Probabilistic small-cell caching: performance analysis and optimization

Small-cell caching utilizes the embedded storage of small-cell base stations (SBSs) to store popular contents, for the sake of reducing duplicated content transmissions in networks and for offloading the data traffic from macro-cell base stations to SBSs. In this paper, we study a probabilistic small-cell caching strategy, where each SBS caches a subset of contents with a specific caching probability. We consider two kinds of network architectures: 1) the SBSs are always active, which is referred to as the always-on architecture, 2) the SBSs are activated on demand by mobile users (MUs), referred to as the dynamic on-off architecture. We focus our attention on the probability that MUs can successfully download contents from the storage of SBSs. First, we derive theoretical results of this successful download probability (SDP) using stochastic geometry theory. Then, we investigate the impact of the SBS parameters, such as the transmission power and deployment intensity on the SDP. Furthermore, we optimize the caching probabilities by maximizing the SDP based on our stochastic geometry analysis. The intrinsic amalgamation of optimization theory and stochastic geometry based analysis leads to our optimal caching strategy characterized by the resultant closed-form expressions. Our results show that in the always-on architecture, the optimal caching probabilities solely depend on the content request probabilities, while in the dynamic on-off architecture, they also relate to the MU-to-SBS intensity ratio. Interestingly, in both architectures, the optimal caching probabilities are linear functions of the square root of the content request probabilities. Monte-Carlo simulations validate our theoretical analysis and show that the proposed schemes relying on the optimal caching probabilities are capable of achieving substantial SDP improvement compared to the benchmark schemes

The Impact of Antenna Height Difference on the Performance of Downlink Cellular Networks

Capable of significantly reducing cell size and enhancing spatial reuse, network densification is shown to be one of the most dominant approaches to expand network capacity. Due to the scarcity of available spectrum resources, nevertheless, the over-deployment of network infrastructures, e.g., cellular base stations (BSs), would strengthen the inter-cell interference as well, thus in turn deteriorating the system performance. On this account, we investigate the performance of downlink cellular networks in terms of user coverage probability (CP) and network spatial throughput (ST), aiming to shed light on the limitation of network densification. Notably, it is shown that both CP and ST would be degraded and even diminish to be zero when BS density is sufficiently large, provided that practical antenna height difference (AHD) between BSs and users is involved to characterize pathloss. Moreover, the results also reveal that the increase of network ST is at the expense of the degradation of CP. Therefore, to balance the tradeoff between user and network performance, we further study the critical density, under which ST could be maximized under the CP constraint. Through a special case study, it follows that the critical density is inversely proportional to the square of AHD. The results in this work could provide helpful guideline towards the application of network densification in the next-generation wireless networks.Comment: conference submission - Mar. 201

Energy and spectral efficiency of multi-tier LiFi networks

In this paper, multi-tier LiFi networks are studied in terms of energy efficiency (EE) and spectral efficiency (SE), which are crucial metrics for LiFi system design. We derived a closed-form expression of the user association probability for different tiers using stochastic geometry based Poisson Voronoi Tessellation (PVT) LiFi network. The performance metrics of the network, EE and SE, are analyzed in terms of different parameters such as transmit power and Lambertian index. Performance evaluations and numerical results show that multi-tier LiFi networks have an optimum transmit power in which EE is maximized. Besides, increasing the transmit power does not increase SE after passing a threshold point. The resulting trade-off between EE and SE is presented

Secrecy Spectrum and Energy Efficiency Analysis in Massive MIMO-enabled Multi-Tier Hybrid HetNets

Massive multiple antenna systems in conjunction with millimeter (mmWave) communication have gained tremendous attention in the recent years owing to their high speed data delivery. However, security in these networks has been overlooked; thereby necessitating a comprehensive study. This paper analyzes the physical layer security performance of the downlink of a massive multiple-input multiple-output (MIMO)-based hybrid heterogeneous network (HetNet) where both mmWave and sub-6 GHz small cells coexist. Specifically, a tractable approach using stochastic geometry is proposed to analyze the secrecy outage probability, secrecy energy efficiency (SEE) and secrecy spectrum efficiency (SSE) of the hybrid HetNets. Our study further characterizes the impact of large antenna arrays, directional beamforming gains, transmit power, and cell density on the above mentioned secrecy performance measures. The results show that at low transmit power operation, the secrecy performance enhances for higher small cell density. It has also been observed that the higher directivity gains at mmWave cells lead to a drop in secrecy performance of the network; thus a tradeoff exists between better coverage or secrecy

Energy-efficient non-orthogonal multiple access for wireless communication system

Non-orthogonal multiple access (NOMA) has been recognized as a potential solution for enhancing the throughput of next-generation wireless communications. NOMA is a potential option for 5G networks due to its superiority in providing better spectrum efficiency (SE) compared to orthogonal multiple access (OMA). From the perspective of green communication, energy efficiency (EE) has become a new performance indicator. A systematic literature review is conducted to investigate the available energy efficient approach researchers have employed in NOMA. We identified 19 subcategories related to EE in NOMA out of 108 publications where 92 publications are from the IEEE website. To help the reader comprehend, a summary for each category is explained and elaborated in detail. From the literature review, it had been observed that NOMA can enhance the EE of wireless communication systems. At the end of this survey, future research particularly in machine learning algorithms such as reinforcement learning (RL) and deep reinforcement learning (DRL) for NOMA are also discussed