Subcarrier Power Allocation Research Articles

Reference Signal Design and Power Optimization for Energy-Efficient 5G V2X Integrated Sensing and Communications

Recently, the development of integrated sensing and communications (ISAC) has attracted great attention. In this paper, the reference signal (RS) in 5G vehicle-to-everything (V2X) communications is leveraged for radar sensing. Particularly, the scattered RS design is adopted under the constraints of communication channel estimation and radar sensing ambiguity functions. To quantify the influence of RS design on channel estimation, the minimum mean square error (MMSE) via two-dimensional Wiener filter is derived in a closed-form for doubly-selective communication channel. The RS spacing and power allocation for RS and each data subcarrier are further jointly optimized to minimize the transmission power, while satisfying the requirements of communication data rate and radar sensing signal to noise ratio (SNR), which results in a mixed-integer non-linear programming (MINLP) problem. To address this problem, the successive convex approximation (SCA) based method is first developed to achieve a suboptimal solution. Next, we show that if the RS spacing is given, the optimal power allocation among RS and each data subcarrier can be achieved by solving three subproblems sequentially via (reverse) water-filling algorithms. Extensive simulations are carried out to validate the effectiveness of our proposed design. Compared to the existing subcarrier-wise RS design with uniform power allocation for data subcarriers, our design can reduce the power consumption by up to 12 dB.

SCMA-Based Multiaccess Edge Computing in IoT Systems: An Energy-Efficiency and Latency Tradeoff

Sparse code multiple access (SCMA) is a kind of code-domain non-orthogonal multiple access (NOMA) scheme, which can support the increasing requirements for high spectral efficiency and massive connections. Meanwhile, multi-access edge computing (MEC) is a promising technology for providing resource-constrained users with computing resources. In this paper, we propose a novel optimization scheme in SCMA-based MEC network from the perspective of energy and latency for the Internet of Things (IoT) devices. Specifically, a system utility is first used to calculate the weighted energy consumption and task execution latency. The initial utility minimization problem is non-convex and then can be subdivided into two tractable subproblems by fixing task offloading decisions, namely, optimal local computing via CPU frequency scheduling and optimal edge computing via SCMA codebook assignment, subcarrier power allocation, and MEC server computing resources distribution. Primarily, a joint SCMA codebook assignment based on the bidirectional matching principle and optimal power allocation algorithm is proposed. Moreover, we come up with CPU frequency scheduling strategies utilizing convex optimization to optimize the computing resources allocation of local devices and the MEC server. Finally, a low-complexity task offloading policy based on simulated annealing is presented. Numerical results show that our proposed joint optimization algorithm for resource allocation and task offloading can achieve a good compromise between time delay and energy consumption for IoT devices. It is demonstrated that the proposed strategy has a remarkable advantage compared to previous SCMA-MEC schemes.

Resource Allocation for OFDM-NOMA Systems under Multiuser Scenarios with Different Speeds

In this paper, we consider an OFDM-NOMA (orthogonal frequency division multiplexing-nonorthogonal multiple access) downlink system, which has multiple subcarriers and multiple users at different locations and speeds from the base station. The goal is to maximize energy efficiency (EE), which is the bit rate per unit of energy consumption, while meeting the user’s quality of service and power constraints. Since the optimization problem of joint user subcarrier allocation and power allocation is a mixed-integer nonlinear programming (MINLP) problem, the use of traversal search will produce an unacceptable amount of calculation. It should be noted that the optimization problem includes the variables of user and subcarrier allocation and power allocation coefficient. In order to effectively solve this problem, we divide the problem into two subproblems: the problem of user and subcarrier allocation and the problem of power allocation between users. We use a matching algorithm to solve the problem of user and subcarrier assignment. An iterative algorithm to ensure convergence is proposed to obtain the power budget between subcarriers. Finally, the effective binary search is used to obtain the power allocation among users. The simulation results show that the proposed algorithm has a faster convergence rate. At the same time, as the number of users and subcarriers increases, EE improves significantly. As the user speed increases, the EE gradually decreases; especially when the number of users is larger, the effect is more obvious. Compared with random matching, the proposed algorithm has higher EE under the same number of users, number of subcarriers, and speed.

Joint Power Allocation and Distributed Equalization Design for OFDM-Based Filter-and-Forward Two-Way Multi-Relay Networks

We consider multi-carrier two-way relaying networks consisting of two user transceivers and multiple filter-and-forward (FF) relays. The FF relaying technique along with the multi-carrier schemes at the transceivers are utilized to suppress the inter-symbol-interference caused by the frequency-selectivity of the end-to-end channel. We jointly optimize subcarrier power allocation at the transceivers and the FIR filters at the FF relays to minimize the total transmit power subject to two quality of service constraints measured by sum-rates at the transceivers. We propose two approaches to tackle this problem: a gradient steepest descent technique, and a semi-closed-form method, which enforces a new constraint on the optimization such that at the optimum, the frequency-selective end-to-end channel is turned into a frequency-flat one. Furthermore, we show that the second method can be used as an initial point for the gradient steepest descent technique. Aiming to evaluate the performance of the proposed methods with a benchmark, we obtain a lower bound for the minimum transmit power at the network. We show that the proposed techniques are within 3 dB of this lower bound. Our numerical results further show that the proposed methods outperform existing solutions for similar networks.

Deep Multiagent Reinforcement-Learning-Based Resource Allocation for Internet of Controllable Things

Ultrareliable and low-latency communication (URLLC) is a prerequisite for the successful implementation of the Internet of Controllable Things. In this article, we investigate the potential of deep reinforcement learning (DRL) for joint subcarrier-power allocation to achieve low latency and high reliability in a general form of device-to-device (D2D) networks, where each subcarrier can be allocated to multiple D2D pairs and each D2D pair is permitted to utilize multiple subcarriers. We first formulate the above problem as a Markov decision process and then propose a double deep $Q$ -network (DQN)-based resource allocation algorithm to learn the optimal policy in the absence of full instantaneous channel state information (CSI). Specifically, each D2D pair acts as a learning agent that adjusts its own subcarrier-power allocation strategy iteratively through interactions with the operating environment in a trial-and-error fashion. Simulation results demonstrate that the proposed algorithm achieves near-optimal performance in real time. It is worth mentioning that the proposed algorithm is especially suitable for cases where the environmental dynamics are not accurate and the CSI delay cannot be ignored.

Joint Power Allocation and Beamforming for Overlaid Secrecy Transmissions in MIMO-OFDM Channels

This paper considers that a transmitter superposes secrecy transmissions to a closer receiver with a high security level in the active MIMO-OFDM channel it is using to send data to a farther receiver with a low security level, by using NOMA techniques. A joint optimization of power allocation and beamforming is important to maximize the secrecy rate to the closer receiver, while keeping the requested data rate of the farther receiver unaffected. The optimization problem is challenging because of coupled variables, and has yet to be addressed. We propose to decouple the problem between subcarrier power allocation and beamforming. Given beamforming, we prove the subcarrier power allocation for the two receivers can be equivalently transformed to two convex problems with waterfilling structures and closed-form solutions. Given power allocation, beamforming can be decoupled between subcarriers by distributing the rate requirement of the farther receiver across the subcarriers. The per-subcarrier beamforming is convexified by applying semidefinite relaxation and the Dinkelbach method. Simulations show the proposed approach can outperform its existing alternatives in terms of secrecy rate, and holistic designs of power allocation and beamforming are critical.

Optimizing the Spectrum and Power Allocation for D2D-Enabled Communication Systems Using DC Programming

Device to device (D2D) communication has recently attracted a lot of attentions since it can significantly improve the system throughput and reduce the energy consumption. Indeed, the devices can communicate with each other in a D2D system, and the base station (BS) can share the spectrum with D2D users, which can efficiently improve the spectrum and energy efficiency. Nevertheless, spectrum sharing also raises the difficulty of resource allocation owing to the serious cochannel interference. To reduce the interference, the transmit power of the D2D pairs and BS to cellular users should be further optimized. In this paper, we consider the resource allocation problem of D2D networks involving the power allocation and subcarrier assignment. The resource allocation problem is formulated as a mixed integer programming problem which is difficult to solve. To reduce the computational complexity, the original problem is decomposed as two subproblems in terms of the subcarrier assignment and power allocation. For the subcarrier assignment problem, the particle swarm optimization (PSO) is adopted to solve it since the subcarrier assignment is an integer optimization problem, and it is difficult to be tackled using the traditional optimization approach. When the subcarrier assignment is fixed, there are only the power allocation variables in the original resource allocation problem. The difference of convex functions (DC) programming is adopted to solve the power allocation problem. Simulation results demonstrate the effectiveness of the proposed resource allocation scheme of D2D networks.

Genetic Algorithm Dependent Qos Aware Adaptive Subcarrier Allocation Scheme in Cognitive Radio Networks

Cognitive Radio Network technology is an efficient and intelligent system with the intention of detecting automatically about the availability of channels in a wireless spectrum and changing transmission parameters to facilitate more communication.In Quality of Service aware spectrum decision making system is locating the spectrum bands all the way through in view of the application necessities by means of the self-motivated nature of the spectrum bands.Here, an innovative adaptive sub-carrier allocation mechanism is proposed for Cognitive Radio Networks by rearranging the functional parameters anchored in the Proportional Resource Algorithm and Genetic Algorithm.This proposed scheme is designed to achieve a divergent performance standard for subcarrier allocation and power allocation system of the Cognitive Radio Network users. The suggested scheme is proven to be effective by maximizing the allocation of data rate for each subcarrier. Moreover, by increasing the overall network throughput, the numbers of satisfied users within bandwidth limits are maintained with the quality of service. In fact, it locates the maximum channel availability.

DC bias and power optimization for AV-DCO-OFDM in optical wireless communication

In this paper, we investigate the optimization of direct current (DC) bias, information-bearing power and subcarrier power allocation in absolute valued DC biased optical orthogonal frequency division multiplexing (AV-DCO-OFDM) systems. First, we analyze the effect of absolute value operation on the system performance using the Bussgang Theorem. Then, we consider the maximization of achievable rate under the optical power constraint. Using the optimality conditions, the optimization problem can be simplified and two efficient algorithms are developed to obtain the solutions in flat channels and dispersive channels, respectively. Simulation results validate the theoretical analysis and the effectiveness of the developed algorithms.

Resource Allocation in NOMA-Enhanced Full-Duplex Symbiotic Radio Networks

This paper studies a full-duplex symbiotic radio network, where a full-duplex access point (FAP) transmits downlink orthogonal frequency division multiplexing signals to a legacy user (LU) and simultaneously receives signals backscattered from multiple passive backscatter devices (BDs) with capability of radio-frequency energy harvesting. A non-orthogonal-multiple-access enhanced dynamic-time-division-multiple-access (NOMA-DTDMA) transmission scheme is proposed to exploit the channel dynamics and further improve the spectrum efficiency. In order to maximize the throughput performance and ensure BD fairness, we maximize the minimum throughput among all BDs by jointly optimizing the FAP's subcarrier power allocation, the BDs' backscatter time allocation and power reflection coefficients, subject to the LU's throughput requirement, the BDs' harvested energy requirements, and other practical constraints. An efficient iterative algorithm is proposed to solve the formulated non-convex problem, by utilizing the block coordinated descent and successive convex optimization techniques. The convergence and complexity of the proposed algorithm are also analyzed. Numerical results show that the proposed NOMA-DTDMA scheme significantly outperforms the benchmark scheme of dynamic TDMA in terms of both throughput performance and BD fairness. Also, the trade-off performances between the BDs' throughput and the LU's throughput requirement as well as the BDs' harvested energy requirements are numerically verified.

Residual self‐interference suppression guided resource allocation for full‐duplex orthogonal frequency division multiple access system

In a wideband full-duplex system, the residual self-interference (SI) channel exhibits significant frequency selective fading characteristics after radio frequency SI cancellation. Resource allocation using the difference between the signal channel and the residual SI channel is a new method of suppressing residual SI and increasing system capacity. In this work, the residual SI channel and the user uplink channel are modelled as multi-path frequency selective fading channels that satisfy the Rayleigh distribution. To improve system capacity, the authors propose to allocate the spectrum resource avoiding occupation of subcarriers with high residual SI, while guaranteeing a fair subcarrier assignment (SA) and power allocation (PA). A sum rate of uplink maximisation is formulated, and an algorithmic solution is developed to effectively conduct SA and PA. Numerical results demonstrate the effectiveness of the scheme in increasing system capacity and suppressing residual SI.

Optimal Resource Allocation in Full-Duplex Ambient Backscatter Communication Networks for Wireless-Powered IoT

This paper considers an ambient backscatter communication network in which a full-duplex access point (FAP) simultaneously transmits downlink orthogonal frequency division multiplexing signals to its legacy user (LU) and receives uplink signals backscattered from multiple backscatter devices (BDs) in a time-division-multiple-access manner. To maximize the system throughput and ensure fairness, we aim to maximize the minimum throughput among all BDs by jointly optimizing the backscatter time and reflection coefficients of the BDs, and the FAP’s subcarrier power allocation, subject to the LU’s throughput constraint, the BDs’ harvested-energy constraints, and other practical constraints. For the case with a single BD, we obtain closed-form solutions and propose an efficient algorithm by using the Lagrange duality method. For the general case with multiple BDs, we propose an iterative algorithm by leveraging the block coordinated decent and successive convex optimization techniques. In addition, we study the throughput region which characterizes the Pareto-optimal throughput tradeoffs among all BDs. Finally, extensive simulation results show that the proposed joint design achieves significant throughput gain as compared to the benchmark schemes.

Flexible Multi-Group Single-Carrier Modulation: Subcarrier Mapping and Power Allocation Optimization

Orthogonal frequency-division multiplexing (OFDM) and single-carrier frequency-domain equalization (SC-FDE) are the two commonly adopted modulation schemes for frequency-selective channels. Compared to SC-FDE, OFDM generally achieves higher data rates but at the cost of higher transmit signal peak-to-average power ratio (PAPR), which leads to lower power-amplifier efficiency. This paper studies a multi-group single carrier modulation scheme, termed flexible multi-group single carrier (FMG-SC), which encapsulates both OFDM and SC-FDE as special cases, thus achieving more flexible rate-PAPR tradeoffs between them. Specifically, in FMG-SC, the total bandwidth is divided into a set of orthogonal subcarriers that, based on the channel gains, are flexibly mapped to multiple non-overlapping groups, and SC-FDE is applied over each group independently to send multiple data streams in parallel. We investigate the joint subcarrier grouping and power allocation optimization problem to maximize the achievable rate of our proposed FMG-SC scheme for both the cases with Gaussian signaling and with practical modulation constellation (e.g., quadrature amplitude modulation), respectively. For both cases, the optimization problem is non-convex in general, for which we propose a two-step approach for finding a high-quality approximate solution efficiently. First, with any given subcarrier grouping, we show that the optimal power allocation can be obtained via convex optimization techniques. Second, with fixed power allocation, we propose low-complexity algorithms for the subcarrier grouping design catering to the SC-FDE receiver. Numerical results show that our proposed algorithms perform close to the optimal solution obtained via exhaustive search over all subcarrier groupings yet with substantially reduced complexity. Moreover, the achievable rate of our proposed FMG-SC scheme with Gaussian signaling approaches the OFDM channel capacity and significantly outperforms that of SC-FDE. Furthermore, with practical modulation constellation, numerical results show that our proposed FMG-SC scheme greatly outperforms the existing single carrier frequency-division multiple access in terms of achievable rate but with moderately increased PAPR.

Energy-efficient multi-cell resource allocation in cognitive radio-enabled 5G systems

In this paper, we propose an energy-efficient resource allocation (RA) algorithm in cognitive radio-enabled 5th generation (5G) systems, where the scenario including one primary system and multiple secondary cells is considered. Because of the high spectrum leakage of traditional orthogonal frequency division multiplexing (OFDM), alternative modulation schemes regarded as the potential air interfaces in 5G are analyzed, e.g., filter bank-based multi-carrier (FBMC), generalized frequency division multiplexing (GFDM), and universal filtered multi-carrier (UFMC). Our objective is to maximize the whole energy efficiency of secondary system defined by the ratio of the capacity to the total power consumption subject to some practical constraints. The general formulation leads to a non-convex mixed-integer nonlinear programming problem with fractional structure, which is challenging to solve due to its intractability and significant complexity. Therefore, we resort to an alternate optimization framework to optimize the variables of subcarrier assignment and power allocation, where successive convex approximation (SCA) is employed so that the general formulation is finally transformed into a solvable convex problem. Numerical results validate the effectiveness of the proposed RA algorithm, and the comparison with some existing RA algorithms is conducted. In addition, the performance of using different 5G candidate waveforms in the energy-efficient RA algorithm is also presented and discussed.

Optimum power allocation in OFDM systems under power amplifier nonlinearity

In this paper, the optimum power allocation for subcarriers in orthogonal frequency division multiple system is derived which maximizes the total rate of system under nonlinearity of practical power amplifier. The rate of system by using analytical signal to interference ratio formula is optimized to derive optimal power scales for subcarriers. The problem is nonconvex and a comprehensive learning particle swarm optimization method is designed to solve the problem. The simulation results demonstrate that by considering nonlinearity in power allocation, better rate can be achieved compared to conventional power allocation methods.

Resource allocation for cache-enabled cloud-based small cell networks

To address the serious challenge of satisfying explosively increasing multimedia content requests from a massive number of users in mobile networks, deploying content caching in base stations to offload network traffic while satisfying content requests locally has been regarded as an effective approach to enhance the network performance. Moreover, content delivery via wireless transmissions in a cache-enabled mobile network needs to be optimized taking the proactive caching policy into consideration. Accordingly, in this paper, we investigate and propose an efficient resource allocation framework for cache-enabled cloud-based small cell networks (C-SCNs) to achieve the benefits of content caching by considering two phases, i.e., content placement and content delivery. In particular, for the content placement phase, we propose a low-complexity distributed popularity-based framework for allocating cache sizes of SBSs to popular contents, in order to offload network traffic and satisfy content requests locally. For the content delivery phase, we propose a low-complexity joint user association and subcarrier-power allocation scheme for min-rate guaranteed content delivery over orthogonal frequency division multiple access (OFDMA) based downlink transmissions. Trace-based simulations and numerical results demonstrate the effectiveness of the proposed schemes in the cache-enabled C-SCNs.

A Joint Subcarrier Assignment and Power Allocation with Statistical Delay QoS Provisioning in Full-Duplex OFDMA Systems

In this paper, we explore a joint subcarrier assignment and power allocation with statistical delay QoS guarantees for full-duplex OFDMA systems. Effective capacity theory which evaluates wireless channel capacity from a novel view of link layer provides a theory basis for modeling the system capacity under the conditions of delay QoS constrains. After modeling the aggregate effective capacity of the system, we formulate joint subcarrier assignment and power allocation problem as a mixed integer programming problem, whose target is to maximize the aggregate effective capacity. To solve this integer programming problem, we allocate initial power on each subcarrier and decompose it into two subproblems, which are subcarrier allocation problem and power allocation problem respectively. Further, we propose an alternative algorithm to achieve joint subcarrier assignment and power allocation, which can meet the “subcarrier continuity” constraint of uplink resource allocation. Simulations have shown that the performance of our proposed scheme is favorite. Additionally, we investigate the effect of the residual selfinterference, inter-node interference (INI) and statistical delay QoS exponent on the performance of systems.

Linear Processing Design of Amplify-and-Forward Relays for Maximizing the System Throughput

In this paper, firstly, we study the linear processing of amplify-and-forward (AF) relays for the multiple relays multiple users scenario. We regard all relays as one special “relay”, and then the subcarrier pairing, relay selection and channel assignment can be seen as a linear processing of the special “relay”. Under fixed power allocation, the linear processing of AF relays can be regarded as a permutation matrix. Employing the partitioned matrix, we propose an optimal linear processing design for AF relays to find the optimal permutation matrix based on the sorting of the received SNR over the subcarriers from BS to relays and from relays to users, respectively. Then, we prove the optimality of the proposed linear processing scheme. Through the proposed linear processing scheme, we can obtain the optimal subcarrier paring, relay selection and channel assignment under given power allocation in polynomial time. Finally, we propose an iterative algorithm based on the proposed linear processing scheme and Lagrange dual domain method to jointly optimize the joint optimization problem involving the subcarrier paring, relay selection, channel assignment and power allocation. Simulation results illustrate that the proposed algorithm can achieve a perfect performance.

Energy-Efficient Subcarrier Pairing and Power Allocation for DF Relay Networks with an Eavesdropper

Algorithms for joint subcarrier pairing and power allocation are investigated in order to maximize the worst-case energy efficiency (EE) in dual-hop decode-and-forward (DF) relay networks in the presence of an active eavesdropper. Accordingly, we study the impact of number of subcarriers on the trade-off in performance between the EE and the spectrum efficiency (SE). The formulated EE optimization problem is the ratio of the secure SE over the entire power consumption in the network, subject to the constraints of total transmit power and subcarrier pairing. A near-optimal iterative algorithm is proposed to perform the subcarrier pairing and power allocation for achieving the maximum EE in the networks. Furthermore, a suboptimal algorithm is proposed with two-step resource allocation. By considering the subcarrier channel quality of the source-to-relay and relay-to-destination links, the subcarrier pairing is first performed, followed by an energy-efficient iterative power allocation scheme to maximize the EE. Numerical results validate the effectiveness and correctness of the proposed algorithms.

Resource Allocation for Multicarrier Device-to-Device Video Transmission: Symbol Error Rate Analysis and Algorithm Design

In resource allocation for a device-to-device (D2D) video transmission system, the performance improvement by applying the exact symbol error rate (SER) is compared with the conventional signal-to-interference-plus-noise-ratio-based SER evaluation method that uses a Gaussian approximation (GA) for the aggregated interference. An analytical SER expression for a D2D system using multicarrier bandlimited QAM is derived and then used in the resource allocation algorithm. We consider centralized resource allocation for the D2D system, given knowledge of the channel state information and the rate distortion information of the video streams, and propose an iterative algorithm for subcarrier assignment and power allocation. Bit-level simulations for different numbers of D2D pairs demonstrate a considerable improvement on user capacity and video peak signal-to-noise ratio by incorporating the proposed SER expression compared with the GA. By invoking the conditions under which the central limit theorem holds, and comparing these conditions with the number of interferers and the power ratio of the dominant interferer in the simulated D2D system, we also study why the GA for the interference degrades performance.