This paper considers a device-to-device (D2D) communications underlaid multiple-input multiple-output cellular network and studies D2D mode selection from a previously unexamined perspective. Since D2D mode selection affects the network interference profile and vice versa, a joint D2D mode selection and interference management is desired but challenging. In this paper, we propose a holistic approach to this problem with interference-free considerations. We adopt the degrees-of-freedom (DoFs) as the mode-selection criterion and exploit the linear interference alignment technique for interference management. We analyze the achievable sum DoF of the potential D2D users according to their mode selections, and derive the probabilistic sum-rate relations between the proposed DoF-based mode selection scheme and the common received-signal-strength-index-based mode selection scheme in Poisson point process networks. Simulation illustrates the theoretical insights and shows the advantages of the proposed DoF-based mode selection scheme over conventional mode selection schemes from various perspectives. The proposed scheme presents a promising proposal for D2D mode selection in 5G communications.

This paper considers a hybrid cochannel MIMO cellular network with both cellular and device-to-device (D2D) users, and studies, from a new perspective, mode selection (cellular or D2D mode) for the potential D2D users joining the network. We propose a new mode selection scheme that takes into account not only the interference caused to the potential D2D users, but also the interference caused by the potential D2D users to the network. We adopt the interference alignment technique for interference management in the network. We theoretically show the conditions under which the potential D2D users will select the cellular or D2D mode for a greater number of guaranteed interference-free transmissions. Simulation illustrates the theoretical insights and shows the advantages of the proposed interference-aware mode selection scheme over conventional mode selection schemes.

Indoor localization technique is a key enabling technology for the future Internet of things (IoT) paradigm. Improving the precision of indoor localization will expand the horizon of indoor IoT applications. In this paper, we propose an enhanced machine-learning indoor localization scheme which incorporates access point (AP) selection and the proposed signal strength reconstruction to enhance robustness in noisy environments. The proposed signal strength reconstruction scheme estimates/reconstructs the received signal strength indicator (RSSI) values of the nonselected APs from those of the selected APs to increase the size of the feature space for enhanced noise robustness. The proposed concept can be applied to various machine-learning frameworks. Simulation results demonstrate improved precision yielded by the proposed method in conjunction with support vector regression (SVR), ensemble SVR, and artificial neural network (ANN) models, as compared to these machine- learning techniques alone.

This paper studies joint subchannel allocation, power allocation, and beamforming for simultaneous wireless information and power transfer (SWIPT) in multiuser downlink orthogonal frequency-division multiple access (OFDMA) systems. We formulate a multi-objective optimization (MOO) problem where the objectives are to maximize both the information rate and the harvested power for all users in the system. We approach the MOO problem with two proposed methods, i.e., semidefinite relaxation-based weighted aggregation (SDR-WA) and multi-objective genetic algorithm (MOGA). Simulation compares the achievable Pareto optimal solution set yielded by these methods, and illustrates the tradeoffs of the sum information rate vs. the sum harvested power in the system.

This paper studies the fairness optimization of dynamic multiuser multicarrier allocation in the cellular downlink of MIMO orthogonal frequency division multiple access (OFDMA) systems. The varying capacity demands of different users motivate the fairness problem. In the resource allocation approaches that maximizing the sum rate or minimizing the total power often leads to poor fairness among users. The allocation is prone to starvation situation for the users with deep fading subchannels. Hence, this work considers the fairness issue and proposes to maximize the minimum rate surplus, where the rate surplus is defined as the difference between the demand data rate and the resulting allocated data rate. The fairness is inverse proportional to the gap of the maximum rate surplus to the minimum rate surplus among all users. In this work, the design of the precoding and decoding matrices for the MIMO structure is also developed. To solve the optimization problem, an iterative algorithm is proposed to optimize the subcarrier assignment with low complexity. Simulation results on multiuser MIMO environment show that the proposed algorithm strikes the balance between sum rate and fairness. Comparing with the state-of-the-art works, the proposed algorithm shows an advantage in keeping the sum rate while the fairness is significantly improved.

In this paper, we study the sum degrees of freedom (DoF) of an uplink two-cell multiuser MIMO interference network with asymmetric number of users in the cells. The achievable DoF is devised based on a two-dimensional space-time spreading code framework with linear precoding/decoding design and finite channel extension. The derivation of the achievable DoF is shown related to a rank minimization problem, which corresponds to the minimization of the dimension of the interference subspace. The problem is solved by the proposed grouping algorithm (GA) based on aligning interfering signals into a low-dimensional subspace as a group and attaining the minimum number of groups. The achievable sum DoF derived based on the proposed GA is shown to be greater than prior arts and achieves the theoretic upper bound in several cases. We also give a closed-form expression of the maximum achievable sum DoF when there is the maximum number of admissible users in the considered finite diversity environment.

This study explores the convergence property of the interference leakage minimization algorithm (ILMA) proposed by S. W. Peters and R. W. Heath Jr. Among the cooperative interference alignment (IA) schemes, we specialize the ILMA for its better feasibility in real applications than others. However, ILMA cannot converge to a global minimum because of its unitary constraints. As an iterative algorithm, no guarantee of global convergence means the tendency towards system instability. Up to the present, most efforts on cooperative IA are focused on pursuing high sum rate or low error floor. None of existing works looks into the convergence issue. In this paper, we explore the global convergence of ILMA. Specifically, we devise a constraint relaxation version of ILMA, named rILMA. Except that rILMA inherits the benefits from ILMA, mathematical proofs given in this paper also show that rILMA converges to a global minimum. This exploration directly results in the improvement of performance. Numerical simulations show that rILMA outperforms Max-SINR and ILMA in sum rate performance.

In this paper, we study in depth the alternating minimization algorithm (AMA) proposed by S. W. Peters and R. W. Heath Jr. Among iterative interference alignment (IIA) schemes, AMA is considered more feasible for real applications than other IIA schemes. However, no research has investigated how AMA is affected by some real system characteristics. Motivated by this, we look into the effects when taking into account the initial conditions and the final conditions of AMA in the MIMO interference networks. We found the sum rate performance of AMA can be improved by well exploiting its initial conditions. On the other hand, by considering the interference leakage residue (ILR) which is the final condition of AMA, we derived a more realistic formulation which fast calculates the smallest possible codebook for precoders.