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In multi-user downlink small cell networks, cooperative resource allocation (RA) within a small cell cluster is a key technique to enhance network capacity. However, capacity-maximizing RA in frequency-selective fading channels requires global channel state information (CSI) of users within a small cell cluster, which makes it infeasible in practical networks with limited direct link capacity. To circumvent this global CSI assumption, most of the existing studies on RA have been based on several CSI assumptions such as local CSI and local CSI at the transmitters (CSIT). Nevertheless, cost functions with local CSI or local CSIT in the literature rely on heuristic formulations, because the sum-rate cannot be computed if without global CSI. In this paper, we propose a deep reinforcement learning-based RA algorithm to maximize the sum-rate for any given limited information on instantaneous CSI or sum-rate at the previous period. The proposed scheme is not restricted to certain CSI assumptions, but attempts to find the best RA for any given information such as quantized local CSI and quantized local CSIT; thus, it is applicable to any given direct link capacity. The proposed algorithm is self-adaptive in time-varying channels, since it is not divided into training and test phases. We modify the target neural network (TNN) scheme to enhance the sum-rate and the convergence speed. Numerical simulations confirm that: i) the proposed algorithm outperforms the conventional algorithms even under the same CSI assumption such as local CSI and local CSIT; ii) a flexible trade-off between the amount of CSI and the sum-rate is realizable in practical systems. [ABSTRACT FROM AUTHOR]

This paper considers the transmission of scheduling information in orthogonal frequency-division multiple-access (OFDMA)-based cellular communication systems such as the Third-Generation Partnership Project (3GPP) long-term evolution (LTE). These systems provide efficient usage of radio resources by allowing users to be dynamically scheduled in both frequency and time. This requires considerable amounts of scheduling information to be sent to the users. This paper compares two basic transmission strategies: transmitting a separate scheduling message to each user versus broadcasting a joint scheduling message to all users. Different scheduling granularities are considered, as are different scheduling algorithms. The schemes are evaluated in the context of the LTE downlink using multiuser system simulations, assuming a full-buffer situation. The results show that separate transmission of the scheduling information requires a slightly lower overhead than joint broadcasting when proportional fair scheduling is employed and the users are spread out over the cell area. The results also indicate that the scheduling granularity standardized for LTE provides a good tradeoff between scheduling granularity and overhead. [ABSTRACT FROM PUBLISHER]

In the area of energy-efficient (EE) resource allocation, only limited work has been done on consideration of both transmitter and receiver energy consumption. In this paper, we propose a novel EE resource-allocation scheme for orthogonal frequency-division multiple-access (OFDMA) networks, where both transmitter energy consumption and receiver energy consumption are considered. In addition, different quality-of-service (QoS) requirements, including minimum-rate guarantee service and best effort service, are taken into account. The time slot, subcarrier (frequency), and power-allocation policies are jointly considered to optimize system EE. With all these considerations, the EE resource-allocation problem is formulated as a mixed combinatorial and nonconvex optimization problem, which is extremely difficult to solve. To reduce the computational complexity, we tackle this problem in three steps. First, for a given power allocation, we obtain the time–frequency resource unit (RU) allocation policy via binary quantum-behaved particle swarm optimization (BQPSO) algorithm. Second, under the assumption of known RU allocation, we transform the original optimization problem into an equivalent concave optimization problem and obtain the optimal power-allocation policy through the Lagrange dual approach. Third, an iteration algorithm is developed to obtain the joint time–frequency power–resource-allocation strategy. We validate the convergence and effectiveness of the proposed scheme by extensive simulations. [ABSTRACT FROM PUBLISHER]

Joint scheduling and resource allocation in uplink orthogonal frequency-division multiplexing (OFDM) systems is complicated and even becomes intractable with a large subcarrier and a large user number. This paper investigates the resource allocation for OFDM-based multiuser multiple-access relay channels (MARCs) with network coding. We formulate a joint optimization problem considering source-node pairing, subcarrier assignment, subcarrier pairing, and power allocation to maximize the sum rate under per-user power constraints. The problem is solved in polynomial time by optimizing three separate subproblems. To further reduce the complexity, three low-complexity suboptimal algorithms are then proposed when fixing partial resource. The simulation results show the performance gains of the proposed algorithms versus per-node transmit power and source-node number, and the impact of relay location is also evaluated. [ABSTRACT FROM PUBLISHER]

In this paper, we consider cross-layer scheduling for the downlink of amplify-and-forward (AF) relay-assisted orthogonal frequency-division multiple-access (OFDMA) networks. The proposed cross-layer design takes into account the effects of imperfect channel-state information (CSI) at the transmitter (CSIT) in slow fading. The rate, power, and subcarrier allocation policies are optimized to maximize the system goodput (in bits per second per hertz successfully received by the users). The optimization problem is solved by using dual decomposition, resulting in a highly scalable distributed iterative resource-allocation algorithm. We also investigate the asymptotic performance of the proposed scheduler with respect to (w.r.t.) the numbers of users and relays. We find that the number of relays should grow faster than the number of users to fully exploit the multiuser diversity (MUD) gain. On the other hand, diversity from multiple relays can be exploited to enhance system performance when the MUD gain is saturated due to noise amplification at the AF relays. Furthermore, we introduce a feedback-reduction scheme to reduce the computational burden and the required amount of CSI feedback from the users to the relays. Simulation results confirm the derived analytical results for the growth of the system goodput and illustrate that the proposed distributed cross-layer scheduler only requires a small number of iterations to achieve practically the same performance as the optimal centralized scheduler, even if the information exchanged between the base station (BS) and the relays in each iteration is quantized, and the proposed CSI feedback reduction scheme is employed. [ABSTRACT FROM AUTHOR]

This paper addresses resource allocation for weighted sum throughput maximization (WSTM) in multicell orthogonal frequency-division multiple-access (OFDMA) networks. Two suboptimal algorithms with polynomial-time complexity are provided. First, we determine a graph-based subcarrier allocation algorithm allowing joint transmission of two interfering links whenever such a transmission fulfills the pairwise WSTM objective. An analytical expression of the optimal power allocation with two interfering cells in single-carrier transmission is obtained, and the capacity region study serves as the basis to build the interference graph. Second, we present a distributed power-control algorithm suitable for any signal-to-interferenceplus-noise ratio (SINR) regime. It rejects users and subcarriers with weighted SINR that is too low before operating in the high SINR regime for the remaining users and subcarriers. Both algorithms and their combination are assessed via dynamic simulations, where the weight of each user is proportional to its queue length. They are shown to significantly decrease resource consumption and efficiently balance users' queue lengths. [ABSTRACT FROM AUTHOR]

This paper investigates subcarrier and powerallocation schemes considering fairness issues in a cooperative orthogonal frequency-division multiple-access (OFDMA) uplink system. A pseudochannel gain is introduced to convert cooperative transmissions to virtual direct transmissions. By utilizing pseudochannel gains, we can simplify a cooperative OFDMA resource-allocation problem to a traditional one. Furthermore, minimum rate requirements are considered to include fairness issues. An algorithm that provides an efficient solution with low computational complexity is proposed. Experimental results show that the fairness with minimum rate requirements is achieved outstandingly, and cooperative transmissions prevail over direct transmissions in the aspect of the total system throughput. [ABSTRACT FROM AUTHOR]

In this paper, we propose a cross-layer resource-allocation scheme for orthogonal frequency-division multiple-access (OFDMA) systems with imperfect channel-state information (CSI). In our modeling, we consider two traffic types: 1) streaming traffic, which requires a maximum guaranteed average delay, and 2) elastic traffic with flexible rate requirements. We then consider queue-state information (QSI) and CSI in a cross-layer framework to maximize the total transmission rate of elastic users while the average delay constraint for streaming users and the maximum transmission power constraint are satisfied. We also consider the impact of imperfect CSI. An algorithm is then proposed based on the dual decomposition method to obtain the subcarrier assignment and power allocation for each user. Using simulations, we evaluate the impact of streaming traffic on the total achievable rate of elastic users for different values of the required outage probability. It is also shown by simulation that, by increasing the variance of CSI estimation, the total rate of elastic traffic is decreased. [ABSTRACT FROM AUTHOR]

This paper studies the resource allocation problem for the relay-assisted orthogonal frequency-division multiple-access (OFDMA)-based multiuser system. A new transmission protocol, named hierarchical OFDMA, is proposed to support two-way communications between the base station (BS) and each mobile user (MU) with or without an assisting relay station (RS) in "relay" or "direct" mode, respectively. In particular, the recently discovered two-way relaying technology, based on the principle of network coding, is applied to MUs in relay mode with two possible relay-operations, namely, decode-and-forward (DF) and amplify-and-forward (AF). By applying convex optimization techniques, efficient algorithms are developed for optimal allocation of transmit resources such as power levels, bit rates, and OFDM subcarriers at the BS, RSs, and MUs. Simulation results show that substantial system throughput gains are achievable by the proposed two-way relaying and optimal resource allocation schemes over the traditional one-way relaying and fixed resource allocation schemes for relay-assisted OFDMA-based wireless networks. [ABSTRACT FROM AUTHOR]

This paper presents efficient dynamic resource allocation schemes with interference mitigation techniques for multimedia services in downlink orthogonal frequency-division multiplexing mobile cellular systems. Performance of the proposed algorithms is evaluated in terms of user quality-of-service and system spectral efficiency. It is shown that the best subcarrier allocation scheme with interference mitigation and macrodiversity techniques gives significant performance gains in terms of system spectral efficiency. Furthermore, sharing the system bandwidth among real-time stream-type voice and bursty data services can support much larger system loads than having a hard division. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the resource allocation problem in a single-user relay-aided cognitive radio (CR) underlay network. Both the CR network and the primary network operate under the orthogonal frequency-division multiplexing (OFDM) scheme. Different from the conventional resource allocation problem, the relay node here is capable of performing subcarrier permutation over two hops such that the signal received over a particular subcarrier is forwarded via a different subcarrier. The objective is to maximize the throughput of the CR network subject to a limited power budget at the secondary source and the relay node and to interference constraints at the primary receiver. Optimization is performed under a unified framework where power allocation at the source node, power allocation at the relay node, and subcarrier pairing at the two hops are jointly optimized. The joint resource-allocation scheme yields an asymptotically optimal solution. We further design a suboptimal algorithm that sacrifices little on performance but could significantly reduce computational complexity. Finally, numerical examples are provided to corroborate the proposed studies. [ABSTRACT FROM PUBLISHER]

Joint channel and rate allocation with power minimization in orthogonal frequency-division multiple access (OFDMA) has attracted extensive attention. Most of the research has dealt with the development of suboptimal but low-complexity algorithms. In this paper, the contributions comprise new insights from revisiting tractability aspects of computing the optimum solution. Previous complexity analyses have been limited by assumptions of fixed power on each subcarrier or power-rate functions that locally grow arbitrarily fast. The analysis under the former assumption does not generalize to problem tractability with variable power, whereas the latter assumption prohibits the result from being applicable to well-behaved power-rate functions. As the first contribution, we overcome the previous limitations by rigorously proving the problem's NP-hardness for the representative logarithmic rate function. Next, we extend the proof to reach a much stronger result, namely, that the problem remains NP-hard, even if the channels allocated to each user are restricted to be a consecutive block with given size. We also prove that, under these restrictions, there is a special case with polynomial-time tractability. Then, we treat the problem class where the channels can be partitioned into an arbitrarily large but constant number of groups, each having uniform gain for every individual user. For this problem class, we present a polynomial-time algorithm and provide its optimality guarantee. In addition, we prove that the recognition of this class is polynomial-time solvable. [ABSTRACT FROM PUBLISHER]

In this paper, resource allocation for energy-efficient secure communication in an orthogonal frequency-division multiple-access (OFDMA) downlink network is studied. The considered problem is modeled as a nonconvex optimization problem that takes into account the sum-rate-dependent circuit power consumption, multiple-antenna eavesdropper, artificial noise generation, and different quality-of-service (QoS) requirements, including a minimum required secrecy sum rate and a maximum tolerable secrecy outage probability. The power, secrecy data rate, and subcarrier allocation policies are optimized for maximization of the energy efficiency of secure data transmission (bit/joule securely delivered to the users). The considered nonconvex optimization problem is transformed into a convex optimization problem by exploiting the properties of fractional programming, which results in an efficient iterative resource allocation algorithm. In each iteration, the transformed problem is solved by using dual decomposition. Simulation results illustrate that the proposed iterative resource allocation algorithm not only converges in a small number of iterations but maximizes the system energy efficiency and guarantees a nonzero secrecy data rate for the desired users as well. In addition, the obtained results unveil a tradeoff between energy efficiency and secure communication. [ABSTRACT FROM PUBLISHER]

Cognitive radio (CR) has been proposed to improve spectral efficiency while avoiding interference with licensed users. In this paper, we propose a resource allocation (RA) algorithm to perform uplink frequency allocation and power allocation among noncooperative multicells with multiuser per cell in CR systems. The maximization of the total information rate of multiple users in one cell is considered for the Rayleigh channel with path loss subject to the power constraint on each user. Since the optimization formulation for rate maximization of multiple users in each cell is an integer optimization problem, the multiple access channel (MAC) technique is proposed. With the aid of MAC, the original integer optimization problem is transformed into a concave optimization problem, thereby establishing a distributed game model in which the base station of each cell, which tries to maximize the sum rate of its own users, is a player. The proposed game-theoretic algorithm for distributed multiuser power allocation is viewed as an extension of the iterative water-filling algorithm, which is applied to distributed single-user power allocation. From the perspective of effectiveness, the proposed game-theoretic algorithm based on the MAC technique is compared with the traditional algorithm based on frequency-division multiplexing access (FDMA). Final numerical results show that the MAC-based RA algorithm can achieve more information rate and better convergence performance than the FDMA-based RA algorithm. [ABSTRACT FROM PUBLISHER]

Radio resource allocation for downlink transmissions in a cellular system based on orthogonal frequency-division multiple access (OFDMA) has been the subject of many research studies over the past few years. Nowadays, increasing attention has turned to cellular systems with relays, but only a few algorithms have been designed for OFDMA-based relay systems. In this paper, resource-allocation schemes are proposed for the cases of one relay station (RS) and multiple RSs in the cell. Due to thespecific design of these algorithms, which operate in an RS-aided base station centralized manner, the amount of required channel-state information and algorithm complexity are minimized, making them suited for practical use. The idea of adaptive RS activation is introduced, where the frame structure is adapted depending on the active RS. Different numbers of relays are considered, and the corresponding algorithms are accordingly adapted. Simulation results show that our algorithms achieve a very good throughput/outage tradeoff. [ABSTRACT FROM AUTHOR]

Conventional resource-allocation schemes in orthogonal frequency-division multiple-access (OFDMA) systems consider only one bit-error-rate (BER) requirement. When these schemes are applied to transmit bitstreams with multiple BER requirements, low resource utilization will occur since the target BER has to be the strictest one. We develop a BER-driven resource allocation for generic bitstreams (BRA-G) algorithm by exploiting multiple predetermined target BERs. Then, this scheme is extended to transmission of realistic scalable bitstreams (BRA-SB) where more important (MI) layers possess lower target BERs as compared with less important (LI) layers. The objective here is to maximize the expected quality of all JPEG 2000 (J2K) scalable bitstreams subject to a total power constraint. Instead of using static target BERs for different layers, BRA-SB water-fills over all layers to determine the number of transmit layers and the target BER of each layer simultaneously based on the importance of each layer in terms of reconstructed quality and average channel conditions. Bit-level results show that BRA-SB provides significant image quality improvement over a scheme that ignores the layer's importance in terms of target BER and a BRA-SB-like scheme with static target BERs. A suboptimal algorithm is also proposed to reduce the computational complexity and its performance is close to the optimal one. [ABSTRACT FROM PUBLISHER]

We study the resource-allocation problem in a multiuser orthogonal frequency-division multiplexing (OFDM)-based cognitive radio (CR) system using a cross-layer approach. The goal is to provide satisfactory quality of service (QoS) to both real-time and non-real-time applications, despite the rapid variations in available resources caused by the activities of the primary users. The dynamic nature of the available spectrum gives rise to two resource allocation issues: 1) problem feasibility and 2)false urgency. To solve the problem-feasibility issue, which arises when resources are insufficient to meet all user QoS requirements, we adopt a goal-programming approach. The false-urgency issue that was caused by variations in available system resources is effectively avoided by a proposed rate-requirement calculation mechanism based on the status of the packets in queue and system resource availability. Simulation results show that the proposed cross-layer resource-allocation algorithm for CR systems performs better than existing algorithms that were designed for multiuser OFDM systems. [ABSTRACT FROM AUTHOR]