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This paper proposes a novel bit loading algorithm to multicarrier non-orthogonal multiple access (NOMA) systems to maximize the system total throughput while satisfying the users’ individual quality of service (QoS) constraints. Although bit loading is generally a non-deterministic polynomial-time (NP)-hard problem, even for orthogonal multiple access (OMA), the mutual interference between the users, and the dependence of power coefficients and modulation orders are additional challenges that add substantial complexity to the optimization problem. Therefore, in this paper, we propose an efficient bit loading algorithm for multicarrier NOMA systems and compare the complexity and throughput with a corresponding OMA system. The obtained results show that for the case of NOMA, bit loading provides an additional degree of freedom that allows non-uniform spectrum sharing among the users. In particular scenarios, the system may experience hybrid modes of operation, where the system switches between NOMA and OMA. The obtained numerical results show that NOMA can outperform OMA by 100% in terms of spectral efficiency, for a two-user scenario. The complexity of the loading process for NOMA is noticeably higher than OMA, which is due to the high complexity associated with the bit error rate (BER) computation in the case of NOMA. [ABSTRACT FROM AUTHOR]

Power control is becoming increasingly essential for the fifth-generation (5G) and beyond systems. An example use-case, among others, is the unmanned-aerial-vehicle (UAV) communications where the nearly line-of-sight (LoS) radio channels may result in very low signal-to-interference-plus-noise ratios (SINRs). The authors in (Chiang et al., 2007) proposed to efficiently and reliably solve this kind of non-convex problem via a series of geometrical programmings (GPs) using condensation approximation. However, it is only applicable for a small-scale network with several communication pairs and practically infeasible with more (e.g., tens of) nodes to be jointly optimized. We therefore in this paper aim to provide new insights into this problem. By properly introducing auxiliary variables, the problem is transformed to an equivalent form which is simpler and more intuitive for condensation. A novel condensation method with linear complexity is also proposed based on the form. The enhancements make the GP-based power control feasible for both small- and especially large-scale networks that are common in 5G and beyond. The algorithm is verified via simulations. A preliminary case study of uplink UAV communications also shows the potential of the algorithm. [ABSTRACT FROM AUTHOR]

This paper proposes a flexible pilot assignment method to jointly optimize the uplink and downlink data transmission in multi-cell Massive multiple input multiple output (MIMO) systems with correlated Rayleigh fading channels. By utilizing a closed-form expression of the ergodic spectral efficiency (SE) achieved with maximum ratio processing, we formulate an optimization problem for maximizing the minimum weighted sum of the uplink and downlink SEs subject to the transmit powers and pilot assignment sets. This combinatiorial optimization problem is solved by two sequential algorithms: a heuristic pilot assignment is first proposed to obtain a good pilot reuse set and the data power control is then implemented. Numerical results manifest that the proposed algorithm converges fast to a better minimum sum SE per user than the algorithms in previous works. [ABSTRACT FROM AUTHOR]

The energy detection technology is recommended in the licensed assisted access (LAA) scheme by 3GPP because of its simplicity and low cost. However, due to its inherent limitation, there may exist imperfect channel detection, which can lead to the decrease of the channel utilization efficiency and the deterioration of fairness. The imperfect detection can generally be represented by the detection probability and false alarm probability, which depend on detection time, signal to noise ration (SNR), sampling rate and energy threshold. However, among the parameters, only the energy threshold can be dominated by LAA small base stations (SBSs) in the LAA scheme. Therefore, the energy threshold should be dynamically adjusted in the changeable channel environment such that the detection accuracy can improved as high as possible. Consider the fact that the optimization theory cannot be used to optimize the energy threshold since the expressions of performance indexes about the energy threshold are extremely complex, a Q-learning based energy threshold optimization algorithm (QLET) is thus proposed in the paper, where LAA SBSs act as the agent, the energy threshold is defined as the agent action, the different combinations of fairness and throughput are defined as the agent states, and the fairness and the reward function are also redefined. In order to ensure the smooth implementation of the proposed QLET algorithm, the information exchange mechanism, where the sending-confirmation mechanism and 1- persistent CSMA are used, is also proposed. Based on the proposed QL framework, the agent can learn the optimal energy threshold by repeatedly interacting with the environment, which enables the coexistence system to obtain the best coexistence performance. A large number of simulation results show that the proposed QLET is superior to the traditional fixed energy threshold scheme (FET) in terms of the fairness, WiFi collision probability and transmission delay, and that QLET is almost the same as FET in term of throughput. [ABSTRACT FROM AUTHOR]