effective noise after equalization. The derived expressions of SER are generalized and simplified as it only depends on the equalizer amplification factor (EAF). The analysis is then used as a quick and accurate method to compare performance over various available subbands (SB's) for subcarrier distribution by BS scheduler with less side information to be exchanged. Since the error rate is directly dependent on EAF, we have used EAF for channel-dependent scheduling (CDS) performed by the base station (BS) for SC-FDMA.

The Internet of Things (IoT) is the vision of a global network that connects various physical world objects to the IT infrastructure through a wireless medium. Despite the availability of a number of mature radio access technologies such as GSM, Long‐Term Evolution, Wi‐Fi, and due to the current progress made in developing 5G technology, more and more IoT operators are opting to use low‐power wide‐area (LPWA) technologies due to their low cost and easy deployment. However, recent studies show that the radio resource allocation used in these technologies is not scalable. This limitation often results in packet collisions, retransmission and unnecessary waste of scarce energy resources. In this paper, we propose a radio resource management framework, based on software‐defined network (SDN), to overcome the inefficient radio resource allocation of LPWA technologies. This is possible through the centralized nature of SDN, which allows collecting network monitoring information to analyze and calculate the optimal channel assignment configuration across the IoT network. We perform software‐defined radio‐based spectrum monitoring within the real IoT network platform in 868 MHz bands in which the latest IoT technologies, ie, LoRa and SigFox, operate. We demonstrate, through extensive simulations, that the proposed approach provides a better radio resource allocation for LPWA, reduces the number of packet collisions, and significantly improves the energy efficiency of the IoT communications.

For the 3G-LTE (Long Term Evolution) up-link, SC-FDMA (single carrier frequency division multiple access) system was adopted by the 3GPP (Third Generation Partnership Project) as standard method. However, similar to the other OFDM-based systems, SC-FDMA system is also very sensitive to frequency synchronization errors, especially in the uplink communications, where different users always have different CFOs (Carrier Frequency Offset). When multiple CFOs exist, the orthogonalities between the subcarriers are lost. ICI (Inter Carrier Interference) as well as MAI (Multi-Access Interference) are generated to degrade the quality of received signals. A novel suppression method based on block type pilots is proposed to overcome the interferences simultaneously. It is confirmed that the proposed suppression algorithm can significantly improve the performance by simulation results.

In the LTE uplink, Single Carrier Frequency Division Multiple Access (SC-FDMA) is adopted as its multiple access scheme. The Channel Estimation (CE) is one of the key technologies in mobile communication system. According to the latest LTE criterion, the pilot-aided CE algorithms of SC-FDMA are researched in this paper. Our research mainly discusses Least-Square (LS), the traditional LS based on Discrete Fourier transform (DFT) and the improved LS based on DFT. Finally, theoretical analysis and simulation result show that: The improved LS based on DFT has more superior estimation performance than the traditional LS based on DFT while it increases the computational complexity in the low SNR.

Despite air travel has not grown as predicted, air travel is still expected to rise to just less than doubling the current figure by 2030. This creates an urging need to develop more efficient Air Traffic Management (ATM) solutions. Around the globe, research and development initiatives have been launched to modernize the air traffic control infrastructures. These modernized infrastructures will be built around continuous information gathering, sharing and transferring of data between aircraft and air navigation service providers and airports ground infrastructure, which will be difficult for current aeronautical communications systems to handle. As a result, new communication infrastructures are required to manage future aeronautical communication traffic demand. This paper proposes an integrated aeronautical communication architecture consisting of four radio access technologies for communications between aircrafts and ground Aeronautical Telecommunication Network (ATN). The design and implementation of a Joint Radio Resource Management (JRRM) framework to manage these radio resources are discussed. The design is verified by a proof-of-concept JRRM prototype which is developed for the management of radio resource between the Inmarsat Broadband Global Area Network (BGAN) and the Aeronautical Mobile Airport Communication System (AeroMACS).

This paper considers the joint design problem of linear precoder, decision feedback equalizer (DFE) and bit allocation for multi-input multi-output (MIMO) broadcast (BC) channels. The problem is formulated as the power minimization problem when the target bit rate and per stream symbol error rate (SER) are specified. For the two-user case, a particular class of joint triangularization (JT) can be applied to obtain the optimal design for the joint optimization problem with quality of service (QoS) constraints, namely, the Minimum Power JT broadcast DFE transceiver (MPJT). For arbitrary number of users, a suboptimal design, the Power Minimized QR DFE broadcast transceiver (PMQR), is also proposed. Numerical results are presented to demonstrate the performance of MPJT and PMQR transceivers under different QoS constraints. 1

In cloud computing, there exist several classes of web services, and each class of web service contains multiple services which have different Quality of Service (QoS) values. To provide QoS for users, determining the best service composition that meet user's preferences is necessity. Finding the best service composition needs a certain method in order to provide the best combination of QoS with minimum response time, while in some cases, the process is bounded by budget constraint provided in user's preferences. In a simple method, for selecting the best service composition, we need to evaluate nm combinations of n classes of service and m services member. This condition leads to a large computing costs and memory consumption that occur during the process of generating the combination of data. To reduce the number of combinations, we propose an approach for finding the best service composition of web services using skyline algorithm which can adapt different user preferences of QoS. While selecting the best composition, we also take consideration in budget as constraint by selecting the closest user's budget with the best skyline result of services. SalSa skyline algorithm have given the new alternative ways to finding the best candidates of services in each class of service in reasonable time. These happened because we only need to evaluate the smaller parts of members in each class which will need much time if the number of services is plenty. Moreover, we also presented a method to find the best composition of services which able to present the composition that meet the user's budget.

For the grid workflow system include a large of candidate services, the activity scheduling in workflow’s ex-ecution base on quality of service (QoS) constraints can pro-vide optimal service for users. This paper established the QoS’s parameter system with applied technology of grid workflow, and introduced the each parameter’s calculation model of activity’s QoS, the grid work-flow’s QoS estimation method based on the basic flow control structures defined by the abstract grid workflow language (AGWL). The experimental results show that the calculation method in this paper can estimate stably QoS parameter values of grid workflow combining with the multi candidate services, and, can provide a reasonable basis for process scheduling based on QoS constraints.

A QoS-guaranteed multicast routing protocol

Enforcing QoS in scientific workflow systems enacted over Cloud infrastructures

This paper investigates the performance of cognitive radio relay networks with interference power constraints in Rayleigh fading channel. Here we consider spectrum sharing approach, whereby secondary user (SU) may be allowed to transmit simultaneously with primary user (PU) as long as its interference to the PU remains below tolerable level. In addition, SU has to meet certain Quality of Service (QoS) constraints of its own link. To support this QoS constraint, what is the maximal data rate that can be reliably transmitted with arbitrarily small error of probability is found. We observe that this capacity is affected by channel quality of secondary transmission links as well as the interference limit allowed from primary user. Close form expression for effective capacity considering delay as a QoS constraint is derived and simulation results are provided to support theoretical results. The approach of finding effective capacity is extended to outage capacity and ergodic capacity in delay insensitive applications.

With the rapid development of e-commerce over Internet, web services have attracted much attention in recent years. Nowadays, enterprises are able to outsource their internal business processes as services and make them accessible via the Web. Then they can dynamically combine individual services to provide new value-added services. With the increasing number of web services having equivalent functionality, the binding procedure is driven by some non-functional, Quality of Service (QoS) criteria, such as the money cost, response time, reputation, reliability or a trade-off between them. Thus, an important problem is, given QoS constraints, how to aggregate and leverage individual service’s QoS information to derive the optimal QoS of the composite service. In this paper, we propose a novel QoS model for performing flexible service selection. The key idea of the model is to relax users’ QoS constraints and try to find the most possible services satisfying users’ QoS requirements. Based on the proposed QoS framework, we develop various algorithms for making service selection on individual and composite services. We also introduce a top-k ranking strategy to reflect a user’s personalized requirements. Experimental evaluation shows the proposed QoS model is efficient and practical.