In this paper, joint designs of data routes and resource allocations are developed for generic half-duplex multicarrier wireless networks in which each subcarrier can be reused by multiple links. Two instances are considered. The first instance pertains to the general case in which each subcarrier can be time-shared by multiple links, whereas the second instance pertains to a special case in which time-sharing is not allowed and a subcarrier, once assigned to a set of links, is used by those links throughout the signalling interval. Novel frameworks are developed to optimize the joint design of data routes, subcarrier schedules, and power allocations. These design problems are nonconvex and hence difficult to solve. To circumvent this difficulty, efficient techniques based on geometric programming are developed to obtain locally optimal solutions. Numerical results show that the designs developed in both instances yield performance that is superior to that of their counterparts in which frequency-reuse is not allowed.
Resource allocation in wireless networks constitutes the task of determining the power allocated for each transmission and the fraction of time over which a particular subcarrier is assigned to that transmission. Instances in which resource allocation techniques were developed are provided in @cite_7 @cite_13 @cite_19 @cite_22 @cite_3 @cite_20 for various network scenarios. For instance, power allocation techniques for single-carrier cellular systems and multicarrier systems were developed in @cite_7 and @cite_20 , respectively. To enable more effective utilization of resources, power allocations were optimized jointly with binary-constrained subcarrier schedules. For instance, the designs developed in @cite_13 and @cite_19 rely on the premise that each subcarrier is exclusively used by one node and the solutions obtained therein are potentially suboptimal. When the binary constraint on the subcarrier schedules is relaxed allowing the subcarriers to be time-shared by multiple nodes, the optimal power allocations can be shown to be the water-filling ones @cite_22 ; a related problem was considered in @cite_3 for a case in which the nodes experience self-noise.
@cite_22 @cite_7 @cite_3 @cite_19 @cite_13 @cite_20
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In this paper, we develop a transmit power adaptation method that maximizes the total data rate of multiuser orthogonal frequency division multiplexing (OFDM) systems in a downlink transmission. We generally formulate the data rate maximization problem by allowing that a subcarrier could be shared by multiple users. The transmit power adaptation scheme is derived by solving the maximization problem via two steps: subcarrier assignment for users and power allocation for subcarriers. We have found that the data rate of a multiuser OFDM system is maximized when each subcarrier is assigned to only one user with the best channel gain for that subcarrier and the transmit power is distributed over the subcarriers by the water-filling policy. In order to reduce the computational complexity in calculating water-filling level in the proposed transmit power adaptation method, we also propose a simple method where users with the best channel gain for each subcarrier are selected and then the transmit power is equally distributed among the subcarriers. Results show that the total data rate for the proposed transmit power adaptation methods significantly increases with the number of users owing to the multiuser diversity effects and is greater than that for the conventional frequency-division multiple access (FDMA)-like transmit power adaptation schemes. Furthermore, we have found that the total data rate of the multiuser OFDM system with the proposed transmit power adaptation methods becomes even higher than the capacity of the AWGN channel when the number of users is large enough. This paper considers the optimum single cell power control maximizing the aggregate (uplink) communication rate of the cell when there are peak power constraints at mobile users, and a low-complexity data decoder (without successive decoding) at the base station. It is shown that the optimum power allocation is binary, which means that links are either “on” or “off.” By exploiting further structure of the optimum binary power allocation, a simple polynomial-time algorithm for finding the optimum transmission power allocation is proposed, together with a reduced complexity near-optimal heuristic algorithm. Sufficient conditions under which channel-state aware time division multiple access (TDMA) maximizes the aggregate communication rate are established. In a numerical study, we compare and contrast the performance achieved by the optimum binary power-control policy with other suboptimum policies and the throughput capacity achievable via successive decoding. It is observed that two dominant modes of communication arise, wideband or TDMA, and that successive decoding achieves better sum-rates only under near perfect interference cancellation efficiency. In this paper, we exploit the theory of majorization to obtain the aforementioned results. In the final part of this paper, we do so to solve power-control problems in the areas of femtocells and cognitive radio and find that, again, optimal solutions have a binary (or almost binary) character. We consider scheduling and resource allocation for the downlink of a cellular OFDM system, with various practical considerations including integer tone allocations, different sub-channelization schemes, maximum SNR constraint per tone, and "self-noise" due to channel estimation errors and phase noise. During each time-slot a subset of users must be scheduled, and the available tones and transmission power must be allocated among them. Employing a gradient-based scheduling scheme presented in earlier papers reduces this to an optimization problem to be solved in each time-slot. Using a dual formulation, we give an optimal algorithm for this problem when multiple users can time-share each tone. We then give several low complexity heuristics that enforce integer tone allocations. Simulations are used to compare the performance of different algorithms. We consider the joint subcarrier and power allocation problem with the objective of maximizing the total utility of users in the uplink of an OFDMA system. Our formulation includes the problems of sum rate maximization, proportional fairness and max-min fairness as special cases. Unlike some previous algorithms, which are iterative and time consuming, our proposed one is non-iterative and with time complexity of only O(KN log2 N), where K and N are the number of users and subcarriers respectively. We prove that it provides a solution that is Pareto optimal within a large neighborhood of itself. Besides, we derive an efficiently computable upper bound of the optimal solution. Simulation results show that our algorithm is nearly optimal. In this letter, we focus on joint subcarrier and power allocation in the uplink of an OFDMA system. Our goal is to maximize the rate-sum capacity in the uplink. For the purpose, we formulate an optimization problem subject to subcarrier and power constraints and draw necessary conditions for optimality, from which we derive joint subcarrier and power allocation algorithms. Simulation results show that our proposed scheme enhances the system capacity, providing almost near optimal solutions with low computational burden. In wireless cellular or ad hoc networks where Quality of Service (QoS) is interference-limited, a variety of power control problems can be formulated as nonlinear optimization with a system-wide objective, e.g., maximizing the total system throughput or the worst user throughput, subject to QoS constraints from individual users, e.g., on data rate, delay, and outage probability. We show that in the high Signal-to- interference Ratios (SIR) regime, these nonlinear and apparently difficult, nonconvex optimization problems can be transformed into convex optimization problems in the form of geometric programming; hence they can be very efficiently solved for global optimality even with a large number of users. In the medium to low SIR regime, some of these constrained nonlinear optimization of power control cannot be turned into tractable convex formulations, but a heuristic can be used to compute in most cases the optimal solution by solving a series of geometric programs through the approach of successive convex approximation. While efficient and robust algorithms have been extensively studied for centralized solutions of geometric programs, distributed algorithms have not been explored before. We present a systematic method of distributed algorithms for power control that is geometric-programming-based. These techniques for power control, together with their implications to admission control and pricing in wireless networks, are illustrated through several numerical examples.