Integrated Radio Resource Allocation for Multihop Cellular Networks With Fixed Relay Stations

Radio resource allocation is always a central and complex computational task in state-of-the-art wireless networks. Computationally efficient implementation of radio resource allocation is certainly desirable in wireless networks, and can be generalized to many resource allocation/management problems in C4ISR systems. Quantum optimization, an emerging quantum computation technology, suggests a new avenue toward (radio) resource allocation particularly potential to reduce computing latency in systems. Quantum approximation optimization algorithm (QAOA) was recently shown to possibly achieve resource allocation (RRA) in limited scope, while the generalization of QAOA for RRA remains an active research subject. Considering OFDMA resource allocation, this paper extends QAOA to multiple radio blocks by quantum state preparation, and takes the interference between adjacent radio resource units into account by introducing the conflict graphs to formulate quantum optimization. Computational experiments successfully verify proposed quantum optimization to RRA.

In the 5th-Generation of Mobile Networks (5G), network slices are formed to provide customized services by sharing the wireless resource and the infrastructures of the system. To improve the wireless resource capacity, it is necessary to multiplex the wireless resource among the slices efficiently. At the same time, wireless resource isolation is significant to meet the quality-of-service (QoS) requirements of one slice when other slices vary. Therefore, how to achieve high wireless resource capacity while achieving the wireless resource isolation among slices is a significant problem. This paper studies wireless resource allocation in sliced 5G networks considering the tradeoff between the multiplexing and the isolation of wireless resource. Firstly, two indicators are defined to denote the wireless resource multiplexing gain and the wireless resource isolation performance, respectively. And the relationship between them is further analyzed. Next, the wireless resource allocation is formulated as an optimization problem, which aims to maximize the wireless resource multiplexing gain with the constraint of the wireless resource isolation requirements. Then, an improved genetic algorithm based on the optimal preservation scheme and the matrix coding is designed to solve the formulated problem. Simulation results show that, compared with the existing scheme without the isolation performance constraints of the slices and the scheme without multiplexing wireless resource among slices, the proposed scheme can achieve a better resource multiplexing gain while guaranteeing the isolation requirements of the slices.

This paper presents a novel downlink resource allocation scheme for OFDMA-based next generation wireless networks subject to inter-cell interference (ICI). The scheme consists of radio resource and power allocations, which are implemented separately. Low-complexity heuristic algorithms are first proposed to achieve the radio resource allocation, where graph-based framework and fine physical resource block (PRB) assignment are performed to mitigate major ICI and hence improve the network performance. Given the solution of radio resource allocation, a novel distributed power allocation is then performed to optimize the performance of cell-edge users under the condition that desirable performance for cell-center users must be maintained. The power optimization is formulated as an iterative barrier-constrained water-filling problem and solved by using the Lagrange method. Simulation results indicate that our proposed scheme can achieve significantly balanced performance improvement between cell-edge and cell-center users in multi-cell networks compared with other schemes, and therefore realize the goal of future wireless networks in terms of providing high performance to anyone from anywhere.

Mobile operators are widely adopting Network Functions Virtualization (NFV) to get the benefits of virtualization, including ease of deployment, flexibility, and cost savings. NFV allows multiple virtualized Radio Access Networks (vRANs) to run on commodity hardware enabling joint signal processing and efficient interference management. In addition, mobile operators can run general-purpose workloads alongside vRANs to utilize spare system resources in the NFV infrastructure. In such a consolidated scenario, it is necessary to ensure that the Key Performance Indicators (KPIs) of vRAN workloads are always met. But the workload consolidation could cause high variability and unpredictability in the performance of the deployed vRANs due to contentions for shared system resources like CPU cores, Last Level Cache (LLC), etc. In order to address this problem, we present JARS – a joint allocation of radio and system resources for the NFV infrastructure – that dynamically adjusts system resources such as CPU cores and LLC-ways, and radio resources such as Physical Resource Blocks (PRBs) to ensure KPIs for vRANs and improve overall resource utilization by workload consolidation. We profile srsLTE to determine the minimal CPU core and LLC resource requirements to satisfy KPIs during different traffic loads, which is used in the JARS. Experimental studies on a prototype system show that the proposed JARS outperforms a state-of-the-art scheme by 23%.

This paper considers the two-hop relay broadcast channel (BC) in a wireless cellular network where the downlink transmission from the base station (BS) to mobile users is assisted by a fixed relay station (RS). Assuming both the BS and RS equipped with multi-antennas, we consider joint optimization of beamforming weights and power allocations at the BS and RS so as to minimize their weighted sum-power consumption under the minimum signal-to-interference-noise-ratio (SINR) constraints at user terminals. We extend the two well-known criteria for design of linear precoding in the conventional MIMO-BC without relay, namely, the SINR balancing and the channel-inversion, to the MIMO relay BC. First, an iterative SINR-balancing algorithm is proposed that in each iteration optimizes transmit parameters at one station (BS or RS) with those at the other fixed. Secondly, a joint BS and RS channel-inversion algorithm is proposed together with a novel technique termed eigenmode switching at the RS. Simulation results show that optimized joint beamforming and power control are able to achieve substantial power savings for the MIMO relay BC.

In this paper we investigate the impact of a Relay Node (RN) placement plan on a heterogeneous wireless network called Hybrid Wireless Network with dedicated delay nodes (HWN*) in terms of system capacity, transmission delay and Quality of Service (QoS). A novel HWN* Heuristic RN Placement (HHRP) algorithm, which considers RN assistance & support for radio resource sharing such as inter-network traffic balancing, TD/CDMA soft handover, routing and user mobility has been implemented. A system level simulation is used to quantify the benefits of HWN* using HHRP algorithm over HWN* with other RN placement plans and multi-hop cellular networks. I. INTRODUCTION A heterogeneous wireless network refers to a wireless net- work that is comprised of wireless nodes with different radio access technologies or equipment with different operating systems. For example, it can be a wireless sensor network including bluetooth based radio access and zigbee 802.15.4 based radio access or a hybrid wireless network including cellular access and 802.11b/g based radio access. Specifi- cally for a hybrid wireless network, the increase of cellular communication intensity results in a reduction in cell size, thus much denser clusters are required resulting in increased infrastructure cost and system complexity. Meanwhile, the bursty nature of communication traffic and path loss results in traffic prediction difficulty. A solution must be found to provide effective resource management, congestion control and routing. The hybrid wireless network which incorporates cellular and MANET interfaced RELAY nodes, provides a practical approach for this. Nevertheless, the dedicated relay infrastructure approach can be also be adapted to the emerging wireless sensor network in order to maintain node connectivity, support sensor data aggregation and optimise power consump- tion.

Multihop cellular networks are expected to play an important role in the evolution towards 4G. In this work, we propose the use of advanced antenna techniques (adaptive antenna arrays and directional antennas) at fixed relay stations in order to enhance the downlink performance of multihop cellular networks. The performance gains of various adaptive antenna configurations are analytically studied by introducing a new system‐level parameter called the interference reduction factor. Moreover, Monte‐Carlo simulation results as a function of various design parameters are presented and analysed in order to further highlight the gains of advanced antenna techniques. Based on the presented analysis, we propose the use of a hybrid antenna configuration at relay stations in order to obtain high gains with limited increase in complexity and cost. Copyright © 2010 John Wiley & Sons, Ltd.

Relaying is expected to play an important role in the design of next generation cellular networks. Integrating multihop communications with the infrastructure-based design of cellular networks results in a new wireless architecture known as multihop based cellular network (MBCN). Studies performed so far on MBCNs mainly assume the use of omnidirectional antennas at relay stations. In this work, we investigate the use of advanced antenna configurations at fixed relay stations in CDMA-based MBCNs. The considered advanced antenna configurations include directional antennas and various adaptive antenna arrays. A new interference reduction factor is derived and studied in terms of overall system performance. Analytical results accompanied by Monte-Carlo simulation results demonstrate that with proper selection of advanced antennas at fixed relay stations, significant performance gains can be achieved.

Mobile cellular networks are now serving all kinds of Internet of Things (IoT) communications. Since current contention-based random access and radio resource allocation are optimized for traditional human communications, massive IoT communications cannot be efficiently accommodated. For this reason, standardization activities for connecting IoT devices, such as Cellular-IoT (C-IoT), have emerged. However, there have been few studies devoted to the evaluation of the performance of the C-IoT communications with periodic data transmissions, despite their being the common characteristics of many IoT communications.Herein, we evaluate the capacity of mobile cellular networks in accommodating periodic C-IoT communications, focusing on differences in performance between LTE and Narrowband-IoT (NB-IoT) networks. To achieve this, we conduct end-to-end performance analyses of both control and data planes, including the random access procedure, radio resource allocation, and bearer establishment in EPC network. Moreover, we determined the effect of immediate release of radio resources considered in 3GPP. Numerical evaluation results show that NB-IoT can accommodate more IoT devices than LTE, although this results in significant latency in data transmission. Furthermore, we find that the number of IoT devices that can be accommodated increases up to 20.7 times with immediate release of radio resources.

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.

The problem of optimal resource allocation in wireless networks usually involves scheduling of the network independent sets (ISs), of which the number increases exponentially in the network scale. To deal with such large-scale optimization problems, traditional approaches often resort to some heuristics or iterative algorithms for obtaining a relatively small set of ISs to solve the problems, but at the cost of suboptimality or long convergence time. In this paper, we consider wireless network resource allocation in dynamic flow environments, aiming at maximizing the network capacity. We propose a learning-based approach to find ISs based on the dynamic flow demands. Specifically, instead of searching for individual ISs, we propose to learn groups of ISs by using a deep belief network (DBN). We present detailed design of the DBN-based learning method including details in the offline training and online running phases. Simulation results demonstrate that our DBN-based method outperforms existing ones in terms of achieved network capacity and computation time.

While Wireless Local Area Network (WLAN) gains growing popularity during the last two decades, it faces several new challenges in meeting the requirements of emerging applications due to spectrum shortage, complicated network management and inefficient radio resource allocation (RRA) in densely deployed scenarios. The operating band has evolved from microwave band (a.k.a. sub-6 GHz) to millimeter-wave band (mmWave for short) or even multi-band to provide higher data rates. However, the existing distributed WLAN architecture works in different bands separately and doesn't support the coordination among access points (APs), making it difficult for efficient network management and improved quality of service (QoS) provisioning. In overcoming these challenges, a centralized control architecture for WLAN with decoupled control and data planes can be used to orchestrate the RRA within the whole network while achieving improved communication performance. Therefore, a new control/data plane decoupled WLAN architecture is designed in this paper to realize efficient network management and optimized RRA by separating the control plane and data plane into sub-6 GHz and mmWave respectively. Then, we propose the sub-6 GHz assisted mmWave beamforming protocol, which can significantly reduce the overhead of beamforming training (BFT). Last, we investigate joint RRA for the control/data plane decoupled WLAN since the control plane affects the RRA in the data plane. Performance analysis and simulation show that the joint RRA mechanism in the control/data plane decoupled network can significantly improve the number of successfully scheduled users and the sum-rate compared with the traditional RRA.

In this paper, a radio resource allocation and scheduling algorithm for multi-user MISO-OFDMA systems with downlink zero-forcing beamforming is proposed to efficiently support the diverse quality of service (QoS) requirements of heterogeneous services. According to the channel state information (CSI) and the queue state information (QSI), the proposed algorithm dynamically assigns subcarriers and selects the users on the same subcarrier. The goal of the algorithm is to maximize the system throughput by fully exploiting multiuser diversity gain in space, time, and frequency domain while guaranteeing the QoS for real time (NT) services and non-real time (NRT) services with minimum data rate requirement. From the system level simulation, it shows that the proposed algorithm significantly improves the system performances.

Device-to-Device (D2D) communications can increase the spectral efficiency of future cellular networks when sharing part of the cellular spectrum. Radio resource allocation mechanisms are then necessary to control the interference that D2D and cellular transmissions can generate to each other. Most of the existing allocation schemes rely on the knowledge of the channel gain of all possible links between cellular and D2D nodes. This paper proposes to reduce the complexity cost and signalling overhead of the allocation process by using location information available at the network level. Using this information, the base station assigns radio resources to new D2D transmissions with the objective to control and limit the interference to the primary cellular users and existing D2D transmissions. The proposed radio resource allocation scheme continuously monitors that the user QoS requirements are satisfied. If it is not the case, it dynamically modifies the resource allocation to the interfering D2D transmissions. The proposed scheme achieves performance levels similar to that obtained with an optimized centralized allocation scheme, but with a significantly lower complexity cost and signaling overhead

The aim of this paper is to propose a resource allocation strategy for dynamic training and inference of machine learning tasks at the edge of the wireless network, with the goal of exploring the trade-off between energy, delay and learning accuracy. The scenario of interest is composed of a set of devices sending a continuous flow of data to an edge server that extracts relevant information running online learning algorithms, within the emerging framework known as Edge Machine Learning (EML). Taking into account the limitations of the edge servers, with respect to a cloud, and the scarcity of resources of mobile devices, we focus on the efficient allocation of radio (e.g., data rate, quantization) and computation (e.g., CPU scheduling) resources, to strike the best trade-off between energy consumption and quality of the EML service, including service end-to-end (E2E) delay and accuracy of the learning task. To this aim, we propose two different dynamic strategies: (i) The first method aims to minimize the system energy consumption, under constraints on E2E service delay and accuracy; (ii) the second method aims to optimize the learning accuracy, while guaranteeing an E2E delay and a bounded average energy consumption. Then, we present a dynamic resource allocation framework for EML based on stochastic Lyapunov optimization. Our low-complexity algorithms do not require any prior knowledge on the statistics of wireless channels, data arrivals, and data probability distributions. Furthermore, our strategies can incorporate prior knowledge regarding the model underlying the observed data, or can work in a totally data-driven fashion. Several numerical results on synthetic and real data assess the performance of the proposed approach.