The standardization activities of the fifth generation communications are clearly over and deployment has commenced globally. To sustain the competitive edge of wireless networks, industrial and academia synergy have begun to conceptualize the next generation of wireless communication systems (namely, sixth generation, (6G)) aimed at laying the foundation for the stratification of the communication needs of the 2030s. In support of this vision, this study highlights the most promising lines of research from the recent literature in common directions for the 6G project. Its core contribution involves exploring the critical issues and key potential features of 6G communications, including: (i) vision and key features; (ii) challenges and potential solutions; and (iii) research activities. These controversial research topics were profoundly examined in relation to the motivation of their various sub-domains to achieve a precise, concrete, and concise conclusion. Thus, this article will contribute significantly to opening new horizons for future research directions.

Recently, cellular networks’ energy efficiency has garnered research interest from academia and industry because of its considerable economic and ecological effects in the near future. This study proposes an approach to cooperation between the Long-Term Evolution (LTE) and next-generation wireless networks. The fifth-generation (5G) wireless network aims to negotiate a trade-off between wireless network performance (sustaining the demand for high speed packet rates during busy traffic periods) and energy efficiency (EE) by alternating 5G base stations’ (BSs) switching off/on based on the traffic instantaneous load condition and, at the same time, guaranteeing network coverage for mobile subscribers by the remaining active LTE BSs. The particle swarm optimization (PSO) algorithm was used to determine the optimum criteria of the active LTE BSs (transmission power, total antenna gain, spectrum/channel bandwidth, and signal-to-interference-noise ratio) that achieves maximum coverage for the entire area during the switch-off session of 5G BSs. Simulation results indicate that the energy savings can reach 3.52 kW per day, with a maximum data rate of up to 22.4 Gbps at peak traffic hours and 80.64 Mbps during a 5G BS switched-off session along with guaranteed full coverage over the entire region by the remaining active LTE BSs.

Television white space (TVWS) technology is approaching the potential roll-out phase for commercial deployment, supported by recent pilot projects being conducted globally. Undeniably, TVWS technology is faced with daunting challenges that require attention. To enable an ecosystem in which TVWS technology can flourish, there is a need for a complete analysis of the challenges, trends and future research direction related to this technology. Database-assisted TVWS technology is market driven, geared toward the spectrum reuse paradigm, and faces fewer technical hurdles. Our goal in this paper is to present a tutorial review of the challenges related to database-assisted TVWS networks using the SLEPT (social, legal, economic, political, and technological) analysis framework. The SLEPT framework is a management model that is extensively used for quantitative analysis. A brief review of TVWS technology using the SLEPT model reveals that the technology has been socially accepted, legal challenges are evident in some countries, economic models are the way forward and are main focus of current research trends, TVWS technology cannot be implemented without political will emanating from spectrum reforms, and there are many coexistence-motivated technological issues confronting TVWS technology. In summary, this paper provides an up-to-date survey on TVWS and presents current trends and future research directions in the TVWS context.

Spectrum trading is an important aspect of television white space (TVWS) and it is driven by the failure of spectrum sensing techniques. In spectrum trading, the primary users lease their unoccupied spectrum to the secondary users for a market fee. Although spectrum trading is considered as a reliable approach, it is confronted with a spectrum transaction completion time problem, which negatively impacts on end-users Quality of Service and Quality of Experience metrics. Spectrum transaction completion time is the duration to successfully conduct TVWS spectrum trading. To address this issue, this paper proposes simple mechanism auction reward truthful (SMART), a fast and iterative machine learning-assisted spectrum trading model to address this issue. Simulated results indicate that SMART out-performs referenced VERUM algorithm in three key performance indicators: bit-error rate, instantaneous throughput, and probability of dropped packets by 10%, 5%, and 15%, respectively.

The IEEE 802.22 Standard targets rural and sparsely populated regions exploiting Television white space (TVWS) technology. In these regions, there are fewer mobile users per density and end-users traffic is light. Hence, there is a need to adopt traffic aware algorithm leveraging on the end-users nonuniform traffic attributes and in essence, promotes spectrum efficiency in TVWS spectrum management regime. This paper investigates mechanism to encourage spectrum sharing during low end-user traffic regime motivated by financial inducement. Since incumbent coexistence has been achieved using market models, it is tractable to apply market assisted spectrum sharing models to address self-coexistence issues in TVWS networks. The purpose of this work is to use the market model to promote self-coexistence in TVWS networks in the uplink self-frequency reuse (USFR). Towards this goal, this paper proposes D-GRACE: Discounted spectrum price Game-based Resource Allocation in a Competitive Environment. Specifically, D-GRACE is a transmit power reduction strategy motivated by financial incentives during light TVWS end-users traffic. When compared to existing non-market inspired TVWS self-coexistence resource allocation algorithm under the same scenario, D-GRACE exhibited superior power saving of about 20 % and converged after five iterations.