Physical Resource Block Research Articles

Enhanced-Power Saving Semi-Persistent Scheduler for VoLTE in LTE-Advanced

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.

Data aggregation of mobile M2M traffic in relay enhanced LTE-A networks

Machine-to-machine (M2M) communication is becoming an increasingly essential part of mobile traffic and thus also a major focus of the latest 4G and upcoming 5G mobile networks. M2M communication offers various ubiquitous services and is one of the main enablers of the Internet-of-things (IoTs) vision. Nevertheless, the concept of mobile M2M communication has emerged due to the wide range, coverage provisioning, high reliability as well as decreasing costs of future mobile networks. Resultantly, M2M traffic poses drastic challenges to mobile networks, particularly due to the expected large number of devices sending small-sized data. Moreover, mobile M2M traffic is anticipated to degrade the performance of traditional cellular traffic due to inefficient utilization of the scarce radio spectrum. This paper presents a novel data aggregation and multiplexing scheme for mobile M2M traffic and thus focuses on the latest 3GPP (3 r d Generation Partnership Project) tong-term-evolution-advanced (LTE-A) networks. 3GPP standardized layer 3 inband Relay Nodes (RNs) are used to aggregate uplink M2M traffic by sharing the Physical Resource Blocks (PRBs) among several devices. The proposed scheme is validated through extensive system level simulations in an LTE-A based implementation for the Riverbed Modeler simulator. Our simulation results show that besides coverage extensions, RNs serve approximately 40 % more M2M devices with the proposed data multiplexing scheme compared to the conventional without multiplexing approach. Moreover, in this paper an analytical model is developed to compute the multiplexing transition probabilities. In the end, the simulation and analytical results of multiplexing transition probabilities are compared in order to analyze the multiplexing scheme.

A New Swap-Based Frequency-Domain Packet Scheduling Algorithm in OFDMA System with Data Queue Size Constraints

This paper aims at the frequency-domain packet scheduling (FDPS) problem in orthogonal frequency division multiple access (OFDMA) system. Under users’ data queue size constraints, a new swap-based FDPS algorithm is proposed to achieve further improvement in system throughput. In this algorithm, the swap of physical resource blocks (PRBs) between different users is introduced to give a comprehensive view of the overall scheduling process. Moreover, the proposed algorithm optimizes the choosing method of swap candidates and always tries to select the user who can maximize the throughput improvement. Simulation results demonstrate that this new algorithm can improve the system throughput significantly as well as reduce the resource waste effectively.

An efficient location aware distributed physical resource block assignment for dense closed access femtocell networks

The demand for higher data-rate in the indoor environment is unrelenting, and has triggered a huge deployment of Femto Base Stations (FBSs) in such environments. A large portion of these FBSs are privately owned and operate in closed access mode. However, due to aggressive spectrum reuse among these FBSs, co-tier interference is very high. Techniques relying on Fractional Frequency Reuse (FFR) will not scale in these dense FBS networks as the effective available spectrum per FBSs will be a little to support any meaningful data-rate at any FBS. Moreover, the techniques that use both power control and sub-channel allocation usually operate in a centralized fashion. Hence, these are a huge burden to the entire system. To handle this additional interference without significantly affecting spectrum efficiency, efficient location aware Physical Resource Block (PRB) assignment among interfering FBSs is necessary. In this paper, we formulate the PRB assignment problem as a Mixed Integer Non-Linear Program (MINLP) that exploits User Equipment (UE) location with respect to its interfering FBSs. Since the optimization problem is NP-hard, we propose a twofold solution which can efficiently reuse the PRBs while maintaining the interference below a threshold. First, an iterative elimination algorithm for PRB allocation and second, an adaptive PRB power control to find the required transmission power which minimizes interference. The proposed technique operates in a distributed manner and improves per FBS PRB reuse, energy efficiency, system blocking. Obtained results are verified using extensive simulations.

Service-differentiated downlink flow scheduling to support QoS in long term evolution

The growing demand of network services, especially for broadband downlink communication, has triggered the evolution of cellular systems. Recently, 3GPP continuously works out the standard of long term evolution (LTE) in response to the oncoming 4G cellular system. LTE adopts the OFDMA technology for downlink communication, and divides the spectral resource into physical resource blocks (PRBs). However, the LTE flow scheduling problem, which asks how to allocate PRBs to downlink flows for communication, is not well addressed in the standard but has great impact on transmission efficiency. On the other hand, LTE categorizes flows into guaranteed bit rate (GBR) and non-GBR classes, where GBR flows usually have higher priorities and shorter delay constraint than non-GBR flows. Although various solutions have been proposed to the LTE flow scheduling problem, many of them generally aim at maximizing transmission efficiency or keeping fair transmission, which may starve non-GBR flows or cannot well support quality of service (QoS) for GBR flows. Therefore, the paper develops a service-differentiated downlink flow scheduling (S-DFS) algorithm by taking the aforementioned difference between flows into consideration. Except for increasing transmission efficiency, S-DFS has two major goals: (1) satisfying the QoS requirement of GBR flows, and (2) ensuring the data transmission of non-GBR flows. Specifically, S-DFS first deals out PRBs to flows according to their channel conditions and QoS class identifier (QCI) defined in LTE. Then, with the mechanism of resource reallocation, S-DFS can assign a dynamic amount of reallocatable PRBs to the flows whose packets are about to be dropped. Experimental results demonstrate that S-DFS can achieve higher LTE transmission efficiency. Furthermore, it not only reduces both dropping ratio and delay of GBR packets, but also improves data throughput of non-GBR flows.

Assessing network energy consumption of mobile applications

Continuous growth in the energy consumption of mobile networks has become a major concern for mobile carriers. Since current mobile networks are dominated by mobile data traffic generated by over-the-top mobile applications, it is crucial for mobile carriers to understand how much network energy is used to deliver these applications. Here, we use real network and application measurements to comprehensively analyze the energy consumption of 12 common mobile applications by breaking down their total energy consumption into data and signaling energy components. The results provide insights into the different proportions of data and signaling energy (due to LTE signaling) for different mobile applications. They show that the energy consumption of a mobile application can vary at different base station cell sites due to different ratios of throughput to physical resource block utilization. We estimate the total monthly energy consumption of all 4G users of a major mobile carrier using conventional mobile services, such as voice and the short messaging service, and two over-the-top applications, i.e. a popular instant messaging application in China and an online video application. The results show that signaling energy consumption may become a major concern for mobile carriers, and that this issue will be exacerbated as the usage of over-the-top applications continues to grow. Energy assessment of mobile applications provides valuable information to enable mobile carriers to improve the energy efficiency of their networks. An energy assessment tool that enables real-time network and service energy monitoring will also assist in developing energy-efficient network policies for diverse applications with different energy consumption profiles. Furthermore, given our signaling energy consumption findings for over-the-top applications, there may be benefits for mobile operators to introduce a surcharge for signaling traffic to mitigate the high signaling energy consumption of some over-the-top mobile applications.

Power headroom report-based uplink power control in 3GPP LTE-A HetNet

In a 3rd Generation Partnership Project Long Term Evolution-Advanced (3GPP LTE-A) uplink, user equipment (UE) has a maximum transmission power limit defined by the UE power class. Generally, the cell edge UE has a higher probability to be constrained by the maximum transmission power level owing to the compensation of the large pathloss. When the UE transmission power is constrained by the maximum level, allocating a higher number of physical resource blocks (PRBs) than the UE power capability can afford will reduce the transmission power to be allocated per PRB, resulting in inefficient use of power resources. To avoid this power inefficiency, the uplink transmission power can be controlled according to the number of PRBs allocated using the power headroom report-based power efficient resource allocation (PHR-PERA) scheme proposed in this paper. Furthermore, adaptive open-loop power control (OL-PC) based on the signal-to-interference-plus-noise ratio (SINR) and the uplink interference is used to improve the cell capacity. By the uplink power control employing the proposed PHR-PERA scheme, the macro and femto UE throughputs were increased by 49.9 and 5 %, respectively, compared to the case of conventional fractional power control (FPC). Additional gains of 21.9 and 4.8 % for macro and femto UE throughputs, respectively, were achieved by adaptive OL-PC. The performance of fast closed-loop power control (CL-PC) based on the received SINR is also evaluated in this paper. The simulation results demonstrate that CL-PC supports OL-PC by compensating the fading effect for the UE uplink SINR to meet the target SINR.

Interference‐aware resource sharing in D2D underlaying LTE‐A networks

AbstractIn a device‐to‐device (D2D) underlaying cellular system, intra‐cell interference is no longer negligible, and such interference exists for sharing both uplink (UL) and downlink (DL) resources. This work presents a power and Physical Resource Blocks (PRBs) allocation algorithm to coordinate UL and DL interference in the D2D underlaying Long‐Term Evolution‐Advanced (LTE‐A) network. The objective is to find an effective and efficient mechanism to mitigate the interference between the two subsystems while maximising the weighted sum throughput. This optimisation problem is formulated as a mixed integer nonlinear programming, which is further decomposed into PRBs assignment and transmission power allocation. Analysis reveals that the proposed PRBs allocation strategy is energy‐efficient, and it suppresses the interference not only suffered by the LTE‐A system but also to the D2D users. In another side, the optimal power allocation is resolved by using a low‐complexity technique, and results show the optimal power resides in one of at most four feasible power vectors. Specifically, the scenario of applying partial channel state information (CSI) is also taken into account in our study. Simulations prove that the optimal power allocation combined with the proposed PRBs assignment achieves higher weighted sum throughput as compared with traditional algorithms, even when partial CSI is utilised. Copyright © 2015 John Wiley & Sons, Ltd.

Scheduling Based on Maximum PF Selection with Contiguity Constraint for SC-FDMA in LTE Uplink

Single-carrier frequency division multiple access (SC-FDMA), which is similar to multi carrier modulation type with orthogonal frequency domain multiple access (OFDMA), has been used as long term evolution (LTE) uplink access method due to its low PAPR and high UE power efficiency. SC-FDMA, however, has a constraint that subcarriers must be consecutively allocated to each user for every time slot due to the single carrier feature of the access type. This paper proposes an LTE uplink scheduling algorithm satisfying the contiguity constraint of resource allocation and ensuring high cell (system) throughput and fairness with low complexity. The proposed scheme, named MSCC, preferentially considers allocation to RBs based on the highest Proportional Fair scheduling metric in each physical resource block (RB) with the contiguity constraint. As a result of simulation analysis, MSCC has better fairness and cell throughput than the previous schemes (i.e., RME, IRME algorithm) by 10% and 17% at most, respectively. We also analyze cell edge user throughput (the gathering with the cell users who have 5% lowest throughput) and PAPR. In the appendix, complexity analysis shows that the time complexity of the MSCC is better than the previous schemes.

Cooperative Bandwidth Sharing for Relaying in LTE-Advanced Using Game Theory

Heterogeneous networks (HetNets) are likely to provide ubiquitous access to mobile broadband services as they involve various small cells, each targeting a different environment in the network. In this paper, we are interested in improving the system performance at the cell edge. Hence, we consider a HetNet composed of an eNodeB (eNB) coexisting with different relay nodes (RNs) situated at the cell border. We propose a cooperative physical resource blocks (PRBs) sharing scheme in a dual-hop LTE-Advanced in which every node can transmit on some of the PRBs assigned to a coexisting node without causing it harmful interference. To do so, we use a powerful mathematical tool, i.e., game theory. First, we consider two nodes in the macrocell and define a new repeated game in which a selfish node is encouraged to cooperate under the threat of punishment. Then, we enlarge the number of RNs in the network and find the optimal pairs of nodes that will cooperate, due to a new coalition formation game. Simulation results show that the proposed cooperative PRB sharing scheme significantly improves the utilization of the spectrum and the achieved total throughput in the system. We also demonstrate that both games rapidly attain Pareto efficiency.

Dynamic resource allocation based on service time prediction for device‐to‐device communication underlaying cellular networks

This work studies the dynamic resource allocation for device-to-device communication and proposes a service time prediction-based dynamic resource allocation mechanism for a cellular system. The proposed allocation mechanism introduces the concept of ‘utility’ as a metric to reflect the quality of experience of users with different service requirements, and takes into account service time prediction in spectral resource allocation. Specifically, it enables a base station to predict the service time of a new user and the remaining service time of existing users in the system, and takes into account the predicted service time and remaining service time in calculating the effective utility gain caused by the arrival of a new user. Based on the calculated effective utility gain, the base station will select the physical resource block (PRB) with the largest effective utility gain every time it allocates a PRB to the new user. Simulation results show that the proposed dynamic allocation mechanism can significantly improve the system performance in terms of the overall average utility and the number of users successfully admitted to the system.

Efficient cooperative access class barring with load balancing and traffic adaptive radio resource management for M2M communications over LTE-A

We propose two efficient cooperative access class barring with load balancing (CACB-LB) and traffic adaptive radio resource management (TARRM) schemes for M2M communications over LTE-A. The proposed CACB-LB uses the percentage of the number of Machine-Type Communication (MTC) devices that can only access one eNB between two adjacent eNBs as a criterion to allocate those MTC devices that are located in the overlapped coverage area to each eNB. Note that an eNB is a base station of LTE-A. In this way, the proposed CACB-LB can achieve better load balancing among eNBs than CACB, which is the best available related work. The proposed CACB-LB also uses the ratio of the channel quality indication that an MTC device received from an eNB over the number of MTC devices that attach to the eNB as a criterion to adjust the estimated number of MTC devices that may access the eNB. As a result, the proposed CACB-LB can have a better set of barring rates of access class barring than CACB and can reduce random access delay experienced by an MTC device, which is also applicable to user equipment (UE). After an MTC device successfully accesses to an eNB, the eNB needs to allocate radio resources for the MTC device. In addition, the proposed TARRM allocates radio resources for an MTC device based on the random access rate of the MTC device and the amount of data uploaded and downloaded by the MTC device in a homogeneous MTC device network, and the priority of an MTC device in a heterogeneous MTC device network. Furthermore, we use the concept from cognitive radio networks such that if there are unused physical resource blocks (PRBs) of UEs, an eNB can schedule MTC devices to use these PRBs to enhance the throughput performance. Simulation results show that either in a homogeneous MTC device network or in a heterogeneous MTC device network, the proposed CACB-LB’s average access delay of UEs/MTC devices and average throughput from UEs/MTC devices are better than CACB’s. The proposed CACB-LB with TARRM’s average throughput from UEs/MTC devices is also higher than CACB’s. Therefore, the proposed CACB-LB with TARRM is feasible for M2M communications over LTE-A.

Interference-Aware Radio Resource Allocation in D2D Underlaying LTE-Advanced Networks

This study presents a power and Physical Resource Blocks (PRBs) joint allocation algorithm to coordinate uplink (UL) interference in the device-to-device (D2D) underlaying Long Term Evolution-Advanced (LTE-A) networks. The objective is to find a mechanism to mitigate the UL interference between the two subsystems and maximize the weighted sum throughput as well. This optimization problem is formulated as a mixed integer nonlinear programming (MINLP) which is further decomposed into PRBs assignment and transmission power allocation. Specifically, the scenario of applying imperfect channel state information (CSI) is also taken into account in our study. Analysis reveals that the proposed PRBs allocation strategy is energy efficient and it suppresses the interference not only suffered by the LTE-A system but also to the D2D users. In another side, a low-complexity technique is proposed to obtain the optimal power allocation which resides in one of at most three feasible power vectors. Simulations show that the optimal power allocation combined with the proposed PRBs assignment achieves a higher weighted sum throughput as compared to traditional algorithms even when imperfect CSI is utilized.

Trading Wireless Capacity Through Spectrum Virtualization Using LTE-A

Markets for spectrum were first proposed by Ronald Coase as a way to efficiently allocate this resource. It took another forty years for primary markets to be developed (in the form of spectrum auctions) as the mechanism for assigning spectrum licenses to users. It is not a secret that secondary markets would be necessary to fully realize the benefits of economic allocation of spectrum. But this is easier said than done, since spectrum is a complex, multi-dimensional product with relatively few buyers and sellers (at least for commercial mobile services), so liquid secondary markets have not emerged, even though spectrum trading through brokers is commonplace. In this paper, we find that liquidity for spectrum markets can be improved over naked spectrum markets when a standardized commodity can be traded that uses the principles of spectrum virtualization. We utilize the Physical Resource Blocks (PRBs) of LTE-Advanced as the traded commodity and modify the SPECTRAD model developed in [5] accordingly. Though much remains to be done, we find that this is a promising approach to finally realizing liquid secondary markets in radio spectrum.

A novel energy efficient packet-scheduling algorithm for CoMP

The increasing energy consumption driven by striking growths in the number of users and data usage has placed cost reduction and energy efficiency at the forefront of system design. As a step towards incorporating more energy friendly mobile platforms in future networks, 3GPP LTE-Advanced has adopted Coordinated Multi-Point (CoMP) transmission/reception due to its ability to mitigate and/or coordinate inter-cell interference (ICI). However, there is room for reducing energy consumption further by exploiting the inherent flexibility of the dynamic resource allocation (DRA) protocols. Pivotal to the DRA is the packet scheduler that plays central key role in determining the overall performance of the 3GPP long-term evolution (LTE) based on packet-switching operation and provide a potential research playground for optimizing energy consumption in future networks. In this paper, a novel energy-efficient scheduling (EES) is proposed that can achieve power-efficient transmission to the UEs while providing both system energy efficiency gain and fairness improvement. The proposed algorithm is based on a novel scheduling metric focusing on the ratio of the transmit energy per bit and allocates the Physical Resource Block (PRB) to the UE that requires the least ratio of the transmit energy per bit. Through computer simulation, the performance of the proposed EES packet-scheduling algorithm using mixed traffic is compared with the State-of-the-Art (SoTA) packet-scheduling algorithms such as Maximum Carrier-to-Interface ratio (MCI), Proportional Fairness (PF), and Round Robin (RR), which eventually shows the significant improvement.

Transmission Scheduling and Game Theoretical Power Allocation for Interference Coordination in CoMP

In 3GPP LTE-A, Coordinated Multi-Point (CoMP) is adopted to enhance the transmission rates of edge users. To maximize the total downlink throughput of all edge users, it is crucial to properly determine the set of simultaneously served users in each physical resource block (PRB) and the cooperative base stations (BSs) for each scheduled user, as well as the transmit power of the BSs. Based on the reference signal receiving power (RSRP) of each edge user, we first propose two simple and integrated transmission scheduling algorithms, one distributed and the other centralized, to choose cell-edge users and cooperative BSs in each PRB. With the scheduling results, the classic Water-Filling (WF) algorithm is carried out over all PRBs at each BS to get an initial single cell power allocation. To take the interference among different cooperative BS sets into account, we further formulate a non-cooperative power allocation game to adjust the initial power allocation for interference coordination, where the initial power allocation provides the strategy space of the game for each BS. This increases the total downlink throughput of edge users over all BSs. We prove that the game has a unique Nash Equilibrium (NE), and design an algorithm to find the NE. Performance gain is then demonstrated through extensive simulation studies.

Ant Colony Optimization Based Sub-channel Allocation Algorithm for Small Cell HetNets

Two-tier heterogeneous networks (HetNets) composed of a conventional macrocellular network and small cell networks (SCNs) have been proposed in the literature with the aim to extend indoor coverage and realize efficient radio resource usage. As SCN shares the same frequency band with the underlying macrocell, the cross tier interference needs to be mitigated since the inter-SCN and cross tier interference at the SCN boundary may result in undesirable network performance degradation. In this paper, we propose an intelligent physical resource block (PRB) allocation as a solution to mitigate the downlink intra-SCN interference as well as the inter-tier interference in OFDM-based systems. The allocation of the PRBs to the network users is formulated as a graph coloring problem, and solved using an ant colony optimization (ACO)-based approach. Simulation results are provided, showing that our ACO-based algorithm outperforms the Received Power-based Allocation (RPA) and Received SINR-based Allocation (RSA) algorithms in terms of average SINR experienced by network users, outage probability, and number of required PRBs.

An Adaptive PRB, Power and MCS Allocation Using AMC for MIMO-OFDMA Systems

Long Term Evolution (LTE) is a promising technology for a high communication quality thanks to its radio link flexibility using Adaptive Modulation and Coding (AMC), Multiple Input Multiple Output (MIMO) and Orthogonal Frequency Division Multiple Access (OFDMA) technologies. In this paper, we address the adaptive resource allocation problem in down-link transmission of MIMO-OFDMA systems. We aim at maximizing the total system capacity to meet the 4G-technologies promises. In order to reduce the computational complexity, the original optimization problem is divided into sub-optimal ones. Firstly, a recursive Physical Resource Block (PRB) allocation algorithm is developed where the Effective Signal-to-Interference-plus-Noise Ratio (ESINR) metric is investigated to properly characterize the channel condition. Secondly, a power allocation procedure is performed. As all resource blocks allocated to the same user must use the same Modulation and Coding Scheme (MCS) according to the LTE standard requirements, power optimization sub-problem is divided into three steps: power allocation among PRBs, power allocation among antennas and power allocation among users. Finally, a more effective MCS is obtained, which ensures a better system capacity. Simulation results prove the efficiency of the proposed method to maximize the total system capacity with a low complexity even in the case of loaded systems compared to Minimum-MCS (Min-MCS) and Maximum-SINR (Max-SINR) algorithms.

Physical Resource Block clustering method for an OFDMA cognitive femtocell system

This paper deals with a heterogeneous Orthogonal Frequency Division Multiple Access (OFDMA) network where a user-deployed low power femtocell operates in the coverage area of a traditional macro-cell using the same frequency band. Femtocells represent a promising solution to increase the network capacity in next-generation wireless networks. However, the arising interference must be mitigated by means of suitable resource allocation policies. In particular, an OFDMA system allows a flexible usage of the resources that are organized in Physical Resource Blocks (PRBs). This flexibility can be fully exploited in a heterogeneous network if the femtocell base station knows how the PRBs are organized and grouped. This paper considers a cognitive femtocell base station that is able to sense the environment and identify the set of PRBs allocated to a given user by the macro-cell base station. A PRBs clustering method is proposed here. Initially, suitable inputs are derived and then provided to the K-means algorithm for a clustering refinement. The method proposed here is able to correctly gather together the PRBs of each user. Performance comparisons with a hierarchical clustering method is presented. The benefits of PRBs clustering on direction of arrival estimation are shown in order to prove the effectiveness of the proposed methods.

Two-Stage Channel Quantization for Scheduling and Beamforming in Network MIMO Systems: Feedback Design and Scaling Laws

This paper proposes an efficient two-stage channel quantization and feedback scheme for the downlink limited-feedback network multiple-input multiple-output (MIMO) system. In the first stage, the users report their best set of base-station antenna and physical resource block combinations, and the base-stations schedule the best user for each antenna in each resource block. The scheduled users are then polled in the second stage to feedback their quantized channel vectors. This paper proposes an analytical framework to show that, under a total feedback budget of B bits, the number of bits assigned to the second feedback stage should scale as log B, and in quantizing channel vectors from different base-stations, each user should allocate feedback bits in proportion to the channel magnitudes in dB scale. Under these optimized bit allocations, the overall sum rate of the system is shown to scale double-logarithmically with B, linearly with the total number of antennas, and logarithmically with transmit power, thus achieving both multiuser diversity and spatial multiplexing gains under limited feedback. Finally, realistic wireless propagation model of an urban small-cell deployment is used to show that the proposed scheme can approach the performance of a network MIMO system with full channel state information with only modest amount of channel feedback.