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Applications of Economic and Pricing Models for Resource Management in 5G Wireless Networks: A Survey

Nguyen Cong Luong, Ping Wang, Dusit Niyato, Ying-Chang Liang, Fen Hou, Zhu Han

arXiv:1710.04771v1cs.GTcs.NI

TL;DR

In proactive content provision, more general scenarios involving multiple content providers and users, or one provider and multiple users, still need investigation. The authors review and compare economic and pricing approaches across user association, spectrum allocation, interference and power management, wireless caching, and mobile data offloading. The survey concludes that economic and pricing models can address resource-management issues across diverse 5G requirements, with most reviewed approaches concerning HetNets and few concerning C-RANs.

  • Problem

    In proactive content provision, more general scenarios involving multiple content providers and users, or one provider and multiple users, still need investigation.

    Source: 3) Profit maximization · p. 28

  • Method

    The authors review and compare economic and pricing approaches across user association, spectrum allocation, interference and power management, wireless caching, and mobile data offloading.

    Source: §IX · p. 32

  • Results

    The survey concludes that economic and pricing models can address resource-management issues across diverse 5G requirements, with most reviewed approaches concerning HetNets and few concerning C-RANs.

    Source: §VIII · p. 30

  • Takeaways & Limitations

    The survey organizes approaches by resource-management issues and objectives and highlights their advantages and disadvantages alongside open challenges and future research directions.

    Source: §IX · p. 32

    The surveyed proactive content-provision work is described as a pioneer study, with more general scenarios involving multiple content providers or users still needing investigation.

    Source: 3) Profit maximization · p. 28

Abstract

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This paper presents a comprehensive literature review on applications of economic and pricing theory for resource management in the evolving fifth generation (5G) wireless networks. The 5G wireless networks are envisioned to overcome existing limitations of cellular networks in terms of data rate, capacity, latency, energy efficiency, spectrum efficiency, coverage, reliability, and cost per information transfer. To achieve the goals, the 5G systems will adopt emerging technologies such as massive Multiple-Input Multiple-Output (MIMO), mmWave communications, and dense Heterogeneous Networks (HetNets). However, 5G involves multiple entities and stakeholders that may have different objectives, e.g., high data rate, low latency, utility maximization, and revenue/profit maximization. This poses a number of challenges to resource management designs of 5G. While the traditional solutions may neither efficient nor applicable, economic and pricing models have been recently developed and adopted as useful tools to achieve the objectives. In this paper, we review economic and pricing approaches proposed to address resource management issues in the 5G wireless networks including user association, spectrum allocation, and interference and power management. Furthermore, we present applications of economic and pricing models for wireless caching and mobile data offloading. Finally, we highlight important challenges, open issues and future research directions of applying economic and pricing models to the 5G wireless networks.

I. INTRODUCTION

Growing mobile-data demand and 5G’s diverse technologies and stakeholders create resource-management challenges that traditional centralized methods may not suit. The survey reviews economic and pricing models as distributed tools for addressing these challenges and organizes prior work by resource-management issue and objective.

II. OVERVIEW OF 5G WIRELESS NETWORKS

  • 5G research responds to surging mobile-data demand; proposed technologies include massive MIMO, dense HetNets, mmWave, full-duplex, D2D, energy-aware communication, C-RAN, and network virtualization.The paper focuses its technology overview on massive MIMO, HetNets, mmWave, and C-RAN, emphasizing base stations’ role in QoS and energy use.Source: §I · p. 1, §II · p. 3

I. INTRODUCTION

  • Traditional resource-management methods may be unsuitable for 5G because they can require centralized control, generate excessive signaling at scale, and make economic implications difficult to incorporate.The challenges include dense, heterogeneous devices and resources, uneven base-station load and coverage, frequent handovers, constrained fronthaul and backhaul, and stakeholders with differing objectives.Source: §I · p. 1

A. What and Why Economic and Pricing Approaches

  • Economic and pricing approaches model interactions among stakeholders and support distributed autonomous decisions based on their strategies and equilibrium analysis.The survey describes them as useful for balancing objectives such as throughput, fairness, load balancing, resource utilization, and operator revenue.Source: A. What and Why Economic and Pricing… · pp. 1–2
  • Economic and pricing models commonly assume rational stakeholders, but limited information or observation can restrict that assumption; evolutionary games are noted for bounded rationality.The survey also identifies congestion-based or tiered pricing for regulating user demand and contract theory for incentivizing small-cell owners to contribute caching resources.Source: A. What and Why Economic and Pricing… · p. 2

B. Contributions of the Paper

  • The survey addresses a gap in prior 5G surveys by reviewing economic and pricing models specifically for resource management.It classifies approaches by user association, spectrum allocation, interference and power management, and wireless caching and mobile data offloading, highlighting advantages and disadvantages.Source: B. Contributions of the Paper · p. 2

A. Massive MIMO

Massive MIMO and HetNets expand the radio resources and service capacity available in 5G, while also complicating resource allocation and management. The survey describes pricing-based approaches alongside these technologies and their performance goals.

A. Massive MIMO

  • Massive MIMO uses very large base-station antenna arrays to support high multiplexing gains, and spatial multiplexing can serve multiple users on the same time-frequency resource.Users may request bundles of resources such as spectrum, power, and antennas; combinatorial auctions can allocate such bundles with little global information requirement.Source: A. Massive MIMO · p. 3

B. Heterogeneous Networks (HetNets)

  • HetNets combine macro- and small-cell tiers, with dense deployments and D2D or M2M links supporting high user densities and performance across capacity, coverage, spectral efficiency, and power consumption.The section identifies resource-management goals including spectrum efficiency, energy efficiency, load balancing, fairness, and interference mitigation.Source: B. Heterogeneous Networks (HetNets) · pp. 3–4
  • In HetNets, RSSI-based association can ignore base-station traffic load, while centralized power-control optimization may incur signaling and computational overhead.The survey describes pricing in which a macro base station penalizes small-cell interference, prompting local power reduction and reducing information exchange and computational complexity.Source: B. Heterogeneous Networks (HetNets) · pp. 4–5

C. Millimeter Wave (mmWave) Communications

mmWave and C-RAN technologies introduce distinct resource-management constraints, while economic models offer tools for handling some of them. The survey also outlines game-theoretic foundations used to model competition among network entities.

C. Millimeter Wave (mmWave) Communications

  • mmWave’s blockage sensitivity, beam misalignment, and need for dense base-station deployment complicate resource management, while pricing models can support fast, dynamic user association.For small cells with radius below 200 m, the text says mmWave can overcome strong path loss; it reports 0.9 dB rain attenuation over 200 m at 28 GHz.Source: C. Millimeter Wave (mmWave)… · p. 5

D. Cloud-Radio Access Network (C-RAN)

  • C-RAN’s limited fronthaul capacity constrains resource allocation; tiered pricing can set higher prices for high-demand users to reduce their resource consumption.Fully centralized C-RAN can increase fronthaul load, latency, and jitter, especially with many RRHs.Source: D. Cloud-Radio Access Network (C-RAN) · p. 5

A. Non-cooperative game

  • A non-cooperative game models selfish players choosing strategies to maximize their own utilities, with a Nash equilibrium when no player benefits by changing strategy alone.The survey notes that existence and uniqueness of equilibrium require checking; Stackelberg games suit settings where at least one player decides before others.Source: A. Non-cooperative game · p. 6

B. Stackelberg game

The section introduces sequential games and auction mechanisms as economic approaches to resource allocation. Their properties and constraints differ, including leader advantage, truthfulness, and the computational difficulty of allocation.

B. Stackelberg game

  • In a Stackelberg game, leaders choose strategies before followers respond, and backward induction can compute the equilibrium.The leader’s utility at the Stackelberg equilibrium is guaranteed to be no less than at the Nash solution, though observing and selecting strategies in dense small-cell deployments is challenging.Source: B. Stackelberg game · p. 6

C. Auction

  • Auctions allocate resources through buyers’ bids, sellers, and an auctioneer, who may also be the seller.The survey presents multiple auction forms for resource allocation.Source: C. Auction · p. 6

1) Vickrey and Vickrey-Clarke-Groves (VCG) auctions:

  • In a Vickrey auction, the highest bidder wins but pays the second-highest bid, which motivates truthful bidding; VCG extends this approach to multiple commodities and charges each bidder the social-value loss due to its allocation.The survey notes VCG use for fronthaul resource utilization in C-RAN.Source: 1) Vickrey and Vickrey-Clarke-Groves… · pp. 6–7

2) Combinatorial auction:

  • Combinatorial auctions let bidders request bundles of heterogeneous commodities, but winner determination is generally NP-hard and lacks a polynomial-time algorithm.Lagrangian relaxation is commonly used for approximate solutions; the survey cites massive-MIMO allocation of antennas and bandwidth.Source: 2) Combinatorial auction · p. 7

3) Ascending Clock Auction (ACA):

  • An ascending clock auction raises prices over rounds until demand is no greater than supply, but users may misreport demand because the mechanism is not truthful.The survey cites uses in massive-MIMO resource allocation and dense-HetNet wireless caching.Source: 3) Ascending Clock Auction (ACA) · p. 7

4) Forward, reverse auction, and double auction:

  • Forward auctions match multiple buyers with one seller, while reverse auctions match multiple sellers with one buyer; double auctions match multiple buyers and sellers.Under certain conditions and settings, double auctions can have truthfulness, individual rationality, balanced budget, and economic efficiency.Source: 4) Forward, reverse auction, and… · p. 7

D. Network Utility Maximization (NUM)-based Pricing

NUM-based pricing uses iterative price updates to allocate network resources according to users’ net utilities. The surveyed user-association applications use economic and pricing approaches for objectives including throughput, load balancing, and fairness.

D. Network Utility Maximization (NUM)-based Pricing

  • NUM maximizes users’ total net utility under network capacity constraints, using iterative prices and user choices to reach a unique optimum when utilities are concave.The method applies dual decomposition and gradient updates; under the stated assumptions, the optimization is convex.Source: D. Network Utility Maximization… · p. 7
  • NUM-based pricing in 5G often targets total user data rate, but the surveyed approaches mostly require strictly concave user utility functions.Source: D. Network Utility Maximization… · p. 7

IV. APPLICATIONS OF ECONOMIC AND PRICING MODELS FOR USER ASSOCIATION

  • User association can optimize throughput, load balancing, or energy efficiency; economic and pricing models offer distributed alternatives to centralized solutions with high signaling and computational demands.Source: §IV · pp. 7–8

A. User Association for Throughput Maximization

1) Distributed auction:

  • In a mmWave auction, users bid for BS connections and the BS raises prices; the scheme improves throughput around 12% over RSSI-based association.Narrower beamwidths increase throughput but add alignment overhead that future bidding-process optimization could consider.Source: 1) Distributed auction · p. 8
  • For harvested-energy HetNets, SBSs bid for users according to probabilistic energy states; the auction outperforms distance-based assignment in aggregate throughput.The paper does not specify how to choose the energy-state probability distribution.Source: 1) Distributed auction · p. 8

2) Utility maximization:

The surveyed approaches use auctions and prices to address user association, including load balancing and fairness. In multi-RAT NUM association, throughput improves over RSSI-based association, while other schemes balance loads through prices or user allocation.

2) Utility maximization:

  • The multi-RAT NUM scheme jointly associates users with BSs and spectrum segments, improving network throughput up to 20% over RSSI-based association.Its distributed price updates are guaranteed to converge to a near-optimal solution; the projected-gradient approach requires synchronized BS price updates.Source: 2) Utility maximization · pp. 8–9

1) Distributed auction:

  • A mmWave distributed auction converts joint association and relaying into a min-cost flow problem; convergence is quick with many users per relay, but overall convergence may be slow.Price increases proportional to relay demand can leave few users accepting an expensive relay, and slow overall convergence may not suit fast mmWave channel variation.Source: 1) Distributed auction · p. 9

2) General pricing:

  • For mmWave load balancing, users minimize payment as BSs update service prices; the scheme outperforms RSSI in convergence and Jain fairness, but iterative updates add communication overhead.The channel-utilization metric is required user data rate divided by channel capacity.Source: 2) General pricing · p. 9

3) Utility maximization:

  • In massive MIMO, NUM pricing allocates user resource fractions across multiple BSs to maximize logarithmic utility and achieve proportional fairness.Each user chooses BSs and resource fractions to maximize its total rate-to-price bang-per-buck.Source: 3) Utility maximization · p. 9

4) Congestion-based pricing:

  • In HetNets, congestion pricing guides MUEs to SBSs by rate relative to shadow and congestion prices, maximizing user utility minus the MBS’s bandwidth cost.Source: 4) Congestion-based pricing · pp. 9–10
  • For M2M routing, routes price services in proportion to connected source nodes, and source nodes choose minimum-price routes as prices adjust to balance demand and supply.Source: 4) Congestion-based pricing · p. 10

5) Paris Metro Pricing (PMP):

  • Paris Metro Pricing assigns RAT access prices according to capacity availability; users choose RATs by utility and congestion sensitivity, but algorithm stability is not proven.Source: 5) Paris Metro Pricing (PMP) · p. 10

6) Forward auction:

The survey reviews pricing and economic models for user association, including forward auctions, energy-efficiency optimization, and cost minimization. It then turns to spectrum allocation, where pricing mechanisms pursue data-rate, revenue, or system-utility objectives.

6) Forward auction:

  • In the forward auction, users bid for BS association and each BS raises its price by the winning bid; temporary associations may discourage participation.The approach targets max-min fairness by maximizing the minimum user SINR.Source: 6) Forward auction · p. 10

C. User Association for Energy Efficiency Optimization

  • For massive-MIMO user association, dual-based service prices support energy-efficiency maximization, while repeated-game ON/OFF switching improves network energy efficiency.The price-based scheme outperforms RSRP association but loses energy efficiency as antenna counts rise; ON/OFF switching outperforms ACTIVE-IDLE switching.Source: 1) Utility maximization · p. 10, 2) Repeated game · p. 11

2) Repeated game:

  • A repeated-game pricing scheme adds service prices to users’ utilities and iterates provider-set prices and user BS choices to maximize provider payment and user utility.The provider broadcasts BS-specific prices before users select their BSs.Source: 2) Repeated game · p. 11

3) Cost minimization:

  • A relay-pricing iteration allocates source data across D2D relays to minimize energy and payment costs, but the source-side optimization method is unspecified.Relay prices adjust inversely to the source’s demand fraction.Source: 3) Cost minimization · p. 11
  • Across reviewed user-association work, utility maximization with price updates is the predominant approach, serving objectives that include throughput, load balancing, fairness, and energy efficiency.The survey notes that limited spectrum and rising data traffic motivate its subsequent review of spectrum allocation.Source: 3) Cost minimization · p. 11

V. APPLICATIONS OF ECONOMIC AND PRICING MODELS FOR SPECTRUM ALLOCATION

  • Spectrum-allocation mechanisms target user data rates, operator revenue or profit, or overall system utility, with auctions commonly used for revenue and Stackelberg games for system utility.For data-rate maximization, the survey describes NUM-based allocation and auctions of massive-MIMO spatial streams.Source: §V · p. 11, A. Data Rate Maximization · pp. 11–12

1) MmWave networks:

The survey reviews pricing approaches for rate allocation across mmWave, massive-MIMO, C-RAN, and HetNet settings. These include games, optimization, matching, and auctions, with reported rate or transmission-success outcomes and implementation caveats.

1) MmWave networks:

  • MmWave user-rate competition can be modeled as a non-cooperative game, but the reviewed scheme does not establish Nash-equilibrium existence or uniqueness.A bargaining alternative maximizes the product of user utilities but requires user cooperation.Source: 1) MmWave networks · p. 12
  • For aggregated mmWave and legacy carriers, NUM allocation converges to optimal rates in simulations, while low carrier resources produce higher user prices that regulate demand.The allocation maximizes user rates across aggregated carriers.Source: 1) MmWave networks · pp. 12–13
  • MmWave backhaul matching assigns BRBs under user-rate requirements and D-BS budgets; it improves average rate by around 30% over best effort, with higher complexity as network size grows.The iterative matching stops when a D-BS meets its rate demand or exceeds its budget.Source: 1) MmWave networks · p. 13

2) Massive MIMO networks:

  • A VCG auction allocates massive-MIMO spatial streams to MVNOs, significantly improving average data rate over equal division but slightly trailing optimal assignment because stream prices use prior-auction estimates.The authors suggest learning algorithms to improve performance.Source: 2) Massive MIMO networks · p. 13

3) C-RANs:

  • In C-RAN, iterative fronthaul pricing divides rate-and-quantization optimization into RRH-local subproblems, but the scheme may be infeasible when cloud resources are too low.Rates are obtained using KKT conditions and the Lagrangian method.Source: 3) C-RANs · pp. 13–14

4) HetNets:

  • For M2M rate allocation, VCG achieves up to 97% transmission success; adding demand estimation before the auction raises this to 99%, an additional 2% over the earlier scheme.The original auction assumes each application’s data rate is constant within each time slot.Source: 4) HetNets · p. 14
  • M2M call-option pricing lets applications reserve rates at a preset price and time, protecting against future price increases and preserving the right to buy after losing an auction round.A convex cost function supports strike-price choices that minimize reservation costs while obtaining guaranteed-rate QoS.Source: 4) HetNets · p. 14

B. Revenue Maximization

The survey examines economic and pricing approaches to operator revenue maximization across mmWave, massive-MIMO, and C-RAN networks. The reviewed mechanisms include spectrum-sharing prices and games, resource-bundle auctions, direct pricing, tiered allocation, and cloud-provider optimization.

1) MmWave networks:

  • MmWave spectrum-sharing prices can give a primary operator an incentive to share: although a higher interference threshold may reduce its own-user rate, secondary-operator payments can offset that loss, and a revenue-maximizing threshold exists.The reviewed study considers one primary and one secondary operator; multiple operators remain an open setting.Source: 1) MmWave networks · pp. 14–15
  • In a two-provider mmWave sharing game, spectrum sharing significantly improves profits over no sharing, but a high-quality provider may share only when the network effect is small.A unique Nash equilibrium exists when the ratio of service-quality levels exceeds a threshold.Source: 1) MmWave networks · p. 15

2) Massive MIMO networks:

  • For massive-MIMO resource bundles, an ascending-clock auction raises spectrum prices under excess demand and selects bids to maximize MNO revenue; lower antenna prices increase revenue through resource complementarity.The approach has high computational complexity; a related LSA version uses 5-MHz blocks and incumbent-use information.Source: 2) Massive MIMO networks · p. 15
  • Direct MNO pricing for massive-MIMO user groups uses NUM optimization; increasing power or antenna prices reduces total rate, with antenna-price increases having the larger effect.The model allocates power and antennas inversely to their respective prices.Source: 2) Massive MIMO networks · p. 15

3) C-RAN:

  • In C-RAN, tiered channel pricing and incremental-revenue allocation balance RRH resources; the scheme matches branch-and-bound system throughput, but its revenue improvement is not demonstrated.Channels go iteratively to the RRH offering the highest revenue increment.Source: 3) C-RAN · pp. 15–16
  • For cloud-provider profit maximization in C-RAN, a knapsack-based approximation selects users by net price after QoS penalty costs, but incurs high computational complexity.Penalty costs may be adjusted iteratively to attract users and recalculate the selected set.Source: 3) C-RAN · p. 16

4) HetNets:

The survey reviews pricing and game-theoretic approaches for spectrum allocation in HetNets, addressing operator revenue and stakeholder utility. It covers tier investment, auctions, congestion pricing, and multi-level games.

4) HetNets:

  • When bandwidth is limited, MNOs invest only in SBSs; with more bandwidth, they invest in both MBSs and SBSs as SBS prices fall.The survey links these investment choices to MNO revenue and notes that the game’s price-adjustment process and equilibrium existence are unspecified.Source: 4) HetNets · p. 16
  • [87] uses a forward auction in which an MNO selects Femto Holders to maximize revenue, while the holders bid for bandwidth to maximize profit.The reported profit-maximizing rental price may not sustain holders’ profit long term because the MNO can reselect winners for its own revenue.Source: 4) HetNets · p. 16
  • [88] repeatedly awards unused spectrum to the highest-bidding D2D user and assigns transmit power per sub-band to limit interference.Source: 4) HetNets · p. 16
  • For M2M access, adjusts channel prices from prior-period utilization to alleviate congestion and lower user access delay, but does not guarantee MBS revenue maximization.Its variable step sizes are intended to prevent utilization fluctuations.Source: 4) HetNets · p. 17
  • [90]’s periodic access pricing raises MBS revenue by up to 50% compared with ’s congestion-based scheme.The scheme sets prices to maximize total revenue rather than address congestion.Source: 4) HetNets · p. 17
  • For relay spectrum, selects bidders by price per requested resource block and uses critical prices to support truthfulness, but does not account for in-band relay-link interference.Source: 4) HetNets · p. 17

1) HetNets:

  • For stakeholder utility maximization, [92] jointly models users, service providers, and an MNO in a two-level Stackelberg game; extending it to multiple MNOs remains open.Users choose services, providers set service prices and leasing policies, and the MNO sets spectrum prices; the cited work notes difficulty proving convexity of providers’ best responses with multiple MNOs.Source: 1) HetNets · p. 17
  • In multi-tier HetNets, [95] reports higher total stakeholder utility with resource sharing than without it; and use multi-RAT pricing, with targeting social welfare but facing high computation as RATs increase.[96] finds MNO revenue can fall when prices become so high that users switch to other MNOs; ensures non-negative participant profit.Source: 1) HetNets · p. 18

2) C-RAN:

The survey describes economic approaches to spectrum allocation in C-RAN and massive MIMO settings, then relates these to interference management and 5G resource objectives.

2) C-RAN:

  • The C-RAN VCG scheme outperforms a greedy algorithm in social welfare; increasing winners and capacity ratio raises welfare, while a larger capacity ratio reduces MNO revenue.The auction allocates spectrum at RRHs and fronthaul bandwidth under limited fronthaul capacity.Source: 2) C-RAN · p. 18
  • A two-level C-RAN auction allocates MNO resource blocks to service providers proportionally to their bids, then uses VCG to allocate them to users.Greedy allocation improves computational efficiency, but the higher-level equilibrium is not always unique because provider utility may lack concavity.Source: 2) C-RAN · p. 18

3) Massive MIMO networks:

  • For massive MIMO, combines combinatorial and VCG auctions to allocate bundles of subchannels and antennas, outperforming fixed sharing in social welfare and average subchannel utilization.Its dynamic-programming solution has high computational complexity; replacing it with a greedy approximation removes VCG incentive compatibility.Source: 3) Massive MIMO networks · p. 19
  • The multiple-MNO double auction in uses shadow prices and pre-negotiated seller-buyer prices to exchange data rates, outperforming random allocation in social welfare.How to set pre-negotiated prices at 5G scale is not explained.Source: 3) Massive MIMO networks · p. 19
  • Across spectrum allocation, the survey associates auctions mainly with revenue objectives and Stackelberg games mainly with overall system utility.Its stated objectives also include spectrum efficiency and total system utility.Source: 3) Massive MIMO networks · p. 19

VI. APPLICATIONS OF ECONOMIC AND PRICING MODELS FOR INTERFERENCE AND POWER MANAGEMENT

  • Ultra-dense 5G makes interference and power management challenging; the survey frames economic pricing as a lower-complexity alternative to centralized power-control methods.It notes that prices set according to interference levels can provide decent outcomes with minimal overhead.Source: §VI · p. 19

A. Interference Mitigation

  • In HetNet interference mitigation, each base station locally measures neighbors’ interference, sets prices, and induces sequential transmit-power responses, making Stackelberg games a common model.Source: A. Interference Mitigation · p. 19

1) Stackelberg game:

For HetNet interference management, the survey reviews Stackelberg pricing and distributed alternatives that couple interference prices with transmit-power or resource decisions. These approaches trade interference control and efficiency against information, convergence, or coordination constraints.

1) Stackelberg game:

  • In, a high interference price significantly reduces femtocell-user transmit power, supporting interference control in a multi-tier HetNet.The MBS leads by setting prices, and femtocell users follow by choosing transmit power.Source: 1) Stackelberg game · pp. 19, 21
  • [107] jointly adjusts channel allocation and SBS transmit power; its pricing scheme outperforms power-control-free allocation in average SBS utility.The MBS lowers an initially high price in steps until its utility is maximized.Source: 1) Stackelberg game · p. 21
  • With incomplete channel-gain information, models SBS interaction as a Bayesian game; convergence is shown to be fast when the initial price is near optimal, though fictitious play generally may not converge.Source: 1) Stackelberg game · p. 21
  • For cross-tier interference, has picocells lead with prices and power choices while the MBS follows; the iterative process does not require complete network knowledge, reducing signaling overhead.Source: 1) Stackelberg game · p. 21
  • The survey notes that predicting other devices’ strategies becomes challenging at 5G’s very high base-station and device density.This qualifies the efficiency of Stackelberg interference management in dense settings.Source: 1) Stackelberg game · p. 22

2) General pricing:

  • For interference-threshold control, updates prices locally and converges to a unique optimal power allocation under a decreasing step-size rule; raising MBS limits increases SBS rates but reduces MBS-user rates.Source: 2) General pricing · p. 22
  • In, simultaneous price and power updates lower average transmit power versus price-free distributed control, but converge more slowly.The scheme uses gradient play based on locally measured SINR, utility, and effective interference.Source: 2) General pricing · p. 22
  • For D2D resource allocation, ’s iterative distributed auction approaches exhaustive-search data rates with less complexity, but its truthfulness remains to be studied.Users bid for resource-block and power-level combinations, while the MBS checks aggregated interference against thresholds.Source: 2) General pricing · p. 22

3) Non-cooperative game:

The survey covers non-cooperative pricing for CoMP interference and economic models for energy trading and cost minimization across mmWave, HetNet, and C-RAN settings.

3) Non-cooperative game:

1) MmWave networks:

  • For mmWave backhaul energy trading, the distributed Stackelberg scheme’s system profit is close to the centralized solution, but drops substantially as backhaul nodes increase.The MNO sets time-dependent prices and power suppliers choose energy storage levels across epochs.Source: 1) MmWave networks · p. 23

2) HetNets:

  • For HetNet energy trading, ’s location-aware double auction reduces electrical-grid usage more than the matching-game approach [129].The matching process emphasizes distance between small cells to reduce grid load.Source: 2) HetNets · p. 23

3) C-RANs:

  • In C-RAN, cost minimization jointly allocates purchased energy, user rates, and BBU virtual-machine computation; a zero-cost balance point uses harvested energy to meet user requirements without energy trading.The trading prices are fixed a priori, and dynamic pricing is left for future work.Source: 3) C-RANs · p. 24
  • For C-RAN energy allocation, the proposed scheme’s total transmit power is much lower than the iterative link-removal algorithm, but complexity increases substantially with cluster size.Source: 3) C-RANs · p. 24

VII. APPLICATIONS OF ECONOMIC AND PRICING MODELS FOR WIRELESS CACHING AND MOBILE DATA OFFLOADING

Wireless caching and mobile data offloading can reduce latency and backhaul traffic, but limited small-cell resources and separate owners create incentives to align. The survey reviews economic models for these services, including Stackelberg games for caching.

VII. APPLICATIONS OF ECONOMIC AND PRICING MODELS FOR WIRELESS CACHING AND MOBILE DATA OFFLOADING

  • Caching and offloading through small cells can reduce latency and backhaul traffic, while pricing models can incentivize third-party owners to provide these services.Small cells have limited power, storage, and bandwidth.Source: §VII · p. 24

1) Stackelberg game:

  • In, one content provider rents fractions of multiple mobile network operators’ small-cell caches, and the operators iteratively set leasing prices to maximize profit.The provider uses water-filling to choose storage fractions; simulations show both sides’ profits rise with its budget, which has a value maximizing provider profit.Source: 1) Stackelberg game · p. 25
  • In, one operator prices cache storage for competing content providers; its profit improves up to 50% over arbitrary pricing, while provider profits fall as their number grows.More providers intensify competition for limited cache storage and raise the price charged to them.Source: 1) Stackelberg game · p. 26
  • For D2D caching in [136], a caching user prices content for peers; its profit rises with network effects but is zero when congestion alone prevents requested transfers.In, caching users instead sell transmission power to an MBS: their profits and MBS utility initially increase with purchased power, but MBS utility later decreases as payments rise.Source: 1) Stackelberg game · p. 26

2) Auction:

Caching auctions address competition for limited small-cell storage, while proactive content provision and contract theory consider other profit and allocation problems. The reviewed approaches include incentive, convergence, and scope constraints.

2) Auction:

  • In, an ascending clock auction raises the operator’s storage price when demand exceeds supply and converges as price rises and demand falls; providers maximize profit only when they do not misreport demand.The operator allocates based on other providers’ bids and uses cumulative clinches to determine payments.Source: 2) Auction · p. 27
  • In, a double auction matches virtual content providers’ storage bids with an operator’s asks, but bandwidth allocation remains outside the storage-sharing model.A content provider is represented by virtual bidders, each associated with a small-cell base station.Source: 2) Auction · p. 27
  • In, a reverse auction selects small-cell caches by ascending price-to-cache-hit-rate ratio to reduce a content provider’s payment, but does not guarantee social-welfare maximization.The scheme guarantees individual rationality and truthful bidding; high seller prices can transfer utility from the provider to operators.Source: 2) Auction · p. 27
  • In, higher content-replacement charges sharply reduce providers’ replacement rates, but also lower their utility and may discourage use of caching services.The additional price reflects backhaul traffic load and providers’ replacement rates.Source: 2) Auction · p. 28

3) Profit maximization:

  • In, a single provider predicts one user’s demand and jointly chooses proactive content and prices; the iterative algorithm is proven to converge globally, though the proof method is unspecified.The work is described as pioneering proactive content provision, but multiple-provider or multiple-user cases remain to be investigated; distributed execution for one provider and multiple users has a convergence-proof challenge.Source: 3) Profit maximization · p. 28

4) Contract theory:

  • In, matching and a swap-based deferred-acceptance algorithm assign storage under a contract model; adds popularity, and both operator and provider profits decrease as that parameter increases.The scheme improves content-provider utility by up to 140% over equal storage allocation by assigning each provider only the storage it needs.Source: 4) Contract theory · p. 28

B. Energy Efficiency Optimization Though Offloading

The survey reviews auction- and game-based offloading models that seek energy efficiency or stakeholder utility, alongside challenges involving mobile users and network security. It reports that most reviewed approaches concern HetNets, with few addressing C-RANs.

B. Energy Efficiency Optimization Though Offloading

  • In and, VCG auctions select offloading resources to improve energy efficiency, but their multi-objective and knapsack optimizations have higher complexity or limited gains when SBS numbers are large.The scheme significantly improves energy efficiency over switching-off, while significantly improves it over fractional frequency reuse; the latter’s gains rise only slightly with many SBSs because inter-cell interference is more severe.Source: B. Energy Efficiency Optimization… · p. 29
  • An ascending clock auction uses local SBS supply decisions and MBS demand to reach equal quantities with small communication overhead, but does not assign users to SBSs by shortest distance.The review also describes Stackelberg models for offloading utility and power pricing, including a price-adjustment algorithm proved to converge to a unique equilibrium using first-order Taylor approximation.Source: B. Energy Efficiency Optimization… · pp. 29–30

VIII. SUMMARY, CHALLENGES, AND FUTURE RESEARCH DIRECTIONS

A. Mobility of users

  • For mobile users crossing cells, online auctions can decide winners as users arrive, unlike the reviewed offline auction that assumes stationary users.The proposed online approach considers information available so far and potential future bids.Source: A. Mobility of users · p. 30

B. Security and privacy

  • For cross-cell security, the survey describes distributed pricing for secrecy without extra key-exchange channels and behavior-based pricing to discourage rational bots from spending power on attacks.The attacks discussed are eavesdropping and distributed denial of service.Source: B. Security and privacy · p. 30

C. Resource Management for Network Function Virtualization (NFV) and Software-Defined Networking (SDN)

This section surveys economic and pricing approaches across NFV/SDN, edge/fog, NOMA, and full-duplex resource management. The paper also synthesizes applications across 5G resource-management problems and identifies open issues and future directions.

C. Resource Management for Network Function Virtualization (NFV) and Software-Defined Networking (SDN)

  • NFV and SDN support programmable virtual network slices, but NFV raises resource-sharing congestion and fairness problems, as well as physical-resource allocation challenges.Source: C. Resource Management for Network… · pp. 30–31
  • Smart-data pricing can incentivize efficient resource use, while iterative auctions or profit maximization can allocate resources to slices; these approaches are generally centralized and computationally costly.Source: C. Resource Management for Network… · p. 31

D. Incentive Mechanisms for Edge/Fog Computing

E. Power Allocation in Non-Orthogonal Multiple Access (NOMA) systems

  • For NOMA, Stackelberg pricing can seek a win-win power allocation, while non-cooperative games model how users’ utilities interact through interference and the base-station power constraint.Source: E. Power Allocation in Non-Orthogonal… · p. 32

F. User Assignment in Full-Duplex (FD) Cellular Networks

  • In full-duplex assignment, forward auctions match uplink bidders with downlink users to minimize interference; required location disclosure creates privacy risk, which cryptographic algorithms can address.Source: F. User Assignment in Full-Duplex (FD)… · p. 32

IX. CONCLUSIONS

  • The survey covers economic and pricing approaches for user association, spectrum allocation, interference and power management, wireless caching, and mobile data offloading, and outlines open issues and future directions.Source: §IX · p. 32
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