NHP2400 Project Planning Report Template of MATLAB implementation and performance evaluation

Student Name: Student No.  

Supervisor Name: Student Email  

Project Title: Project ID  

Flexible spectrum usage is a promising enabler of spectral efficiency for next generation wireless broadband networks. In order to deliver the next order of magnitude gains in terms of overall spectral and radio efficiency envisioned for f the next wireless generation network .Recently, licensed shared access has been proposed as a new paradigm for dynamic spectrum access in upcoming 5G wireless communications networks. This project will study licensed shared access techniques and work on design and development of these techniques for 5G wireless communications networks.

Project Background   

Spectrum sharing is one of the promising solutions to meet the spectrum demand in 5G networks that results from the emerging services like machine to machine and vehicle to infrastructure communication. The idea is to allow a set of entities access the spectrum whenever and wherever it is unused by the licensed users. In the proposed framework, different spectrum provider (SP) networks with surplus spectrum available may rank the operators requiring the spectrum, called spectrum users (SUs) hereafter, differently in terms of their preference to lease spectrum, based for example on target business market considerations of the SUs. Similarly, SUs rank SPs depending on a number of criteria, for example based on coverage and availability in a service area. Ideally, both SPs and SUs prefer to provide/get spectrum to/from the operator of their first choice, but this is not necessarily always possible due to conflicting preferences.

Licensed Shared Access (LSA), which is described by the EU Radio Spectrum Policy Group (RSPG) as “An individual licensed regime of a limited number of licensees in a frequency band, already allocated to one or more incumbent users, for which the additional users are allowed to use the spectrum (or part of the spectrum) in accordance with sharing rules included in the rights of use of spectrum granted to the licensees, thereby allowing all the licensees to provide a certain level of QoS

The operation of Licensed Shared Access (LSA) systems has to be designed in a way that Quality of-Service (QoS), in the form of spectral, energy and cost efficiency is guaranteed for both incumbent and licensee systems, when they have access to the spectrum. However, in order to achieve a joint, requested QoS objective, cooperation needs to characterize the operation between licensee operators/devices, as well as the concurrent transmissions of incumbent and licensee systems. For instance, since spectrum and resources such as antennas are expensive and, at the same time, cost efficiency constitutes a crucial factor for the successful deployment of an LSA system, cooperative techniques have to be designed towards such directions

In this this projects , a number of algorithms and schemes that facilitate cooperative communication between entities belonging to a LSA network, is described, with reference to the investigated scenarios and use cases. First, focusing on the small-cell/cloud-Radio Access Network (C-RAN) scenario, the case where a number of users requires a wireless service from a Virtual Mobile Network Operator (VMNO), is studied. More specifically, we  will study the Cost-efficient allocation of spectrum and antennas for the smallcell/C-RAN scenario

However, since the use of such resources is characterized by a cost, the VMNO has to select the appropriate number of antennas, along with an LSA bandwidth, such that the sum information rate per currency unit, is maximized. Motivated by the above situation, a cost efficiency metric is proposed, and then, based on that, as well as on the Zero-Forcing (ZF) distributed Multiple-Input-Multiple-Output (MIMO) precoding technique, we will evaluate the optimal solution, in terms of cost efficiency, in comparison to an arbitrary, uncoordinated strategy. It is interestingly shown that the optimal scheme achieves a gain in cost efficiency, for a number of system scenario

Obj 1:  Study licence shared access techniques. 

Obj 2:  Investigate the cost-efficient allocation of spectrum and antennas for the small cell/C-RAN scenario.

Obj 3:  investigate the statistically coordinated precoding with distributed CSIT for the macro-cell scenario

Obj 4:  Build simulation model the investigated algorithms 

Obj 5:  Evaluate and analyse the proposed algorithms

Project Deliverables:

D1 – Project Plan.  

D2 - Research background. 

D3 – Investigation about cost-efficient allocation of spectrum and antennas for the small cell/C-RAN scenario.

D4–  Investigation about statistically coordinated precoding with distributed CSIT for the macro-cell scenario

D5 – Performance evaluation and analysis of the proposed technique. 

D6 – Project poster.

D7 – Project report. 

Workpackage/Task list :

WP1 - <Doing Research background about licence shared access><Insert workpackage description here>

The main aim of this stage is to study the idea licence shared access, Focusing on licence shared access

Algorithms and schemes that facilitate cooperative communication between entities belonging to a LSA network. Different resources  such as published paper, books will be used to achieve this purpose   

WP2 - < investigate a cost-efficient allocation of spectrum and antennas for the small cell/C-RAN scenario

In the small cell/cloud RAN scenario, the ZF distributed MIMO technique will be investigated as precoding technique. It  is related to the trade-off between the spectrum and the antennas associated with centralized resource allocation

WP3 – investigate statistically coordinated precoding with distributed CSIT for the macro-cell scenari

In this section, we will focus on a MISO LSA system in the downlink, which consists of an incumbent MNO along with a licensee MNO. The two systems coexist and transmit simultaneously, thus, a SS task is not performed at the licensee side. Since a major objective of LSA systems is to guarantee QoS for all involved entities, the goal of a joint transmission design is the maximization of the average information rate of the licensee system, subject to the fact that the average rate of the incumbent lies above a given threshold (which depends on the service requirements of the incumbent terminal)

Using MATLAB software we will implement a macro-cell MISO LSA system. The system model will compress of a multiple antenna incumbent transmitter, TX 1, which consists of M antennas, along with its single antenna assigned receiver, RX1. The system will able to generate the proper results which will be later on in performance evaluation.

This part will summarise what we have done in the project including projects aim and objectives, research background, system modelling and performance evaluation. After that, the final report of the project will be submitted

M1 – Project plan submission.<Insert milestone description here>

M2 – Research Background completion.

M3 - investigate a cost-efficient allocation of spectrum and antennas for the small cell/C-RAN scenario completion. 

M2 - investigate statistically coordinated precoding with distributed CSIT for the macro-cell scenario completion.

M2 - MATLAB implementation and performance evaluation completion.

M2 - Project poste submission.

M7 – Project report submission.

Technical Risk Analysis (100 words) – Identify any technically risky work packages/tasks i.e. those whose outcomes that may be uncertain. Assess the criticality of those work packages and describe possible approaches to mitigation.

Workpackage / Task Risk Criticality Mitigation

<WP xxx> <High/Med> <High/Med/Low> <Describe mitigation>

WP1 - <Doing Research background about licence shared access><Insert workpackage description here>

The main aim of this stage is to study the idea licence shared access, focusing on licence shared access

Algorithms and schemes that facilitate cooperative communication between entities belonging to an LSA network.

  The concept of the Licensed Shared access was initially developed, which is enabled by the used of vacant resources of spectrum, in 2.3-2.4 GHz band for the mobile band by the long term static licenses. The system of the LSA is developed for the guarantee LSA licensee with the predictable quality of services as well as large access, which is shared by the resources of spectrum. Architecture as well as development for the LSA of 2.33-2.4 GHz band, and it is comprised of LSA with a brief spectrum access system. There is the need to update along with the replaced enable spectrum form the current exclusive spectrum of the licensed (Kalliovaara, a, & l, 2018). The demand for novel framework permits the efficient sharing’s which is available for the underutilized spectrum emerges to overcome the pressing effects of the fragmentation of spectrum. The concepts of LSA enables the spectrum sharing which is very advanced among end user of limited number by two entities which are incumbent and current holder of the spectrum. The framework of the LSA   enables the controlled spectrum of sharing among primary users along with secondary users where both having the access of portion of spectrum by given location along with time is shown in the below to figure 1 (Sadreddini & al, 2018). 

Communication is based on the industry for the initiative of promoted spectrum of sharing access across wireless industry along with diverse types of incumbents. The approach which is regulatory it aims is that to facilitate radio communication, where the systems are operated through the number of limited licensed  under the individuals licensing regime of the frequency band. From the harmful interface the LSA model guarantees for the protection by prediction of quality services for LSA license for the incumbent. For managing the dynamic of LSA spectrum the LSA architecture consists of two different elements (Yrjola, 2017) 

The performance of the fair dynamics with the management spectrum of an algorithm in the single incumbent along with multi operator of the LSA network .The algorithm works like a building block for the challenging scenario which is discussed in this work. The schemes of the spectrum licensing are given below; 

 

WP2 - < investigate a cost-efficient allocation of spectrum and antennas for the small cell/C-RAN scenario

In the small cell/cloud RAN scenario, the ZF distributed MIMO technique will be investigated as a precoding technique. It is related to the trade-off between the spectrum and the antennas associated with centralized resource allocation

Suppose the demand of K user for the wireless services is from VMNO and operator, which rent spectrum and antenna from LSA and C-RAN system to transmit information for time needed .By using the infrastructure like antennas, processing, and backhaul along with the LSA spectrum, which has cost in currency per unit seconds. The purpose of network operator is to select an optimal number of bandwidth, W, antennas M, like number of transmission as the bits per currency which are maximized in the below figure 

 

Suppose the cloud based MD-MIMO RAN by theM_max of downlink, that is available for transmit antennas, then the select subset of antenna for the network operator which transmit data to K≤M≤M_max for single user of antennas. The Super cell is area which is covered by M antennas.  By assumption of the SDMA” Spatial Division Multiple access” the k user has whole bandwidth at same time. In data centre by ZF, data k streamed to each user and converted into the M signals, which are transmitted into antenna by optical fibber network. Received and transmitted signal, y, and x are vectors for K elements, which is also the K^th symbol of element which is received and transmitted at the K^th by user antenna, and model of systems is 

y=FHx+n

Whereas, H∈C^msK and H∈C^kxM are precoding and channel matrices, and vector n is complex Gaussian noise vector. On the precoding metric F, the power transmitted by each antenna depends on the loss of generality which is also neglect antenna power constraints. Denote m^th  and h_km channel among transmission of antenna by k^th user and it is supposed the fading channel i.e

h_km (d_km )=√(β(d_km ) σ_km  ) w_km

β(d_km )= β_o d_km^(-1)

It is path of loss function of distance among m^th and k^th  transmit of the antenna with d_km. 

The random variable which is approximated through exponential distribution by the average; 

E{r_k }~P(M-K+1)

For super cell the sum of rate capacity is 

C=W∑_(k=1)^K▒log_2⁡(1+r_k/(N_o W)) 

W is Bandwidth which is acquired from LSA pool

For 1s form LSA by using 1MHz is the spectrum cost c_w. c_m is the antenna cost which is using the one distributed antenna for the 1s. c_o is the operative cost which is the cost by using the cloud infrastructures, e.g. processing and backhaul for the 1s. Cost efficiency is a number of transmissions which is used for the bits per cost units, and it is also given like the ratio of the total are of cost. 

η(M,W)=(W ∑_(k=1)^K▒log(1+r_k/(N_0 W)) )/(c_m M+c_w W+c_0 )

Suppose the downlink of the cloud based MD-MIMO RAN, and it is also supposed the application of the SDMA by user access of whole bandwidth at same time. The efficiency which is obtained through the arbitrary strategy with optimal cost efficient which either maximize the number of antenna with the bandwidth (Miguelez & et.al, 2014)

WP3 – investigate statistically coordinated precoding with distributed CSIT for the macro-cell scenario

In this section, we will focus on a MISO LSA system in the downlink, which consists of an incumbent MNO along with a licensee MNO. The two systems coexist and transmit simultaneously. Thus, an SS task is not performed at the licensee side. Since a major objective of LSA systems is to guarantee QoS for all involved entities, the goal of a joint transmission design is the maximization of the average information rate of the licensee system, subject to the fact that the average rate of the incumbent lies above a given threshold (which depends on the service requirements of the incumbent terminal)

 

In the above figure 4 , the macro-cell MISO LSA system is below the investigation which is shown , and it is also comprised for the different antenna of incumbent of transmitter  TX1, and it also consists for the M_1 antenna by single antenna for receiver, which is assigned RX1.teh focus is on downlink , for the incumbent who is willing to share an available spectrum by the MISO system license and it is also compromising different antenna of the receivers and the transmitters which is equipped by the M_2 antennas. The signal is received at the  RX_i,i∈{1,2} can be expressed;

y_i=h_(i,i)^H w_i s_i+h_(i,¯i)^H w_( ¯i) s_i+n_i

Whereas w_i   presented the beam forming of the vector at TXi and it is also supposed which w_i   = √(P_i ) u_i with the P_i≤P_i^max and the ‖u_i ‖=1whereas the P_i^maxis the instantaneous maximum power. By analyzing eth above equation the instantaneous information is the rate of RX i ,i∈{1,2} is given by; 

R_i=log_2⁡(1+(P_i |h_(i,i )^H u_i |^2)/(N_o+P_¯i |h_(i,¯i)^H u_¯i |^2 ))

The problem is the joint of the downlink for the precoding which is combined the local CSIT, and it is also formulated. 

At the TXi,i∈{1,2} is capitalizing is available for the optimization of problem which is also maximizing average rate of the license systems and it is also subjected for average rate of constrains for the RX1 and it could also be formed for the functional of the optimization problem by the different functional dependencies which are available for the CSI (Filippou, 2016). The optimization problem is explained as; 

(w_1^*,w_2^* )=argmaxE[R_2 (w_1 (h_1,1 ),w_2 (h_2,2 )  ]

E[R_1 (w_1 (h_1,1 ),w_2 (h_2,2 ))]≥τ_1>0,0≤|(|w_1 (h_1,1 )|)|^2≤P_1^max,0≤|(|w_2 (h_2,2 )|)|^2≤P_2^max

Whereas τ_1  is used for quality of services and it is demand for the RX1in the terms of an average rate 

WP4 – MATLAB implementation and performance evaluation

Using MATLAB software we will implement a macro-cell MISO LSA system. The system model will compress of a multiple antenna incumbent transmitter, TX 1, which consists of M antennas, along with with its single antenna assigned receiver, RX1. The system will able to generate the proper results which will be later on in performance evaluation

Multiple antenna incumbent transmitters, RX 1

By using the MATALB, the average rates of RX 1, respectively, are plotted as a function of the system’s transmit Signal-to-Noise Ratio (SNR) when N =2,M = 2 antennas. 

This part will summarise what we have done in the project, including project aim and objectives, research background, system modelling, and performance evaluation. After that, the final report of the project will be submitted

Summing up all the discussion as shown in the above, the aim and the objective of research report is achieved. The concepts and research study of the LSA network are also explained with detailed analysis. In the 2nd objective and work packages determine cost efficient, it is obtained by considering the large number of MVNOs by the various accessing capabilities which are shared by C-RAN infrastructures. To modulate the signals the MVNOs are capable by the high bandwidth of the efficient waveforms. For the 5G by using waveform suppose possibility of the MVNOs which is capable of this. All detailed analysis with calculations is shown in the above discussions.

Filippou, M. C. (2016). Coordinated Shared Spectrum Precoding With Distributed CSIT. IEEE Transactions on Wireless Communications, 15(8), 5182–5192. doi::10.1109/twc.2016.2554112 

Kalliovaara, J., a, e., & l. (2018). Licensed Shared Access Evolution to Provide Exclusive and Dynamic Shared Spectrum Access for Novel 5G Use Cases. Licensee IntechOpen, 55-72. doi:10.5772/intechopen.79553

Miguelez, I. G., & et al. (2014). Cloud-RAN platform for LSA in 5G networks — Tradeoff within the infrastructure. 2014 6th International Symposium on Communications, Control, and Signal Processing (ISCCSP). Retrieved from https://sci-hub.tw/10.1109/ISCCSP.2014.6877927

Sadreddini, Z., & al, e. (2018). Dynamic Resource Sharing in 5G with LSA: Criteria-Based Management Framework. Hindawi Wireless Communications and Mobile Computing, 12.

Yrjola, S. (2017). Licensed Shared Access evolution enables early access to 5G spectrum and novel use cases. EAI Endorsed Transactions on Wireless Spectrum. doi:10.4108/eai.12-12-2017.153463