Joint Transmit Antennas for Energy Efficiency in Downlink Massive MIMO Systems

Massive multiple-input-multiple-output (MIMO) systems are an exciting area of fifth-generation (5G) technology and very important in maximizing energy efficiency (EE) and saving battery technology.  Obtaining energy efficiency without sacrificing the quality of service (QoS) has become increasingly important for mobile devices. In this paper, we investigate the maximal EE for downlink massive MIMO systems using zero-forcing beamforming (ZFBF), dependent on the number of antenna elements and the optimal number of users inside the cell to optimize the transmit power. The linear precoding ZFBF is able to mitigate interbeam interference, in addition to noise, due to expanding the reception at low  power transmission.  The simulation results reveal that the maximal energy efficiency  can be obtained dependent on increasing the number of antennas M and choosing the  , where the number of antennas is greater than the critical number of antennas   , which minimizes the received interference due to increased transmit power

Network efficiency enhancement by reactive channel state based allocation scheme

Now a day the large MIMO has considered as the efficient approach to improve the spectral and energy efficiency at WMN. However, the PC is a big issue that caused by reusing similar pilot sequence at cells, which also restrict the performance of massive MIMO network. Here, we give the alternative answer, where each of UEs required allotting a channel sequences before passing the payload data, so as to avoid the channel collision of inter-cell. Our proposed protocol will ready to determine the channel collisions in distributed and scalable process, however giving unique properties of the large MIMO channels. Here we have proposed a RCSA (Reactive channel state based allocation) scheme, which will very productively work with the RAP blockers at large network of MIMO. The position of time-frequency of RAP blocks is modified in the middle of the adjacent cells, because of this design decision the RAP defend from the hardest types of interference at inter-cell. Further, to validate the performance of our proposed scheme it will be compared with other existing technique

Multipair Two-Way DF Relaying with Cell-Free Massive MIMO

We consider a two-way half-duplex decode-and-forward (DF) relaying system with multiple pairs of single-antenna users assisted by a cell-free (CF) massive multiple-input multiple-output (mMIMO) architecture with multiple-antenna access points (APs). Under the practical constraint of imperfect channel state information (CSI), we derive the achievable sum spectral efficiency (SE) for a finite number of APs with maximum ratio (MR) linear processing for both reception and transmission in closed-form. Notably, the proposed CF mMIMO relaying architecture, exploiting the spatial diversity, and providing better coverage, outperforms the conventional collocated mMIMO deployment. Moreover, we shed light on the power-scaling laws maintaining a specific SE as the number of APs grows. A thorough examination of the interplay between the transmit powers per pilot symbol and user/APs takes place, and useful conclusions are extracted. Finally, differently to the common approach for power control in CF mMIMO systems, we design a power allocation scheme maximizing the sum SE.Comment: 15 pages, 8 figures, This work was accepted in IEEE Trans. Green Commun. Net. Copyright may be transferred without notice, after which this version may no longer be accessibl

Low-complexity antenna selection techniques for massive MIMO systems

PhD ThesisMassive Multiple-Input Multiple-Output (M-MIMO) is a state of the art technology in wireless communications, where hundreds of antennas are exploited at the base station (BS) to serve a much smaller number of users. Employing large antenna arrays can improve the performance dramatically in terms of the achievable rates and radiated energy, however, it comes at the price of increased cost, complexity, and power consumption. To reduce the hardware complexity and cost, while maintaining the advantages of M-MIMO, antenna selection (AS) techniques can be applied where only a subset of the available antennas at the BS are selected. Optimal AS can be obtained through exhaustive search, which is suitable for conventional MIMO systems, but is prohibited for systems with hundreds of antennas due to its enormous computational complexity. Therefore, this thesis address the problem of designing low complexity AS algorithms for multi-user (MU) M-MIMO systems. In chapter 3, different evolutionary algorithms including bio-inspired, quantuminspired, and heuristic methods are applied for AS in uplink MU M-MIMO systems. It was demonstrated that quantum-inspired and heuristic methods outperform the bio-inspired techniques in terms of both complexity and performance. In chapter 4, a downlink MU M-MIMO scenario is considered with Matched Filter (MF) precoding. Two novel AS algorithms are proposed where the antennas are selected without any vector multiplications, which resulted in a dramatic complexity reduction. The proposed algorithms outperform the case where all antennas are activated, in terms of both energy and spectral efficiencies. In chapter 5, three AS algorithms are designed and utilized to enhance the performance of cell-edge users, alongside Max-Min power allocation control. The algorithms aim to either maximize the channel gain, or minimize the interference for the worst-case user only. The proposed methods in this thesis are compared with other low complexity AS schemes and showed a great performance-complexity trade-off