Data Rate Requirements Of Users Research Articles

Enabling Green Wireless Networking With Device-to-Device Links: A Joint Optimization Approach

Device-to-device (D2D) communication has emerged as a promising technique for improving capacity and reducing power consumption in wireless networks. Most existing works on D2D communications either targeted CDMA-based single-channel networks or aimed at maximizing network throughput. In this paper, we, however, aim to enable green D2D communications in OFDMA-based wireless networks. We formally define an optimization problem based on a practical link data rate model, whose objective is to minimize total power consumption while meeting user data rate requirements. We propose solving it using a joint optimization approach by presenting two effective and efficient algorithms, which both jointly determines mode selection, channel allocation and power assignment. It has been shown by extensive simulation results that the proposed algorithms can achieve over 68% power savings, compared to several baseline methods.

Cost-Reliability Tradeoff in Licensed and Unlicensed Spectra Interoperable Networks with Guaranteed User Data Rate Requirements

Unlicensed spectra access holds the promise of alleviating licensed spectra scarcity and providing super high-rate mobile data services, which has been viewed as one of the key technologies of fifth generation (5G) cellular networks. In this paper, we first design a framework for 5G licensed and unlicensed spectra interoperable networks based on the cloud radio access network technology and the control/data decoupled architecture. Then, we investigate network-level cost-reliability tradeoff from two aspects. First, we study a fundamental tradeoff between the cost and the reliability by minimizing cost for a given reliability level. Second, we define a quality of experience efficiency utility as the complementary measure to characterize the cost and the reliability, offering an inherent tradeoff between them. Moreover, an interference power estimation method is proposed to more accurately estimate channel states to guarantee resource allocation effectiveness. Finally, we conduct extensive simulation study and demonstrate the effectiveness of the interference power estimation method.

Optimal Resource Allocation for Energy-Efficient OFDMA Networks

This paper focuses on radio resource allocation in OFDMA networks for maximizing the energy efficiency subject to the data rate requirements of users. We propose the energy-efficient water-filling structure to obtain the closed-form optimal energy-efficient power allocation for a given subcarrier assignment. Moreover, we establish a new sufficient condition for the optimal energy-efficient subcarrier assignment. Based on the theoretical analysis, we develop a joint energy-efficient resource allocation (JERA) algorithm to maximize the energy efficiency. Simulation results show that the JERA algorithm can yield optimal solution with significantly low computational complexity.

Hybrid‐genetic‐algorithm‐based resource allocation for slow adaptive OFDMA system under channel uncertainty

A resource allocation algorithm for the slow adaptive orthogonal frequency division multiple access system under channel uncertainty is considered. The optimisation objective maximises the long-term system throughput over subcarrier assignment and the constraint condition satisfies the short-term data rate requirements of individual users, except occasional outage. Such an objective has a natural chance-constrained programming formulation. To solve the chance-constrained optimisation, the neural network and the genetic algorithm (GA) are integrated to develop a hybrid GA (HGA) which could satisfy the user data rate requirement with the target outage probability. The simulation tests verify that the HGA yields a higher long-term system throughput than the Li algorithm with the Bernstein approximation.

Resource Allocation and Scheduling in Multi-Cell OFDMA Systems with Decode-and-Forward Relaying

In this paper, we formulate resource allocation and scheduling for multi-cell orthogonal frequency division multiple access (OFDMA) systems with half-duplex decode-and-forward (DF) relaying as a joint optimization problem taking into account multi-cell interference and heterogeneous user data rate requirements. For efficient multi-cell interference mitigation, we incorporate a time slot allocation strategy into the problem formulation. We transform the resulting non-convex and combinatorial optimization problem into a standard convex problem by imposing an interference temperature constraint, which yields a lower bound for the original problem. Subsequently, the transformed optimization problem is solved by dual decomposition and a semi-distributed iterative resource allocation algorithm with closed-form power and subcarrier allocation policies is derived to maximize the average weighted system throughput (bit/s/Hz/base station). Simulation results illustrate that our proposed semi-distributed algorithm achieves practically the same performance as the centralized optimal solution of the original non-convex problem and provides a substantial performance gain compared to single-cell resource allocation and scheduling schemes.

Slow Adaptive OFDMA Systems Through Chance Constrained Programming

Adaptive OFDMA has recently been recognized as a promising technique for providing high spectral efficiency in future broadband wireless systems. The research over the last decade on adaptive OFDMA systems has focused on adapting the allocation of radio resources, such as subcarriers and power, to the instantaneous channel conditions of all users. However, such "fast" adaptation requires high computational complexity and excessive signaling overhead. This hinders the deployment of adaptive OFDMA systems worldwide. This paper proposes a slow adaptive OFDMA scheme, in which the subcarrier allocation is updated on a much slower timescale than that of the fluctuation of instantaneous channel conditions. Meanwhile, the data rate requirements of individual users are accommodated on the fast timescale with high probability, thereby meeting the requirements except occasional outage. Such an objective has a natural chance constrained programming formulation, which is known to be intractable. To circumvent this difficulty, we formulate safe tractable constraints for the problem based on recent advances in chance constrained programming. We then develop a polynomial-time algorithm for computing an optimal solution to the reformulated problem. Our results show that the proposed slow adaptation scheme drastically reduces both computational cost and control signaling overhead when compared with the conventional fast adaptive OFDMA. Our work can be viewed as an initial attempt to apply the chance constrained programming methodology to wireless system designs. Given that most wireless systems can tolerate an occasional dip in the quality of service, we hope that the proposed methodology will find further applications in wireless communications.

Optimal Resource Allocation for Multiuser MIMO-OFDM Systems With User Rate Constraints

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> With the proliferation of wireless services, personal connectivity is quickly becoming ubiquitous. As the user population demands greater multimedia interactivity, data rate requirements are set to soar. Future wireless systems, e.g., multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM), need to cater to not only a burgeoning subscriber pool but also to a higher throughput per user. Furthermore, resource allocation for multiuser MIMO-OFDM systems is vital for the optimization of the subcarrier and power allocations to improve overall system performance. Using convex optimization techniques, this paper proposes an efficient solution to minimize the total transmit power subject to each user's data rate requirement. Using a Lagrangian dual decomposition, the complexity is reduced from one that is exponential in the number of subcarriers <formula formulatype="inline"><tex>$M$</tex></formula> to one that is only linear in <formula formulatype="inline"> <tex>$M$</tex></formula>. To keep the complexity low, linear beamforming is incorporated at both the transmitter and the receiver. Although frequency-flat fading has been known to plague OFDM resource allocation systems, a modification, i.e., <emphasis emphasistype="boldital">dual proportional fairness</emphasis>, seamlessly handles flat or partially frequency-selective fading. Due to the nonconvexity of the optimization problem, the proposed solution is not guaranteed to be optimal. However, for a realistic number of subcarriers, the duality gap is practically zero, and optimal resource allocation can be evaluated efficiently. Simulation results show large performance gains over a fixed subcarrier allocation. </para>