Performance Analysis of a Dynamic Spectrum Assignment Technique for 6G

We consider dynamic Routing and Spectrum Assignment problem in elastic optical networks with multiple fibers per link. The proposed path selection and spectrum management scheme is demonstrated to improve spectrum efficiency.

Novel applications running on top of wireless sensor networks set new QoS requirements, demand devices' seamless connectivity, and call for new communication paradigms such as opportunistic spectrum access. However, realization of opportunistic spectrum access without causing interference to primary users poses significant challenges that are further exacerbated by the power constrained nature of wireless sensors and the QoS requirements of the supported applications. In this work we consider the problem of dynamic spectrum assignment in opportunistic access spectrum bands in the context of cognitive wireless sensor networks. We propose a dynamic spectrum assignment scheme that establishes a virtual link, between wireless sensors, which exhibits minimal power consumption and deterministic capacity. The virtual link is created on top of opportunistic and free access bands and to this end we utilize cognitive radio technology. The proposed DSA scheme is based on a two-stage stochastic integer program with non-fixed recourse. Numerical results verify the effectiveness of the proposed dynamic spectrum assignment scheme.

Elastic optical networks are seen as a promising solution to improve spectrum utilization efficiency by utilizing flex-grid optical orthogonal frequency division multiplexing technology and facilitating flexible bandwidth allocation to services with heterogeneous demands. With dramatic growth of Internet traffic and imminent fiber capacity exhaustion, multiple fibers per link are required to accommodate increasing demands. In this paper, we investigate path selection and spectrum management for heterogeneous bandwidth requests by taking the network topology and expected traffic pattern into account. A novel and efficient solution for path selection and spectrum assignment that optimizes the state of the network after assignment is proposed. We first develop integer linear programming formulations to calculate path selection probabilities offline. Then, we propose a spectrum partition scheme that provides a particular spectrum range for each request size. A next-state-aware spectrum assignment algorithm with resource sharing among partitions is then introduced. We use the demand blocking ratio for dynamically arriving requests as the performance indicator. Simulation results indicate the effectiveness of our proposed schemes for routing and spectrum assignment, as they perform 1 to 2 orders of magnitude better than several baseline schemes.

In a cognitive wireless network, sudden arrival of a primary user could force one or more secondary users to terminate their ongoing communication. This situation leads us to propose a cognitive wireless network which dynamically assigns service rates to the secondary users depending on the available network spectral resource. Our analysis is based on a two-dimensional Markov chain with three state variables. Performance metrics for the secondary users such as dropping probability, blocking probability and completion probability are derived. Our numerical result demonstrates that, depending on the network condition, the proposed dynamic spectrum assignment scheme is capable of reducing the dropping probability substantially and yielding higher throughput via increasing the maximum allowable service rate. Analysis of blocking probability implies that higher value of the maximum allowable service rate may or may not make the network more selective in terms of admitting new secondary users.

This paper considers distance adaptive dynamic routing and spectrum assignment (RSA) for elastic optical networks with shared backup path protection (SBPP). Efficient heuristic algorithms based on spectrum window planes are proposed for implementing distance and modulation format adaptive RSA so as to maximize spare capacity sharing among multiple protection lightpaths. A differentiated sharable frequency slot cost was also defined for the first time to more efficiently share protection resource. Network performance is evaluated in terms of bandwidth blocking probability (BBP) through simulations. The proposed SBPP technique is effective in reducing BBP and improving spectral efficiency compared to conventional SBPP schemes and 1+1 path protection. The impact of transponder tunability on bandwidth blocking performance is also evaluated to show that a limited tuning range is sufficient to achieve a BBP performance close to that with full tunability.

The IEEE standard 1900.4 “Architectural Building Blocks Enabling Network-Device Distributed Decision Making for Optimized Radio Resource Usage in Heterogeneous Wireless Access Networks” was published in February 2009. It considers three use cases: distributed radio resource usage optimization, dynamic spectrum assignment, and dynamic spectrum sharing. In the dynamic spectrum sharing use case, several radio systems share the same frequency band. One example of this use case is when several radio systems operate in unlicensed spectrum. Another example is when a secondary radio system operates in white space of a TV broadcast operator frequency band. The last example starts to attract a lot of attention recently including a lot of standardization activities. The IEEE draft standard P1900.4a for “Architecture and interfaces for dynamic spectrum access networks in white space frequency bands” is one of such activities. P1900.4a uses IEEE standard 1900.4 as a baseline. It defines additional entities and interfaces to enable efficient operation of white space wireless systems. This paper gives detailed description of P1900.4a system. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Also, it shows experimental results that our prototype system conforming to the draft standard can detect the white space and reconfigure itself to operate on it.

Research into cutting-edge systems that can satisfy the rising needs for greater data rate and increased reliability is becoming more and more necessary as wireless communication technologies continue to evolve. To enhance the spectral and energy efficiencies, this study examine the performance of massive multiple input multiple output (mMIMO) communication systems using wavelet-order orthogonal frequency division multiplexing (W-OFDM). Also, a communication system was created based on W-OFDM Massive MIMO using MATLAB to model its operation. The simulated system's various stages of operation was examined through detailed analysis and its performance was compare with prior studies and practical networks on OFDM. Key performance metrics, specifically the data throughput, was evaluated to assess the effectiveness and viability of the system. The simulation results indicates that W-OFDM can achieve up to 35% reduction in peak-to-average-power-ratio (PAPR) compared to the traditional OFDM, with a 20% improvement in BER performance under Rayleigh fading conditions. Also, the spectral efficiency of W-OFDM mMIMO outperforms convectional OFDM by 15%, making it a viable technology for future communication networks requiring high data rates and reliability. This implies that, the analytical evaluation confirms that W-OFDM mMIMO provides substantial benefits for improving energy and spectral efficiencies in wireless communication systems, addressing the challenges of increased user demands for high spectrum. This study contributes to the understanding of MIMO systems' performance optimization, highlighting the importance of antenna configuration and cell distance. Comprehensive analysis of spectral efficiency and energy efficiency trade-offs in MIMO systems and identification of optimal antenna configuration for maximum energy efficiency, addressing some of the challenges of future wireless communication systems.

In this paper the process of spectrum assignment in narrowband power line communication is studied. Due to the frequency-selective and time-variant nature of the PLC channel, spectrum assignment must be performed dynamically throughout the transmission. This requires constant channel state information at the PLC transmitter which in turn increases undesired overhead in the system and degrades throughput. An adaptive pursuit strategy has been proposed in order to overcome this problem by enabling the transmitter through a reinforcement learning method to select the best part of the spectrum dynamically and without prior knowledge of the channel. Simulation results show a fast adaptation to the channel conditions which results in an increase in spectral efficiency and improves the performance of the transmission.

With the ubiquitous data-rate demand in cellular networks, total power consumption is growing rapidly. Thus, it is essential to design the future networks with energy and spectral efficient strategies. Using heterogeneous networks (HetNets) is one possible way of improving spectral and energy efficiencies in cellular networks. In this paper, under coverage ratio and area spectral efficiency constraints, we consider deploying strategies with low power small cells in order to improve the spectral and energy efficiencies of the conventional macrocell only cellular network. To compare the efficiencies of different scenarios, we employ area spectral efficiency and power consumption per bit criterion. As a result, we observe that deploying microcells increases both energy and spectral efficiencies.

Energy efficiency is a basic need of the fifth-generation (5G) wireless networks, because the energy sources and capability of the battery are very narrow against the huge demand. Massive MIMO is a great invention of 5G cellular network with a large number of antennas. In this research, the prime aim is to maximize the energy efficiency and spectral efficiency simultaneously of massive MIMO system. Spectral and energy efficiency are formulated with fixed power, number of users, number of antennas with the impact of multiple users and without the impact of multiple users, the ratio of number of antennas and number of users. Trade-off also has been analyzed among the co-located, distributed and hybrid massive MIMO with the Nakagami-m fading channel, which can subtract the co-channel interference in a system and compared with each other. From the simulation results, it is seen that the energy efficiency of distributed massive MIMO is higher than the other two with the variation of fixed power, number of user and the ratio of number of antenna and number of user with respect to spectral efficiency. The energy efficiency of co-located massive MIMO is higher than the other two with the variation of number of antenna.

In heterogeneous networks (HetNets), small cells are overlaid within a large cell in order to increase capacity and coverage beyond the initial deployment of a large cell. Previous studies assumed that small cells are deployed only in the cell edge area of a large cell to support low-SINR users in that area. However, in a future communication environment, we need more small-cells to support much higher user data rates. We propose a spectral-, energy-, and cost-efficient small cell deployment scheme for maximizing the following spectrum, energy, and cost related metrics: spectral efficiency, spectral and energy efficiency, and deployment cost efficiency. We find the optimal values of these metrics for varying the number of small cells and the transmit power of small cells.

In fifth-generation cellular networks (5G), a large multiple-input multiple-output (MIMO) technique is critical for optimizing the trade-off between energy efficiency (EE) and spectral efficiency (SE). The challenges for the next generation depend on increasing the high data traffic in the wireless communication system for both EE and SE. The trade-off between EE and SE in downlink massive MIMO systems has been investigated in this paper using the first derivative of transmit antennas and transmit power. The EE-SE trade-off has been investigated using a multi-objective optimization problem to reduce transmit power. The EE and SE based on constraint maximum transmits power allocation and a number of antennas by computing the first derivative of transmit power to maximize the trade-off EE - SE has been improved. The optimal trade-off between EE and SE can be obtained based on the first derivative by selecting the optimal antennas with a low cost of transmit power showing good simulation results. Therefore, based on an optimal optimization problem is flexible to make trade-offs between EE-SE for distinct preferences.

Cell discontinuous transmission (DTX) has been proposed as a solution to reduce the energy consumption of cellular networks. This paper investigates the impact of network traffic load on the spectral and energy efficiency of cellular networks with DTX. The signal-to-interference-plus-noise ratio (SINR) distribution as a function of traffic load is derived first. Then, the sufficient condition for ignoring thermal noise and simplifying the SINR distribution is investigated. Based on the simplified SINR distribution, the network spectral and energy efficiency as functions of network traffic load are derived. It is shown that the network spectral efficiency increases monotonically in traffic load, while the optimal network energy efficiency depends on the ratio of the sleep-mode power consumption to the active-mode power consumption of base stations. If the ratio is larger than a certain threshold, the network energy efficiency increases monotonically with network traffic load and is maximized when the network is fully loaded. Otherwise, the network energy efficiency first increases and then decreases in network traffic load. The optimal load can be identified with a binary search algorithm. The power ratio threshold depends solely on the path loss exponent α, e.g., 56% for α = 4. All these analytic results are further validated by the numerical simulations.

10 - Improving energy efficiency in next-generation IoT networks with beam adjustment technique

Non-orthogonal multiple access (NOMA) deployment in future wireless networks has been recently considered a promising radio access technology to enhance spectral efficiency (SE). However, gain in SE comes always with the cost of energy efficiency (EE). In this paper, we investigate the SE and EE tradeoff in downlink NOMA with the consideration of quality of service (QoS) requirements based on three population-based multi-objective evolutionary algorithms (MOEAs): multi-objective particle swarm optimization (MOPSO), non-dominated sorting genetic algorithm-II (NSGA-II) and strength Pareto evolutionary algorithm-2 (SPEA2). The tradeoff is optimized and Pareto optimal solutions are obtained through MOEAs. The effectiveness of the algorithms is evaluated based on the hypervolume metric and the capability of solving multi-objective optimization problems. Simulation results reveal that SPEA2 outperforms NSGA-II and MOPSO. Furthermore, NSGA-II is the loser among all algorithms in terms of finding Pareto optimal results.