Resource allocation in Green Wireless Communication Dr. Muhammad Naeem COMSATS University Islamabad, Wah Campus & WINCORE Lab, Ryerson University Toronto, Canada 1 Outline Why Green Emerging Wireless Paradigms Resource Allocation Convex and Non-Convex Resource Allocation Difference between traditional and green communication modeling Some formulations 2 Future wireless systems challenges Wireless spectrum availability High data rate Coverage Global warming Limited resources 3 Green Communication Information and communication technologies are responsible for 5% of global greenhouse gas emissions. Green communication deals with the issue of designing environmental friendly and energy efficient communication systems 4 Green Communication In year 2002, ICTs caused 150 mega tons of CO2 emissions In year 2020, ICTs will cause 350 mega tons of CO2 emissions 5 Global rise in energy demand – 37% annually From 2013 to 2035 80 % met from Fossil fuels Coal, Gas and Oil Cellular Energy Consumption Single BS 1.3 KWh 8,800 KWh/yr 12-15,000 Cell Sites 2G & 3G 3 Antenna 736,000 MWh/yr Mobile broadband growth • It is assumed that by 2025, 50 billion devices will be connected to the global IP network 3/18/2019 7 Mobile broadband growth • “Smart living” will require constant and ubiquitous mobile connectivity to the network to upload activity data and IoT control commands 3/18/2019 8 Mobile broadband growth • 5G/B5G will provide seamless compatibility with dense heterogeneous networks (HetNets) to satisfy the high demand of real-time traffic • 5G : high throughput, low-latency, high reliability, increased scalability, and energy efficient mobile communication technology 3/18/2019 9 Mobile Communication Growth & GHG Emissions Global mobile communication growing rapidly 2020 7.6 billion Increased energy consumption of networks 2020 98 TWh Global warming from green house gas emissions ICT 7.8 gtCO2 Most ICT Energy Consumption Data Centre Base Station Green Base Station 3/18/2019 12 Resource Allocation Power Relay Selection User scheduling Routing Delay QoS Fairness Subcarrier Allocation 13 Resource Allocation 14 Resource Allocation Power Inputs :(any combination) Relay Selection Number of users User scheduling Number of primary/Secondary users Routing Number of relays/hops Delay Interference threshold on primary users QoS Cooperative protocol type Fairness Channel state/side information Subcarrier Allocation Geographic locations of Users Network related custom inputs 15 Resource Allocation Power Relay Selection Find: (any combination) User Power Source/Relay power User scheduling Assignment/Selection of relays Routing User Selection/Scheduling Delay Subcarrier/Band width Allocation Routing QoS Relay deployment/placement Fairness Network related custom variables Subcarrier Allocation 16 Resource Allocation Power Objectives:(any combination) Minimize: Total power Relay Selection Minimize: Per node power/ Investment/Emissions User scheduling Minimize: Bit error rate Routing Minimize: Waste Material Minimize: Outage probability Delay Minimize: Delay//Operating Cost/Fuel Cost QoS Maximize: Sum-Rate/Revenue Fairness Maximize: Weighted Sum-Rate Maximize: Energy-Efficiency/ Life Span Subcarrier Allocation Maximize: Utility / Reliability/ Production Max-Min: (Worst user’s) SNR/Capacity Max/Min: Network related custom objectives 17 Resource Allocation Constraints :(any combination) Power Source/Relay power constraint Relay Selection Interference constraint User scheduling Relay selection/Assignment constraint QoS constraint Routing Bandwidth/Delay/Fairness constraint Delay Fairness constraint QoS Probabilistic interference and QoS constraints Topology constraints Fairness Outage constraints Subcarrier Allocation Economic constraints 18 Green Multi-Objective Optimization Conflict Graph QoS Network/Battery life N/B QoS Coverage Cov Cost D PER Cost T Throughput PER Conflicting Design dependent Delay Supporting 19 Green Multi-Objective Optimization Conflict Graph Relation between conflicting objectives. Max-R: maximize revenue, Min-E: minimize emissions, Max-RL: maximize reliability, Max-P: maximize production, Min-OC: minimize operating cost, Min-I: minimize investment,20 Min-FC: minimize fuel cost, Max-LS: maximize life span, Min-WM: minimize waste. Difference Between Green and Traditional Resource Allocation : A CRN case Channel between the mth PU and the kth SU Channel between the BS and the kth SU α do hk = Φ Ko dk Protected distance based PU Model K Secondary Users M Primary Users 21 Difference Between Green and Traditional Resource Allocation 1. Cognitive radio technology itself has a built in green communication capability. 2. Cognitive radio sets an upper limit on the interference level on the primary users that eventually limit the transmit power of the secondary users. Main Objectives: 1. Maximize Total Throughput or Minimize Total Power 2. Protect the Cognitive radio users 22 Traditional Resource Allocation Maximize Total throughput of cognitive radio network while meeting the interference and total power constraints. Max ( Total Spectral Efficiency ) 23 Traditional Resource Allocation Sum-Rate Maximization pk hk log 1 + ∑ 2 p No k =1 Subject to K max C1 : C2 : C3 : ∑ ∑ K k =1 K k =1 pk g m ,k ≤ I m , ∀m pk ≤ P pk ≥ 0, ∀k Maximize Total throughput of cognitive radio network while meeting the interference constraints due to the primary users and total power constraint 24 Traditional Resource Allocation Sum-Power Minimization Sum-Rate Maximization K pk hk log 1 + ∑ 2 p No k =1 Subject to K min max C1 : C2 : C3 : ∑ ∑ K k =1 K k =1 pk g m ,k ≤ I m , ∀m pk ≤ P pk ≥ 0, ∀k Maximize Total throughput of cognitive radio network while meeting the interference constraints due to the primary users and total power constraint p ∑p k k =1 Subject to Dual C1 : ∑ K k =1 pk g m ,k ≤ I m , ∀m pk hk C 2 : log 2 1 + No C 3 : pk ≥ 0, ∀k ≥ Rk , ∀k Minimize Total Power of cognitive radio network while meeting the interference constraints due to the primary users and rate constraint 25 Difference Between Green and Traditional Resource Allocation Sum-Rate Maximization Traditional ph max ∑ log 2 1 + k k p No k =1 Subject to K C1 : s p g ∑ k =1 k m,k ≤ I m , ∀m Green Spectral efficiency pk hk log 2 1 + ∑ No k =1 max K p φ + ∑ k =1 pk K K C2 : ∑ C3 : pk ≥ 0, ∀k K k =1 pk ≤ P Energy Efficiency Total power Subject to C1 : s p g ∑ k =1 k m,k ≤ I m , ∀m K C2 : ∑ C3 : pk ≥ 0, ∀k K k =1 pk ≤ P Maximize energy efficiency in bits per joule of cognitive radio network while meeting the interference constraints due to the primary users 26 Difference Between Green and Traditional Resource Allocation Sum-Rate Maximization Traditional ph max ∑ log 2 1 + k k p No k =1 Subject to K C1 : s p g ∑ k =1 k m,k ≤ I m , ∀m Green Fractional Spectral efficiency pk hk log 2 1 + ∑ Programming No k =1 max K p φ + ∑ k =1 pk K K C2 : ∑ C3 : pk ≥ 0, ∀k K k =1 pk ≤ P Convex Optimization Energy Efficiency Total power Subject to C1 : s p g ∑ k =1 k m,k ≤ I m , ∀m K C2 : ∑ C3 : pk ≥ 0, ∀k K k =1 pk ≤ P Maximize energy efficiency in bits per joule of cognitive radio network while meeting the interference constraints due to the primary users Non-Convex Optimization 27 Energy Efficiency Objective The Optimization Objective is non-convex function of SUs’ Power But The objective function has special structure as shown in the figure 28 Energy Efficiency Objective The Optimization Objective is non-convex function of SUs’ Power But The objective function has special structure as shown in the figure 0.05 0.138 0.045 1 0.04 0.035 0.6 0.172 0.03 p1 (watts) Normalized EE 0.8 0.4 0.207 0.025 0.02 0.2 0.241 0.015 0 0.05 0.04 0.05 0.03 0.276 0.01 0.04 0.03 0.02 p1 (watts) 0.414 0.005 0.517 0.02 0.01 0.01 0 0 0 p2 (watts) 0.31 0.345 0.379 0 0.005 0.01 0.015 0.02 0.025 0.03 p (watts) 0.035 0.04 0.045 2 The objective function is a Quasi-Concave function Local Maximum is the Global Maximum 29 0.05 Optimal and epsilon Optimal Solutions Optimal 1. Introduce a new variable and two constraints to transform the min term inside the objective 2. Apply Charnes-Cooper Transformation to get an equivalent convex optimization problem 3. Get optimal solution with any standard convex optimization tool 30 Optimal and epsilon Optimal Solutions Optimal Transformed Original pk hk log 2 1 + ∑ No k =1 max K p φ + ∑ k =1 pk yk hk max t ∑ log 2 1 + y ,t > 0 tNo k =1 Subject to Subject to C1 : K K C1 : C2 : C3 : ∑ ∑ K k =1 K k =1 pk g s m,k ≤ I m , ∀m pk ≤ P CCT ∑ ∑ K k =1 C2 : K k =1 yk g m ,k −I m ≤ 0, ∀m yk − tP ≤ 0 C 3 : tφ + ∑ k =1 yk = 1 K pk ≥ 0, ∀k Convex Optimization Non-Convex Optimization Parametric or charnes cooper transformation 31 ε–optimal power allocation Iterative Power Allocation 1. The EE fractional maximization problem is transformed into EE parametric optimization problem. 2. The optimal solution of the EE parametric optimization gives the optimal solution of EE fractional maximization. 3. For any nonnegative ε, the algorithm ensures the solution is always within ε of optimal. 32 ε–optimal power allocation 33 Simulation Parameters 1. Maximum BS coverage distance = 1000m 2. Static and leakage power, Φ = 10-6 Watts 3. Ko = 50 4. PU protected distance = 10m 5. Reference distance for the antenna far field, do = 10, 6. Path loss exponent, α = 3 34 EE and SE comparison with K = 5 and M = 1 5 5 x 10 1.8 4.5 1.6 4 1.4 3.5 1.2 3 -6 10 -5 10 -4 10 Im (Watts) -3 10 SE(bits/s/Hz) EE(bits/Joule/Hz) EE SE 1 -2 10 35 Performance of IPA with number of iteration Only few iterations required by the iterative algorithm to converge 7 EE(bits/Joule/Hz) 10 More number of user means more EE 6 10 K = 5, Im = 1µW K = 25, Im = 1µW K = 45, Im = 1µW K = 5, Im = 1mW 5 10 K = 25, Im = 1mW K = 45, Im = 1mW 2 4 6 8 10 Number of Iterations 12 With increase in SUs, IPA needs more number of iteration for its convergence 14 Due to cognitive radio’s built in green communication capability, the iterative algorithm requires less number of iterations to converge at low interference threshold 36 Effect of ε on the iterations of IPA with Im=1µW 16 14 ε = 0.1 Increase in number of ε = 0.000001 PUs decreases the number iterations of (K,M)=(45,1) Number of iterations 12 (K,M)=(45,20) (K,M)=(5,1) 10 IPA to converge to a given epsilon 8 (K,M)=(5,20) 6 4 2 0 1 2 3 4 37 Effect of ε on the iterations of IPA with Im=100µW 18 ε = 0.1 ε = 0.000001 16 (K,M)=(45,1) Number of iterations 14 (K,M)=(5,1) (K,M)=(45,20) Increase in number of PUs decreases the number iterations of IPA to converge to a 12 (K,M)=(5,20) given epsilon 10 8 6 4 2 0 1 2 3 4 38 Effect of ε on the iterations of IPA with Im=1W ε = 0.1 18 ε = 0.000001 (K,M)=(45,1) 16 (K,M)=(5,1) (K,M)=(45,20) PUs decreases the number iterations of 14 Number of iterations Increase in number of (K,M)=(5,20) IPA to converge to a 12 given epsilon 10 8 6 4 2 0 1 2 3 4 39 Effect of ε on the iterations of IPA Im=1uW Im=100uW 18 16 ε = 0.1 ε = 0.000001 16 ε = 0.1 ε = 0.000001 14 (K,M)=(45,1) (K,M)=(45,1) 12 (K,M)=(5,20) 10 8 6 (K,M)=(45,20) (K,M)=(5,1) 10 8 (K,M)=(5,20) 6 4 4 2 2 0 12 (K,M)=(45,20) Number of iterations Number of iterations 14 (K,M)=(5,1) 1 2 3 4 0 1 2 3 4 40 Future Directions Radio Resource Management for the following Renewable Energy Assisted Base Station Placement Green Aerial Base Stations Energy Harvesting Green Maintenance Scheduling in Smart Grid Green Sensor Network With Wireless Power Transfer Green Cyber Physical Systems Energy Efficient D2D and M2M Networks Energy Efficient Cloud Computing 41 Thank You 42