Public Warning Systems (PWS) are critical infrastructures for protecting lives during emergencies, yet many users increasingly ignore or disable alerts. Prior research has focused on attentive recipients, overlooking those who disengage mentally or behaviorally. We examine disengagement as a gradual process of psychological detachment shaped by alert fatigue, trust erosion, and perceived inefficacy. Focusing on South Korea’s high-frequency cell broadcast system, averaging 80 messages per day, we conducted a qualitative study with 37 participants classified as responders, ignorers, or blockers, drawing on EPPM and PADM. Through interactive message evaluation and interviews, we traced cognitive and emotional pathways from message reception to protective action or inaction. Our findings reveal structural and psychological barriers, including fixed cognitive anchors that preemptively dismiss alerts, information-seeking behaviors rarely leading to action, and divergent adaptations to repeated false alarms. We reframe emergency alerts as adaptive user–system interfaces shaped by cumulative experience, not static channels. We show how PADM pathways become non-linear, truncated, or collapsed under saturated alert environments. We contribute design implications for more adaptive, trustworthy, and user-sensitive emergency alert systems.

In this paper, we propose a novel deep learning (DL)-based framework for three-dimensional (3-D) trajectory design and resource allocation in unmanned aerial vehicle (UAV)-enabled wireless networks to maximize the minimum average spectral efficiency (SE) among moving ground nodes (GNs). The proposed framework incorporates an obstacle avoidance algorithm for 3-D trajectory planning and employs a practical blockage-aware channel model that accounts for the blockage effects on air-to-ground links caused by obstacles, while also accounting for the stochastic movement of GNs. To this end, we develop a new mathematical approximation based on a point cloud method to efficiently determine whether the channel between the UAV and the GN is blocked by obstacles and whether the trajectory of the UAV intersects with obstacles. Subsequently, we introduce a DL framework that integrates deep neural network (DNN) structures designed to solve the formulated problem with an unsupervised learning-based training methodology. This approach enables efficient modeling of 3-D trajectory and resource allocation while also facilitating the effective training of the DNN without the need for labeled data. Through performance evaluations, we demonstrate that the proposed scheme accurately accounts for the location-dependent channel blockage effects caused by obstacles and successfully avoids potential UAV collisions with obstacles. Furthermore, the proposed scheme outperforms baseline schemes in achieving the minimum average SE by jointly optimizing the 3-D trajectory and resource allocation while maintaining low computation times for real-time operation.

This study investigates the joint optimization of trajectory and resource allocation in unmanned aerial vehicle (UAV)-enabled wireless-powered two-way communication (WPTWC) under probabilistic line-of-sight (LoS) channel models. In this communication protocol, the UAV transmits signals over wireless links while the ground nodes (GNs) simultaneously receive information and harvest energy based on either a power splitting (PS) or time switching (TS) policy. Each GN then uses the harvested energy to transmit collected data back to the UAV. Due to the nature of downlink broadcast transmission in UAV-enabled WPTWC, where GNs at different locations receive signals simultaneously, both LoS and non-LoS components significantly influence the channel characteristics. To capture these location-dependent effects accurately, we reformulate both components into equivalent convex forms, which enhances analytical tractability. Subsequently, we jointly optimize the time allocation, the three-dimensional UAV trajectory, the transmit powers of the UAV and GNs, and the energy harvesting ratio of the GNs to maximize the minimum uplink spectral efficiency (SE) of the GNs while satisfying the downlink SE requirements for all GNs. To tackle the nonconvexity of the optimization problem, we first decompose it into four subproblems and then transform each subproblem into a convex problem with respect to its corresponding optimization variable via successive convex approximation and advanced optimization methods. Thereafter, we propose a low-complexity algorithm that employs the block coordinate descent method to iteratively find the optimal solution for each convex subproblem. Simulations reveal distinct trajectory and resource allocation behaviors under the PS and TS policies, influenced by the downlink SE requirement. Furthermore, by adaptively optimizing the UAV trajectory and radio resource allocation based on the network conditions, the proposed scheme significantly improves the minimum uplink SE of the GNs relative to baseline schemes.

In this paper, we investigate robust trajectory design and resource allocation in unmanned aerial vehicle (UAV) enabled wireless networks to maximize the minimum average spectral efficiency (SE) among mobile nodes (MNs) on the ground while coping with uncertainties in trajectory. Our work specifically addresses practical challenges encountered during trajectory planning, namely: 1) the mobility of MNs, causing changes in their locations over time; 2) the positioning error of UAV, leading to deviations from its planned trajectory; and 3) the presence of no-fly zones (NFZs), which must be avoided during UAV flight. Taking these practical aspects into account, we propose a deep learning (DL) framework that integrates a deep neural network (DNN) structure with an unsupervised learning-based training methodology. The former enables efficient modeling of UAV trajectory and resource allocation, while the latter allows effective training of DNNs without labeled data. Through performance evaluations, we demonstrate that the proposed DL-based scheme outperforms the comparative baseline schemes in terms of the minimum average SE by optimizing trajectory and resource allocation with low computation time. Furthermore, we validate the robustness of our proposed scheme against the uncertainty associated with positioning errors.

In this paper, we investigate the optimal resource allocation in unmanned aerial vehicle (UAV)-assisted wireless-powered two-way communications. The communication process considered here consists of two steps. First, the UAV transmits a control signal over wireless links while ground terminals (GTs) receive information and harvest energy simultaneously, with each GT then using the harvested energy to send data to the UAV. We aim to maximize the minimum uplink throughput among GTs while ensuring the minimum requirement of the downlink throughput for each GT by optimizing the time allocation, the transmit power and the trajectory of the UAV along with the energy harvesting ratio of GTs. First, we propose an effective optimization-based approach to address the non-convexity of the formulated problem, which is difficult to solve. Specifically, we apply a successive convex optimization technique to approximate the convex problem for each optimization variable and find the optimal resource management strategy through a block coordinate descent algorithm. To reduce the high computational complexity of the optimization-based approach, we also develop a deep learning (DL)-based approach consisting of an efficient deep neural network framework and a novel training methodology. Simulation results confirm that the proposed schemes show significant performance improvements over existing baseline schemes. We also confirm that the DL-based scheme achieves performance comparable to the optimization-based scheme with a much shorter computation time.

In this study, we explore a three-dimensional trajectory and pick-up design of an unmanned aerial vehicle (UAV) for parcel delivery. In particular, we consider the real-world scenario in which a weight-restricted UAV cannot pick up all parcels within a single route; therefore the parcel delivery must be divided into multiple trajectories while avoiding no-fly zones. We formulate this problem mathematically as the minimization of total delivery time, which jointly optimizes the pick-up indicators, the lengths of the time slots, and the horizontal and vertical trajectories. To address the non-convexity of the formulated mixed-integer nonlinear programming, we employ a successive convex approximation to convert the problem into a convex form concerning optimization variables and utilize a penalty convex-concave procedure to preserve the binary characteristics of the pick-up indicators. Subsequently, we propose an iterative algorithm based on a block decent algorithm to efficiently identify the optimal solution by solving the relaxed convex problem. To address the problem of high computational complexity associated with the optimization-based algorithm, we also present an unsupervised deep learning (DL)-based heuristic algorithm. The simulation results confirm that the proposed schemes achieve considerably shorter delivery times than the baseline schemes in various scenarios. Furthermore, the DL-based scheme requires about 10% longer delivery time than the optimization-based scheme, but it can approximate the UAV strategy with substantially reduced computation time.

This paper presents a novel framework that combines an optimization-based approach with a deep learning (DL)-based approach to devise a cooperative strategy for unmanned aerial vehicles (UAVs) in wireless-based secure communications by leveraging the strengths of both approaches and addressing their respective limitations. We first formulate a joint optimization problem to maximize the minimum achievable secrecy rate while guaranteeing the minimum harvested energy requirement for each ground node by optimizing scheduling, transmit power, and trajectory. To address the difficulty of solving the formulated non-convex mixed-integer nonlinear programming problem, optimization techniques, such as continuous convex approximation and the block coordinate descent algorithm, are used to efficiently find feasible solutions. To tackle the challenges posed by the high computational complexity and initialization sensitivity of the optimization-based approach, we also propose an unsupervised learning-based deep neural network (DNN) structure with a specialized loss function tailored to our goals that allows the DNN to effectively approximate the optimal strategies for UAVs. Finally, we design a pioneering method that integrates the strengths of the two aforementioned approaches, in which the output of the trained DNN serves as the initial values of the optimization variables, and subsequently, optimization techniques are applied to fine-tune these optimization variables, leading to further performance improvements. Through intensive simulations, we confirm that the integrated scheme provides superior performance without constraint violation compared to the DL-based scheme, while guaranteeing faster convergence than the optimization-based scheme.

In this paper, we present a realistic and novel protocol for two-way communication in multiple-input-single-output (MISO) wireless-powered interference channels, namely, a simultaneous wireless information and power transfer (SWIPT) then wireless information transfer (WIT) protocol. In the protocol considered, transmitters first perform SWIPT in a forward link (FL) and receivers and then execute WIT using the harvested energy in a backward link (BL). Given that the operation of SWIPT in the FL affects the performance of WIT in the BL, our aim is to find a resource allocation strategy that maximizes the sum spectral efficiency (SE) of the FL and BL, which requires the joint optimization of the transmit beamforming vector, transmit power, and energy harvesting (EH) ratio in the FL and the receive beamforming vector in the BL. To deal with the non-convexity of the optimization problem, a deep learning (DL) framework is devised, in which the optimal resource allocation strategy is approximated by a well-designed deep neural network (DNN) model consisting of six independent DNN modules where the sigmoid and softmax functions are jointly utilized to properly model each control parameter. Furthermore, a two-stage training method is proposed where the DNN model is initialized using suboptimal solutions that are found using a low complexity algorithm in a supervised manner before it is fine-tuned using the main training based on unsupervised learning. Through intensive simulations performed in various environments, we confirm that the proposed method improves the training performance of the DNN model while reducing the training overhead. As a result, the proposed DL-based resource allocation achieves a near-optimal performance in terms of the sum SE with a low computation time.

In this article, we consider wireless-powered two-way communication in an$N$-user interference channel with imperfect channel state information (CSI). In the system considered, the receivers harvest energy and receive information simultaneously from data signals sent by transmitters using a time switching (TS) policy, before transmitting response signals back to the transmitters in a subsequent phase using the harvested energy. We aim to find the resource allocation that allows the transmit power and TS ratio to be determined jointly to maximize the sum rate of the response links while guaranteeing a predetermined rate requirement for each data link, even in the presence of errors in the estimated CSI. To deal with the nonconvexity of our optimization problem, we first introduce a gradient algorithm with a barrier function that finds suboptimal solutions heuristically. Moreover, to overcome the limitations of the gradient algorithm, e.g., its high computational complexity and vulnerability to channel error, we devise a robust strategy for resource allocation based on deep learning, in which artificially distorted CSI is fed into the deep neural network (DNN) during training to compensate for the incompleteness of the derived solutions caused by channel error. The performances of the considered schemes are examined through simulations, in which the proposed DNN scheme achieves a near-optimal performance with respect to the sum rate of the response links and outage probability under imperfect CSI, which validates its usefulness and robustness.

In this article, we consider a practical scenario for secure wireless-powered communication in the presence of imperfect channel state information (CSI) with simultaneous energy harvesting, in which it is required to keep information secret from an untrusted energy receiver allowed only to harvest energy from the transmitted signals. We aim to find the robust transmit power control (TPC) strategy to maximize the secrecy rate whilst ensuring the spectral efficiency of transceiver pairs and the amount of energy harvested by the energy receiver, even when the CSI is inaccurate. To deal with the nonconvexity of the formulated optimization problem, we first derive a suboptimal form of TPC in an iterative manner by adopting dual methods. In order to overcome the drawbacks of the conventional optimization-based approach regarding the suboptimality of performance and requiring long computation time, we devise a deep learning (DL)-assisted TPC as an alternative means of deriving the TPC. In the considered DL-assisted TPC, a deep neural network (DNN) is trained to compensate for the distortion caused by channel errors in an unsupervised manner. More specifically, artificially distorted CSI, which reflects the difference between actual and estimated CSI, is fed into the DNN during training and used to update the weights and biases of the proposed DNN using a bounded loss function, which allows a robust TPC strategy to be approximated by the DNN. Simulation results reveal the robustness of the proposed DL-assisted TPC against channel errors, such that it achieves a near-optimal performance with a lower computation time, even when the CSI is incorrect.

In this paper, a deep learning (DL) framework for the optimization of the resource allocation in multi-channel cellular systems with device-to-device (D2D) communication is proposed. Thereby, the channel assignment and discrete transmit power levels of the D2D users, which are both integer variables, are optimized for maximization of the overall spectral efficiency whilst maintaining the quality-of-service (QoS) of the cellular users. Depending on the availability of channel state information (CSI), two different configurations are considered, namely 1) centralized operation with full CSI and 2) distributed operation with partial CSI, where in the latter case, the CSI is encoded according to the capacity of the feedback channel. Instead of solving the resulting resource allocation problem for each channel realization, a DL framework is proposed, where the optimal resource allocation strategy for arbitrary channel conditions is approximated by deep neural network (DNN) models. Furthermore, we propose a new training strategy that combines supervised and unsupervised learning methods and a local CSI sharing strategy to achieve near-optimal performance while enforcing the QoS constraints of the cellular users and efficiently handling the integer optimization variables based on a few ground-truth labels. Our simulation results confirm that near-optimal performance can be attained with low computation time, which underlines the real-time capability of the proposed scheme. Moreover, our results show that not only the resource allocation strategy but also the CSI encoding strategy can be efficiently determined using a DNN. Furthermore, we show that the proposed DL framework can be easily extended to communication systems with different design objectives.

In this paper, we consider a wireless-powered two-way communication, calledtransmit-harvest-respond, with co-channel interference. The two-way communication considered here comprises three steps: i) transmitters send data signals, ii) receivers decode information and harvest energy simultaneously from the received signals using a policy of time switching (TS) or power splitting (PS), and iii) receivers transmit responses back to transmitters using this harvested energy. We aim to find the transmit power and energy harvesting ratios that maximize the sum rate of the forward links while ensuring a minimum rate requirement for each backward link. Due to the non-convexity and NP hardness of the optimization problem considered here, we first derive suboptimal solutions using an iterative algorithm (IA) on the basis of asymptotic strong duality. In view of the high computation time of the IA, we then design an efficient deep neural network (DNN) framework and novel training strategy as a means of combining supervised and unsupervised training. Specifically, DNNs are pre-trained using the suboptimal solutions obtained by the IA in a supervised manner, as a means of initialization; further training is then applied to DNNs using a well-designed loss function in an unsupervised manner to enhance performance. Simulation results reveal that the pre-training technique using IA solutions is beneficial for improving the performance of the DNN. The proposed hybrid scheme thus achieves near-optimal performances with a lower computation time, compared with the use of IA or DNN alone.

In this paper, we consider a two-way relay network consisting of a single relay node and two source nodes, where both the relay node and source nodes are equipped with multiple antennas. Two source nodes are assumed to transmit data with spatial modulation (SM) and the relay node is assumed to try to decode the network-coded packet (via bit-wise exclusive OR operation) of the two packets received from two source nodes, respectively. We propose a maximum-likelihood (ML) signal detection technique for the physical-layer network coded packet with SM for the relay node. Extensive simulation results show that the bit-error rate (BER) at the relay node becomes significantly improved with the proposed SM-based physical-layer network coding (PNC) technique, compared with the conventional PNC technique that achieving the same data rate. In particular, the performance of the proposed technique becomes excellent when the number of antennas at the nodes is large and the data rate is high, which implies that the proposed technique is suitable for the next-generation wireless communication system, i.e. 5G. Note that the proposed SM-based PNC technique does not require channel state information at transmitter (CSIT) and thus it can be implemented easily in practice.

A pricing‐based distributed spectrum access technique for cognitive radio (CR) networks which adopt the geolocation database (GD) is proposed. The GD contains which frequency bands are occupied by the primary system in a particular location. Given that multiple CR systems may attempt to transmit data over the same frequency band when the GD is used, the achievable rate of the CR systems becomes deteriorated due to interference. In the proposed technique, each (secondary) CR system determines whether it utilises vacant frequency bands by considering the cost of using them, which is calculated by taking into account the interference. The authors analyse the behaviour of the CR systems based on game theory. In particular, it is shown that the sum capacity of CR systems is maximised when the number of utilised bands is proportional to the relative channel gain with respect to the average channel gain at each CR system. In addition, the authors obtain the optimal cost for the vacant bands, which achieves the maximum sum capacity of CR systems. Finally, it is shown that the sum capacity of the (secondary) CR systems is significantly improved via proper pricing policy on the vacant frequency bands through extensive computer simulations.

We herein consider directional transmission based on adaptive beamforming in which multiple vehicles are served at the same time using multiple directional beams; this represents one promising technology that could improve the capacity of vehicle-to-infrastructure (V2I) communication. We propose an iterative resource-allocation method to maximize the capacity of V2I communication by taking into proper account the interference between directional beams, which reduces capacity. We also consider a heuristic resource allocation scheme, which has significantly less computational complexity than the iterative scheme and negligible performance degradation. We make use of computer simulations to show that the capacity of V2I communication can be improved using our proposed schemes.

Integration of distributed generation based on renewable energy sources into the power system has gained popularity in recent years. Many small-scale electricity suppliers (SESs) have recently entered the electricity market, which has been traditionally dominated by a few large-scale electricity suppliers. The emergence of SESs enables direct trading (DT) of electricity between SESs and end-users (EUs), without going through retailers, and promotes the possibility of improving the benefits to both parties. In this paper, the cooperation between SESs and EUs in DT is analyzed based on coalitional game theory. In particular, an electricity pricing scheme that achieves a fair division of revenue between SESs and EUs is analytically derived by using the asymptotic Shapley value. The asymptotic Shapley value is shown to be in the core of the coalitional game such that no group of SESs and EUs has an incentive to abandon the coalition, which implies the stable operation of DT for the proposed pricing scheme. Unlike the existing pricing schemes that typically require multiple stages of calculations and real time information about each participant, the electricity price for the proposed scheme can be determined instantaneously based on the number of participants in DT and statistical information about electricity supply and demand. Therefore, the proposed pricing scheme is suitable for practical implementation. Using computer simulations, the price of electricity for the proposed DT scheme is examined in various environments, and the numerical results validate the asymptotic analysis. Moreover, the revenues of the SESs and EUs are evaluated for various types of SESs and different numbers of participants in DT. The optimal ratio of different types of SESs is also investigated.

We compare the capacity of two most popular methods for acquiring the state of the spectrum in cognitive radio technology: geolocation-database-based schemes and spectrum-sensing-based schemes. For the comparison, we use a new Hidden Markov Chain based channel model, because recent measurements show that a conventional two-state model, which has been widely used, is not appropriate for modeling the behavior of channels in the cognitive environment. We also consider more generic cognitive environments in which each wireless system has its own licensed bands and uses unlicensed bands in addition to its licensed bands to increase its capacity. This type of wireless systems can comprise conventional cognitive radio systems by letting the number of licensed bands be zero. Moreover, we have derived the optimal number of unlicensed bands to be used for maximizing the capacity of wireless system by taking into account interference from neighboring wireless systems. Through simulations, we compare the capacity of wireless systems with two channel state acquisition schemes and show the counterbalancing relation between two schemes, which has never been investigated in previous works. To the best of our knowledge, this is the first work to compare the capacity of these two schemes for acquiring the state of the spectrum.

The directional transmission can be applied to wide range of wireless system, which ranges from indoor WPAN to outdoor cellular system. In the system which uses directional transmission, the interference estimation will be different from that with omni-directional transmission, because the interference from other cell will come from various directions. To solve this problem, we have proposed adaptive interference estimation scheme which uses directional transmission. Through the system level multi-cell simulation which is based on the actual wireless environment, we have shown that our proposed scheme can improve the performance of system in the view of spectral efficiency and fairness.

In a WSN (Wireless Sensor Network), cooperative communications can provide improved energy efficiency which is one of the most important metric given that sensors usually have constrained energy. In this paper, we consider a WSN where sensors operate selfish without centralized entity like as base station. The most of sensors do not tend to help spontaneously data transmission for others in a distributed WSN. We proposed the fair cooperative communication scheme which encourages sensors to participate in cooperative communication by giving some reward. In other words, sensors can participate in the same cluster and help each other to cooperatively transmit data if the following two conditions are satisfied: 1) sensors have a plan to transmit data to nearby area, and 2) sensors are able to decode message coming from a cluster representative. Hence, proposed scheme is more realistic and fair compared with existing scheme which makes sensors to participate in cooperative communication without reward. Moreover, simulation results show that the proposed scheme is more energy efficient than the existing schemes.

We propose a spectrum-sensing and data-transmission scheme that utilizes multiple-input and multiple-output (MIMO) technology to enable the spectrum to be sensed and data to be transmitted simultaneously. By using our proposed scheme, the degradation of quality of service (QoS) that is caused by spectrum sensing can be reduced and the system throughput of a cognitive radio (CR) system can be increased, while the accuracy of the spectrum sensing is unaffected. Even the CR system transmits data during the spectrum sensing, the interference to a primary user which is caused by the CR system will not increase compared to a conventional scheme. We also analyze the performance of the proposed scheme in the presence of channel estimation errors. Through performance evaluations, we show that the proposed scheme can increase the throughput and QoS of CR systems.

In this paper, we compare the performance of two major spectrum information acquisition schemes in cognitive radio system, which are geo-location database based scheme and spectrum sensing based scheme. Although these two schemes are the major spectrum information acquisition schemes which are mainly considered in current researches on cognitive radio system, the comparison of two schemes has not been considered in previous works. In the comparison, we propose a new channel model for the cognitive environment which has three states, because recent measurements show that two-state Markov Chain model which has been widely used is not appropriate to model the cognitive environment. And based on the new channel model, we propose a new spectrum sensing scheme to reduce sensing overhead. Through performance analysis and numerical results, we show that our analysis is accurate and that the number of spectrum sensing can be reduced by using our proposed scheme so that the throughput of a cognitive radio system can be improved. We also find that the throughput of a spectrum sensing based scheme is better than that of a geo-location database based scheme in general cognitive environment.

In this paper, we propose a group handover scheme in multi-access networks, which utilizes adjusted delay to prevent handover blockings caused by a group handover. In the group handover, a lot of users try to initiate a handover at the same time, which causes network congestion and increases the probability that the handover would be blocked. In our proposed scheme, to prevent these problems of a group handover, each user which participates in the group handover, optimally selects an access point(AP) based on the remaining resources of the AP, and each user initiates a handover after adjusted delay to prevent network congestion. To find an optimal AP selection strategy, we formulate an optimization problem whose objective is to minimize handover blocking probability, and derive an optimal solution by using the Karush-Kuhn-Tucker (KKT) condition. Through performance analysis and simulation results, we show that our proposed scheme can reduce handover blocking probability in a group handover compared to a conventional scheme and keep handover blocking probability to be less than the maximum allowable value of handover blocking probability.

In this paper, we propose a downlink power control scheme for a wireless system which utilizes a smart antenna technology for space division multiple access(SDMA). In the wireless system which we consider, an access point (AP) forms multiple beams by using multiple antennae and the users of wireless systems get services at the same time by using these beams. By doing so, the spectral efficiency of the wireless system can be increased because multiple data streams can be transmitted at the same time. In this paper, we propose a downlink power control scheme in which the transmission power of each beam is allocated by considering the interference to neighboring APs so that overall system throughput can be improved. Through performance analysis and simulation results, we show that our proposed scheme can increase overall system throughput. We also show that our proposed scheme can improve the throughput even though the information of neighboring APs is inaccurate, which implies that our proposed scheme is robust to errors.

We propose a new neighbor discovery scheme for cellular communication systems that utilizes the azimuth spread (AS), delay spread (DS) and shadow fading of a mobile station (MS). Given that the AS, DS and shadow fading are spatially correlated, MSs that are close to each other will have similar AS, DS and shadow fading values. In our proposed scheme, we use the AS, DS and shadow fading of an MS to find its neighbor MSs. The proposed scheme filters out MSs that are unlikely to be neighbor MSs and uses the Kolmogorov-Smirnov (K-S) test for more refined neighbor discovery. Through analysis and simulation, it is shown that we can effectively find neighbor MSs.

We propose a new velocity estimation scheme for cellular communication systems that utilizes the azimuth spread (AS), delay spread (DS) and shadow fading of a mobile station (MS). Since the AS, DS and shadow fading are spatially correlated, the variation of the AS, DS and shadow fading of an MS is related to the velocity of the MS. In our proposed scheme, we use the AS, DS and shadow fading of an MS to estimate the velocity of the MS. Through analysis and numerical results, we show that we can accurately estimate the velocity.

In OFDMA systems, channel quality information (CQI) feedback is used to determine the modulation and coding level of a mobile station (MS). When the MS uses diversity, it transmits the CQI feedback information which contains channel condition which is averaged over all the sub-bands allocated to itself. In conventional CQI feedback scheme, every MS should send its own CQI feedback. So, as the number of MSs in the system increases, the amount of CQI feedbacks increases too, and it results in the reduction of system throughput. To solve this problem, we have proposed a new CQI feedback scheme. In our CQI feedback scheme, MSs which are close each other make a CQI feedback group. And only one representative CQI feedback per one CQI feedback group is transmitted because MSs which are in the same CQI feedback group have similar channel condition. In this paper, we also have derived the PER equation of MSs when the MSs use a representative CQI feedback. And also, through simulations and numerical results, we have shown that by using our proposed scheme, CQI feedbacks can be reduced.