Resource Block Allocation Algorithm Research Articles

Energy-efficient deep-predictive airborne base station selection and power allocation for UAV-assisted wireless networks

Wireless networks with unmanned aerial vehicles (UAVs) as the airborne base station (ABS) become a promising technology to enhance terrestrial users’ coverage area. Moving at high speed over the wireless network covered by UAV requires an effective ABS selection scheme during the handover process to maintain the quality of service (QoS). On the other hand, the network load must be distributed fairly between the ABSs to prevent overloading at some base stations. Due to the limited power of ABSs, power saving is an essential task. Energy efficiency is a measure that balances data rate and power consumption. This study proposes a deep-predictive target ABS selection, resource block (RB), and power allocation algorithm that jointly maximizes energy efficiency and load balancing. The proposed method constructs the neighbor lists based on the reference signal received power (RSRP), signal to interference plus noise ratio (SINR), and the sojourn time estimated by the deep neural network model to reduce the unnecessary handovers. The simulation results show that the proposed method reduces the ping-pong rate and the outage probability and increases load balancing, spectral, and energy efficiencies compared to previous works.

Uplink Resource Allocation in Energy Harvesting Cellular Network With H2H/M2M Coexistence

We consider uplink transmission using a single carrier frequency division multiple access (SC-FDMA) scheme in a cellular network with human-to-human (H2H) and machine-to-machine (M2M) communications. The M2M traffic is relayed through energy harvesting (EH) gateways. An optimization framework is developed to minimize the data dropping in the EH gateways caused by delay constraint violation of the M2M traffic while taking into account the rate requirements of the H2H communication, the data and energy causality constraints as well as the SC-FDMA transmission constraints. By introducing two transforms, the original problem is expressed in a linearly separable form in terms of its discrete and continuous variables with the convexified continuous part. Then, Generalized Benders Decomposition is applied to solve the problem by decomposing it into primal and master problems. Due to the NP-hardness of the optimal solution, a low-complexity method is proposed by combining the solution of the primal problem with a heuristic resource block allocation algorithm. Simulation results show that the proposed heuristic method performs better than two alternative heuristic methods when applied to small scale and large scale networks.

Joint Task Offloading and QoS-Aware Resource Allocation in Fog-Enabled Internet-of-Things Networks

Fog computing is an advanced technique to enhance the Quality of Service (QoS), decrease network latency and energy consumption for Internet-of-Things devices (IDs). In this article, to minimize the overhead of the fog computing network, including the task process delay and energy consumption, while ensuring multiply QoS requirements of different types of IDs, we propose a QoS-aware resource allocation scheme, which jointly considers the association between fog nodes (FNs) and IDs, transmission and computing resource allocation to optimize the offloading decisions while minimizing the network overhead. First, an analytic hierarchy process-based evaluation framework is established to find the preference of QoS parameters and the priority of different types of ID tasks. Second, we introduce a resource block (RB) allocation algorithm to allocate RBs to IDs based on the IDs priority, satisfaction degree, and the quality of RBs. Moreover, a QoS-aware bilateral matching game is introduced to optimize the association between FNs and IDs. Finally, the offloading decisions are based on the previous steps to minimize the network overhead. The simulation results demonstrate that the proposed scheme could efficiently ensure the loading balance of the network, improve the RB utilization, and reduce the network overhead.

A Channel and Queue-Aware Scheduling for the LTE Downlink Based on Service Curve and Buffer Overflow Probability

In this letter, we propose a joint channel and queue-aware resource block allocation algorithm for the long-term evolution (LTE) downlink based on buffer overflow probability estimation. To this end, we first derive an equation for the buffer overflow probability bound. Furthermore, we propose a novel service curve in order to estimate maximum delay for LTE system users considering the Effective exponential signal-to-noise ratio mapping to the SNR values of the LTE sub-bands. Simulation results reveal that in general, compared to other approaches, the proposed scheduling algorithm provides lower delay and buffer overflow rate while maximizing throughput.

Energy Harvesting Aided Device-to-Device Communication Underlaying the Cellular Downlink

The specific family of device-to-device (D2D) communication underlying downlink cellular networks eliminates the reliance on base stations for its transmission by allowing direct transmission between two devices in each other’s close proximity that reuse the cellular resource blocks for enhancing the attainable network capacity and spectrum efficiency. By considering downlink resource reuse and energy harvesting (EH), our goal is to maximize the sum-rate of the D2D links, without degrading the quality-of-service requirement of the cellular users. We formulated a sum-rate maximization problem of joint resource block and power allocation for the D2D links, which resulted in a non-convex problem that was then transformed into a more tractable convex optimization problem. Based on the results of our Lagrangian constrained optimization, we propose joint resource block and power allocation algorithms for the D2D links, when there is non-causal (offline) and causal (online) knowledge of the EH profiles at the D2D transmitters. The performance of the algorithms is quantified using simulation results for different network parameters settings, where our online algorithm performs close to the upper bound provided by our offline algorithm.

Radio Resource Management for D2D-Based V2V Communication

Direct device-to-device (D2D) links are proposed as a possible enabler for vehicle-to-vehicle (V2V) communications, where the incurred intracell interference and the stringent latency and reliability requirements are challenging issues. In this paper, we investigate the radio resource management problem for D2D-based V2V communication. First, we analyze and transform the latency and reliability requirements of V2V communication into optimization constraints that are computable using only the slowly varying channel information. This transformation opens up the possibility of extending certain existing D2D techniques to cater to V2V communication. Second, we propose a problem formulation that fulfills the different requirements of V2V communication and traditional cellular communication. Moreover, a Separate resOurce bLock and powEr allocatioN (SOLEN) algorithm is proposed to solve this problem. Finally, simulations are presented to evaluate different schemes, which illustrate the necessity of careful design when extending D2D methods to V2V communication, as well as show promising performance of the proposed SOLEN algorithm.

Full-duplex communication for future wireless networks: Dynamic resource block allocation approach

In this paper, we discuss a full-duplex (FD) communication scenario, where multiple FD user equipments (UEs) share same spectrum resources (or resource blocks) simultaneously. The FD eNodeB deploys digital precoding and successive interference cancellation with optimal ordering algorithm, to allow coexistence of multiple UEs in downlink and uplink, respectively. The sharing of same resource blocks, results in co-channel interference (CCI), in downlink of a UE, from uplink signals of other UEs. To mitigate the interference, a smart antenna approach is adopted. The approach includes using multiple antennas at UEs to form directed beams towards eNodeB and nulls towards other UEs. However, the approach fails when the UEs due to their mobility align themselves in the same direction with respect to the eNodeB (eNB). In this paper, we propose a dynamic resource block allocation (DRBA) algorithm for avoiding CCI due to mobility of UEs, sharing the spectrum resource, in a FD communication scenario. The proposed algorithm shows significant improvement of the quality of service (QoS) of the communication links.

Group-Sparse-Based Joint Power and Resource Block Allocation Design of Hybrid Device-to-Device and LTE-Advanced Networks

In this paper, a joint power and resource block (RB) allocation (JPRBA) algorithm with low complexity is proposed, which addresses the intra-and-inter-cell interference management problem for a multicell device-to-device (D2D) communication underlaying LTE-Advanced network. We first introduce a power control and resource allocation vector (PORAVdm) to each D2D transmitter, and the set of all PORAVdm has two functions: one is to select appropriate reused RBs for each D2D link, whereas the other is to determine the optimal power for D2D transmitters on each selected RB. To obtain the appropriate PORAVdms, we exploit the group sparse structure to formulate a sum rate maximization problem (referred to as the group least absolute shrinkage and selection operator programming). Then we derive the stationary solution by solving its equivalent sparse weighted mean square error minimization problem. Finally, simulation results show that the proposed JPRBA algorithm can efficiently improve the total throughput.

Energy-Efficient Resource Allocation for LTE-A Networks

Energy-efficient resource allocation can significantly reduce power consumption of network and, consequently, operating expenses. In this letter, we study the energy efficiency of long-term evolution (LTE) networks with carrier aggregation, where user equipments are able to utilize multiple component carriers simultaneously. Furthermore, an LTE-compliant optimal resource block allocation algorithm to maximize throughput while minimizing energy consumption is proposed. Single-cell multi-user simulation results show that the proposed algorithm offers significant gains in terms of cell throughput and energy consumption over the state-of-the-art solutions.

Green Opportunistic and Efficient Resource Block Allocation Algorithm for LTE Uplink Networks

This work proposes a new radio resource allocation scheme for the green uplink Long-Term Evolution (LTE) networks. The proposed scheme aims at efficiently allocating the resource blocks (RBs) and the transmission power of the user equipment (UE). The allocation of RBs is based on the opportunistic and efficient RB allocation (OEA) algorithm, whose objective is to maximize the aggregate throughput while being subjected to single-carrier frequency-division multiple-access (SC-FDMA) constraints. An evolution of the algorithm, which is called quality-of-service (QoS)-based OEA, providing QoS differentiation, is also presented. It allocates a given maximum number of RBs to each UE, which is set according to the user's QoS requirements. Once the allocation of RBs is completed, the allocation of the UE transmission power is performed. The transmission power considers the UE channel conditions over its whole allocated RBs. Its value is reduced as much as possible and adjusted such that the user's required QoS satisfies throughput demand previously determined by the RB allocation. The proposed radio resource management algorithm has several interesting properties. It maximizes the aggregate throughput, decreases the ratio of inefficiently allocated RBs, cancels the RB's wastage, and increases the energy efficiency without affecting the user's throughput. Furthermore, the OEA algorithms can be implemented in LTE hardware equipment devices on account of their low computational complexity.

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.

Two-Step Resource Block Allocation Algorithm for Data Rate Maximization in LTE Downlink Systems

This study focuses on resource block allocation issue in the downlink transmission systems of the Long Term Evolution (LTE). In existing LTE standards, all Allocation Units (AUs) allocated to any user must adopt the same Modulation and Coding Scheme (MCS), which is determined by the AU with the worst channel condition. Despite its simplicity, this strategy incurs significant performance degradation since the achievable system throughput is limited by the AUs having the worst channel quality. To address this issue, a two-step resource block allocation algorithm is proposed in this paper. The algorithm first allocates AUs to each user according to the users' priorities and the number of their required AUs. Then, a re-allocation mechanism is introduced. Specifically, for any given user, the AUs with the worst channel condition are removed. In this manner, the users may adopt a higher MCS level, and the achievable data rate can be increased. Finally, all the unallocated AUs are assigned among users without changing the chosen MCSs, and the total throughput of the system is further enhanced. Simulation results show that thanks to the proposed algorithm, the system gains higher throughput without adding too many complexities.

Dynamic physical resource block allocation algorithms for uplink long term evolution

The authors investigate dynamic physical resource block (PRB) allocation for the uplink long-term evolution (LTE) system with single carrier-frequency division multiple access (SC-FDMA). Three dynamic PRB allocation algorithms are proposed, which are referred to as the maximum greedy (MG), mean enhanced greedy (MEG) and single mean enhanced greedy (SMEG) algorithms, respectively. Simulation results show that the proposed algorithms significantly outperform the previous two-dimensional (2-D) algorithm in terms of bit error rate (BER) and data rate fairness. The MEG algorithm is shown to provide a performance close to the Hungarian algorithm (optimal algorithm to maximise the SE) in terms of spectral efficiency (SE), while requiring a much lower computational complexity. SMEG further reduces the complexity of MEG with little performance degradation. Furthermore, the effects of imperfect channel estimation, root mean square (RMS) delay, Doppler spread and channel estimate feedback delay on performance are investigated.