# Effective 5G Wireless Downlink Scheduling and Resource Allocation in Cyber-Physical Systems +
## 3 Problem Formulation  
Figure 2 shows the downlink time-slotted architecture inspired by [13]. It consists of mainly two parts: a base station and User Equipment (UE), as shown in the figure. The base station has \(N_{\rm T}\) transmit antennas with transmitting power \(P_{T}\). In a real environment, multiple mobile stations are connected with a base station. For simplicity, we show just one UE in the figure and assume that all the Mobile Stations \(MS=\{MS_{1},...,MS_{N}\}\) follow the same architecture. Each of these mobile stations supports multiple-input-multiple-output (MIMO) technology with an array of \(N_{T}\times N_{R}\) transmitting and receiving antennas. \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Category** & **Dependent Parameter** & **Algorithm Name** & **Resource Allocation Summary** \\ \hline \multirow{9}{*}{Channel-Independent} & \multirow{4}{*}{Classical Algorithms} & Proportional Fair (PF) [22; 23; 24] & Allocate resources to users in proportion to their weights \\ \cline{3-5}  & & First-In-First-Out (FIFO) [25; 26; 27] & Allocate resources based on their arrival order \\ \cline{3-5}  & \multirow{2}{*}{Classical Algorithms} & \multirow{2}{*}{Round Robin [28; 29; 30]} & Allocate resource to each user for a fixed time interval \\ \cline{3-5}  & & Weighted Fair Queuing [31; 32] & Allocate resources based on users’ weights inversely proportional to costs \\ \cline{3-5}  & & Blind Equal Throughput [33; 34; 35] & Allocate resources to maintain minimum throughput requirements \\ \cline{3-5}  & & Largest Weighted Delay First [36; 37; 38] & Allocate resources based on users’ weights and delay sensitivities \\ \cline{3-5}  & \multirow{2}{*}{VoIP} & \multirow{2}{*}{Delay sensitive [39; 40; 41; 42; 43]} & Prioritize VoIP traffic and provide best effort service to other traffic \\ \cline{3-5}  & & Dynamic Adaptive Streaming Over HTTP (DASH) [44; 45; 46; 47] & Ensure a guaranteed bit-rate to high-rank users based on the channel quality \\ \hline \multirow{9}{*}{Channel-Dependent} & \multirow{4}{*}{Guaranteed Bit-Rate (GBR)} & Priority Based [48; 49; 50; 51] & Allocate resources based on user priority \\ \cline{3-5}  & & Quality of Service (QoS) Aware Schedular [52; 53; 54] & Prioritize users and allocate resources accordingly \\ \cline{3-5}  & & Hybrid Schedulers [55; 56] & Allocate resources based on users’ QoS and delay sensitivity requirements \\ \cline{3-5}  & & Weighted Delay First [57; 58; 59] & Assign a higher weight and more resources to a user close to its target \\ \cline{3-5}  & \multirow{2}{*}{Deline-Dependent} & Hybrid Automatic Repeat Request (HARQ) Aware Scheduling [60; 61; 62] & Prioritize users based on the average throughput and delay \\ \cline{3-5}  & & Exponential/Proportional Fair (Exp/PF) [63; 64] & Maximize throughput while providing a fair level of services \\ \cline{3-5}  & & Two-Level Schedular [65; 66] & Prioritize real-time and non-real-time data to allocate resources \\ \cline{3-5}  & & Delay-Prioritized Scheduling [38; 67] & Assign resources based on users’ delay requirements \\ \cline{3-5}  & & Exp and Log Rule [68; 69] & Assign resources to a user based on his/her position in the queue \\ \cline{3-5}  & & Game Theory-Based Scheduling [70; 71] & Fairly distribute the resources among the participating users based on game theory \\ \hline \multirow{9}{*}{Cross-Layer Algorithm} & Overload-State Downlink Resource Allocation [72; 73; 74] & Assign resources based on the queue state information \\ \cline{3-5}  & & Greedy Resource Block (RB) Allocation [13] & Assign resources based on the queue and channel state information \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Taxonomy of Scheduling and Resource Allocation (SRA) algorithms. In this architecture, we have considered a busy base station with an infinite traffic queue: there is a continuous traffic flow from the upper layer to the DLC and PHY layers of the base station, as shown in Figure 2. Requests from different mobile stations are queued at the base station.