Visible light communication (VLC) is a promising technology for next-generation indoor wireless broadband communication systems. Physical layer security (PLS) is one proposed method to ensure data security for VLC in public areas. However, most PLS studies in the literature neglect the reflections in the VLC channel and do not consider inter-symbol interference (ISI). This paper investigates the ISI effects on the secrecy rate in multiple-input single-output VLC systems where the reflections are not neglected for the cases of perfect and imperfect knowledge of the eavesdropper’s channel state information. We aim to compensate for the ISI effects by designing zero-forcing (ZF) and artificial noise (AN) beamformers. In addition, we formulate optimization problems to maximize the secrecy rate resulting in non-convex and NP-hard problems. Thus, we utilize the particle swarm optimization algorithm for solutions and also convert non-convex optimization problems into easily solvable linear problems. Furthermore, robust ZF and AN beamformers for the worst-case scenario are considered for the cases of ISI and no ISI. The results reveal that ISI severely degrades the secrecy rate; however, well-designed beamformers can reverse the ISI effects.

A traditional array (TA) multiple-input multiple-output (MIMO) architecture in mmWave with hybrid beamforming suffers from high power consumption and hardware overhead. Therefore, a lens antenna subarray (LAS)-MIMO architecture has been recently proposed as a promising technology for a power-efficient system and reducing hardware cost and complexity. Additionally, the LAS-MIMO can offer spectral efficiency (SE) performance close to TA-MIMO and higher than single-lens antenna array (SLA)-MIMO. In this paper, we propose a hybrid precoding algorithm for the LAS-MIMO in mmWave to efficiently control the LAS design. The precoding problem is formulated as a sparse reconstruction problem due to the sparse behavior of mmWave channel. The proposed algorithm is an iterative process developed jointly using artificial bee colony (ABC) optimization with orthogonal matching pursuit (OMP) algorithms. In each iteration, the algorithm first selects the switches for each lens randomly using ABC and then uses OMP to approximate optimal unconstrained precoders. This process continues until achieving maximum SE. The simulation results show that LAS has around a 30% increase in SE compared to SLA while providing a significant gain in energy efficiency (EE) for single radio-frequency (RF) chain and multi RF chain scenarios.

Massive MIMO enabled ultra-dense heterogeneous networks (UDHNs) have been considered as the indispensable and emerging approach to meet the demand on growing data traffic for next generation networks. Although deployment of large number of antennas causes high circuit power consumption in massive MIMO UDHNs, there is always a tradeoff between energy efficiency (EE) and spectral efficiency (SE). Therefore, an energy efficient and spectral efficient user-cell association will become crucial and challenging in massive MIMO UDHNs. In this paper, we address a user-cell association problem for EE and SE tradeoff. To this end, we formulate a convex multi-objective optimization problem (MOP) and convert it into a single-objective optimization problem (SOP) where a priority is assigned for EE and SE with a weighting factor which means the problem can be adjusted whether priority is on EE or SE. The problem aims to maximize the weighted sum of the EE and SE. As a solution, Lagrange duality analysis is performed and a distributed game theoretical user-cell association (GTUCA) algorithm considering the fairness among users is developed. The results confirm that the proposed algorithm outperforms the baseline algorithm, namely maximum rate-based cell selection in terms of EE and SE, when the weighting factor is set properly.

With increasing cell density and the heterogeneity in the network, optimal user-cell association which is a well known open problem, will become an even more challenging issue. Contrary to the current studies that address user-cell association problem for convectional HetNets with massive MIMO deployments in HF (high frequencies) ranges, in this paper we investigate user-cell association problem for dense two-tier networks with massive MIMO deployment both at macro and femto-tier operating in HF and mmWave spectrum, respectively. We evaluate the performance of four user-cell association algorithms for massive MIMO deployment in a two-tier network under two different deployment scenarios: 1) HF-HF (both tiers operating in HF band); 2) HF-mmWave (MBSs operating in HF while FBSs in mmWave bands. To this end, we model the association problem in form of a convex network utility maximization problem as a function of the downlink user throughput. Contrary to the existing load aware association schemes that preclude the effect of bandwidth disparity in HF and mmWave bands, we propose a modified utility function that takes into account the effect of large bandwidth at mmWave bands. The problem is solvable through centralized as well as distributed or user centric load aware user association schemes.