Shujuan Hou

On the Spectral-Energy Efficiency and Rate Fairness Tradeoff in Relay-Aided Cooperative OFDMA Systems

In resource constrained wireless systems, achieving higher spectral efficiency (SE) and energy efficiency (EE), and greater rate fairness are conflicting objectives. Here, a general framework is presented to analyze the tradeoff among these three performance metrics in cooperative OFDMA systems with decode-and-forward relaying, where subcarrier pairing and allocation, relay selection, choice of transmission strategy, and power allocation are jointly considered. In our analytical framework, rate fairness is represented utilizing the α-fairness model, and the resource allocation problem is formulated as a multi-objective optimization problem. We then propose a cross-layer resource allocation algorithm across application and physical layers, and further devise a heuristic algorithm to tackle the computational complexity issue. The SE-EE tradeoff is characterized as a Pareto optimal set, and the efficiency and fairness tradeoff is investigated through the price of fairness. Simulations indicate that higher fairness results in a worse SE-EE tradeoff. It is also shown imposing fairness helps to reduce the outage probability. For a fixed number of relays, by increasing circuit power, the performance of SE-EE tradeoff is degraded. Interestingly, by increasing the number of relays, although the total circuit power is increased, the SE-EE tradeoff is not necessarily degraded. This is thanks to the extra degree of freedom provided in relay selection.

Energy and Spectral Efficiency Tradeoff With User Association and Power Coordination in Massive MIMO Enabled HetNets

In this letter, we investigate the tradeoff between energy efficiency (EE) and spectral efficiency (SE) while ensuring proportional rate fairness in massive multiple-input multiple-output enabled heterogenous networks, where user association and power coordination are jointly considered. It is first formulated as a multi-objective optimization problem, and then transformed into a single-objective optimization problem. To solve this mixed-integer non-convex problem, an effective algorithm is developed, where the original problem is separated into lower level power coordination problem and master user association problem. Simulation results verify that our proposed algorithm can significantly improve the performance of EE-SE tradeoff and obtain higher rate fairness compared with other algorithms.

Energy- and Spectral-Efficiency Tradeoff with

In this letter, we adopt multi-objective optimization to investigate the tradeoff between energy efficiency (EE) and spectral efficiency (SE) in downlink orthogonal frequency division multiple access (OFDMA) systems. In the proposed model, α-fair utility function is applied to take account of the rate fairness among users. We then transfer the original multi-objective optimization into a single objective optimization employing the weighted sum method to obtain the solution set characterized as a Pareto set. The obtained Pareto set demonstrates the tradeoff between EE and SE while α-fairness guarantee is in place. We further consider price of fairness, as a metric to quantify the loss of EE due to enforcing fairness requirements. Such a metric enables the network operators to determine an acceptable operation point in terms of EE-SE tradeoff when certain level of fairness is required. Simulation results indicate that higher fairness results in lower system EE, and the price of fairness is significantly raised with the increase of overall SE.

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In this paper, we propose a general framework to study the tradeoff between energy efficiency (EE) and spectral efficiency (SE) in massive multiple-input-multiple-output-enabled heterogenous networks while ensuring proportional rate fairness among users and taking into account the backhaul capacity constraint. We aim at jointly optimizing user association, spectrum allocation, power coordination, and the number of activated antennas, which is formulated as a multi-objective optimization problem maximizing EE and SE simultaneously. With the help of weighted Tchebycheff method, it is then transformed into a single-objective optimization problem, which is a mixed-integer non-convex problem and requires unaffordable computational complexity to find the optimum. Hence, a low-complexity effective algorithm is developed based on primal decomposition, where we solve the power coordination and number of antenna optimization problem and the user association and spectrum allocation problem separately. Both theoretical analysis and numerical results demonstrate that our proposed algorithm can fast converge within several iterations and significantly improve both the EE-SE tradeoff performance and rate fairness among users compared with other algorithms.

-Fairness in Downlink OFDMA Systems

In this paper, the power allocation problem for downlink massive multiple-input multiple-output (MIMO) systems with inter-user interference is investigated considering the tradeoff between energy efficiency (EE) and spectral efficiency (SE). The minimum mean-square-error channel estimate and the rigorous closed-form expression of achievable downlink rate are first derived. Then, the multi-objective optimization problem (MOP) is formulated subject to maximum total transmission power constrain, where conflicting EE and SE are maximized simultaneously. To obtain the solution set characterized as a Pareto set, the MOP is transformed into a single-objective problem (SOP) using weighted sum method. We further convert the SOP into a convex optimization problem by adding an additional interference constraint, and the optimal power allocation algorithm is proposed by using dual method to balance EE and SE efficiently. Simulation results demonstrate the effectiveness of the proposed algorithm and illustrate the fundamental tradeoff between EE and SE for different parameter settings.

On the Energy and Spectral Efficiency Tradeoff in Massive MIMO-Enabled HetNets With Capacity-Constrained Backhaul Links

The multi-packet reception (MPR) technique enables access points to correctly receive simultaneously transmitted packets. Therefore, the network saturation throughput can be significantly improved by introducing MPR into wireless local area networks (WLANs). In this letter, we develop a novel analytical model to evaluate the saturation throughput of a WLAN with MPR. In contrast to previous models, we adopt the stationary distribution of backoff counter value after a generic overlapping transmission process to characterize the behavior of a contending station. Then, we derive the saturation throughput based on this distribution. Comparisons with simulation results show that our analytical model provides a close evaluation of the saturation throughput. Moreover, the impacts of several parameters on the saturation throughput are also investigated using the proposed model.

Energy- and spectral-efficiency tradeoff in massive MIMO systems with inter-user interference

In this paper, a novel “delay- and utility-oriented (DUO)” resource allocation algorithm for the mixture of real-time (RT) and non-real-time services in orthogonal frequency division multiple access systems is proposed. The proposed DUO algorithm considers delay-utility function and rate-utility function simultaneously, scheduling RT packets approaching the deadline first and then achieving as much aggregate rate-utility as possible. Simulation results reveal that it can not only satisfy RT users’ delay requirements in terms of packet drop ratio, but also obtain much more aggregate rate-utility for mixed services than other reference algorithms. Besides, the rate-utility fairness of all users is also improved substantially.