Effective area spectral efficiency for wireless communication networks with interference management

Abstract

In this paper, we introduce a new metric, namely, effective area spectral efficiency (EASE), to quantify the spectral efficiency as well as the spatial properties of point-to-point transmission systems and decode and forward (DF) relaying communications networks with interference management. For each transmission mode, we derive a closed-form expression for the maximum transmission range under Rayleigh fading environment. Based on the maximum transmission range, we define and derive the average affected area and the average ergodic capacity. We then introduce the EASE expression to quantify the spatial spectral utilization efficiency. For DF relaying, the EASE metric is based on a newly introduced index, namely, source relay communication index (SRCndx). SRCndx is used to validate the communication possibility between a source and a relay for given transmission parameters in a given environment and provides information about the necessity of using relaying communications. Through mathematical analysis and numerical examples, we show that the EASE metric provides a new perspective on the design of wireless transmissions, especially the transmission power selection process.

Evolutionary Approach to jointly resolve the Power and the Capacity Optimization problems in the multi-user OFDMA Systems

“…We hope that this approach will give interesting results when applied to the case of multi-cell system and in radio cognitive systems. In future work, the integration of new metric of QoS measurement of cellular systems such as an effective spectral efficiency area [9], will constitute the basis of a new development in order to achieve a more effective resolution of the resources allocation problem in radio mobile systems.…”

On the Application of Massive MIMO Systems to Machine Type Communications

“…The sentiment shared by most researchers nowadays is that the foreseen increase in data rate will be achieved by combined gains [32] provided by (i) increasing the network density, i.e., the addition of more radio sites with smaller cell coverage areas to the same region (extreme network densification), which consequently improves the area spectral efficiency [33] (ii) increasing spectrum availability such as the introduction of new spectrum bands like mmWaves [34], [35], (iii) improving the use of licensed, unlicensed and licensed-shared spectrum bands [36] with more efficient and intelligent sharing techniques, (iv) and increasing spectral efficiency of digital communications systems through advances in MIMO techniques. One of the benefits resulting directly from the powerful processing gains provided by the use of large arrays of antennas (i.e., massive MIMO systems) is that the majority of the physical layer signal processing and and resource allocation (i.e., scheduling) issues are simplified, if not solved, which is clearly not the case for systems employing only a moderate to small number of antennas [37].…”

On the Application of Massive MIMO Systems to Machine Type Communications

“…The sentiment shared by most researchers nowadays is that the foreseen increase in data rate will be achieved by combined gains [32] provided by (i) increasing the network density, i.e., the addition of more radio sites with smaller cell coverage areas to the same region (extreme network densification), which consequently improves the area spectral efficiency [33] (ii) increasing spectrum availability such as the introduction of new spectrum bands like mmWaves [34], [35], (iii) improving the use of licensed, unlicensed and licensedshared spectrum bands [36] with more efficient and intelligent sharing techniques, (iv) and increasing spectral efficiency of digital communications systems through advances in MIMO techniques. One of the benefits resulting directly from the powerful processing gains provided by the use of large arrays of antennas (i.e., massive MIMO systems) is that the majority of the physical layer signal processing and and resource allocation (i.e., scheduling) issues are simplified, if not solved, which is clearly not the case for systems employing only a moderate to small number of antennas [37].…”