Multi-User Resource Allocation Techniques

Question: Discuss about the Design of Multi-User Resource Allocation Techniques for Uplink of LTE-Advanced. Answer: Evolution of LTE-Advanced Systems Scope Communication is one of the vast changing and demanding part of science. The evaluation of the fourth generation mobile communication system aimed at performance and efficiency in the high mobility environment. LTE-Advanced is considered as one of the 4G technologies. LTE advanced technology fulfill the service demands of multiuser heterogeneous traffic environments for high data rate applications. Optimal operations of 4G technology can be achieved by intelligent implementation of the radio resource management layer. LTE systems can be scheduled at three different levels packet level, admission level and class level [1]. Both LTE and LTE advanced technology take the advantage of Orthogonal Frequency Division Multiple Accesses (OFDMA). OFDMA is a multi carrier scheme as it allocates resources to multiple users. OFDM technique is used by OFDMA while SC-FMDA technique is recommended as the 3GPP LTE transmission technique. SC-FMDA is a modified technique as compared to the OFDMA while it almost has the same complexity level as OFDMA has. Basic scheduling and resource allocation is the set of users that determine RB users and set transmit power on each RB. To avoid interference, single user is assigned at single RB. For the purpose of transmission, maximum amount of power is always available at each base station and mobile user [2] [3]. Multi user downlink: Vast theoretical and experimental research has been done on multi user downlink that stated that multiplexing gains are available in downlink multi-user MIMO. Transmit beam forming technique is used to distinguish the user channels and serve multiple users simultaneously [4]. Multi user uplink: A very limited work has been performed on distributed multi-user uplink WLANs. Till yet distributed multi-user uplink are neither standardized nor commercialized for WLANs. Sequential contention is used to enable multiplexing by the Existing WLAN uplink multi-user solutions. The limitation of sequential contention is that it incurs control overhead which ultimately increases the linearly with group size [5]. Requirements LTE systems include cell capacity, latency, mobility, peak data rate, flexibility of the spectrum, provided and extended. The peak data rate must be 100 mbps in downlink and 50 mbps in uplink and bandwidth should be within 20 MHz. The cell capacity must support 200 active users within a bandwidth of 5 Mhz. Latency must be 5ms in user plane and mobility must be optimized to support 0-15 km/h. Spectrum flexibility must be between 1.25 and 20 MHz. Key elements in the LTE systems are wider bandwidth upto 20 MHz, flat IP architecture that supports direct communication between base stations, MIMO supports multiple data transmission and reception and it also increases the signal quality and data reception. The current spectrum flexibility of LTE is between 1.25 and 2.5 MHz. the mode of operation are FDD and TDD. Current peak data rate for downlink is 326.4 mbps and for uplink is 86.4 mbps [6] [7]. A couple of crucial requirements were proposed by Young Yong Kim and it's associated in his paper for the novel and energy efficient internet of Things (IOT) communication scheme. They stated that a large number of communication devices must be supported with low power consumption ability [8]. Critical Review of LTE and LTE Advanced LTE stands for Long Term Evolution network. The main approach to develop the LTE Advanced is to facilitate the fourth generation cellular wireless systems of the mobile services. The most promising techniques used in the current scenario is Device - to- device (D2D) communications as it improve the spectrum utilization and also provides wireless peer to peer services in the LTE advanced. Initially to communicate between user equipments (UEs) one has to establish both Uplink and Downlink communication in the mobile mode passing through the base station but now D2D communication technique facilitate one user equipments to directly converse with the other user equipment by reuse of cellular resources. Basically two major challenges are faced by the users while enabling D2D communication technique in a cellular network. The first challenge is related to the quality of service of the D2D communication technique as minimum quality of service is needed to be guaranteed. The second challenge is related to the performance of the cellular devices as it is affected by the obstruction in the D2D devices caused by the cellular users. To address the above defined challenges ajoint scheme of block scheduling and power control scheme has been introduced forD2D communications in LTE-Advanced networks. This scheme addresses the above challenges and also maximizes the spectrum utilization [9]. Literature Review In past years mobile industry has experienced a tremendous growth of subscribers. Growth in number of subscribers has increased demand for the advancement of the current mobile networks. Increase in the number of subscribers has delayed the speed and restricted the bandwidth of various applications such as web browsing, video streaming, video chat, and voice over IP (VoIP) etc. Eventually LTE network has come as the most trusted methods to overcome these problems [10]. LTE is recognized by the Third Generation Partnership Project (3GPP) with an aim to lower down the latency, deliver high throughout and enhance spectral efficiency with respect to previous 3G networks. 3GPP mobile networks changes shifted the use of Code Division Multiple Access (CDMA) to OFDMA. OFDMA is a powerful way to resolve the ISI problems as it simplifies the channel equalizers. The concept of resource allocation in LTE networks is significant to understand as it includes algorithm for both uplink and downlink scheduling. The first algorithm was designed to work in the downlink. For real time application in LTE networks, downlink algorithm is scheduled in two stages. In the first stage, digital filter theory based on resource allocation mechanism is presented. This is called as moving average scheduler (MAS). This moving average scheduler defines sampling time, amount of data for each real stream to fulfill the requirements of the quality of service of each stream. The second stage is related to the allocation of resources. This stage uses a professional fairness (PF) scheduler. The second algorithm is related to the uplink work and it is dependent on the Genetic Algorithms (GA). This tool is designed especially for handling the complex optimization problems. Genetic Algorithms (GA) is an important tool currently used for resource distribution for the LTE uplink [11]. As per Kwan, Leung and Zhang [12] a novel multiple scheduler improved the overall performance of the LTE networks with increasing co-relation among OFDMA sub-carriers. This shows that in case of large number of users sub-optimal scheduler is more attractive than any other scheduler [12]. El-Hajj and Dawy [13] stated that joint uplink and downlink resource distribution can be performed in OFDMA networks. For this regularization can be done for coupling between downlink and uplink directions. This additional term is beneficial in controlling the resource allocation through minimizing difference between the downlink and uplink rates [13]. Anchora and its associates explored a novel approach of resource allocation in the LTE systems based on the game theory. They explored a cross layer approach where limited amount of information is exchanged by a scheduler and a radio resource to provide both a high throughout and a satisfactory level of equality among flows [14]. Lee, Shin and Lee [15] studied various Uplink multi user selection scheme in a Multicell environment especially for MIMO systems and they proposed an Interference Aware User Selection Scheme (IAUSR) for this system. This scheme works in a distributed manner. The Author has focused on the uplink multiuser MIMO systems in a multicell environment and tried to build up a user selection scheme. For the development of the user selection scheme they proposed two steps. The first step is related to the beam forming vector of each multiple mobile station. In the next step each base station chooses a set of users for serving them at the same time to analyze the multiuser diversity and the interference of neighboring cells on them. The development of user selection scheme is necessary to get the desired link performance. The requirements for the development of multiuser selection scheme are significant to analyze in order to achieve the highest sum rate and to embrace a comparative fairness amo ng the users [15] [16]. Xi and Zoltowski [17] considered transceiver design for uplink multiuser MIMO communications. In transceiver design multiple transmitters choose beam forming at the receiver to access the linear spatial equalizer simultaneously. Authors have investigated the crucial issues in the design of beam forming weights for all the transmitters. They presumed that information related to the state of user channel is available with each of the users. On the basis of the analysis of the design, a class of orthogonal Joint Beam Forming Design Scheme was proposed by the researchers. An Eigen beam forming scheme (Eig-BF) was also proposed by the researchers along with ordered successive interference cancellation minimum mean square error receiver (OSIC-MMSE). Further these two schemes were evaluated with the optimal joint transceiver design to calculate the overall mean square error (MSE). After comparison, the results showed that Eig-BF OSIC-MMSE scheme showed near-optimum performance with much sim pler transmitter design at much lower complexity. The design of the Eig-BF OSIC-MMSE scheme can be implemented in a decentralized way due to efficiency to know its CSI. On the other hand the orthogonal beam forming schemes carry out the straightforward receiver structure at much higher cost [17] [18]. Multiuser access scheme are developed for spectrum sharing system. Primary users can utilize spectrum shared by secondary users under some specific conditions. One condition is that interference at the primary use must be below to the predefined threshold. Basically there are two scheduling schemes to select a user from those who have fulfilled the interference constraints and achieved an up to standard signal to noise ratio level as per the predefined signal to noise ratio level at the secondary base station. The first suggested scheme is for the users who have reported best channel quality. To lighten the high feedback from the users of the first scheme, the second scheme proposed was dependent on the concept of switched diversity. In the second scheme the work of base stations is to scan the users one by one till an appropriate user is found [19]. Simulation of the uplink resource allocation scheme used in LTE-Advanced Figure 1: Simulation of the uplink resource allocation scheme used in LTE-Advanced using Matlab Recent Developments Yaacoub and Dawy [20] described that wireless technologies are continuously advancing the new systems to overcome the growing demands of customers for high speed mobile internet services. OFDMA is one of the technologies which has been chosen as the multiple access scheme. The reason for choosing this scheme is to satisfy the demand for high speed reliable data services. Need for delay sensitive applications along with high speed internet data to play games on mobile and to facilitate video conferencing has been increasing day by day and it has mandated the need for upgrading the state of art OFDMA based wireless communications system with efficient resources allocation schemes. Initially, uplink and downlink were treated independently for resource allocation. Recently, Queue Aware Resource Allocation Schemes are anticipated in this segment for providing delay guarantees for real time services. This scheme has been studied for both uplink and downlink directions independently. Good q uality is required in both the directions to achieve end user satisfaction. When coupling is performed between the downlink and uplink directions in order to determine the quality of service of a particular user, in this situation the problem objective and formulation changes accordingly. For example when a user uses a mobile game application it has a good channel situation in the uplink direction but downlink direction goes in bad channel condition. In this situation the network suffer from bad quality. This situation occurs because data is sent at a very high speed while receiving speed is very low which notably affects the service interactivity [20]. Referneces Dr. Abhijit Mitra, "A Curriculum Development Cell Project Under QIP," Indian Institute of Technology Guwahati, 2009. Rukhsana Ruby, "Uplink Scheduling And Resource Allocation Schemes For Lte-Advanced Systems That Incorporate Relays Or Carry Heterogeneous Traffic," University of British Columbia, 2015. radio-electronics. (2016) www.radio-electronics.com. [Online]. https://www.radio-electronics.com/info/cellulartelecomms/lte-long-term-evolution/lte-ofdm-ofdma-scfdma.php Adriana B. Flores, Sadia Quadri, and Edward W. Knightly, "A Scalable Multi-User Uplink for Wi-Fi," Department of Electrical and Computer Engineering, Rice University, Houston, TX, 2016. Hua Wang, Claudio Rosa, and Klaus I Pedersen, "Radio resource management for uplink carrier aggregation in LTE-Advanced," EURASIP Journal on Wireless Communications and Networking, vol. 1, no. 1, 2015. B. Archana and T.P. Surekha, "Resource Allocation in LTE: An Extensive Review on Methods, Challenges and Future Scope ," Communications on Applied Electronics, vol. 3, no. 2, pp. 2394-4714, 2015. Jeanette Wannstrom. (2013) www.3gpp.org. [Online]. https://www.3gpp.org/technologies/keywords-acronyms/97-lte-advanced Woojin Ahn, Young Yong Kim, and Ronny Yongho Kim, "An Energy Efficient Multi-User Uplink Transmission Scheme in the Next Generation WLAN for Internet of Things," Yonsei University, 2015. Phond Phunchongharn, Ekram Hossain, and Dong In Kim, "Resource allocation for device-to-device communications underlaying LTE-advanced networks," IEEE Wireless Communications, vol. 20, no. 4, 2013. Elias Yaacoub and Zaher Dawy, Resource Allocation in Uplink OFDMA Wireless Systems: Optimal Solutions and Practical Implementations.: WILEY- IEEE Press, 2012. Panagopoulos and D. Athanasios, Handbook of Research on Next Generation Mobile Communication Systems.: IGI Global, 2015. Raymond Kwan, Cyril Leung, and Jie Zhang, "Resource allocation in an LTE cellular communication system," in ICC'09 Proceedings of the 2009 IEEE international conference on Communications, 2009, pp. 3915-3919. A. M. El-Hajj and Z. Dawy, "On optimized joint uplink/downlink resource allocation in OFDMA networks," in ISCC '11 Proceedings of the 2011 IEEE Symposium on Computers and Communications, 2011, pp. 248-253. Luca Anchora, Luca Canzian, Leonardo Badia, and Michele Zorzi, "A Characterization of Resource Allocation in LTE Systems Aimed at Game Theoretical Approaches," 2010. Byong Ok Lee, Oh-Soon Shin, and Kwang Bok Lee, "Distributed user selection scheme for uplink multiuser MIMO systems in a multicell environment," Journal on Wireless Communications and Networking, vol. 1, no. 1, 2012. Robert Schober, Wolfgang Gerstacker, Shahram Zarei, Yongpeng Wu, and Jiayi Zhang, "Massive MIMO Systems," Institute for Digital Communications, 2014. Songnan Xi and Michael D. Zoltowski, "Uplink Multiuser MIMO Transceiver Design with Transmitting Beamforming under Power Constraints," in Military Communications Conference, 2006. M. Kottkamp, A. Roessler, and J. Schlienz, "LTE-Advanced Technology Introduction," 2012. Qaraqe and Marwa, "Uplink Multiuser Scheduling Techniques for Spectrum Sharing Systems," Texas AM University, 2012. Elias Yaacoub and Zaher Dawy, "A Survey on Uplink Resource Allocation in OFDMA Wireless Networks," IEEE Communications Surveys Tutorials, vol. 14, no. 2, 2012.