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User circuit consumed power against distance from the BS for bit rate 920 kbps. Performance comparison when spatial multiplexing is implemented.
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In this paper, we consider a distributed virtual multiple-input multiple-output (MIMO) coalition formation algorithm. Energy savings are obtained in the reverse link by forming multi-antenna virtual arrays for information transmission. Virtual arrays are formed by finding a stable match between two sets of single antenna devices such as mobile stat...
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Citations
... Nevertheless, a way to cope with this issue is through MIMO systems. WSN may take advantage of spatial diversity only if they work as a group, this concept is called virtual MIMO link [10]- [12]. A cluster sensor is organized in a cooperative way by using routing tables, where a leader or leader nodes are selected to transmit with other cluster-heads or gateways, creating a system with virtual diversity [13]. ...
Multiple-input Multiple-output (MIMO) systems are an attractive choice to enhance the communication performance by increasing the number of antennas at transmitter and receiver. However, the physical implementation of MIMO systems has several issues such as high cost and high computational complexity for large antenna arrays. Additionally, due to space limitations, huge MIMO systems cannot be implemented in small devices. Hence, antenna selection techniques emerge as a solution to maintain the benefits of MIMO with an affordable trade-off between complexity and implementation cost. Besides, cooperative wireless sensor networks (WSN) may consider virtual MIMO systems. Thus, in this paper, we propose an antenna selection algorithm called Antenna Adaptive Selection of Information (ASSI) for being implemented on WSN. AASI adapts the transmission information according to channel characteristics. Our algorithm achieves affordable results in performance and capacity close to the optimal antenna selection for a specific signal-to-noise ratio with a reduced computational complexity.
... The work of E. Jorswieck One canonical tool for distributed resource allocation is game theory. Distributed resource allocation by means of non-cooperative game theory is studied in [9], [10], whereas [11], and [12]- [14] employ coalitional games and bargaining theory, respectively. Overviews of game-theoretic approaches for distributed resource allocation are provided in [15]- [17]. ...
... As for E busy , we set P tone = P max and T tone = 5 µs. 11 Then, for the distributed Algorithms 2 and 3, we assumed feedback slots of equal duration, i.e. T max,n,i = T o /2I, for all i, n. ...
... 3are rarely encountered in the uplink of cellular networks. Moreover, it should be stressed that these results have been derived by setting P tone = P max , which is clearly a worst-case scenario for the distributed methods, in particular for the auction-based approach, which requires more rounds than Algorithm 3. In11 This is the length of an OFDM symbol in the IEEE 802.11 standard (4 µs) plus 1 µs as guard interval12 The lower-bound L has been obtained by randomly selecting a feasible point, whereas the upper-bound U has been obtained by considering the ideal scenario in which each user transmits on its best subcarrier, without suffering any multiuser interference. ...
This work deals with the problem of distributed resource allocation in multiple-input multiple-output (MIMO) multi-carrier (MC) multiple-access channel (MAC) networks. The assignment between users and subcarriers is allocated together with the users’ transmit powers for energy efficiency maximization, by means of a novel approach which merges the popular Dinkelbach’s algorithm with the frameworks of distributed auction theory and stable matching. Two distributed algorithms are presented, which can be implemented in a fully decentralized way. The former is guaranteed to converge to the global optimum of the system energy efficiency, up to a threshold which can be set in advance, while the latter enjoys weaker optimality properties, but has an even lower computational complexity. Additionally, we develop a novel energy consumption model which explicitly accounts for the energy consumption due to feedback transmissions. Employing this new model, it is shown that the proposed distributed algorithms can even outperform centralized resource allocations which require a larger feedback energy consumption.
A potential way to handle the future requirements of wireless data traffic is the Massive Multiple Input Multiple Output (MIMO) antenna systems. The most effective method to satisfy the demand for wireless data traffic is to enhance the Spectral Efficiency of the existing spectrum since the wireless spectrum is a limited resource. In the MIMO network, cell-free, energy-efficient, and user-centric are considered as most important parameters to achieve effective communication. Therefore, a new energy efficiency optimization scheme is developed in a massive MIMO system to improve the system’s capacity and spectral efficiency. The multi-channel optimization problem is effectively rectified with the help of this newly designed energy efficiency optimization scheme. Here, the “Singular Value Decomposition (SVD)” method is utilized for the implementation of a sub-channel grouping scheme, where the sub-channels are arranged in descending order based on the results attained from SVD. After arranging the sub-channels, the sub-channel grouping is carried out, and then the energy efficiency optimization is provided with the help of Integrated Fruit Fly with Salp Swarm Optimization (IFFSSO). This energy-efficient algorithm improves the system capacity and spectral efficiency. The experimental outcome is revealed through various conventional models to ensure the energy efficiency of the recommended model.
Energy consumption has become a major research topic from both environmental and economical
perspectives. The telecommunications industry is currently responsible for 0.7% of the
total global carbon emissions, a figure which is increasing at rapid rate. By 2020, it is desired
that CO2 emissions can be reduced by 50%. Thus, reducing the energy consumption in order
to lower carbon emissions and operational expenses has become a major design constraint for
future communication systems. Therefore, in this thesis energy efficient resource allocation
methods have been studied taking the Long Term Evolution (LTE) standard as an example.
Firstly, a theoretical analysis, that shows how improvements in energy efficiency can directly
be related with improvements in fairness, is provided using a Shannon theory analysis. The
traditional uplink power control challenge is re-evaluated and investigated from the view point
of interference mitigation rather than power minimization. Thus, a low complexity distributed
resource allocation scheme for reducing the uplink co-channel interference (CCI) is presented.
Improvements in energy efficiency are obtained by controlling the level of CCI affecting vulnerable
mobile stations (MSs). This is done with a combined scheduler and a two layer power
allocation scheme, which is based on non-cooperative game theory. Simulation results show
that the proposed low complexity method provides similar performance in terms of fairness
and energy efficiency when compared to a centralized signal interference noise ratio balancing
scheme.
Apart from using interference management techniques, by using efficiently the spare resources
in the system such as bandwidth and available infrastructure, the energy expenditure in wireless
networks can also be reduced. For example, during low network load periods spare resource
blocks (RBs) can be allocated to mobile users for transmission in the uplink. Thereby, the user
rate demands are split among its allocated RBs in order to transmit in each of them by using
a simpler and more energy efficient modulation scheme. In addition, virtual Multiple-input
Multiple-output (MIMO) coalitions can be formed by allowing single antenna MSs and available
relay stations to cooperate between each other to obtain power savings by implementing
the concepts of spatial multiplexing and spatial diversity. Resource block allocation and virtual
MIMO coalition formation are modeled by a game theoretic approach derived from two
different concepts of stable marriage with incomplete lists (SMI) and the college admission
framework (CAF) respectively. These distributed approaches focus on optimizing the overall
consumed power of the single antenna devices rather than on the transmitted power. Moreover,
it is shown that when overall power consumption is optimized the energy efficiency of the users
experiencing good propagation conditions in the uplink is not always improved by transmitting
in more than one RB or by forming a virtual MIMO link. Finally, it is shown that the proposed
distributed schemes achieve a similar performance in bits per Joule when compared to much
more complex centralized resource allocation methods.
